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Seventeen years ago, cell biologist Donald Ingber and his colleagues at Harvard University’s Wyss Institute for Biologically Inspired Engineering submitted a paper to the journal Science describing their model human lung. It was smaller than a USB stick and made of a clear polymer slab containing narrow channels, which were lined with the type of cells that line a lung’s air sacs and blood vessels. When air was pumped through hollow chambers beside the channels, the device rhythmically expanded and contracted—it “breathed.” This lifelike movement was a dramatic change from previous generations of lung models, which typically used static cultures of lung tissue that were unable to simulate the movements essential to lung function. When exposed to inflammatory proteins and bacteria, Ingber’s artificial lung reacted much as living lungs would. And exposure to silica nanoparticles used to model the effects of ultrafine particulates revealed that movement affected how tissues absorbed them. Donald Ingber led the team that developed the first human lung-on-a-chip at Harvard University’s Wyss Institute. Sam Ogden It was a powerful proof-of-principle demonstration of a system that could be used to test drugs and other chemicals, providing a complement and even an alternative to testing in tissue cultures or in animals. Even so, the editors at Science were hesitant. They rejected the paper and suggested that Ingber’s team also run the tests in mice. It wasn’t an unreasonable request: Harvard’s lung system was new and comparing the results it generated to results from mice would help validate it. Ingber’s team ran the suggested experiments and resubmitted their study a year later, in 2010, at which point it was published. (It has since been cited by nearly 5,400 other papers.) Still, the incident spoke to how animal models have been the default of modern biomedical research. An early lung-on-a-chip developed at Harvard’s Wyss Institute used microfluidic channels lined with human cells to reproduce key features of lung function. Wyss Institute at Harvard University A recent story told by Ilka Maschmeyer, a translational toxicology researcher and executive at the German biotech company TissUse, shows how much things have changed. TissUse specializes in building organ-on-a-chip systems—the conversational name for systems like Ingber’s lung—that are used by pharmaceutical companies for research. A few months ago, says Maschmeyer, a pharmaceutical company approached TissUse after being denied permission by the U.S. Food and Drug Administration to run a clinical trial of a new drug. The problem: It had presented animal data, but the FDA wanted data from organs-on-a-chip or some comparable alternative. The standards had come full circle. A Breathing Lung-on-a-Chip The moment spoke to a trend, perhaps even the early days of a fundamental shift, away from the use of animals in toxicology and drug development. “It’s rare still,” says Maschmeyer, “but I think it’s going to be more and more frequent.” A host of these kinds of alternatives to experiments on animals have been developed over the years. Collectively they’re known as NAMs, an acronym that stands, depending on whom you’re talking to, for new approach methodologies, novel alternative methods, or nonanimal methods. Most NAMs have yet to be rigorously tested, but early studies suggest their potential. As NAMs have become more sophisticated, the question of how they will be implemented has become less about their technical qualities and more about the practical next steps needed to realize their potential. Validating NAMs—standardizing the systems, conducting head-to-head comparisons with animal experiments—is an enormous challenge. Moreover, simply outperforming animal models is necessary but not sufficient. The adoption of NAMs will require changes in policy, training, and culture. “This transition process is much more complicated than you would think,” says Thomas Hartung, a toxicologist and director of the Center for Alternatives to Animal Testing at Johns Hopkins University. “It is more about change management than it is about the technology.” The Technologies Replacing Animal Testing For decades, animal advocates and many scientists have criticized both the morality and usefulness of experimenting on animals. An estimated 92 percent of all drugs that enter U.S. clinical trials fail to reach the market, sometimes for business reasons but often because the drugs prove ineffective or unsafe in ways that were not predicted by animal experiments. Failure rates are even higher in drugs for heart disease, cancer, and diseases of the brain. These statistics don’t automatically mean that a reliance on animals is to blame. Flawed study designs are a problem too, and also the sheer confounding complexity of disease. But there’s little question that animals have made poor surrogates for many conditions. And just as animal experiments may mistakenly suggest efficacy or fail to predict harm in humans, they might also erroneously suggest that drugs are ineffective or harmful when they could actually work in humans. Some researchers argue that if aspirin or acetaminophen had been discovered after the advent of modern testing requirements, they might have been abandoned. TissUse’s Humimic systems use microfluidic chips to culture human tissues and model interactions between organs. A researcher images tissues in a chip during an experiment [top], Humimic chips sit in a temperature-controlled unit [center], and a researcher prepares chips for use [bottom].TissUse (3) Researchers developing NAMs have pushed these systems far beyond old-fashioned tissue cultures. The new technologies include organoids that more closely mimic the structure, composition, and function of human organs. More humanlike still are organ-on-a-chip systems; alongside Ingber’s lung-on-a-chip are brains, hearts, kidneys, and even placentas on a chip. As many as 10 such organs have been linked together, yielding multi-organ systems that promise to recapitulate many aspects of human physiology—not perfectly, but better than a mouse or a monkey would. Supporting these systems are computational simulations of organs and organisms, and also artificial intelligence tools that analyze data generated by other systems and inform future experiments in a high-powered iterative loop. Yet even as studies piled up and some pharmaceutical companies started using NAMs in-house, the U.S. regulatory system governing drug developing and testing remained an obstacle to their wider use. NAM proponents were overjoyed, then, when in late 2022 the FDA Modernization Act 2.0 passed into law. It explicitly authorized the use of NAMs in the preclinical studies required of new drugs before they could enter human trials. Previous regulations had mandated animal testing; now the door was open to alternatives. It was a landmark moment. “That was something I didn’t expect to see in my life,” says Maschmeyer. Although immediate in-the-lab impact was limited, the FDA’s decision was a harbinger of things to come. In 2025, the FDA pledged “to make animal studies the exception rather than the norm” for drug safety testing. Then, in September 2026, the agency followed up by issuing a rule that, if it takes effect, will replace references to “animal tests” in its drug-development regulations with the broader term “nonclinical tests.” The change makes explicit that validated alternatives such as human-cell systems, organs-on-chips, and computer models can be used when appropriate. Also in 2025, the U.S. National Institutes of Health, the world’s largest public biomedical research funder, announced that researchers applying for grants to study animal models would also need to incorporate nonanimal research, such as real-world data or studies of NAMs. Meanwhile, the European Commission and United Kingdom have announced their own plans to phase out animal testing, and the intergovernmental Organisation for Economic Co-operation and Development updated its influential guidelines to allow for expanded use of NAMs. NAM proponents say these shifts were essential: If regulators won’t accept NAM results, there’s less incentive to adopt them, especially for researchers already working with animals. Maschmeyer says TissUse’s clients increasingly include scientists whose research has been focused on animals. “I see, within the last year, a change,” says Maschmeyer. “It’s more people who are working with animal models who now have to also add in vitro models.” She traces it mainly to the regulatory shift—a trend Ingber calls “game-changing.” Proving That NAMs Work It’s not enough for regulators to say that NAMs can or should be used, though. Even more important is the regulatory apparatus dedicated to assessing how they should be used. This begins with their validation: the process by which experimental methodologies and devices are determined to be reliable and trustworthy. A prototype brain-on-a-chip designed to model a rare neurological disease might work fine in the lab that developed it—but to be validated, the system needs to work in the real world. “You read about all the organ chips that come out of academic labs, which is great—but that’s not going to change their uptake by the FDA, because you have to get the same results anywhere in the world. It has to be a commercial product. It has to be mass-produced and meet very fine performance criteria,” says Ingber. For example, even minute variations in the hydrogels used as tissue scaffolds in organ chips can produce very different growth patterns. Emulate’s Organ-Chips are connected to the company’s automated culture system, which supplies the chips with nutrients and controls the flow of fluid through them. Emulate Workflows and procedures need to be uniform, too. One obstacle to wider use of vascularized tumor-on-a-chip platforms in developing cancer therapies, for example, is the different metrics used by different research groups to characterize blood-vessel function and geometry. Experimental guidelines, workflows, checkpoints, metrics, reporting criteria: All need to be standardized in order for researchers to compare their work and collaborate across platforms. Members of Ingber’s lab coach industry researchers on how to use chips developed by Emulate, a company founded by Ingber. But even with instructions, they still need help with the finer points of tending to stem-cell cultures. When a NAM is ready for commercial use and researchers know how to use it, the most important test—whether it provides clinical benefit—still remains. A rare-disease organ chip might be reliable, but are the biomarkers it measures actually relevant? If so, are the algorithms that extrapolate chip results to the drug’s in-body effects truly predictive? Microscopic images reveal the human tissues grown inside Emulate’s Organ-Chips. Bacteria, shown in magenta, interact with mucus and airway cells in a LungChip [top]; an IntestineChip develops structures resembling those that absorb nutrients in the small intestine [center]; and tiny hairlike cilia grow on cells in another LungChip, where they help move mucus and trapped particles out of the airway [bottom].Emulate (3) Such questions have been answered for some NAMs. For example, a liver-on-a-chip system from Emulate correctly flagged about seven out of every eight drugs that had safely passed animal trials but proved toxic to human livers. A similar study was conducted by researchers from Oxford University and Janssen Pharmaceutica (later renamed Johnson & Johnson Innovative Medicine). That team showed that their computational simulations of human heart cells flagged compounds that caused a type of dangerous heart arrhythmia with 89 percent accuracy, compared to animal studies that were 75 percent accurate. Such studies, however, are complicated and costly. Emulate’s study required 870 chips and the labor equivalent of 16 full-time employees working for 16 weeks—efforts far beyond the reach of the average lab. If the researchers wanted regulatory approval to use their chip to predict large-molecule drugs rather than the small-molecule drugs they tested, they would have needed to run another such study for that particular use. And comparable studies ostensibly need to be conducted for every commercially available NAM and every context in which they would be used—a vast undertaking. “That’s a challenge,” says Kathy Archibald, founder of Safer Medicines Trust, a United Kingdom–based group that considers animals to be poor models of human biology. “It takes too long and costs too much, and small companies can’t afford to do it.” Ingber thinks that academic scientists need to collaborate more with industry researchers on NAMs, and that governments should fund those projects. He and other NAM proponents also stress the importance of having access to the necessary data: Without information from preclinical animal studies and human clinical trials, comparisons are difficult, but much of that data is now proprietary. Pharmaceutical companies and regulators need to share it, they say, and the FDA has called for an open-access repository of drug toxicity data. Hartung of Johns Hopkins also suggests that new animal experiments be run in parallel with NAMs, producing side-by-side comparisons. To Christian Maass, a computational biologist at the German biotechnology company ESQlabs, NAMS are overdue for a showdown with animal models. His company makes “digital twin” systems in which data from organ chip systems inform whole-human simulations of drug outcomes and disease progression. “I love what we are doing,” says Maass, speaking not only of his company but of the whole field. But he adds that researchers have not yet provided “the evidence and the proof that we are doing better or as good as the animal models.” Maass thinks that head-to-head comparisons are essential to good science. After all, if a NAM doesn’t outperform an animal model, or works best as a complement rather than a replacement, that needs to be known. He also believes such studies could convince skeptics. Maass mentions the debut of the iPhone, when people saw for the first time how well a phone could work without buttons. “That was an ‘aha!’ moment,” he says. But for NAMs, “that moment is still lacking.” Changing Scientific Habits Even when those head-to-head comparisons are made, though, and regulations are appropriately changed, adoption can be slow. In the mid-1990s, researchers developed and validated the monocyte activation test—an assay that uses human blood cells to predict immune response—to replace the rabbit pyrogen test, which involves injecting a compound into a rabbit’s ear and monitoring the animal’s rectal temperature. But it wasn’t until 2010 that the European Pharmacopeia—the official Europe-wide standards for such testing—accepted the monocyte activation test as a replacement. And rabbits are still widely used for this test worldwide. Why the slow pace of change? In part because updates to guidance documents referring to animal tests lagged behind, but also because of inertia within the culture and institutions of science. “The formal requirement may disappear, but the informal expectation persists,” says Kathrin Herrmann, a veterinary scientist and colleague of Hartung’s at the Center for Alternatives to Animal Testing. Regulators, grant reviewers, peer reviewers, journal editors—the human infrastructure of science—often still expect to see animal data and are unfamiliar with NAMs. Herrmann is now overseeing a survey of early-career researchers working with, or trying to make the switch to, NAMs. “We consistently hear concerns that NAM-only proposals are perceived as risky by funders, that there is pressure to ‘add an animal experiment’ for credibility, that access to NAM infrastructure is limited, and that career trajectories become uncertain when departing from established animal models,” says Herrmann. The vast majority of drugs entering clinical trials in the United States fail to reach FDA approval [failure rates in pink], with particularly high failure rates in some therapeutic areas. Animal models are embedded in databases, training programs, and the very culture of research. Scientists who use animals may be reluctant to change; their identities as researchers are tied to animals and, more practically, they’ve spent their careers learning the techniques. A toxicologist who has used rats for decades might understandably look askance when asked to take a chance on unfamiliar chunks of polymer and stem cells—especially when human well-being, or millions of dollars, may ride on the choice. Likewise, an academic scientist whose career was built on animal models may not welcome NAMs; a switch may represent the loss of jobs for lab members whose expertise is no longer relevant. “I could see why it’s a hard thing for people to take it up,” says Ingber. Education and training is vital, say NAM proponents. The NIH and FDA now offer resources for researchers interested in NAMs, as do their counterparts in other countries embracing the technologies. Herrmann helps run webinars where researchers and regulators learn to use and evaluate NAMs; Hartung’s modules on Coursera, the online learning platform, have been taken by about 12,000 students so far. “These trainees will set up their own labs. They will go to industry. They will replace the old guard,” says Joseph Wu, director of Stanford University’s Cardiovascular Institute. Wu is also a cofounder of Greenstone Biosciences, a company that uses stem-cell-derived human tissues and AI to model disease and predict drug responses. He’s used that position to introduce researchers to NAMs, helping convince the company’s directors to freely share Greenstone’s large library of stem-cell lines with any academic researchers who want to use them. “I really believe that people should understand how this platform works,” says Wu. “At the end of the day, we’re just trying to advance science.” With enough time—and funding, incentives, training, education, collaboration, and generational turnover—the research culture of drug development and safety testing may shift. Whether NAMs will be used in other areas of science, though, is an open question. Early-stage drug development and regulatory testing account for roughly 30 percent of animals used in experiments; the rest are used in basic biological research. Replacing those animals is less straightforward, but it may be possible: Ingber describes organ-on-a-chip-based insights into inflammatory bowel disease, preterm birth, and treating viral infections that couldn’t have been made in animals. Hartung calls the adoption of NAMs in toxicology a “lighthouse function,” helping guide the way for other types of research. “Suddenly, all the dams have opened,” he says.
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Building useful robots starts with understanding the people who use them. For Charlie Kemp, cofounder and chief technology officer of Hello Robot, that means developing assistive robots that can help people with everyday tasks and support greater independence. In this Robots Guide profile, Kemp shares his path from studying artificial intelligence at MIT to building Stretch, explains how working with people with disabilities has shaped his approach, and offers advice for aspiring roboticists. Read the full profile on IEEE’s Robots Guide.
IEEE TryEngineering is dedicated to inspiring intellectual curiosity in children. The technologies shaping our world, including in the realms of artificial intelligence, electric vehicles, and ocean exploration, are evolving rapidly. Helping young learners understand the concepts is essential to preparing the next generation of problem-solvers, creators, and engineers. TryEngineering has introduced a STEM book series for youngsters ages 8 to 12 through the Lerner Publishing Group. The series, Tomorrow’s Technology With TryEngineering, Powered by IEEE, makes complex topics more approachable and engaging, with each book combining age-appropriate explanations, real-world examples, and design challenges that encourage curiosity and critical thinking. The series is based on ebooks and videos available at tryengineering.org. For the series, TryEngineering partnered with several other IEEE groups including the Communications, Computer, and Oceanic Engineering societies and the Transportation Electrification Council. Whether used in the classroom, a library, or at home, the books can help pupils connect STEM concepts to the technologies they encounter every day, including computers and smartphones. Six topics in the collection Here are the books in the new collection: Artificial Intelligence: The Future of Smart Technology explores the systems behind streaming services, search engines, and health care. Readers learn how AI works while exploring ethical concerns such as bias, deepfakes, hallucinations, and privacy. Pupils can better understand one of the most influential technologies of our time as it evolves. Communication Technology: From Morse Code to Smartphones teaches readers about smoke signals, semaphore towers, telephones, and wireless networks. The pupils can gain an understanding of how engineers are changing communications technology through innovations such as 6G and space-based networks. The book highlights career opportunities in the aerospace and telecommunications fields. Electric Vehicles: Powering the Future of Transportation covers how e-cars, e-bikes, e-scooters, and electrified trains are transforming the way people travel. Readers can discover how hybrid and fully electric vehicles operate and how the batteries that power them work. They also can learn about the roles engineers play in developing smarter transit systems. Ocean Engineering: Protecting Our Ocean Environments highlights the vital ecosystem role played by the world’s oceans, which require careful stewardship. play in our ecosystem. Readers can learn how engineers study the underwater world using submersibles and floats, how they address pollution, and how they protect marine environments. Semiconductors: The Building Blocks of Modern Electronics focuses on the technology behind nearly every electronic tool we use. Microchips power smartphones, computers, and countless other products, all thanks to semiconductors. Readers can learn about insulators and conductors, how microchips are manufactured, and why semiconductor engineering is a fertile field for innovation. Signal Power: The Hidden Waves Behind Modern Tech explores how engineers analyze and manipulate signals to make technologies work more effectively. Whether it is a phone call reaching the correct person despite background noise or a medical device monitoring a patient, signal processing plays crucial roles in modern life. The book introduces different wave types, the signal processing workflow, and careers in the field. The book series can help children understand the technologies shaping the world around them while encouraging them to think like engineers and innovators. By connecting STEM concepts to real-world applications, the series can make learning more meaningful and engaging. The Tomorrow’s Technology With TryEngineering series is available through Amazon, Bookshop, and Lerner. More about the collection may be found here.
It’s 6 a.m. on a cold January morning in 2002 in New Delhi. It’s still dark outside, and I’m in the kitchen preparing breakfast, packing lunches, and getting my two children ready to catch the school bus when, for the third time in a week, the power goes out. No lights, no mixer to finish my daughter’s puttu—her favorite rice dish—no kettle, no toaster. The bathroom is dark, and the kids are upset. It will probably be hours before the power comes back on, so I grab a flashlight and light the candles that are set up around the house for these occasions. We’re behind schedule now. We pack the food we have, bundle up as the house turns chilly, and head outside, leaving a mess in the kitchen. We make our way to the bus stop in the dark—the streetlights are out, too—only to discover my daughter has missed her ride. Again. I’ll be late for work at Jamia Millia Islamia, a university where I am a professor of electrical engineering and teach power systems and smart grids. I just hope the power is on there. This was a common scene for my family and all of Delhi in the early 2000s. Power outages happened almost daily and lasted hours. When the power was on, the quality was so poor that it would dim lights, flicker screens, and wreak havoc on appliances. Customer service at the power utilities essentially didn’t exist. A child walks in July 2007 past a store in New Delhi specializing in reconditioned generators. The fear of power cuts during summer heat spurs demand for these generators so that residents can produce their own power.Nicholas Bradley/AFP/Getty Images These problems had been getting worse through the 1980s and 1990s. The cause: an aging distribution grid bereft of crucial technologies, and electricity providers with little accountability. The situation became so bad that the city was losing more than half of its power through obsolete equipment and theft. These staggering losses meant that utilities got paid for only a fraction of the electricity they were trying to deliver. And the lack of funds prevented them from investing in better grid infrastructure. But over the last quarter century, a remarkable effort by the government and the city’s distribution utilities has turned Delhi’s grid into a reliable, modern system. Power losses have shrunk from over 50 percent in 2002 to 5 to 6 percent in 2026—on par with France and Belgium, and better than Greece and Serbia. Delhi’s grid reliability index, a measure of how often electricity can be counted on, stood at around 70 percent in 2002 and has now topped 99.9 percent. The bustling Main Bazar in the Paharganj neighborhood of Delhi increasingly uses more nighttime electricity, but reductions in electricity loss help counter demand. iStock With reliable power, businesses across the city have blossomed. The streetlights are bright. The number of electric vehicles, including city buses, is growing daily. Quality of life has improved. Today, my family is comfortable year-round in our home despite Delhi’s scorching summers and cold winters. The chaos of losing power no longer hinders me from getting to work. The city still has problems—pollution, overcrowding, noise—but thankfully, reliable power is no longer among them. The transformation of Delhi’s grid can serve as a model for other cities that suffer from decrepit power infrastructure. Regions of Albania, Argentina, Bangladesh, Brazil, Estonia, India, Kenya, Pakistan, Sri Lanka, Uganda, and Venezuela are reeling from heavy losses in their distribution grids. Their problems look like Delhi’s 25 years ago. I believe it’s possible to improve electricity in these places by adapting the changes Delhi made. Here’s an inside look at how the city accomplished it. Delhi’s Power Grid and Energy Mix The city of Delhi hosts the capital of the Republic of India, and sits along the Yamuna River in the northern part of the country. It’s home to about 23 million people and is one of the most densely populated areas in the world. Delhi’s grid includes thousands of kilometers of power lines, and peak electricity demand reached an all-time high this year of 8,748 megawatts. The city currently buys 76 percent of its power from central generating companies and private players from neighboring states. Energy generation within the city is restricted to natural gas and renewable sources. Nearly 48.5 percent of the city’s power comes from coal, about 26.5 percent from natural gas, and the rest from carbon-free sources, led by hydropower at 15.6 percent. Tata Power replaced about 5 kilometers of overhead lines with underground cables, which reduced electricity loss and improved the aesthetics of Delhi’s streets, such as the Janta Flats in the Shalimar Bagh neighborhood.Tata Power-DDL By the early 2000s, Delhi’s nearly 100-year-old power distribution system was in serious disrepair. Everything was old—lines, transformers, circuit breakers, switches. New grid technologies were needed to keep up with new kinds of electricity loads, but there was little money to upgrade components. The shabby state of the grid caused many problems, most notably high electricity losses, where electricity vanishes primarily as heat. The cause of the losses was a classic electrical problem: too much current flowing through a network that wasn’t designed to carry it efficiently. To understand the problem, it helps to understand how modern power grids work. Typically, they include generation, transmission, and distribution. After power is generated, transformers convert the electricity to high voltage levels—typically 132, 220, 400, or 765 kilovolts in India. Transmission lines then carry the power over long distances to receiving substations that are closer to where customers need electricity. Transformers then step down the voltage (to 66, 33, or 11 kV in India) and distribution lines branch out, carrying the power to customers. The whole grid works primarily on alternating current. Electricity Losses by Country, 2002 vs. 2023 Distribution networks carry both active and reactive power. Active power is the energy used to perform useful work (and is measured in watts). Reactive power is the power that flows back and forth in an electric circuit, building electric and magnetic fields (measured in volt-ampere-reactive, or VAR). Although it doesn’t perform useful work, reactive power is necessary for many devices, such as induction motors, transformers, and computers (typically any circuit or device with inductance or capacitance elements). When there are a lot of devices consuming reactive power on the same line, the overall current carried by the line—the sum of the active and reactive current—must increase. The more current in the line, the more the line heats up and the more energy that’s wasted as heat. In addition to current, resistance in the line will increase losses as well. Resistance is when electrons encounter opposition as they move through the conductive material (typically aluminum in a power grid). Longer lines with many branches and connection points will increase resistance. The rule of thumb is that line loss equals the square of the current multiplied by the resistance. Reactive power creates a second problem: It causes the voltage along the line to drop. And when the voltage falls, many modern electrical devices try to maintain roughly the same level of performance by drawing more current. That higher current produces even greater losses in the line and causes the voltage to fall further. Meter Technology Impacts Electricity Losses In a healthy grid, the utility will take compensatory measures to lower the current and maintain the voltage all the way to the ends of the lines. But in Delhi, this wasn’t happening. The result was a vicious cycle. Reactive loads increased the current, the higher current increased energy losses and lowered the voltages, lower voltages forced devices to draw more current and further increased the losses. In some parts of Delhi, the effect was so severe that residents took matters into their own hands. A colleague of mine who lived in a different part of the city constantly experienced voltage that was too low for her appliances to operate reliably, so she had to install her own voltage stabilizer. At my home, we bought an inverter and battery system to keep a fan and a few lights running during the many outages. Electricity Loss and Theft in Delhi The losses in Delhi weren’t caused solely by technical problems. Theft of electricity was rampant, by both the powerful and the powerless (in both senses of the word). Businesses, residential customers, and utility employees with vested interests would siphon electricity from the grid. It was easy to illegally hook into a streetlight or a distribution line running close to one’s house or factory. Utilities didn’t have the resources to identify theft or penalize offenders. Even if they could, the courts were already overburdened, and an electricity regulatory commission that could push for reforms had not yet fully formed. Updated meters have made billing easier and more accurate. Tata Power-DDL Making matters worse, the utilities and their employees were rarely held accountable for their actions, and so corruption plagued the system. Junior engineers and line workers, many of them lacking appropriate technical skills, were tasked with handling nearly every issue, including outages, flickering, and bill payment. This was too much authority in the hands of people with too little training. On top of that, customers didn’t pay their bills. Meters were old, frequently faulty, and easily tampered with. Utility employees would take a meter reading by visiting the customer’s property, noting the reading in a book, entering it in a ledger or on a computer back at the office, and converting it into an electricity bill that would get dropped off at the customer’s property. This process left a lot of room for incorrect billing. To pay a bill, customers had to stand in long queues at the utility offices, which had limited business hours. Not wanting to take off a half day of work for this, many customers simply didn’t pay. And there was no penalty for not paying—there were no regulations allowing the utilities to cut off a customer’s power. (I paid my bill by having a family member stand in line for me.) The combined commercial and technical losses left Delhi’s utilities collecting payment for less than half of the electricity they were supplying in the early 2000s. India’s Electricity Act and Power Reforms Such problems weren’t unique to Delhi. On average in 2002, state utilities across India experienced electricity losses of nearly 37 percent. My country desperately needed systemic reforms, but authority over electricity was split between the central and state governments so any decision-making was fractured. States managed most of the generation, as well as transmission and distribution, while the central government oversaw generation that supplied multiple states, such as hydropower, fossil fuel plants, and nuclear plants. The central government could push reforms, but the states determined whether those reforms would succeed. Making matters worse, most states put a single organization in charge of generation, transmission, and distribution, giving that entity too much control and reducing transparency and competition. A team of technicians with BSES Rajdhani Power maintains an insulator string on a large power transformer in 2011. BSES Rajdhani Power In 2001, India’s central government began writing some historic legislation that became the landmark Electricity Act, 2003. Among the grand reforms aimed at transforming the country’s power industry, it unbundled state oversight of grid networks, creating separate entities for generation, transmission, and distribution. It also opened up the power sector to privatization. It allowed large electricity customers to bypass local distribution companies and purchase electricity from competitors or build their own power plants. It created a central regulatory agency responsible for determining interstate tariffs and promoting market competition in the power sector. And it created mechanisms for prosecuting electricity theft. Hundreds of capacitor banks have been installed in Delhi to supply reactive power at strategic locations and help stabilize voltage.Tata Power-DDL In 2002, Delhi was already taking drastic action to fix its grid. The organization overseeing Delhi’s distribution, the Delhi Vidyut Board, was broken up and two private companies—BSES (now Reliance Infrastructure), and Tata Power—took over distribution. They faced a Herculean task. Tata Power, serving the northern half of Delhi, would have to tackle a combined commercial and technical electricity loss of 53.5 percent. BSES, whose territory was split between two subsidiaries, was facing 51.5 percent losses in South Delhi and 63.1 percent losses in East Delhi. “The company inherited a deteriorated and overloaded network, massive power theft, weak billing and collection systems, inaccurate consumer records, and an aging, largely untrained workforce,” Dwijadas Basak, CEO of Tata Power, told me. There were over 100,000 unresolved billing complaints, 20,000 pending connection applications, and frequent supply failures, which had severely eroded consumer trust, he added. Both Tata and BSES devised sweeping reforms and human resource development initiatives. The companies followed their own paths over the years, but ultimately implemented similar changes, with similar results. Delhi’s Electricity System Overhaul Fixing Delhi’s grid was a journey that involved all stakeholders, including customers, city authorities, and utility employees at all levels. The utilities revamped their organizational structures, diminishing the power of junior staff and creating separate teams to focus on specific tasks. Long-term employees of the erstwhile Delhi Vidyut Board received training from the up-and-comers at the new companies. On the technical side, both companies installed digital control systems that let them monitor and operate the grid from a central location. Known as SCADA, or supervisory control and data acquisition, the systems offered a bird’s-eye view of the infrastructure, including the status of equipment, voltage, current, power flow, and switch positions, with updates in seconds. This helped the companies identify areas of high loss and theft and make faster decisions based on accurate information. The SCADA (supervisory control and data acquisition) system at Balaji Estate in Delhi’s Kalkaji neighborhood serves as the nerve center of BSES Rajdhani Power’s distribution network in South and West Delhi. It enables real-time visibility, remote control of grid operations, fault identification and isolation, and load management. BSES Rajdhani Power The utilities also replaced aging transformers and circuit breakers and created extensive maintenance plans for equipment. In 2002, 11 percent of the transformers in the region were failing at any given time. That rate is less than 1 percent today, according to Tata. Crucially, the companies installed hundreds of capacitor banks, including some mobile ones, to supply reactive power at strategic locations. This improvement reduced the total current flowing in the distribution lines and helped stabilize the voltage. They also installed voltage regulators at points in the system where voltage tends to drop. To reduce theft, the companies replaced bare distribution wires with insulated lines—a single cable for three phases—which made it harder to tap into the lines. The cables also reduced outages because they’re better at preventing ground faults, which can occur when, say, a tree branch falls on the line. Workers received better sensors and tools to do their jobs safely and accurately. For instance, they were given helmet-mounted voltage sensors, which are safer than handheld ones, and thermal scanning tools to detect hidden defects in the insulation of high-voltage equipment that could otherwise have led to catastrophic failures. To reduce inaccurate billing and meter tampering, the companies replaced the old electromechanical meters with digital ones that are read with handheld devices. In some locations, radio-frequency-based group metering systems were installed by Tata to consolidate multiple customers’ meters into one. The data is then wirelessly transmitted to a central database, eliminating the need for individual meter readings. The companies are now trying smart meters, which give consumers more control over their electricity bills and give utilities remote control of some equipment (with the customer’s consent). To encourage people to pay their bills, the utilities installed kiosks that are available 24 hours a day, and they created a web-based payment system and mobile app. Incentives for early bill payment and community-engagement programs also helped. Assistance from Delhi’s law enforcement considerably reduced electricity theft. Tata Power hired women living in the 223 slums it serves in the northern parts of the city to knock on neighbors’ doors and remind them to pay their power bills. These payment collectors [left and center], known as abhas, were photographed while speaking with a customer [right] in the Sanjay Basti area of New Delhi in 2017. Prashanth Vishwanathan/Bloomberg/Getty Images In areas where theft was particularly rampant and losses were as high as 83 percent, according to Tata, the companies took a different strategy. These pockets of Delhi were predominantly occupied by low-income families. Tata Power, and later BSES, worked to improve the water supply for these residents and provide educational opportunities, such as instruction in reading and writing in Hindi as well as financial literacy. These efforts focused on the women, who were at home more, and paid them to collect electricity payments from their neighbors. Bill payment rates from these areas are now on par with those of other parts of Delhi. In recent years, some customers have been installing rooftop solar panels to take advantage of subsidies and incentives. This trend can reduce electricity losses further because the energy generated at the customer end reduces current in the distribution lines. Customers are also installing more LED lights and energy-efficient appliances, reducing the load in the system. BSES is using AI to help detect theft. The algorithms analyze consumption patterns in pockets where losses are higher than they should be. The company is also using AI to forecast demand, fine-tune operational efficiency, and provide chatbots for customers. Quality of Life Improves in Delhi Life in Delhi is better than it was 25 years ago. I’m not worried that the power may go out and force me to reschedule my activities. My uninterrupted Wi-Fi gives me peace of mind, and my heating and cooling systems keep me and my family comfortable. I rarely need to use our old inverter and battery. The sharp rise of e-rickshaws in Delhi has increased demand on the power grid. Sajjad Hussain/AFP/Getty Images The number of businesses in Delhi has increased substantially, in part because of the access to quality power. People can confidently buy products that depend on electricity. In fact, the city’s peak electricity demand has tripled since 2002 due to the increase in population, commercial activity, and use of electrical gadgets. And then there’s the benefits to the planet. One unit of electricity that isn’t frittered away is one less unit that must be generated, not to mention the reductions in carbon emissions. Still, there’s work to do. Some areas of Delhi continue to have high losses, driven partly by the illegal charging of e-rickshaws. Elsewhere in India, the states of Himachal Pradesh, Madhya Pradesh, Maharashtra, and Telangana still experience losses of about 17 to 23 percent despite the sweeping Electricity Act, 2003. There are many reasons for the ongoing losses: long distribution lines to remote villages, less digitization, and inefficiencies in billing and collection of payments. These regions, and others around the world, can learn from Delhi’s grid comeback. Recently, power losses have increased substantially in countries such as Argentina, Greece, Jamaica, and Morocco, according to the World Bank, and some of the causes are similar to those that Delhi faced back in 2002. Meanwhile, Australia, most countries in North America and Europe, and a few countries in Asia and Africa experience low electricity losses as they invest regularly in their distribution infrastructure and the ethical enforcement of rules. In China, for example, losses have gradually been cut in half, from 7.1 to 3.4 percent. In Latvia, losses plummeted from 25 to 5.8 percent. What’s important is a comprehensive approach. Technologies like smart metering, AI, and analytics certainly help, but equally important is that people in the field are trained and take responsibility for their jobs, and that laws are enforced and payments collected. “Sustainable loss reduction cannot happen through technology alone,” Abhishek Ranjan, CEO of BSES Rajdhani Power told me. “Technology is an important enabler, but long-term success comes from combining it with disciplined execution, operational accountability, and strong consumer engagement.”
My job is to translate dry and unrelenting code into a user interface of surpassing beauty. With my mouse, I roll one pixel after another up the vast anthill of the internet. My dream is to translate the visions of the holy ones into a communication protocol of universal wonderment. I want to launch shreds of light into the air to fall like a layer of diamonds on the endless mountains of the Web. Don’t imagine these dreams are limited by the LANs of the software lab. Between here and the ultimate unlimited interface of my aspirations lives a dazzling darkness, wide as the universe and thin as a hair.
Even before France approved legislation banning social media for children under 15 last January, 13-year-old Benjamin was already wondering what life without social media would look like. “If we want to play football, we won’t be able to organize it. What will we do? Send letters instead?” he joked in an interview for Le Monde. His reaction captured the central challenge behind the growing wave of youth social media bans: Removing access is one thing; understanding what those platforms mean in children’s lives is another. Within weeks of Australia’s similar ban, the country’s eSafety Commissioner reported that platforms had restricted access to 4.7 million under-16 accounts. Two months later, though, one in five Australian teenagers under 16 was still using TikTok and Snapchat, according to a parental-control data company. But even if all children’s social media accounts were to disappear, do such bans actually make children safer online? Governments are moving ahead without answering that question as they follow Australia’s lead. Indonesia’s child-safety framework, which took effect in March, bars children under 16 from holding accounts on “high-risk” platforms. The U.K. government has announced plans to ban social media for under-16s, add default overnight social media curfews for 16- and 17-year-olds, and extend child-safety rules to cover risky AI features. And on 17 September, the European Commission proposed the EU KIDS Act, which would bar children under 13 from social media, set 15 as the EU-wide minimum age for opening an account independently, and require platforms to show that their services are age appropriate and safe by design. But based on my experience working on Child Online Protection initiatives with the International Telecommunication Union (ITU) across Southeast Asia and the Pacific, I know the bans don’t address the real problems. Instead, we should be paying more attention to the systems that generate harm in the first place—namely, recommender algorithms, engagement-maximizing design, opaque moderation, and extractive data practices. Account removals are not the same as online child safety My experience working on protecting children’s online safety has taught me three main lessons: First, the public institutions responsible for child online protection often lack the staff, budget, or technical capacity to enforce complex online safety policies. Indonesia is illustrative. A 2026 UNICEF evaluation found capacity constraints among service providers, long-term funding uncertainty, and a need for specialized personnel. At the local level, some staff lacked digital skills, while budget constraints left some areas reliant on external support. Second, many children, and often their parents, lack the digital literacy and critical thinking skills needed to navigate online risks safely. My policy research on child online protection in Indonesia, published earlier this year in Digital Society, found substantial gaps that account removals cannot repair: Many children lacked guidance on navigating the internet safely, and large numbers did not know how to report harmful experiences. And third, the platforms have limited independent oversight as they identify underage users, design age-verification systems, and report their own compliance. In Indonesia, platforms themselves are responsible for carrying out age verification, while the Ministry of Communication and Digital Affairs oversees compliance. TikTok’s appeals process for users flagged as underage, for instance, can require a government-issued ID and selfies, which is a problem because it involves collecting the additional personal data on an ID card, beyond that needed to confirm age. Will government regulators ensure that TikTok handles that data responsibly? The privacy paradox of proving age Every age-based ban creates an engineering problem: How can a platform reliably determine that a user is old enough, without intruding on other information? Governments and companies may use identity documents, parental authorization, app-store checks, or facial age estimation. Each approach has trade-offs among accuracy, privacy, accessibility, and resistance to circumvention. There are also technical issues. One tool, facial age estimation, draws on enormous databases but it is probabilistic, not exact, because people vary so much. It’s also been shown to misclassify both children and adults. The challenge should not merely be to “verify age.” It should be to prove that someone is above a threshold, without disclosing their identity, birth date, or other information third parties might use to create a marketing profile. The European Commission’s age-verification blueprint challenges companies to verify ages without collecting all that additional information. Privacy-preserving technologies offer promising ways to achieve this. Zero-Knowledge Proofs (ZKPs) can confirm that someone meets an age threshold without revealing their identity or exact date of birth. W3C Verifiable Credentials are cryptographically verifiable digital claims that can disclose only the information needed, such as “over 16.” And device-based age signals can allow a phone or app store to share an age range without revealing a user’s exact birth date. But these methods still require rigorous security testing, common standards, independent oversight, and clear limits on data retention. Otherwise, poorly designed child-safety policies risk creating permanent identity infrastructures in which businesses, not people, control personal data. Where connection goes when a platform closes Blocking access to a platform redirects some young people, but not always where expected. Early anecdotal reports in Australia pointed to teenagers migrating to smaller, less-regulated platforms like Yope, a pattern the Cato Institute flagged as a “whack-a-mole” problem for regulators. But industry data collected two months later found no broad-based shift of that kind, aside from a small uptick in WhatsApp use. Many teens simply found a way to stay on the banned platforms. This points to a deeper gap in current society: the erosion of youth “third places“ physical spaces where young people have room to socialize and build identity outside home and school. As those spaces have diminished, commercial communications platforms have absorbed that role. For many teenagers, social media workarounds are merely inconvenient. But for isolated, marginalized, disabled, or LGBTQ+ youth who depend on online communities for support that’s otherwise unavailable, displacement can mean losing certain kinds of belonging, or having to move to a platform with even weaker oversight. How to design safer online systems for children If blanket social media bans don’t work, then what will? The platforms have created many of the conditions that governments are now trying to contain: engagement-optimized recommenders, intrusive data practices, weak safeguards against unwanted contact, and features such as infinite scroll, autoplay, streaks, and persistent notifications. These design patterns increasingly face regulatory scrutiny, including what’s required under the European Union’s Digital Services Act. A 2026 study from the 5Rights Foundation that tracked children’s device use minute by minute found that the user interfaces shape children’s attention, sleep, and well-being in real time. A more durable response would regulate those interfaces directly, treating children as legitimate users whose privacy, agency, and well-being are required protections, not afterthoughts. That means designing for safety from the outset. One example would be for children’s apps to have high-privacy defaults, such as private accounts and location sharing switched off for minors. They could also have recommender systems that explain the main factors shaping a feed and give young users more control over personalization. The European Commission has published age-appropriate interaction guidelines that limit unsolicited contact and prevent minors from being added to groups without consent. Rules could also prohibit engagement-maximizing features that demand users’ attention, such as autoplay, infinite scroll, usage streaks, read receipts, and push notifications, by disabling or limiting them by default. Governments should define measurable outcomes and fund independent evaluation, platforms should give researchers meaningful data access, and engineers should audit age-assurance systems for bias and data leakage. Schools, parents, and children themselves need a seat in designing the technology that’s designed to protect children. If policymakers still decide to remove an infrastructure for youth connection, they should offer something better in return. Social media bans may reduce some forms of exposure to harmful content and may be justified for particular ages, services, or risks. But they are just one tool, not a comprehensive substitute for safer design, accountable platforms, digital literacy, institutional capacity, and noncommercial digital “third places”—moderated communities, creative spaces, and public-interest platforms designed for youth participation rather than profit. The first wave of social media restrictions isn’t enough to keep children safe. Governments are still measuring what’s easiest to count, while neglecting harder-to-measure outcomes such as children’s access to safe third places and meaningful social connection, both online and offline. Until governments can show evidence that harm has actually declined, they will keep mistaking account removal for safety.
In Guadalajara, Mexico, many high schools have motivated teachers and talented students with an interest in science, technology, engineering, and mathematics, but they lack access to advanced tools such as robotics laboratories. The resources shortfall limits the students’ opportunities for hands-on learning on cutting-edge applications. A team from ITESO, Universidad Jesuita de Guadalajara, is working to change that. Through the EPICS in IEEE initiative, a multidisciplinary group of 15 engineering students, faculty advisors, and IEEE Guadalajara Section volunteers developed RoboMeshA. The portable, self-contained educational platform brings robotics and AI experiences into classrooms. EPICS is administered by IEEE Educational Activities and funded by the IEEE Robotics and Automation Society. A mobile laboratory Rather than requiring a school to build a dedicated computer lab or install complex software, RoboMeshA operates as an all-in-one mobile learning network. “RoboMeshA brings robotics and AI to students who don’t have access to specialized facilities or preinstalled software,” says team member Fernando Vidal Luna, an IEEE student member and a mechatronics engineering major at ITESO. Students connect directly to the platform from a user-friendly web browser. They can interact with the robot manually or use its control modes to watch it move and detect and avoid obstacles. “The project combines mechanical design, embedded systems, control engineering, computer vision, and AI into a single robotic system that functions as a mobile learning laboratory,” says faculty advisor Jorge A. Lizarraga. The team says young students are interested in technology, programming, and robotics but don’t have an opportunity to work with systems that combine mechanics, electronics, software, and control. “RoboMeshA allows students to see how all these disciplines work together in a tangible and understandable way,” says team member José S. González, who also is studying mechatronics engineering. The team has built two units and is developing a modular coupling framework to expand the system’s capabilities for research and classroom demonstrations. The structured system design approach connects independent software components while minimizing internal dependencies, enabling four RobotMeshA robots to operate together. Overcoming design challenges The team faced significant hurdles while designing the project. “One key challenge involved the robot’s structural design,” Luna says. “It wasn’t only about making a chassis where all the components fit and the design had sufficient stability, rigidity, and weight distribution. It was also about ensuring that the electronics were protected while still being accessible for maintenance, testing, and modifications.” “It was also challenging to design a platform that could be used by students with different levels of experience,” González adds. “When students realize the technology they develop can inspire others and improve lives, engineering becomes far more meaningful.” —Luis Fernando Luque-Vega The team partnered with the CETI Colomos and Prepa ITESO high schools to validate the platform in classroom settings. “We wanted the first interactions with the robot to be simple and intuitive,” González says, “such that students could simply power the robot, connect to its network, and begin interacting with it, rather than having to deal with software installation, extensive configuration, or troubleshooting.” Engineering with social impact Many of the students who participated were from ITESO’s applied professional projects program. The experience offered them practical training in project management, system integration, and user-centered design. The team also presented a research paper and a project poster in May at the Engineering Congress of the Jesuit University System. “Seeing a design move from a digital model to a physical system was invaluable,” González says. “Working with students from different backgrounds taught us to listen to end users and design for their actual needs.” Project lead Luis Fernando Luque-Vega, an IEEE member, says he’d like the venture to serve as a blueprint for engineering education. “I hope RoboMeshA is adopted by schools, universities, and IEEE student branches across Mexico and internationally as a model for integrating technical innovation with community engagement,” Luque-Vega says. By pairing engineering talent with community service, initiatives such as EPICS in IEEE demonstrate how targeted support can turn academic concepts into real-world solutions. “When students realize the technology they develop can inspire others and improve lives, engineering becomes far more meaningful,” Luque-Vega says. For more information on service-learning opportunities, visit the EPICS website.
I’ve seen two total solar eclipses and have been duly impressed by what happens as the moon casts its shadow on Earth. But recently I’ve become even more intrigued by a similar phenomenon that doesn’t involve the sun or the moon—something called an asteroid occultation. That’s what happens when an asteroid orbits around the solar system and blocks the light of a distant star you’re viewing from Earth. Like the moon during a solar eclipse, the asteroid casts a predictable moving shadow on a swath of Earth’s surface—a small silhouette in the dim light bathing us from that one star. When such a fortuitous alignment occurs, amateur astronomers can discern things about the asteroid that professionals can’t readily measure, even with their giant telescopes on high mountains. That’s because amateurs are nimble: They can be in just the right place at just the right time to measure an asteroid’s fleeting shadow, which could be just a few hundred meters wide and traveling at tens of kilometers per second. With enough observers, they can collectively map that shadow, revealing the asteroid’s shape. Even folks on a limited budget can do this, because the size of an asteroid you can measure doesn’t scale with the size of your telescope. If the occulted star is relatively bright, you don’t need much of a telescope at all. How Do You Catch an Asteroid Occultation? My own efforts along these lines have been with a modest 5.1-inch-aperture (130-millimeter) Newtonian telescope that sells for about US $300. I attach it to a small equatorial mount ($150) that can track the stars by virtue of some added stepper motors driven by an open-source telescope controller called OnStep. (You could save yourself the time, trouble, and expense of all that DIY hacking by purchasing a motorized mount for as little as $300.) A flasher provides a calibrated time base for light-curve measurements. It relies on a GPS module [top] to provide a high-accuracy pulse once per second, is gated by an Arduino nano [middle] to prevent flashes occurring at the moment of occultation, and is then passed to a LED [bottom].James Provost I bought an inexpensive astronomy color camera on Amazon for $260 to take images at the video rates required to capture the rapid changes during an occultation. I chose this camera because it has a relatively large sensor, Sony’s IMX585, which provides a large field of view. A monochrome camera would be better for asteroid occultations, but the monochrome version of this camera is harder to come by and more expensive. If you’re looking for a cheaper option, the monochrome ToupTek G3M662M (about $200) would be a good choice, although its sensor is smaller. Knowing where and when to catch an occultation in your area is of course critical and can be calculated using free PC software found on the International Occultation Timing Association (IOTA) website. If you plan to contribute your observations to IOTA to increase the body of scientific knowledge about asteroids, you will need to calibrate the timing of your images. You can’t just depend on the time stamps your computer adds to the video frames, which can be way off. For time calibration, many practitioners use a flasher: a red LED driven from the pulse-per-second signal from a GPS receiver. Asteroid observers use such a pulsing LED positioned in front of their telescopes to calibrate the timing of the images they take. With some effort, it’s possible to reduce the uncertainty to just a handful of milliseconds. The flasher I built uses a GPS module that I had on hand. But I’d recommend you purchase a different one that accepts an external active antenna. HiLetgo’s NEO-7M $12 module might be a good choice—but don’t forget to remove its antenna-coupling capacitor (marked as C2 on the circuit board) if you do attach an active external antenna to it. How Do You Make a Telescope Flasher? You can’t let the flasher just blink away every second, though, because its light might stomp on the very signal you’re trying to detect. So alongside the GPS module, my flasher also contains an Arduino Nano, plus two transistors, three resistors, and a switch. I wired these components together so as to drive the LED directly from the pulse-per-second signal coming from the GPS. The signal passes through a transistor controlled by the Arduino so that the flashes can be started and stopped at prescribed times. I can then program the flasher to produce calibrating pulses near the start and end of each recording session, while suppressing the flashing around the occultation itself. Over time, an asteroid such as Duccio will pass in front of multiple stars [below]. Each time it does, it will block the light from a star [above] for a time that depends on its width along the line of transit. By combining multiple light curves, it is possible to map the shape of the asteroid.James Provost So far, I’ve managed to record four occultations that have occurred within easy driving distance of my home in North Carolina. The first was quite short, by an asteroid a mere 4 kilometers wide. The star involved was rather dim, so I really had to squint at my laptop screen to see the star momentarily blink out. The star in my second occultation was brighter, and the dimming much longer, so no squinting was required. My third observation tested the limits of my little telescope with a very dim target star, requiring quite long exposures per video frame (about a third of a second). Thankfully, the asteroid was a big one (120 km wide), so the occultation lasted a few seconds, and I could discern it. The asteroid I targeted last, named Duccio, is about a dozen kilometers wide and orbits in the main asteroid belt between Mars and Jupiter. Its shadow, moving at a clip of some 24 km per second, took about a half second to pass over me. The star this asteroid blocked was bright enough for me to record the event very distinctly at 24 frames per second, providing excellent time resolution. Asteroid occultations offer a wonderful natural experiment. And unlike a solar eclipse, observable events probably take place near you multiple times each month. So with a little knowledge and the right gear, you can observe them. You just have to wait for the stars—and the asteroids—to align.
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This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free! If you have kids, they’re probably back in school right now after the summer break. Mine are too. My kids range from 9 to 20 years old, and lately I’ve been thinking a lot about what their future careers are going to look like. Some schools are embracing AI in the classroom. Others are banning it completely. I’m not sure either side has figured out the right answer yet—I’m not sure any of us have. What I do know is that AI is already changing how engineers, and nearly every other knowledge worker, does their job. So I’ve been thinking: What skills do I actually want my kids to develop for an AI-augmented workforce? 1. Learn to write The more I use AI, the more convinced I am that it’s a multiplier, not a substitute. Give a great researcher AI tools and they can accelerate their research. Give an experienced software engineer the same tools and they can build at an incredible rate. But put those tools in the hands of someone without foundational knowledge and you often get something else you’re used to seeing way too much of: slop. We’ve all seen the vibe-coded applications that barely work. Our inboxes are filled with emails that sound suspiciously identical. Teachers are reading papers that sound like they were all written by the same person. That “person” has the last name GPT. When everyone has access to the same tools, having an actual voice becomes a differentiator. Ironically, strong writing skills might be MORE important because of AI, not less. Text is still the primary way we communicate with these models, so being able to clearly articulate what you want improves what you get back. But more importantly, writing teaches you to develop ideas of your own. AI can help you express your opinion. It shouldn’t manufacture one for you. 2. Learn to speak Like written communication, speaking skills are now at a premium—but for a different reason. We live in a strange moment. We’re more digitally connected than ever, while often feeling increasingly isolated from one another. At the same time, we’re seeing renewed interest in conferences, meetups, communities, and in-person experiences. When human interaction feels scarce, communication becomes more valuable. So learn how to explain an idea in front of a room. Learn how to disagree without being disagreeable. Learn how to tell a story. Learn how to listen. Learn how to persuade someone. There’s a timeless book, originally published 90 years ago, that teaches these interpersonal skills, and it’s probably more valuable for engineers than another white paper on how neural networks work: How to Win Friends and Influence People. An AI can generate a presentation for you, but it can’t convincingly deliver it to a skeptical audience. That takes emotional intelligence. 3. Learn to be bored This might be the hardest one. We have engineered boredom almost completely out of our lives. There’s always a podcast to listen to, a notification to check, a video to watch, a feed to scroll, or now an AI to talk to. Go for a walk without headphones. Eat without looking at a phone. Sit in the car without immediately reaching for something to fill the silence, and let your mind wander. Because boredom isn’t wasted time. It’s a breeding ground for original ideas. The danger I worry about isn’t that AI becomes too intelligent, but that we become too willing to outsource the uncomfortable parts of thinking. The students going back to school today will enter a workforce filled with technology that I couldn’t have imagined when I was their age. I have no idea what the dominant AI model will be by then or even what interacting with a computer will look like. That’s exactly why I don’t want to optimize their education around today’s tools. I want them to learn the skills that will outlive the tools. Write clearly. Speak confidently. Think independently. And every once in a while, embrace boredom. —Brian AI Slop Is Changing How Engineers Review Code Software engineers have entered a new era of code review. The strategies they’re now testing will determine whether AI can actually provide code that’s faster and more reliable when you factor in the review process. If it can, what does that mean for the entry-level engineers who are still learning to code on their own? Read more here. U.S. Tech Firms Change Strategies to Hire International Talent In response to a barrage of actions by the U.S. federal government to limit legal immigration, tech companies are adapting their search for top talent. IEEE Spectrum looked into the responses to proposed changes, like higher fees for H-1B visa applications. Read more here. What It Takes to Be an Adaptable Engineer As AI changes the job market and day-to-day work of engineers, young professionals are often told they need to be adaptable. But what does adaptability actually look like in practice? The skill has different definitions depending on who you ask, but with the right mindset and support from leadership, adaptability can help keep you afloat. Read more here.
Many youngsters fascinated by exploring outer space dream of becoming an astronaut, but few do. One who had the right stuff is Pedro Duque, who was Spain’s first astronaut. The aeronautics engineer flew aboard the space shuttle Discovery and the International Space Station. After retiring as an astronaut, he headed Spain’s Ministry of Science, Innovation, and Universities. Today he is the chairman of HispaSat, a Spanish satellite company. Pedro Duque Employer HispaSat in Madrid Title President and chairman of the board Member grade Honorary member Alma mater The Polytechnic University of Madrid Nearly 30 years after his first mission, Duque is still Spain’s most famous astronaut. Three public schools have been named after him, and he has received numerous awards. This year, the IEEE Board of Directors made him an IEEE honorary member for “contributions to space exploration, leadership in collaborative science and technology programs, and serving as a role model for younger generations.” He was unable to attend the 24 April ceremony in New York City, but he expressed his gratitude in recorded acceptance remarks in his award presentation shown during the event. “We engineers of all specialties recognize the leadership of your institute—the largest and most important engineering society in the world,” he says in the video. “What an honor it is to belong now to an organization whose purpose it is to foster technological innovation and excellence for the benefit of humanity.” Inspired by Apollo 11 Duque says he knew he wanted to be an engineer from a young age. It’s not surprising that he became interested in aeronautics, as his father was an air traffic controller who explained to him how airplanes worked. In 1969, when he was 6 years old, he was inspired to become an astronaut after watching the Apollo 11 moon landing. “I didn’t know anyone who wasn’t attracted to space exploration,” after the moon landing, he says, laughing. “It was presented in such an epic manner, with declarations about its impact on society. The landing made us aware that humanity was exploring new places, and I was keen on knowing more about them.” His dream of becoming an astronaut was unrealistic at the time, he says, because the country had no space program. Spain was ruled by Francisco Franco, who spent little to no money on scientific innovation, Duque says. That changed after Franco died in 1975. The country transitioned to a democratic constitutional monarchy and began participating in research and development programs, particularly with the European Space Agency (ESA). Astronaut duties Duque earned an aeronautical engineering degree in 1986 from the aeronautical and space engineering school at the Polytechnic University of Madrid. His first job was as an engineer in the flight dynamics group at GMV, a space and technology company based in Madrid. He was a member of the orbit determination group and worked at ESA’s European Space Operations Centre, in Darmstadt, Germany. He helped develop algorithms, orbit computational software, and computer models. He was also a member of the space agency’s flight control team for the European Remote Sensing-1 satellite, launched in 1991, and the European Retrievable Carrier, launched in 1992 on the space shuttle Atlantis’s STS-46 mission. In 1990, ESA recruited candidates for its astronaut program—which Duque says rarely happens. He and several colleagues applied. “Why not?” he says. “What we thought we wanted to be when we were little was now possible.” After a considerable selection process, Duque was chosen in 1992 to join the agency’s Astronaut Corps. He trained at the European Astronaut Centre and at the Russian Cosmonaut Training Center (now known as the Gagarin Research and Test Cosmonaut Training Center). In 1994, he served as the prime crew interface coordinator on the ESA–Russian EuroMir space station. He managed communication between the astronauts on the Mir station and the European scientists to help ensure orbital experiments and operations ran smoothly. NASA selected Duque to be an alternate payload specialist on the ground for space shuttle Columbia’s STS-78 mission in 1996. In that role, he was trained to operate and manage scientific experiments, equipment, and cargo during a crewed mission. He also supported the flight from the ground as a crew interface coordinator. His first flight into space was in 1998 as a mission specialist representing the ESA on the space shuttle Discovery’s STS-95 mission. He managed in-orbit tasks, experiments, spacewalks, and equipment operations. He served as a flight engineer in 2003 onboard the Soyuz TMA-3 Cervantes, a joint mission between Russia and Spain to the International Space Station. He operated the station systems, managed daily maintenance tasks, and performed scientific experiments assisted by the mission commander. Memories of flying with John Glenn The years of training and preparation to become an astronaut were rigorous, he says, but the experience was fulfilling. Even though he spent only about 10 days on each mission, he says, “they were very rewarding times because all the preparation paid off, and we got the results we needed.” Some of his favorite memories were viewing Earth during the day and night for the first time, and watching it pass from one phase to the other. Another was seeing the moon flattened to almost a sliver during the few seconds before it set. Experiencing microgravity was a thrill as well, he says. “I also cherish the companionship of the people who worked alongside me in space and on the ground,” he adds. One was John H. Glenn, the first American to orbit the Earth. Duque flew with Glenn on Discovery. They talked about the selection criteria for astronauts. Glenn told him those on board the Apollo and Gemini missions were chosen because they were test pilots, who were thought to be most likely to handle problems or failures effectively and proactively. “The technology in today’s missions is advanced enough that astronauts won’t be needed to handle probable catastrophic equipment failures,” Duque says. “Instead, they will perform experiments and update or fix the devices used in space capsules. But flights to the moon and even Mars will use new types of spacecraft that might not necessarily work as planned, so those astronauts will have to know how to fix them and possibly solve critical problems on their own. “Also, future astronauts will have to live with others in a confined space for months, and not everyone can do that.” The price of sudden fame As Spain’s first astronaut, Duque became a celebrity. Among his honors are Russia’s Order of Friendship and Spain’s Great Cross of Aeronautical Merit and Princess of Asturias Award. He also received NASA’s Space Flight Medal, which is given to an astronaut who flies aboard a U.S. space mission. Learning to navigate sudden fame and being treated like a celebrity was challenging, Duque says. Like many engineers, he was accustomed to working behind the scenes and out of the public eye. “Being famous, both in the profession and the public, was quite difficult in the beginning,” he says. “Being a celebrity doesn’t come easily to me, but after so many years, somehow I learned how to deal with it.” People might assume that an astronaut’s leadership skills come effortlessly, Duque says, but that’s not always the case. “Everybody gives so much importance to your opinion, and sometimes I was surprised by that,” he says. “Being an astronaut, you tend to have a certain kind of leadership style because it’s what you have done for years without knowing it.” Minister of science and other leading roles His leadership style has served him well. After he retired from the ESA in 2018, he was appointed as Spain’s minister of science, a role he held until 2021. He oversaw the government’s policies on scientific research, technological development, innovation, space programs, and higher education. During his term, Spain committed to contributing US $800 million (€701 million) between 2020 and 2026 to the ESA—which at the time was the largest overall investment in the agency’s history. After retiring as an astronaut, Pedro Duque headed Spain’s Ministry of Science, Innovation, and Universities. Today, the IEEE honorary member is the chairman of HispaSat, a Spanish satellite company.Isaac Buj/AP In 2022, he joined Destinus Spain, a European defense manufacturer that develops technologies for aircraft propulsion and auxiliary systems. He advised its strategic committee. The following year, Spain’s government appointed him president and chairman of the HispaSat board. Headquartered in Madrid, the satellite operator provides broadcasting and broadband services for Europe, North Africa, and the Americas. The public-private partnership was in its origin a joint initiative between the Spanish government and private telecom companies. The IEEE honor was a surprise Duque says he was surprised to learn that IEEE added him to its membership ranks. He was aware that a colleague had nominated him, but he deemed it unlikely the nomination would be supported. “When I started looking into who its members were, I wondered why they selected me,” he says. “Obviously, electrical engineering is not my branch, but it could have been, because growing up, I was just as interested in telecommunications as I was in aeronautics. “Most engineers know IEEE for its standards and the work it does in achieving consensus in standards development.” After discussing with several colleagues who were IEEE members about the significance of the IEEE honorary membership—which is bestowed for a significant achievement and impact on society— Duque feels the award is a significant honor. “I’m still in the early phase of figuring out how I can contribute to IEEE,” he says, “and what I can do for the many hundreds of thousands of members, all whom have impressive qualities.”
Throughout her career, roboticist Barbara Mazzolai has turned to nature for inspiration. Now she wants to ensure the technology she builds gives back to the environment, too. After starting her career as a biologist, a chance opportunity saw Mazzolai switch streams to engineering and become an early pioneer of bioinspired robotics. Building on her knowledge of biology’s ability to solve a diverse set of problems, she has developed robots based on octopuses, plant roots, and even seeds. “I’ve always been fascinated by living organisms, [and] by the extraordinary variety of solutions in nature, selected by the evolutionary process,” she says. Barbara Mazzolai Employer: Italian Institute of Technology Occupation: Associate director for robotics; director of the Bioinspired Soft Robotics Laboratory Education: Master’s degree in biology, University of Pisa; master’s degree in eco-management and audit schemes, Scuola Superiore Sant’Anna; Ph.D. in microsystems engineering, University of Rome Tor Vergata But Mazzolai, now the associate director for robotics at the Italian Institute of Technology, in Genoa, also believes engineering needs to reckon with its own impact on the natural world. That’s why she is advocating for a new field of research she calls “sustainability robotics.” In a manifesto published in Nature Machine Intelligence in July, she and her collaborators outline a vision for a new approach to designing robots that’s meant to improve the relationship between nature, humanity, and technology. “We need to reduce the footprint of our technology,” she says. “It’s really about thinking in a different way to open new possibilities for robotics [and] for society.” In this new mode of thinking, Mazzolai considers sustainability a core component of the design. A child of nature Mazzolai traces her fascination with the living world back to her childhood growing up on Italy’s Tuscan coast, close to the port city Livorno. Her father was a public-health inspector and a professional mycologist, and the family spent a lot of time exploring forests and learning about the local fungi and plants. After toying with the prospect of pursuing art, her other major passion, Mazzolai ultimately decided to enroll at the University of Pisa in 1987 to study biology. She was particularly drawn to marine biology, but shortly before graduating with a master’s degree in 1995, she secured a research position at the Italian National Research Council’s Institute of Biophysics studying the cycles of heavy metals like mercury through both living and nonliving parts of the environment. This involved collecting and analyzing samples from water, soil, vegetables, and even humans to understand the impact these metals have on health and the environment. She balanced this work with studying environmental management at the Scuola Superiore Sant’Anna, in Pisa, graduating with a master’s degree in 1998. During that time, however, she learned that the university was recruiting biologists to help design new devices for environmental monitoring. She applied for and got the job in 1999 and began working as a research assistant under renowned bioroboticist Paolo Dario, first developing sensors and then robots meant to monitor air, water, and soil. Even before entering a doctoral program, Mazzolai was promoted to assistant professor in 2004 and shortly afterward made her first foray into bioinspired robotics. In collaboration with colleagues at Sant’Anna, she helped design a soft robot inspired by the octopus. “We proposed it as a paradigm for launching this idea of soft robotics: demonstrating that [robots] can be soft, but at the same time apply strong force to the environment, like the animal does,” she says. Back to school In 2007 Mazzolai enrolled in a Ph.D. in microsystems engineering at Tor Vergata University of Rome, which she balanced with her role at Sant’Anna. She was already relying heavily on microfabrication techniques to develop sensors for her robots, and she was keen to push that part of the field forward. While robots frequently feature sensors designed for perception, such as tactile or proprioceptive sensors, these systems typically focus on understanding the robot’s position in its environment, she says. “But there are few robots that integrate physical or chemical sensors to really understand the environment they move in,” she adds. “I’ve always been fascinated by living organisms, [and] by the extraordinary variety of solutions in nature.” Mazzolai was appointed as a team leader at the Center for Micro-BioRobotics of the Italian Institute of Technology in 2009, where she continued her work on the emerging field of bioinspired robotics. Two years later, she completed her Ph.D. and was promoted to director of the center. Planting the seeds Around this time Mazzolai says she became interested in using plants as a model for new kinds of robots, expanding bioinspiration beyond just animals. In particular, she was captivated by the ability of roots to efficiently explore the underground environment, and she imagined machines with the same deftness could have applications in both environmental modeling and precision agriculture. While many bioinspired robots mimic animals, plants also serve as a muse for Mazzolai. This tendril-like bot can coil around other structures like a vine. Italian Institute of Technology When she first proposed the idea, colleagues were somewhat skeptical of robots based on seemingly static organisms. But in reality, she says, plants move nonstop through a process known as indeterminate growth. “They really grow for their entire life,” she says. “They adapt their morphology, their behavior to the external environment; they repair, they sense, they communicate.” Trying to mimic a system that operates on such different principles to conventional robotics required some serious thinking, however. Mazzolai says that working in bioinspired robotics sometimes requires you to have “two separate brains”—one of a biologist and one of an engineer. The process often involves deep study of the target organism to learn the underlying principles that shape how it operates before trying to engineer a robot capable of mimicking them. “It’s not a copy of natural organisms,” says Mazzolai, because a living organism is both difficult to replicate and has different goals. In the case of plant roots, what makes them so efficient at exploring the soil is that they reduce friction by growing only at the very fine tip of the structure, while the thicker base of the root remains static. This significantly reduces the amount of energy required to push through the earth compared to that of a more conventional drill, which must push the entire structure from above. To realize this principle in a robot, her team developed a miniaturized 3D printer that sits at the machine’s tip and feeds thermoplastic filament through a heated nozzle to build a snakelike body behind it. This allows the robot to push through the soil efficiently. The tip also contains sensors that allow it to avoid obstacles and detect nearby nutrients or water. Making robotics sustainable After spending so much of her career borrowing from nature, Mazzolai is now eager to return the favor. Many modern technologies, including plastics and car batteries, have been developed with little thought about how they will affect the environment at the end of their life cycles, she says. She wants to ensure that robotics doesn’t follow the same path. This is the inspiration for what she and collaborators now call sustainability robotics. The approach has three central pillars: ensuring that robots have minimal impact on the environment; that they’re available to people from across the world and all socioeconomic backgrounds; and that they’re “symbiotic,” providing benefits to both humans and nature. More concretely, Mazzolai would like to incorporate the concept of a life cycle into the design of robots, so that at the end of their useful life these machines can be reused, recycled, or even biodegraded. While that might sound ambitious, she’s confident that all the ingredients to make it a reality are in place. And it’s a vision that she is certain will inspire future roboticists. “There are younger people who want to really work in this field because this is the future, their future,” she says. Facing the threat of ongoing environmental damage, “they want to develop something that can help.”
This article is brought to you by CoolIT, an Ecolab Company. Beyond 250 kW a server rack can no longer be cooled by a hybrid approach of liquid and air. At this density a 70/30 liquid-air split leaves 75 kW of air load. The air cooling system needed to move it brings cost and complexity few operators will accept. The answer is near-total heat capture. Liquid takes effectively all the heat, air falls below 1 percent of the load, allowing the server to run fanless. CoolIT builds these loops today from modular coldplate blocks proven across six generations of fanless designs. Processor thermal design power (TDP) keeps climbing generation over generation. This rising heat load is now cascading into the memory, networking, storage, and power components that once ran comfortably on air. The heat escaped the chip For years the story stayed simple. Cool the processor and let air handle the rest. That balance has shifted. As TDP climbs, heat spreads outward from the processor and cascades into the components around it. Memory, networking, storage, and power now run hot enough to demand liquid of their own. Engineers designing the next generation of AI servers face a board where heat capture rises with every launch. Beyond 250 kW per rack, air cooling becomes the bottleneck. Near-total liquid heat capture enables fanless AI server designs built for the next generation of computing. New parts, new rules Unlike processors, which are cooled as flat rectangular packages, these peripherals come in a wide range of shapes, sizes, and mounting requirements, each with its own thermal limits. Some run cooler than the processor case temperature, others run hotter, which leaves them sensitive to a design tuned only for CPUs and GPUs. Operators need purpose-built solutions here, matched to the part rather than stretched across the board. CoolIT engineers meet this with a deep toolkit. Conductive plates, vapor chambers, heat pipes, and thermal transfer plates move heat from components closer to the liquid path. Riding coldplates enable pluggable components. Each solution stays true to the component it serves. CoolIT Customer Showcase: How GWDG Cools HPC & AI Systems with CoolIT’s Direct Liquid Cooling CoolIT One loop, one server Cooling the parts is one challenge. Uniting them is the real work. Full heat capture means folding every one of these solutions into a single server loop that distributes coolant effectively and remains easy to install. Connection reliability, coolant routing, and the time it takes to assemble the loop at rack integration determine whether a design thrives in production or stalls on the bench. CoolIT builds these loops from proven modular blocks, so operators gain performance and deployment speed within the same solution. Density forces the decision Rack power continues to climb toward 1 MW, and the case for liquid grows stronger at every step. A 70/30 split of liquid to air holds comfortably at lower density. Past roughly 250 kW it stops working. The 30 percent left to air becomes a 75 kW load inside a single rack, and moving that much heat demands a parallel air system whose cost and footprint few operators will accept. Adding density only widens the gap. As rack power continues to climb toward 1 MW, CoolIT’s modeling places full heat capture as the standard server design for flagship rack-scale products through 2028. The simpler, more efficient answer is to capture the heat in liquid and drop air to less than 1 percent of the total load. True 100 percent remains almost impossible to reach in the strictest sense, so the honest and achievable target is near-total capture. That distinction matters to engineers who value precision, and the direction stays clear either way. Full heat capture moves from a premium option to a mainstream requirement as density rises, and CoolIT’s modeling places it as the standard server design for flagship rack-scale products through 2028. CoolIT delivers it CoolIT scales heat capture all the way to 100 percent using modular coldplate building blocks proven across six generations of fanless server designs. Engineering teams are already working on designs for the maximum density racks coming next. As the cascade spreads and racks grow denser, near-total heat capture becomes the design that keeps AI running. Talk to CoolIT about building a server loop engineered for total heat capture.
Shortwave radios offer a way to connect one location on Earth to practically anywhere else with minimal infrastructure. But these radios come with some drawbacks—a significant one being that, unlike satellite communications, their transmission rates for digital data are typically measured in just hundreds of bits per second. Peter Bloom Peter Bloom is the founder of Rhizomatica, a nonprofit that works with remote, indigenous, and off-grid communities around the world to build shortwave and cellular-communication infrastructure. Peter Bloom is the founder of Rhizomatica, a Philadelphia-based nonprofit that has open-sourced a digital shortwave-radio set called the High-frequency Emergency and Rural Multimedia Exchange System, or HERMES. The set operates in the high-frequency (HF) band from 3 to 30 megahertz, as does Mercury, its digital modem. Rhizomatica staff travel around the globe to remote locations in countries like Bangladesh, Brazil, and Ecuador. Wherever they go, they use HERMES to help connect locals to the rest of the world. Bloom spoke with IEEE Spectrum about how HERMES brings better data rates and encryption to shortwave radios. How does HERMES connect remote locations? Peter Bloom: We use the ionosphere as our satellite—or mirror—which helps us move information, voice, and data over really long distances. We’re using small radios that put out about 20 watts of power, and we can pretty reliably do 400- to 600-kilometer links between two radios. We’re talking about places that are not easy to reach, where it’s not simple to put terrestrial infrastructure. What can HERMES send that a basic voice radio can’t? Bloom: HERMES is a software stack—it’s a set of different programs that all work together in order to be able to send data over HF. HERMES allows you to send pretty much any file. Depending on what the file is, whether it’s a photo or an email or a voice memo, it just sends it as a file. It’s like a data pipeline over HF. Why does sending files and data matter more than just voice? Bloom: In emergency situations, people send their latitude and longitude over HF to say, “Hey, I’m here at this place.” People need to be able to send data over HF if there’s a manifest, a parts list, telemedicine—here’s what we have, here’s what we need. Instead of trying to read that out over the air, it’s much easier to just send the file. Same with a photo—if we need evidence that an area was logged illegally, we can just have someone send that over HF, rather than spending days getting down the river to get the photo where it needs to go. Why did you build in encryption that amateur-radio regulations in many countries don’t allow? Bloom: Encryption [regulations] for ham radio operators are different in each country. So it’s all optional—you turn it on, you turn it off. The reason we built the encryption is that some of the partners we work with are in very sensitive areas and don’t want to be sending out information that can be easily captured and used against them. How has HERMES made an impact? Bloom: We’ve been working with artisanal fishers in Bangladesh on a pilot project. There’s 10 or 11 boats that have HERMES systems on them. Pretty soon after we installed those, one of the boats had a mechanical issue in the Bay of Bengal, 100 or 200 kilometers offshore. They were able to send their GPS position and an SOS that they were having trouble. They were able to coordinate the rescue of the crew and the boat. So that was a really cool moment of HERMES in action that we’re super happy about. This article appears in the October 2026 print issue as “Peter Bloom.”
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Transitioning from years of working in a senior technical or executive role to a leadership position is one of the most challenging phases of a STEM career. It requires moving away from making decisions on your own to mentoring others, making strategic decisions for the organization, and collaborating with coworkers from different generations. The shift in mindset is known as “legacy leadership,” a philosophy whereby success is no longer measured by personal achievements but by how effectively a senior leader empowers others. To help seasoned professionals and senior experts navigate the transition, the inaugural IEEE International Leadership Conference (ILC) will provide attendees practical advice on cultivating collaborations and guiding emerging talent. “Early in our STEM careers, we measure success by what we have achieved,” says Jeewika Ranaweera, cochair of the IEEE ILC program committee. “Later, we should measure success by what we enable, how many people we mentor, how much knowledge we transfer, and how many doors we open for the next generation.” The ILC is scheduled for 3 and 4 October in Budapest. Registration is open. Letting go of the “expert” identity For decades, seasoned technologists have been valued primarily for their technical expertise. Shifting from that identity can feel uncomfortable, but legacy leadership requires measuring success by different standards. They include a leader’s influence on the staff, the ability to uphold the company’s mission, and empowering others to lead and succeed. The transition requires leaders to find purpose outside their corporate titles, shifting their focus to the long-term sustainability of their teams, their organization, and the broader technical community. “A professional legacy is not measured only by what we have achieved but also by sharing our knowledge, experience, and opportunities with others,” says Sudhanshu S. Jamuar, another program committee cochair. “The real transition from expert to a legacy builder happens when we stop asking, ‘What more can I accomplish myself?’ and start asking, ‘How many others can I enable to accomplish more?’” Neeli Rashmi Prasad, IEEE ILC treasurer and sponsorship cochair, adds that the transition transforms a lifetime of technical work into a platform for future innovation. “The true value of experience is not in how much knowledge we accumulate but in how intentionally we transfer it,” Prasad says. “When we partner with, mentor, and create space for others to lead, our expertise becomes a foundation for progress far beyond our own careers.” Moving beyond advice-giving True knowledge transfer requires coaching rather than advising, and mastering the art of active listening. To build deep trust with early-career colleagues and ensure a seamless transfer of leadership to the next generation, senior leaders must avoid offering unsolicited or outdated anecdotes. Effective mentorship is a collaborative loop in which senior experts contribute hard-won industry wisdom while remaining curious and learning from the fresh, cutting-edge perspectives of talented coworkers. “Knowledge transfer is most powerful when it is a two-way bridge: experience flows from one generation to the next, while new ideas and perspectives flow back,” says Sudeendra Koushik, president of the IEEE Technology and Engineering Management Society and an ILC keynote speaker. “This approach transforms mentorship from simply passing on information into one where the next generation can question, experiment, innovate, and ultimately surpass what came before them.” Seasoned professionals should encourage independent, disruptive thinkers rather than carbon copies of themselves, Prasad says. “Legacy leadership is about building continuity,” she says. “We should not simply prepare the next generation to follow the paths we created; we should give them the confidence, knowledge, and networks to challenge those paths, create new ones, and take technology further than we imagined.” Designing your next chapter Leadership does not need to stop when one’s job ends; it can evolve. Seasoned, retired professionals can continue contributing meaningful service through pathways that align with their personal passions. They include: Advisory boards: steering corporate, technical, or community organizations. Civic engagement: applying engineering methodologies to solve community challenges. Volunteerism: mentoring the next generation of grassroots innovators through professional networks including IEEE. Those pathways offer experienced professionals an opportunity to redefine success, not in terms of position, authority, or personal achievement but in terms of sustained impact. “Retirement from a job should never mean retirement from purpose,” Koushik says. “Our experience becomes even more valuable when we use it in service of the profession, society, and the generation that follows.” At the same time, the collaborative continuum relies on a proactive younger generation. Emerging leaders need to take responsibility for building their professional connections, Prasad says, advising: “Be bold, stay curious, and build your network early!” A space for continuity The conference is designed to combine a drive to cultivate emerging innovators with a deep reservoir of industry stewardship and strategic perspective. Rather than a single classroom session, the conference will feature a dedicated career-readiness and mentorship track where attendees can explore practical frameworks for building strategic professional networks, connecting with peer advocates, and establishing reciprocal knowledge-sharing opportunities across generations. By participating, seasoned professionals can ensure their decades of expertise continue to yield dividends for generations to come. “Our professional legacy is not the technology we build, the titles we earn, or the awards we receive,” Ranaweera says. “It is the knowledge we share, the lives we influence, and the future we help others create.” You can view the agenda, read speaker biographies, and secure your seat at the ILC online.
This article is brought to you by VicOne. Robot safety has traditionally asked: Can a machine remain safe when something goes wrong? Physical AI raises a harder question: Can a machine remain safe when an attacker changes what it sees, decides, or does even when nothing appears to have failed? As AI and robotics continue to advance at an unprecedented pace, modern robots perceive through multimodal sensors, interpret context using AI models, and translate those interpretations into physical action. As they move into dynamic environments, their safety increasingly depends on the integrity of the data guiding their decisions. That dependence creates risks that conventional safety assessments may not fully capture. Recent research has demonstrated that manipulating what a robot sees, hears, or interprets can influence its behavior without requiring direct control. Such manipulation can occur anywhere across its complex sensing and decision-making system — a layered attack surface encompassing training pipelines, system infrastructure, and runtime perception. Layer One: Corrupting intelligence at its source In 2017, BadNets demonstrated that a model could behave normally under most conditions, yet fail in the presence of a specific hidden trigger. In one example, a subtle pattern caused a stop sign to be misclassified as a speed limit sign without affecting the model’s behavior on other inputs. What began as a classification vulnerability has since evolved into action manipulation. At NeurIPS 2025, researchers introduced BadVLA a backdoor attack targeting Vision-Language-Action (VLA) models that allow robots to see, interpret instructions, and produce coordinated physical movement. Rather than altering a single label, the attack caused conditional deviations in the robot’s action trajectory when a trigger was present. Without the trigger, the model largely preserved normal task performance, while the backdoor remained effective under task transfers and model fine-tuning. A related study in 2025, GoBA, showed that ordinary objects such as a coffee mug could serve as a reliable trigger. The researchers reported a 97 percent attack success rate without degrading performance on clean inputs. A critical safety question today is whether Physical AI models remain within their task and safety boundaries under adversarial conditions. These studies expose a blind spot in model validation: A model may pass testing yet produce corrupted behavior when a hidden trigger appears in operation. So a critical safety question today is whether Physical AI models remain within their task and safety boundaries under adversarial conditions. Simulation tools such as NVIDIA Isaac Sim, when paired with VicOne Radeis, can test the effects of manipulated inputs before deployment. VicOne LAB R7 demonstrates Radeis, a Physical AI safety validator for NVIDIA Isaac Sim that tests how adversarial visual inputs affect robot behavior before deployment.VicOne Layer Two: System vulnerabilities as gateways to AI control Even a securely trained model can be subverted if the surrounding system stack is vulnerable. In September 2025, researchers disclosed UniPwn, a Bluetooth exploit chain affecting quadruped and humanoid robots from a major manufacturer. Hardcoded cryptographic keys allowed traffic decryption, authentication checks were bypassed, and command injection enabled root-level execution. The exploit is also described as “wormable.” A compromised robot could scan nearby units and potentially affect an entire fleet. VicOne Lab R7’s demo shows how chaining three wireless exploits can trigger uncontrolled robot behavior within 60 seconds, resulting in operational disruption.VicOne Middleware creates another exposure point. Vulnerabilities in ROS 2 and DDS-based systems can enable arbitrary code execution or abuse unauthenticated topics to deliver malicious commands. With sufficient access, an attacker could override motor commands or replace AI model weights without directly attacking the model architecture. In this case, the components may still function as designed. What has changed is the trustworthiness of the commands flowing through the system. Vulnerability management can help teams identify known risks before deployment, while continuous monitoring can surface emerging threats. Layer Three: Manipulating perception and reasoning at runtime At runtime, manipulating inputs that shape perception or reasoning may require neither firmware modification nor a network breach. In 2024, RoboPAIR demonstrated how carefully structured prompts could redirect LLM-controlled robots into unsafe trajectories. BadRobot exposed a deeper architectural weakness: in several cases, a robot verbally refused a dangerous command while its motion controller executed the action anyway. Vision-based manipulation is equally powerful. VLAttack showed that an adversarial patch within the camera’s view could reduce a VLA model’s task success rate to zero. FreezeVLA showed that a single adversarial image could freeze a robot’s decision-making loop, making it unresponsive to subsequent instructions. Runtime assurance must therefore look beyond whether individual components remain available and assess whether cyber events are beginning to affect physical behavior. In each case, the camera may still work, the model may still run, and the controller may still respond. Yet the resulting behavior can be unsafe because the robot is acting on manipulated perception or reasoning. Runtime assurance must therefore look beyond whether individual components remain available and assess whether cyber events are beginning to affect physical behavior. Security event correlation, behavioral-impact assessment, and policy-bounded response supported by edge AI, can help contain the affected path without unnecessarily stopping the entire robot fleet. From point-in-time safety to lifecycle assurance The risks across these three layers reveal the missing layer in robot safety assurance: cybersecurity. Functional safety addresses failures and unexpected operating conditions; cybersecurity extends that assurance to deliberate manipulation, including attacks that may leave the underlying system apparently functional. This requires assurance across the robot’s lifecycle. During design, teams need to understand which cyber risks could invalidate assumptions behind intended behavior. Before deployment, they should test whether realistic attacks can cause a robot to deviate from its task or safety boundaries. In operation, monitoring should identify whether cyber events are beginning to affect behavior, contain the affected path, and preserve safe operation where possible. VicOne’s lifecycle approach combines AI model and vulnerability scanning, simulation-based validation, and continuous monitoring to help secure robots from development through operation.VicOne While cybersecurity does not replace functional safety, it helps ensure that Physical AI remains within acceptable boundaries even when what it sees, decides, or does is under attack. For a deeper look at the cybersecurity risks and defense strategies shaping autonomous robotics, download our whitepaper “Securing the Rise of AI Robots: Cyber Risks, Real-World Threats, and Defense Strategies.”
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Since about 2020, AI has largely focused on training bigger and better models. Large language models (LLMs) ballooned from millions of parameters to trillions. This proved effective: The largest version of OpenAI’s GPT-3, released in 2020, correctly answered just 43.9 percent of questions on a popular knowledge-and-reasoning benchmark. Just four years later, GPT-4o reached a score of 88.7 percent on the same exam, effectively matching those of human experts. Advanced AI labs are still training ever larger models, but that training has somewhat receded to the background of the AI conversation. In 2026, inference—the use of trained models to produce code, write essays, or make images of ourselves as elves—has come to the forefront. “It’s like training is yesterday’s news,” says Matt Kimball, principal data-center analyst at Moor Insights & Strategy. “All that any chief information officer wants to talk about is inference.” Nvidia CEO Jensen Huang, speaking at the company’s GTC 2026 conference, touted this change as the “inflection point of inference.” Part of what’s caused the shift is very simple: LLMs are becoming useful, so people are using them. On top of that, many models on the market today are reasoning models. In response to a user’s query, they run inference not just once but multiple times, reprompting themselves in a process called chain of thought. Reasoning models generate longer outputs, and models with high reasoning effort can produce up to 20 times as much text as those with low or no effort. Adding even more to the world’s inference workload, the rise of agentic AI has resulted in inference running not just as a real-time response to a user’s query but also around the clock, working autonomously toward a user-defined goal. Amazon’s Trainium chip was originally designed for AI training. However, Amazon Web Services chose to break up AI inference into two parts, with Trainium running the more computationally complex portion and Cerebras’s wafer-scale engine taking on the more memory-intensive portion.Amazon The resulting explosion in inference demand has led to unexpected alliances among tech giants. OpenAI and Amazon have deployed chips the size of a dinner plate designed by Cerebras, despite Amazon having its own Trainium chips. Nvidia bought key talent and intellectual property from AI-inference startup Groq in a controversial deal worth US $20 billion. And Anthropic is paying LLM competitor SpaceXAI over a billion dollars per month to lease spare compute. Although they might seem similar, AI training and AI inference are computationally different. These big moves from tech giants signal that in order to support the inference demand, we’re going to need a very different mix of hardware than experts may have expected even a couple of years ago. How does AI inference differ from AI training? An untrained LLM is like a jumble of Scrabble tiles on a table. Instead of single letters, though, the tiles show fragments of words, called tokens. Everything you’d need to write almost anything is present, but nothing makes sense. Training a model organizes this jumble using a guessing game played at scale. The model is shown real text with the next token hidden and asked to predict what comes next. After each guess, the correct token is revealed and then compared to the prediction, and the difference is used to calculate the model’s accuracy. The game is played not with a single sentence but over billions of passages. While a real game of Scrabble can be played over a bag of chips and a few drinks, AI training is computationally intense. The model updates its parameters through backpropagation, a process that repeatedly calculates how each of a model’s billions or trillions of parameters should shift to make the next prediction better. This is why tech giants are building larger data centers than ever before. Eventually the model’s creator decides further training isn’t worth the cost, and the guessing game stops. Backpropagation ends, the parameters are frozen, and the LLM becomes a pretrained model. Fine-tuning—a short training run on smaller, more specialized data—adds final tweaks, and the model is deployed. Next comes inference. This is the process of using the deployed model, which, now that it’s been trained, has learned to spit out Scrabble tiles—tokens—in a sensible order. You might think that AI inference is less computationally demanding because the backpropagation calculations used to update parameters are eliminated. But Sudeep Bhoja, founder and CTO of the inference-hardware company d-Matrix, explains that inference adds new challenges. The models are “autoregressive” in nature. That is, the next output depends on the previous one. “So to generate the next token, you have to read all of the weights and all of the [context] from the previous token,” explains Bhoja. The context includes all of your prompts, all of the LLM’s replies, and all of the files you upload. It’s a lot of data and a lot of processing. An LLM generates its reply in two phases: prefill and decode. Prefill is the model reading a prompt. It processes every token at once, computing how each token relates to all the others. This operation is called attention, and it’s a defining characteristic of the transformer architecture behind modern LLMs. It allows them to respond to a word in its sentence, paragraph, and larger context rather than on its own. Think of it like arranging Scrabble tiles before you place them in a game. Many players move tiles around to imagine how they connect. Self-attention plays a similar role, though instead of moving physical tiles, each token sends a query to the others and receives a score indicating the token’s relevance. These queries result in two types of vectors: the keys and values. They are typically placed in a store called the KV cache. This isn’t strictly required, as a model could instead recompute these vectors with each new token it generates. But nearly all LLMs use a KV cache to reduce how much computing they do. The KV cache is stored in memory and becomes a scratchpad to which the LLM can return to understand a conversation, and though it starts small, it can swell to dozens of gigabytes. Prefill is a problem that can be easily divided up and worked on in parallel. This is why GPUs became the dominant AI accelerator as LLMs surged in popularity. Graphics rasterization (computing the color of every pixel on a screen) is also massively parallel, so GPU architectures were a natural fit. Next comes decode. Here, the model generates its reply one token at a time. At each step it takes the most recent token, weighs it against everything in the KV cache, uses that information to predict the next token, and adds the new token’s key and value to the cache. Then it repeats in sequence, token by token. This is where the autoregressive nature of the model works against inference speed. Predicting each token requires reading the entire model from memory, and that model consists of possibly tens to hundreds of gigabytes of parameters (the numbers representing what the model learned in training). Crucially, this is in addition to the memory required to store the KV cache. As a result, the movement of all this data through memory often requires more bandwidth than inference hardware has available. So at least some of the computing parts of a GPU sit idle as it waits for data. Researchers found that Nvidia H100 GPUs running open-source LLMs sit idle 50 to 80 percent of the time. Memory’s role in inferencing Shahriar “Sha” Rabii, former head of silicon engineering at Meta and cofounder of the AI startup Majestic Labs, says idled processors are why many companies that are trying to improve AI-inference performance are laser-focused on memory. “With the GPU-based approach, you end up greatly over-provisioning compute and starved on memory. That’s driving the big [memory] scale out,” he says. Bhoja’s d-Matrix and Rabii’s Majestic Labs both focus on this memory bottleneck. However, their companies imagine different solutions. d-Matrix’s second-generation AI accelerator, Raptor, aims to improve inference performance by minimizing the distance between compute and memory. The GPUs in most current AI-inference deployments do this by placing high-bandwidth memory (HBM) around the perimeter of the GPU. Each HBM is a stack of DRAM dies linked together and connected to a superfast interface to the GPU. This is great for training, but for inference, the amount of memory you can stack this way and the bandwidth it can provide leave something to be desired. d-Matrix’s stacked-die architecture d-Matrix’s Raptor removes that bottleneck by stacking an AI accelerator on a DRAM die. Instead of stacking memory, d-Matrix stacks memory and compute. Bhoja says this reduces the distance that data must travel to “micrometers instead of millimeters.” Like building a skyscraper, going vertical makes it possible to do more inside the same physical footprint. Majestic takes the opposite approach. Instead of trying to minimize the length that data must travel between compute and memory, the company is focused on improving the memory interface to accommodate longer wire traces while keeping bandwidth high. Longer wires allow Majestic to connect memory stacks that aren’t directly next to the GPU, removing the space limitation of HBM. “A memory interface has a very short physical distance it can operate over. In the case of HBM, it’s up to 2 or 3 millimeters. You have this shoreline around the periphery, which is the only place where you can put HBM,” says Rabii. Majestic claims its memory interface can transmit bits as far as about a meter. That’s achieved with a proprietary copper link and a memory-aggregator chip that coordinates data. “The aggregator is the endpoint for the high-speed interface and a way to fan out to many, many commodity DRAM chips,” says Rabii. Because of this, Majestic can support up to 128 terabytes of DRAM memory in a single server rack—a significant increase over Nvidia’s GB300 NVL72 rack, which has about 20 TB of HBM3E. Majestic Labs’ memory-aggregation architecture d-Matrix and Majestic have one thing in common: Instead of HBM, they both use off-the-shelf DRAM. This is the most common type of computer memory in the world; it’s in everything from smartphones to cars. Memory analyst Jim Handy says HBM costs two to three times as much as DRAM. d-Matrix and Majestic chose DRAM in part because of this price advantage. However, the proponents of HBM, which include memory giants like Samsung and SK Hynix, aren’t sitting idle. HBM4, the latest version of HBM memory, is now in production and will be used by Nvidia’s Vera Rubin GPU, which is expected to ship in the second half of 2026. Hoshik Kim, head of memory-systems research at SK Hynix, says HBM4 “will decisively break the memory bottlenecks constraining AI inference today” by doubling HBM’s maximum memory bandwidth and increasing the amount of HBM memory per stack. Combining chips for faster inference The big players—Nvidia and Amazon—are going for an all-chips-on-deck approach. Nvidia’s GPUs and Amazon’s Trainium training accelerators are still great for part of the inference workload: the prefill stage, where all the context keys and values are calculated. But to accelerate decode, the part where new tokens are generated, they are looking to new, memory-centric architectures from smaller players. In Nvidia’s case, the smaller player was Groq (not to be confused with Grok, the family of LLMs trained by SpaceXAI). Nvidia purchased intellectual property and hired talent from Groq at the end of 2025, and just three months later at the Nvidia’s GTC 2026 conference, Jensen Huang unveiled the Nvidia Groq 3 language-processing unit (LPU). Groq’s architecture relies on memory—in its case, SRAM—built directly into the chip’s architecture. Unless you’re a chip architect, or a hardcore PC gamer, you probably never give SRAM a thought. SRAM has the benefit of being tightly integrated into a compute chip’s architecture—it’s on the same piece of silicon as the processor—and has the drawback of being less dense and more expensive than DRAM. Most chips include only a few dozen megabytes of SRAM. AI inference, however, has ignited new interest in SRAM as a means of bringing the model weights stored in memory closer to compute. Ian Buck, vice-president and general manager of hyperscale and high-performance computing at Nvidia, says the LPU has a much different set of priorities than the company’s GPUs. The LPU has far less raw computing power than a standard GPU, but it gains 500 megabytes of on-die SRAM connected directly to its floating-point math units. “The benefit is the memory bandwidth. The LPU has seven times the memory bandwidth of the GPU,” he says. Between the Rubin GPU and the Groq LPU, prefill and decode can both be accelerated to get the best of both worlds, the theory goes. “We do all the attention math and context processing on the Vera Rubin [GPU] rack,” explains Buck. “For all the expert calculations…the matrix multiplications, we do that part on the LPU.” The company packs 256 LPUs into the Groq 3 LPX, a system the size of a data-center rack. Nvidia’s two-chip approach to inference Amazon Web Services (AWS), for its part, struck a deal with Cerebras, to pair the Trainium accelerator with Cerebras’s Wafer-Scale Engine 3 (WSE-3). Cerebras takes a similar approach to Groq, though at a much larger scale. WSE-3 turns an entire silicon wafer into a single chip that contains over 4 trillion transistors. The design doesn’t connect to external memory but instead etches 44 gigabytes of SRAM into each wafer. “We store the [model] weights on the SRAM,” says James Wang, formerly director of product marketing at Cerebras who has since moved to SpaceXAI. “So that’s easily 40 to up to 80 billion parameters that we can support on one chip.” Amazon plans to use AWS Trainium chips for prefill, and Cerebras for decode. But Cerebras’s chips can also go it alone in inference. WSE-3 was deployed by OpenAI to power GPT-5.3-Codex-Spark, a variant of the company’s coding mode, outputting over 1,000 tokens per second. For comparison, OpenAI’s standard GPT-5.4 deployment outputs 50 to 125 tokens per second. Amazon Web Services’ two-chip inference strategy Cerebras can also tackle prefill without moving the workload to different specialized chips. For this, it networks together multiple WSE-3 chips to form a single pool of memory. Cerebras has demonstrated it can serve models with up to 1T parameters, such as Moonshot AI’s Kimi 2.6, though Wang says “the architecture has no innate limitation in terms of how many parameters it will do.” Despite these differences in strategy, Nvidia and AWS seem to agree that the future of AI inference will be solved by a systems approach that pools different kinds of chips together to tackle the largest LLMs. Or, as Buck says: “To do modern AI inference, you need all the chips.” Learning to do more with less (bits) Nvidia became the world’s most valuable tech company because it designed the world’s most desired GPUs. But not all of the attention is focused on improving AI-inference hardware. AI researchers are also learning how to optimize LLM software and hardware in tandem to make the best use of the memory and compute components. Most computers store numbers in a 32-bit or 64-bit format. These determine how many bits are available to represent a single number. If too few bits are available, the number can’t be stored without losing information. The quality of an LLM benefits from more-precise number formats, but this creates a problem for inference performance. More-precise numbers aren’t free. The bits that describe them take up more space in memory and require more silicon and energy to compute. Gilles Backhus, cofounder of the AI-accelerator company Tensordyne, says this creates a tension between model size and number precision. “Would you prefer a model that is size x but runs in 8-bit, or would you prefer a model that is twice the size but runs in 4-bit?” The size of each model will be roughly the same in terms of memory and compute, “but the 4-bit approach gives you twice as many synapses, if you will. And people are figuring out that [the 4-bit approach] is worth it.” The process of converting an LLM from a more-precise number format to a less-precise format is called quantization, and it’s been in use for several years. However, researchers are finding new ways to quantize models down while retaining a large majority of the model’s quality. Nvidia recently created a new 4-bit number format, NVFP4, for this purpose. AMD, Intel, and Qualcomm have instead rallied around a competing 4-bit number format called MXFP4 that Nvidia also contributed to developing. “It’s the black art of AI,” says Buck, of Nvidia. When Nvidia quantized DeepSeek-R1 from FP8 to NVFP4, scores on seven major benchmarks degraded by less than one percent while performance improved by three times, the company says. Quantization is likely just the tip of the spear, as AI researchers and startups are investigating a diversity of opportunities for optimization, some of which could dramatically change the silicon found in AI-inference hardware. Tensordyne’s unique approach to AI inference combines a logarithmic number format with bespoke hardware in the company’s Napier chip. Tensordyne Tensordyne is expected to accelerate AI inference with a logarithmic number system that leans on a property of logarithms: The log of A times B equals the log of A plus the log of B. So, storing numbers as their exponents lets the chip add where it would otherwise multiply. That matters in silicon because multiplier circuits draw more power and use more die area than adders do. Tensordyne says its rack-scale hardware, called Napier, can produce up to 1,300 tokens per second per user, and can do so while using less than a tenth as much power as comparable Nvidia hardware. Etched, a startup based in San Jose, Calif., is even designing AI accelerators that translate the transformer architecture used by LLMs directly into silicon. Rather than building general-purpose GPUs, the company is wiring up the connections needed for efficient transformer calculations into its chip, making the chip much less flexible but more efficient for the tasks most performed by current LLMs. The company says its first AI accelerator, Sohu, can run Meta’s Llama 70B model at a stunning 500,000 tokens per second, though this approach also means it won’t be able to run LLMs that move away from a typical transformer architecture. Whether these ideas will prove fruitful remains to be seen. Etched just shipped their first rack in August. Tensordyne believes its first hardware will be available in 2027. Even so, these startups show how the demand for inference performance is fueling unconventional ideas. Inference is everyone’s game The sheer variety of approaches to AI-inference acceleration—stacking compute on memory, extending interfaces from millimeters to meters, using an entire silicon wafer for SRAM, squeezing models into 4 bits—raises a question: Which is going to win, and which is going to lose? But that’s likely not the right question, experts say. The demand for AI is currently insatiable, and while fears of an AI bubble stalk the industry, it has yet to hamper growth. On the contrary, Kimball of Moor Insights & Strategy thinks inference could drive intense demand for AI hardware in the long term, because it’s not obvious where that demand will end. “You could add a million agents into your organization,” he says. “These things work 24 hours a day; they don’t go home at five at night like we do.” If AI inference remains as desirable as Kimball expects, the evolution is likely to follow the same trajectory as the CPU. The CPU didn’t improve along a single axis but instead across multiple fronts simultaneously. Once transistor scaling slowed, chip and system architecture innovations of all kinds proliferated. The list of individual innovations that led to today’s ubiquitous, powerful personal compute could fill dozens of books. A few decades from now, the history of AI inference innovation will show similar depth.
In 2003, Martin Eberhard, a cofounder of Tesla Motors, decided it was time to start wooing investors. To do that, however, he needed an electric car. So he talked to Alan Cocconi and asked if he could borrow the tZero, the revolutionary and blazingly fast electric roadster that Cocconi had built at AC Propulsion. Eberhard’s idea was to drive the tZero up and down Sand Hill Road in the heart of Silicon Valley and do demonstrations for curious entrepreneurs and VCs. Eberhard was joined by Tom Gage, Cocconi’s partner at AC Propulsion, on many of the visits. Like Tesla, AC Propulsion was also seeking investors, but to build a considerably more utilitarian EV. Adapted with permission from The EV Guys: How Caltech Engineers Reinvented the Electric Car, by Charles J. Murray, published by Purdue University Press. In December 2003, Eberhard also proposed a demo at Buck’s of Woodside, a popular restaurant frequented by tech entrepreneurs. At 5 o’clock on any evening, Buck’s probably had more VCs per square foot than any building in the country. Eberhard’s plan was to “show off what a real electric sports car can do,” he wrote in an email to Buck’s owner, Jamis MacNiven. MacNiven happily obliged. In some ways, the tZero was a hit. When a VC would ride shotgun in the car with Eberhard at the wheel, Eberhard would implore them to touch the dashboard. As they reached forward, he’d punch the accelerator. As the car accelerated and the g forces piled up, the VC was literally unable to touch the dashboard. That was how powerful the tZero’s acceleration was, Eberhard would say. Many of the VCs were astounded. Some even questioned whether the car was really electric. Many owned Ferraris or Lamborghinis. They knew sports cars—but this? They could never have imagined it was possible to do this with an electric drivetrain. On December 13, 2003, Martin Eberhard brought AC Propulsion’s tZero electric roadster [yellow] to Buck’s of Woodside, a popular hangout for entrepreneurs and venture capitalists. Next to the tZero is a Scion xB, which AC Propulsion’s principals thought they could turn into a mass-market electric vehicle.Martin Eberhard Still, the demo at Buck’s garnered little investor interest—with one exception. Google cofounders Sergey Brin and Larry Page were both at Buck’s that day, and they told Gage they knew an individual whose funds were liquid, as he’d recently sold his stake in a startup. What’s more, this individual liked fast cars. The man’s name was Elon Musk. Elon Musk, Meet the tZero In 2004, it wasn’t apparent to anyone that Elon Musk had a future in the auto industry. He was notable for cofounding PayPal, which he then sold to eBay in 2002 for a whopping US $1.5 billion. He had already launched Space Exploration Technologies Corp., or SpaceX, with the stated goal of paving the way to a sustainable colony on Mars. Musk did love fast cars. He owned a million-dollar silver McLaren F1, one of only 64 road-going F1s in the world, as well as a 400-horsepower BMW M5 sports car and a 1967 XK-E Series 1 Jaguar roadster. But he’d never expressed an interest in building cars or starting an auto company, at least not publicly. Elon Musk was photographed in 2008 at Tesla’s headquarters, then in San Carlos, Calif., around the time when Tesla’s Roadster was being delivered to its first customers.Patrick Tehan/MediaNews Group/Bay Area News/Getty Images Still, Musk’s affinity for fast cars made it almost impossible for him to ignore an email from Gage on 21 January 2004. “Sergey Brin and JB Straubel both suggested you might be interested in driving our tZero electric sports car,” Gage wrote. (Straubel was the young Stanford engineering graduate who would later serve as Tesla’s chief technology officer.) “The tZero goes quite well,” the email continued. “We ran it against a Viper last Monday and it won four of five sprints on a 1/8th of a mile track. I lost one because I was carrying a 300-pound cameraman. Do you have time for me to bring it by?” Musk quickly replied. “Sure, I would really enjoy seeing it. Don’t think it could beat my McLaren (yet) though I’m in town Feb 2nd through 4th.” “Hmm, a McLaren, boy that would be a feather in my cap,” Gage wrote back. “I can have it there on Feb. 4.” Two of the principals behind AC Propulsion were businessman Tom Gage, left, and engineering genius Alan Cocconi.Left: Tom Gage; Right: Alec Brooks The emails marked the beginning of Musk’s involvement in electric cars and in the auto industry. Gage drove the car to SpaceX headquarters, a warehouse in El Segundo, Calif., about 30 kilometers southwest of Los Angeles. In Musk’s cubicle in the “office” portion of the warehouse, Gage made his pitch. There was a void in the market, he said. GM had abandoned the EV1. Toyota, Honda, Ford, and Chrysler were shutting down their electric car programs. California’s zero-emission vehicle (ZEV) rules, which mandated the sale of increasing numbers of vehicles with no tailpipe emissions, had been plundered. But electric vehicle technology, he said, was getting a bad rap. Here was the tZero, an electric car that could take off like a jet. The tZero proved that the technology was readily available. He and Cocconi wanted to use that technology to make an electric car that was useful and practical: the eBox, an electrified Toyota Scion. After building the tZero roadster, AC Propulsion’s principals pinned their hopes on an electrified version of the Toyota Scion they called the “eBox.” It did not appeal to Elon Musk.Jeff Chiu/AP For Musk, Gage’s introduction of the eBox was unexpected. He was meeting with Gage because he was interested in the tZero. It was the car’s performance that appealed to Musk. He wasn’t interested in the eBox. He then drove the tZero and offered to buy it. The lithium-ion version of the tZero electric roadster could get 515 kilometers on a charge and go from zero to 97 km/hr (60 miles per hour) in 3.6 seconds. Only three tZeros were built and only two survive. Scott Sorbe Gage told him it wasn’t for sale. Undeterred, Musk offered a quarter million dollars if AC Propulsion would squeeze its lithium-ion battery pack into his Porsche. Gage declined again. AC Propulsion needed money to electrify the Toyota Scion, Gage said. Musk shook his head. The idea seemed incredible to him. “Who wants to take an ugly $20,000 car and buy it for $65,000?” he asked incredulously, as he later recalled during an interview with Vanity Fair magazine. “I wouldn’t want to drive it. My wife certainly wouldn’t want to drive it.” Many years later, Musk would tell his biographer Walter Isaacson, “Nobody is going to pay anything near that for something that looks like crap.” Musk believed that the way to start a car company was to build high-priced cars first and then let the technology trickle down to the mainstream. It was a classic Silicon Valley approach. Alan Cocconi, the engineering whiz behind AC Propulsion, stands next to the company’s legendary tZero electric roadster in a picture taken in the early 2000s. The small yellow wheeled pod on the other side of the car is a trailer with a small gasoline engine that, when connected to the tZero, turned it into a hybrid gas-electric vehicle.Martin Eberhard In Musk’s mind, it was all very obvious. He liked fast cars. He liked the tZero and believed it “could change the world.” He couldn’t even imagine why Gage was sitting here trying to sell him on the idea of the eBox. “Gage and Cocconi were sort of madcap inventors,” he told Isaacson. “Common sense was not their strong suit.” Gage concluded that he wasn’t going to convince Musk to invest in AC Propulsion. “Well, if you want to do a sports car, then you should talk to Martin Eberhard,” Gage said. A few weeks later, Gage sent an email to Eberhard introducing him to Musk. “Elon Musk heads up SpaceX, is a car enthusiast,” Gage wrote. “He would be interested in hearing about your activities at Tesla Motors.” Elon Musk, Meet Tesla Motors As it happens, Eberhard and Marc Tarpenning, Eberhard’s partner and cofounder at Tesla, had considered contacting Musk even before Gage’s email arrived. They’d known of Musk and appreciated the way he thought. A few years earlier, they saw him speak at a Mars Society conference at Stanford University. Musk had talked about the rather improbable idea of sending mice to Mars. The presentation gave them a window into Musk’s unconventional approach to high-tech entrepreneurism and to life in general. Eberhard and Musk agreed to meet, and then Eberhard emailed Gage. “Any chance of my borrowing the car for next week?” he wrote. Gage, of course, complied. Martin Eberhard posed next to an electric motor at Tesla’s San Carlos, Calif., headquarters in 2006. Paul Sakuma/AP By this time, Tesla was nine months old. It still had just three employees—Eberhard, Tarpenning, and Ian Wright, a New Zealand-born engineer and neighbor of Eberhard’s. The founders were arranging to pay the licensing fee on AC Propulsion’s drivetrain technology. And they were making arrangements to build their first cars using the chassis of the Lotus Elise two-seat roadster. They estimated they needed $6.5 million to go further. And that’s where Musk came in. The original Tesla Roadster prototype, or “Mule,” was built inside the chassis of a 2002 Lotus Elise. Dylan Stewart/Image of Sport/Sipa/Alamy Eberhard and Wright flew to Los Angeles on a Friday and met Musk in his cubicle at SpaceX. The meeting was supposed to last a half hour, but Musk’s questions came virtually nonstop, and as the meeting progressed, he repeatedly shouted to his assistant to cancel his next meeting. Over the following weekend, Musk called Tarpenning to get his input about their financial model. “I just remember responding, responding, and responding,” Tarpenning said, according to a 2015 book by Ashlee Vance. The Tesla founders were all impressed with Musk. He was unlike any of the VCs they’d met with in the previous months. He was technically astute. He’d earned a bachelor’s degree in physics from the University of Pennsylvania, and in his two-day stint as a Ph.D. student at Stanford, he’d intended to do a dissertation on solid-state capacitors for use in electric cars. Moreover, he wasn’t averse to risk—at least not intelligent risk. He loved technical challenges, and he loved proving that the impossible was possible. “You’re presenting an electric car company to this person on the other side of the table, and he’s doing something even crazier,” Tarpenning said later. “He’s building rocket ships.” On the Monday after their first meeting at SpaceX, Eberhard and Tarpenning flew back to Los Angeles. Musk agreed to invest $6.35 million. He would become the biggest shareholder as well as chairman of the company. Now, Tesla Motors was really in business. All it needed was someone to design and build a groundbreaking electric car. “All Electric Cars Have Sucked” No one at AC Propulsion believed that Tesla Motors had even a remote chance of success. The whole idea—building and selling electric vehicles and competing against the giants in Detroit, Japan, and Germany—seemed impossible. Even Toyota, which was having so much success with the hybrid Prius, was not planning to build pure electric cars. The prospect of starting any kind of auto company was unbelievably daunting. Automotive startups had been the undoing of many ambitious entrepreneurs, including Henry Kaiser, Preston Tucker, and John DeLorean. Such endeavors required mountains of money, connections, and expertise. There were unseen obstacles around every corner. And the people who’d launched Tesla, as smart as they were, were almost certainly unprepared for what lay ahead. Years later, Musk would contend that their struggles were caused by the fact that Tesla had been founded on “two false premises.” The first was that Tesla’s founders believed they could simply convert an existing gasoline sports car to electric. The second was that they could use the existing AC Propulsion technology with little or no modification. “That turned out to be, in retrospect, staggeringly dumb,” Musk said. In April or May of 2004, Tesla engineers worked on an early test vehicle, or “mule,” of the Roadster. The group included (clockwise from upper left) mechanical engineer Gene Berdechevsky, in the pink shirt, electrical engineer Phil Cole, and mechanical engineer Dave Lyons, in the dark blue shirt.Martin Eberhard He also later concluded that their early path almost doomed them. “It ended up being much worse than if we had designed the car from scratch,” he said. But Tesla decided it could not go back and start over. It could only deal with the problem at hand. Eberhard and Tarpenning were terrified of going into production with the existing analog motor controller and drivetrain electronics, which were unreliable and jittery. If those problems weren’t fixed, they knew their new vehicle would fail, and so would the company. Another looming issue was the safety of the Tesla lithium-ion battery pack. To test it, Eberhard brought the engineering team to his home, where they dug a pit in his backyard. They took a brick of cells, covered it with a sheet of Plexiglass, and then remotely heated one of the cells with an electrical wire. As they expected, the heated cell burst into flame, setting neighboring cells on fire. The cells went off one at a time—pop, pop, pop. “We had a conflagration,” Eberhard said. “One cell caught fire, and it blasted right through the pack.” Buyers of sports cars were known to be forgiving. In their quest for performance, they could put up with poor reliability. But the fire hazard was another matter, and one that had the potential to take down the company. Eberhard took the news right to Tesla’s board of directors. “It was my first big oh-shit moment to my board,” he said. “I told them we’ll have a day-to-day schedule stop until we figure this out.” Working with friends from his Stanford days, Straubel began developing a new pack in his garage. The team acquired 7,000 lithium-ion cells from LG Chem, then constructed battery bricks, each with 69 cells, and tested them with different kinds of liquid-cooling systems. By October 2004, they’d finished a prototype pack and used a crane to lower it into the back of a Lotus Elise sportscar. A few months later—in January 2005—the team had completed a working prototype of that first car. At the end of the month, they showed it off at a board meeting, and Musk took it for a spin. Impressed by its performance, he invested $9 million more, and Tesla completed a $13 million round of funding. Now the vehicle had a name—Roadster—and a tentative production schedule. The plan was to begin delivering it to customers in early 2006. Tesla’s struggles with the Roadster were not apparent to the outside world, especially to those invited to the reveal of the Roadster at the Santa Monica Airport in July 2006. By then, the yellow test car, or “mule,” had evolved into two prototypes: a red car and a black car, both of which would be available for drives at the event. Tesla unveiled the Roadster, its first vehicle, at the Santa Monica, Calif., airport on 19 July, 2006.Glenn Koenig/Los Angeles Times/Getty Images The prototypes were more advanced than the mule, with more of a production-type design. But Musk and the team didn’t know what to expect at the reveal. The company was just coming out of stealth mode, and at that point, Tesla had received no media coverage. It had no customers, no deposits, and no sales team. Still, Musk planned a huge party for the unveiling—an “awesome event,” in his words, staged inside the airport’s Barker Hangar. He told his personal assistant to invite 350 guests, including Michael Eisner of Disney, movie producer Richard Donner, actor Ed Begley Jr., California Governor Arnold Schwarzenegger, and many other luminaries. All were told to bring their checkbooks in case they wanted to write a $100,000 check to put a deposit on an electric Roadster. Meanwhile, a Roadster prototype zipped around a makeshift road inside the hangar, out the door, down a runway, and back inside again. Musk took center stage, telling the audience that they were witnessing the start of a new era in automotive technology, according to CNET’s coverage of the event. “Until today, all electric cars have sucked,” he told the audience. “Electric cars play into the strength of Silicon Valley. A lot of the things inside the car are conventional automobile technology. The magic is the battery technology and the software and the controllers.” Tesla Hooks Arnold Schwarzenegger, George Clooney Musk’s message was perfect for such an event, especially for the dozens of reporters who were there to publicize the reemergence of the electric car. They adored the Roadster. It was small, powerful, electric, and above all, cool. It was anti-Detroit—a new kind of car that burned no gasoline and was born in Silicon Valley instead of an antiquated factory in Michigan. The night was also a financial success for Tesla, with twenty $100,000 checks gathered from prospective buyers. And the momentum continued. A few days after the event, Joe Francis, creator of the adult entertainment franchise Girls Gone Wild, sent an armored truck to Tesla’s San Carlos office to drop off $100,000 in cash. A few days after that, Schwarzenegger put his money down, as did actor George Clooney. Within two weeks, Tesla had presold 127 Roadsters. California Governor Arnold Schwarzenegger was among the first buyers of the Tesla Roadster on the day the car was officially unveiled, 19 July, 2006.Glenn Koenig/Los Angeles Times/Getty Images Meanwhile, though, the company’s manufacturing woes continued. The mechanical problems weren’t even the biggest issue. The biggest issue was the supply chain. This was ironic, because some in the media admired Tesla for its global approach. They liked the fact that the battery pack, the motor, the chassis, and the assembly had an international flavor. It was a world car, they thought. But for Tesla, it was a nightmare. The battery pack was being assembled in Thailand by a manufacturer of barbecue grills. The facility was 3 hours from Bangkok, literally in a jungle where the heat was almost unbearable, and the factory building consisted of a truss roof held up by some steel columns. There were no walls because no one there wanted to work indoors. And because the pack assembler was inexperienced, Tesla engineers were repeatedly flying to and from Thailand to direct the effort. They would find animal droppings on the battery packs, which were sitting out in the open air all day and all night. For the umpteenth time, Musk wondered if the company would be able to survive. “We’re doomed if we don’t in-source the battery pack,” he told one of Tesla’s manufacturing engineers, “because we have a supplier in Thailand who is great at making barbecues but not great at battery packs. And the supply chain is so long that it takes six months from when the cells are built to when the battery pack is done and in a car. So that means the capital cost is gigantic because we have to pay for all that inventory and process. And inevitably, there are mistakes in the design or fabrication of the battery pack, and then we have six months’ worth of battery packs that don’t work.” Never mind that this chaotic approach was central to their plan. Tesla had never been envisioned as an old-fashioned, Detroit-style, vertically integrated manufacturer. From the beginning, it had been a Silicon Valley–type enterprise that would rely on others for the bulk of its manufacturing. Only now, as the fledgling company sent batteries and motors and assembled cars back and forth across two oceans, was its plan beginning to appear untenable. “We had this misguided idea that everything must be cheaper and better if built in Asia,” Straubel later said. Tesla’s Chances of Success: 10 Percent From the beginning, Musk had never been optimistic about Tesla’s chance of success. He repeatedly said he thought it was approximately 10 percent. “In 2004, the idea of starting a car company was extremely stupid,” he said. “The idea of starting an electric car company was stupid squared.” As he watched Tesla struggle with its supply chain, his earlier words were starting to look prescient. The only chance, the engineering team concluded, was to bring the manufacturing of all of the subsystems, such as the battery packs, motors, and inverters, in-house. They disassembled their overseas operations and moved them to California, starting with battery pack manufacturing. Assembly stations were loaded into huge shipping containers, transported back to one of the company’s new facilities on Bing Street in San Carlos, and then reassembled there. It took five and a half months. They also redesigned the battery packs and developed machines for automating their assembly. In 2008, with mass production of the Tesla Roadster just getting underway, Elon Musk gave an interview at the company’s headquarters, then in San Carlos in northern California. At the time, Tesla was merely a startup in a precarious position, bleeding cash and grappling with many manufacturing problems.Ryan Anson/Bloomberg/Getty Images Musk concluded that the key to success was not the design of the car itself but rather the manufacturing. Henry Ford had reached the same conclusion a hundred years earlier. It was “the realization of how important it is to build the machine that builds the machine,” Musk said at the Tesla Annual Shareholder Meeting in 2016. “And how much harder it is to build the manufacturing system that builds the product, than it is to create the product in the first place. You can create a demo version of a product…with a small team in maybe three to six months. But to build the machine that builds the machine takes at least a hundred to a thousand times more resources and difficulty.” Gradually, Tesla’s idea of letting others do its manufacturing slipped away. Packs were built in San Carlos, and then installed in the Lotus Elise chassis there instead of in England. “We had control now,” said manufacturing engineer Jason Mendez. “We had all the engineers there. We didn’t have batteries on the water, not from Thailand to England and not from England to here.” Musk began to talk about a new vision. He called it the gigafactory. Raw materials would enter at one end, and a car would exit at the other end. This was the ultimate in vertical integration, and it sounded a lot like Henry Ford’s vision for the River Rouge plant in Dearborn, Mich., in 1917. Tesla Motors was becoming an auto manufacturer.
Social media plays a significant, multifaceted role in adolescents’ development, influencing how they communicate, learn, socialize, and express themselves. The benefits, however, are accompanied by risks that can undermine youngsters’ character as well as their cognitive and social development. The potential problems include excessive screen time, social media addiction, cyberbullying, misinformation, radicalization, privacy violations, exposure to inappropriate content, sextortion, and doomscrolling. A recent study published in Nature: Human Behaviour found that adolescents who begin using social media at an early age tend to have significantly lower academic performance. A Mashable article highlights additional issues including effects on mental health, self-harm, addiction to social media, compulsive, repetitive checking, and exposure to pornography and violent material. Protecting minors has largely fallen to parents, schools, and self-regulation by some social media providers. But that approach has proven ineffective and inadequate, so some governments and policymakers have stepped in and placed responsibility on social media providers. Australia’s nationwide ban Australia was the first country to legislate a nationwide social media ban on children younger than 16—which I wrote about in January for Communications of the ACM. Enacted in December, the ban initially applied to 10 platforms: Facebook, Instagram, Kick, Reddit, Snapchat, Threads, TikTok, Twitch, X, and YouTube. It excluded messaging, gaming, and nonsocial platforms including Discord, GitHub, Roblox, WhatsApp, YouTube Kids, and educational tools. The law places the responsibility for enforcement on the platform providers through age-assurance mechanisms, requiring the platforms to take “reasonable steps” to prevent those 15 or younger from creating or holding accounts. It does not, however, apply to content consumption. Children can view publicly available posts and videos without logging in; they cannot comment or post, according to the law. The legislation mandates that the user’s age be verified with tools such as government-issued identification, biometric or facial age-estimation tools, behavioral or inference algorithms, and self-declaration with optional checks. Penalties for noncompliance can reach US $35.6 million. The 10 platforms subsequently removed nearly 5 million accounts of young users. Although the ban received widespread support, human rights organizations and digital freedom advisory groups have argued that it limits young people’s freedom of expression and access to useful information. They say the ban might contribute to social isolation and the loss of support networks, particularly among marginalized youth. Promising early outcomes The Australian ban is producing positive outcomes, according to a recent Time magazine article, “What the World Should Learn From Australia’s Social Media Law.” Early findings indicate it has reduced account ownership and social media use among young children. A YouGov survey of Australians found that 61 percent of parents of children age 16 and younger reported positive changes including more face-to-face interaction, greater presence and engagement, and improved parent-child relationships. Three in five Australians surveyed called the ban effective. The ban has encouraged social media platforms to reconsider their features. Snapchat is moving toward a friends-only experience for 13- to 15-year-olds, for example. The law is stimulating the development of purpose-built online spaces for children younger than 16 that can support their developmental needs, offering alternatives to mainstream social media. The longer-term impact could be more significant if “no social media account before age 16” becomes an accepted norm, making it easier for parents and schools to support delayed social media use. Implementation struggles Despite the early encouraging outcomes, one study found that online platforms struggle to implement age checks. Many under-16 users in Australia have continued to access platforms with little difficulty, the study said. They children have found workarounds to subvert restrictions, such as using a free VPN to bypass age checks—some of which have questionable data-collection practices. Seven in 10 children retained their existing accounts on restricted platforms, the study found. Other teens created new accounts using incorrect age information. Some were incentivized to seek unregulated offshore platforms not subject to Australia’s law. The workarounds prompted Australia to double the maximum fine and warn of court action against tech giants for noncompliance. Emphasis on age verification A number of other countries are implementing or considering social media restrictions. They include Brazil, Canada, France, Greece, Indonesia, Norway, Poland, Thailand, Türkiye, and the United Kingdom. The European Union is contemplating its own restrictions. The countries’ mandates for age verification or age assurance shift the policy focus from whether to verify age to how to do so effectively while protecting user privacy. An article on think tank New America’s website, “Age Assurance and Verification,” describes some methods: Age gating and screening. Users self-attest their age by checking a box or inputting a birth date. Age estimation. Several techniques are available, including profiling the user’s online activity and scanning the user’s face. Age verification. One way is providing a government-issued identification document. Other approaches include digital identity systems, digital wallets, and third-party verification. Reliable age verification is technically challenging and raises privacy concerns, as outlined in “The Age-Verification Trap,” written by Cinderpoint consultant Waydell D. Carvalho and published in February in IEEE Spectrum. Carvallo says platforms need to balance age verification with protecting users’ personal information. IEEE’s contributions IEEE is working on initiatives to provide a safer online environment for children. To help developers build age-appropriate social media platforms and websites, the IEEE Standards Association (IEEE SA) has published two guidelines. The IEEE Standard for Online Age Verification (IEEE 2089-2024) provides a framework for designing, specifying, evaluating, and deploying verification systems. The standard includes requirements for privacy protection, data security, and information management specific to the age-assurance process. It also provides procedures for verifying a user’s age or age range with a high degree of accuracy. Based on the 5Rights Foundation’s Principles for Children, the other standard (IEEE 2089-2021) provides practical steps to qualify online products and services for children. It requires systems to present information in an age-appropriate way and to uphold the rights established for youngsters in the U.N. Convention on the Rights of the Child. IEEE SA also offers an online age-verification-certification program, which assesses systems for compliance with the IEEE 2089.1 standard. The program certifies that organizations implement robust processes before granting access to age-restricted products and services, prioritizing children’s safety, privacy, autonomy, and rights. As outlined in The Institute article “IEEE Makes Strides to Improve Online Safety for Kids,” certification is based on six key indicators: accuracy, frequency of assurance, counter-fraud measures, authenticity, frequency of authenticity checks, and birth date confidence. Indonesia used key provisions from the two IEEE guidelines to inform its child-protection regulation, which was signed into law last year. IEEE’s ethically aligned design framework prioritizes human well-being, transparency, accountability, privacy, and protecting vulnerable populations including children. Calls for platform reforms Although social media bans would be globally significant policy responses, deeper structural issues remain largely unaddressed. Platform architecture and features contribute to social media harm. The focus needs to shift from constraints on account provisioning and content moderation to safer platform design. Meta in August agreed to pay $17.1 billion to settle a lawsuit brought by U.S. states. The suit said Meta designed its social media to be addictive to children, and the company concealed internal research showing Instagram’s addictive effects on teenagers. As part of the settlement, Meta agreed to implement child-safety measures such as setting daily time limits and disabling Facebook and Instagram push notifications during school hours. The company still faces other lawsuits that could have far-reaching implications, pressuring other tech companies to design safer social media platforms. Architecture-driven features such as infinite scrolling, algorithmic recommendations, addictive platform design, data-driven engagement, and personalized advertising to minors are other contributing factors to social media addiction. IEEE Senior Member Katina Michael, professor at the University of Sydney business school and founding editor in chief of IEEE Transactions on Technology and Society, shared her perspective: “Social media bans may offer a short-term response to growing concerns, but they are not a long-term solution,” she says. “IEEE 2089-2021 advocates for socio-technical systems that are designed to promote human well-being, safety, and flourishing. Rather than relying on prohibition alone, we should focus on better design, building digital platforms that embed ethics, accountability, transparency, and human values from the outset.” Collective responsibility Protecting children online would require a combination of policy measures, improved platform design, digital literacy, parental involvement, and cultural change. Building a safe, secure, and inclusive digital ecosystem that supports adolescents’ cognitive, social, and emotional development would require collaboration among technology companies, platform providers, content creators, parents, educators, policymakers, and young people themselves. Professional organizations such as IEEE can continue contributing through standards development, education, certification while promoting trustworthy and responsible digital technologies. This article was updated on 15 September 2026.
This interactive webinar will introduce the different types of AI, address the concerns with AI, share how we IBM are approaching Responsible AI, and offer guidance to students about what they can do - as individuals, and members of their IEEE chapters. Participants will also have the opportunity to to apply the Responsible AI approach to a particular use case - IBM Bob, a software development life cycle agent, and Q&A. This will be an interactive session, so have phones ready to engage! Register now for this free webinar!
For this year’s IEEE annual election, the IEEE Tellers Committee will recognize the Sections with the highest voter turnout (based on total eligible voters) in each Region with an incentive reward after the election results are accepted by the IEEE Board of Directors.The incentive initiative is managed by the Tellers Committee, which retains full authority over all rules, operations, and decisions regarding the program. Incentive guidelines The Section sizes and their respective reward amounts are: Large sections consist of more than 1,501 eligible voting members. The top-performing large section in each IEEE region will be rewarded with US $1,000. Medium sections consist of 501 to 1,500 eligible voting members. Each region’s top-performing medium section will receive $600. Small sections consist of 500 or fewer eligible voting members. The top-performing small section in each region will receive $400. To learn more about the incentive program, visit the IEEE annual election website. If you haven’t voted in the 2026 elections, you can learn about the candidates and vote here. Send questions to: elections@ieee.org.
Franklin “Frank” Kuo Codeveloper of ALOHAnet Fellow, 91; died 14 April Kuo helped develop ALOHAnet, a pioneering computer system at the University of Hawaii at Mānoa, in Honolulu. The system went online in 1971 and represented the first public demonstration of a wireless packet data network. It was an inspiration for Robert Metcalfe’s development of Ethernet a couple of years later. In 2020 ALOHAnet was designated as an IEEE Milestone. Kuo earned bachelor’s, master’s, and doctoral degrees in electrical engineering from the University of Illinois, Urbana-Champaign. After earning his Ph.D. in 1960, he joined Bell Labs in Murray Hill, N.J., where he conducted research in computer communications. After six years at the company, Kuo left to become a professor of electrical engineering at the University of Hawaii. From 1968 to 1971 he and one of his colleagues, IEEE Life Fellow Norman Abramson, developed ALOHAnet. The network connected computers on Hawaiian islands using ultrahigh-frequency radio, transmitting information over radio waves instead of cables. ALOHAnet became the foundation for modern networks. Kuo pioneered the concept of a random-access protocol, or sharing a single channel without central coordination—which led to the packet-switching principles that underpin modern Wi-Fi and mobile networks. Kuo authored or coauthored several books including Computer Communication Networks. Published in 1972, it was one of the earliest textbooks on the subject. He served as director of the university’s Cosine committee, a project funded by the U.S. National Science Foundation to develop computer engineering courses. He took a sabbatical from 1975 to 1977 to work at the U.S. Pentagon as director of information systems in the defense secretary’s office. He oversaw computer communications applications used in command, control, and intelligence programs. During the 1980s and ’90s, he helped develop China’s Internet. In 1982 he joined SRI International (formerly the Stanford Research Institute), in Menlo Park, Calif., as a researcher. He also was a consulting professor in Stanford’s electrical engineering department and taught computer networking at Shanghai Jiao Tong University. As a UNESCO lecturer in Beijing in 1994, he helped Peking University, Tsinghua University, and the Chinese Academy of Sciences connect to the Internet. He also worked with Tsinghua University to develop CERNET, the country’s first nationwide education and research computer network, which was managed by the Chinese Ministry of Education. For his work, he received an honorary degree from Shanghai Jiao Tong University. In the mid-1990s, Kuo helped found General Wireless Communications, a developer of mobile phone messaging services and games that was renamed Mtone Wireless. Muhammad Rezaul Karim Bell Labs researcher Life senior member, 86; died 18 May Karim was a distinguished member of the technical staff at Bell Labs in Murray Hill, N.J. His work was instrumental in the development of modern cellular communications technology. He joined Bell Labs in 1972 and worked in its mobile telecommunications laboratory as part of the team tasked with creating one of the earliest cellular networks. In 1975 Illinois Bell Telephone petitioned the U.S. Federal Communications Commission to develop and test a cellular system. The FCC, which now regulates radio, TV, telephone, Internet, satellite, and wireless services, authorized the project in March 1977. Karim and his team helped develop key elements of the technology, including the Bell Labs logic that controlled the cellular system, turning the concept into a working one. They also built radio receivers, transmitters, control systems, and cell-site equipment used in the first trial of the cellular system. The following year, Bell Labs and Illinois Bell deployed the Advanced Mobile Phone Service system across Chicago, with its switching office located in Oak Park, Ill. The initial test used approximately 100 mobile phones to work through hardware, software, and system-design problems. A subsequent test in 1979 involved 2,500 mobile users, providing a demonstration of the cellular technology in practice. The trials in Illinois helped establish the technical foundation for the commercial cellular networks that followed. Later in his career, Karim worked on the asynchronous transfer mode (ATM) technique, a high-speed networking technology crucial to the transition from traditional telephone networks to broadband and digital ones. In 2000 he published ATM Networks: Application, Systems, and Design, a textbook that served as a guide for designing and implementing ATM-based services. Karim received a bachelor’s degree in electrical engineering from the Bangladesh University of Engineering and Technology, in Dhaka. He then earned a master’s degree in EE from the University of Manchester, England, and a Ph.D. in EE from Stevens Institute of Technology, in Hoboken, N.J. Harry Bostic Former IEEE Region 4 director Life senior member, 86; died 18 March Bostic was an active IEEE volunteer who served as the 1998–1999 director of IEEE Region 4. In 2007 he received a lifetime achievement award from the IEEE Central Indiana Section for “outstanding commitment and dedicated service as regional advisor to the volunteers and members of Region 4 and the Institute.” He was an engineer for 30 years at U.S. Navy’s avionics facility, a research, development, and manufacturing concern in Indianapolis. He worked on flight control, navigation, and weapons systems there. (The facility closed in 1996.) Edwin C. Jones Jr. Professor Life Fellow, 91; died 10 March Jones was widely recognized for his contributions to engineering education, curriculum development, and accreditation through decades of service to IEEE, ABET, and the American Society for Engineering Education. He earned a bachelor’s degree in electrical engineering in 1955 from West Virginia University in Morgantown. The following year he earned a diploma of membership (equivalent to a master’s degree) from Imperial College, London. He went on to serve in the U.S. Army Signal Corps for two years. After his service ended, he studied engineering education at the University of Illinois, Urbana-Champaign, earning a Ph.D. in 1961. Jones then joined the university’s faculty. The following year, he left Illinois to join Iowa State University, in Ames, as an assistant professor. He was promoted to professor in 1995. Two years later he became associate chair of the electrical and computer engineering department and served in that position until 2001, when he retired and was named professor emeritus. In recognition of his commitment to students, Iowa State established a scholarship in his honor. In 2006 he accepted a part-time position as an adjunct professor in Minnesota at the University of St. Thomas, in St. Paul. He advised graduate students and helped develop the university’s systems engineering program. An active IEEE volunteer, he served as 1975–1976 president of the IEEE Education Society. He was a member of the IEEE Educational Activities Board, helping strengthen the relationship among engineering education, professional practice, and accreditation organizations. He received an IEEE Centennial Medal in 1984 and the EAB Meritorious Achievement Award in Accreditation Activities in 1986. The IEEE Education Society later named its Meritorious Service Award in his honor. Jones was elected a Fellow of ABET in 1986. During his years of service as a program evaluator and leader, he helped advance the quality of engineering education and accreditation programs. ABET recognized him with its Grinter Distinguished Service Award, its highest honor. Alexander Robert Spitzer Clinical neurology researcher Life senior member, 70; died 27 February Spitzer was a neurologist for 40 years at the Wayne State University School of Medicine, in Detroit, where he also was a director of the electromyography laboratory at Harper University Hospital. The lab studied patients’ brain and spinal cord activity in response to sensory stimuli. The evaluations assessed nerve pathway integrity to help diagnose multiple sclerosis, spinal cord injuries, and other conditions. After earning his medical degree from the Einstein College of Medicine, in New York City, Spitzer completed a fellowship at the U.S. National Institutes of Health, in Bethesda, Md. He then joined Wayne State as a clinical neurology researcher. His pioneering research in applying neural network analysis to electromyography and clinical neurophysiology resulted in peer-reviewed publications, grants, and several U.S. patents. He mentored generations of neurologists in electrodiagnostic medicine, a medical specialty that uses nerve-conduction and electromyography tests to evaluate and diagnose muscle and nerve disorders. In 2020 he founded Mackinac Neurology, a telemedicine-based practice that treated patients virtually during the COVID-19 pandemic. A longtime IEEE volunteer, he held numerous roles on the IEEE Regional Activities Board, now known as the Member and Geographic Activities Board. He was a member of the IEEE Ethics and Member Conduct and Nominations and Appointments committees, as well as the IEEE Educational Activities and IEEE-USA boards. He served as 1977–1979 director of the IEEE Central Indiana Section. Donald Leo Dietmeyer Professor Life Fellow, 93; died 13 February Dietmeyer was a professor of electrical and computer engineering for 40 years at the University of Wisconsin-Madison. He developed a lifelong interest in radio and electronics at high school in Wausau, Wisc., and earned a Ph.D. in electrical engineering in 1959 from the University of Wisconsin. He’d joined the university’s electrical engineering faculty as a professor in 1958 while pursuing his doctorate. Dietmeyer’s research focused on computer-aided design in the areas of switching theory, hardware description languages, and the decomposition of Boolean functions. His research contributed to the development of automation tools for integrated circuit design. He worked with Jim Duley, a former student, to pioneer the use of the digital system design language. He wrote the textbook Logic Design of Digital Systems, published in 1978. In the early 1980s, Dietmeyer worked with researchers to develop ConLan, a language-construction method that combined hardware description languages in one underlying framework. He served as associate dean of the University of Wisconsin’s electrical and computer engineering department from 1983 to 1995. In 1998 he retired and was named professor emeritus.
This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free! I lost a cushy engineering management position at the same time I purchased a business, with a mortgage, three kids, and every other financial obligation of being an adult. It was one of the most stressful stretches of my life. If something similar has happened to you, I feel your pain. You lose more than the income For many of us, our work became our identity, so we don’t just think, “I don’t have a job anymore.” We start thinking, “I’m not an engineer anymore.” An apple tree in winter is still an apple tree. A car parked in a driveway is still a car. You’re still an engineer, the same way you were still one every evening you clocked out, and the same way you’ll be one at the next job. Your skills took years to build and won’t evaporate because a company stopped paying for them. This feeling can be particularly rough if part of your identity was tied to a recognizable employer. However, this can also be a chance to catch up on what you’ve been putting off: working out, being present with your kids, writing, whatever hobby got shelved for a deadline three years ago. Once you’ve caught your breath, here are some tips for when the actual work starts: 1. Don’t sprint on day one I started applying for jobs the next morning after the layoff. It felt productive, but it gave me no time to settle my nervous system or consider what I actually wanted next. I took the first offer that came along, at a company I already knew wasn’t right, and quit after exactly 30 days. Give yourself a few days before deciding anything. Plans built out of desperation rarely work out well. 2. Audit your spending Go through every subscription and recurring charge and cut what isn’t essential. Every dollar you stop bleeding buys you patience instead of forcing a bad offer out of fear. 3. Build a list wider than LinkedIn Start with former colleagues and vendors, or businesses with a relationship to your previous employer. They already know you or your company, which gives you the halo effect: Some of the trust from your employer carries over to you automatically. LinkedIn is table stakes and is the most popular place to find work, but that doesn’t mean your search should stop there. Try Facebook, Instagram, and other social media channels too, where plenty of people who aren’t on LinkedIn might have leads for open roles. I found my first job years ago from a Facebook post, and both roles I landed after being laid off came from startup job boards and recruiters I found entirely outside LinkedIn. Your mileage may vary, but LinkedIn isn’t the only game in town. Don’t skip your inner circle either: Text your family and friends. Good leads rarely come from someone you know directly, but they do come from someone that person knows. Work this list daily and track who you’ve contacted. 4. Eight hours is a long time With no work to fill your day, you may default to treating the search like an eight-hour shift. You can’t apply productively for 8 hours straight, and that’s why people burn out. Use a focused morning block for the list in step 3, then spend the rest of your day on activities you’ve been putting off. 5. Catalog your wins before you study interview questions Cataloging your wins is a higher-leverage move than drilling practice questions this early. Can you explain the most impactful project you worked on in the last year? Probably not. Most people skip this step, then default to generic answers when a recruiter asks about their last role. Write down specific stories showing leadership, technical ability, and grace under pressure. They’ll come up once you’re in interviews, and cataloging them rebuilds your confidence along the way too. Save the deep prep for once an interview is on the calendar. Ask yourself daily whether today’s work is generating interest in you, or leading you to someone who might hire you. If not, consider skipping it. One last thing A layoff rarely reflects your skills. It’s usually a company protecting revenue—nothing more personal than that. Knowing that doesn’t make it easier, but hopefully this gets you back on your feet faster. —Brian IEEE Global Careers Fair: September 23-24 Looking for a job? For the first time, IEEE is taking its Career Fair worldwide. The inaugural IEEE Global Virtual Career Fair runs September 23 (5:00 PM EST) through September 24 (8:00 PM EST), following the sun across every region to connect engineering and technology professionals with employers around the world. Read more here. United States Invests in Industry Partnerships for Ph.D. Training While most engineering Ph.D. grads end up in jobs at commercial companies, academia and industry often operate in their own bubbles. Now, the U.S. National Science Foundation is investing in a program to integrate industry experience into STEM doctoral programs and help bridge the gap. Modeled after similar programs in other countries, students in the I-PhD will spend at least one year working on research at an industry site and receive a combination of funding from the company, NSF, and the university. Read more here. AI Efficiency Could Cost Us the Next Generation of Experts When systems engineer Richard Mitchell designed a digitally-controlled nuclear plant, he made a counterintuitive decision: including manual steps for the human operator that a machine could execute on its own. The strategy was meant to keep the operator sharp, and it’s one that could help address one of the biggest issues facing the workforce today: What happens to human expertise when AI does the work that used to build it? Read more here.
I’m sitting in a Rivian R1S SUV as it drives itself down the leafy streets of Palo Alto, Calif., through areas crowded with touchstones of tech history. We cruise near the landmark HP Garage, the one-car workshop where Hewlett-Packard, and, arguably, Silicon Valley, was founded in 1939. I skirt Stanford University, where a team led by computer science professor Sebastian Thrun won a US $2 million DARPA Grand Challenge in 2005. The team’s Volkswagen SUV, named Stanley, became the world’s first vehicle to navigate a grueling 212-kilometer Mojave Desert course with no human intervention. This Rivian might look like any other electric SUV in this affluent town, with its concentration of tech bros, venture capital, and startups. But inside this boxy EV is something special: an Autonomy+ system that will allow owners to enter an address, sit back, and let the vehicle drive to any mapped destination in the U.S. and Canada. This point-to-point system is one of the most advanced semiautonomous-driving systems coming to market. It is also a precursor of the company’s bid to make self-driving cars a reality, for robotaxis and—eventually—for everyday car buyers. After years of incremental advances and frustrating setbacks, self-driving has been swept up in the great AI resurgence, and is now a top priority for investors and global automakers, who envision vast new streams of profits. So here I am, 21 years after that DARPA challenge, riding shotgun in Stanley’s vastly more advanced descendant. Rivian’s Autonomy+ is intended to operate seamlessly on suburban streets like these, sensing and responding to traffic lights, crosswalks, and stop signs. That point-to-point system is set to debut on Rivian’s all-new R2 SUV by roughly the end of this year, and via over-the-air updates for its newest R1S and R1T models. Rivian says it will charge $49.99 a month, or $2,500 up front, versus Tesla’s $99 per month for its rival system, which is somewhat misleadingly called Full Self-Driving (Supervised), or FSD. Mercedes, meanwhile, plans to charge $3,950 for a three-year subscription for the forthcoming MB.Drive Assist Pro on its CLA-Class EV; that system still requires at least one hand on the steering wheel. Video released by Rivian shows the company’s R1 SUV being driven on a variety of urban and rural roads, according to the company. Rivian plans to introduce this self-driving system to compete with Tesla’s offering before the end of 2026. Rivian Impressive as it is, Autonomy+ is only a Level 2+ system in the classification system established by the Society of Automotive Engineers. Level 2+ means that a human driver must pay full attention and be ready to retake control at any moment. Rivian, along with a horde of deep-pocketed rivals, is aggressively working toward more impressive (and potentially lucrative) levels of autonomy. At Level 3, drivers could “check out” behind the wheel for limited periods, to scroll through emails or watch a movie—but not to sleep. The big race right now is to deliver Level 4 autonomy: A car you could (in theory) dispatch to pick up a pizza, and have it carted home on the heated, unoccupied driver’s seat—or in which you could spend the ride lounging alone in the back seat, enjoying a private slice while reading a newspaper. At Rivian’s software lab in Palo Alto, Calif., a technician evaluated code for the company’s self-driving system.Jason Henry/Bloomberg/Getty Images Robotaxis currently roaming select cities in the U.S., China, and the Middle East have proved that driverless, Level 4 autonomy is possible. These cars operate in relatively tiny numbers in a couple of dozen cities, and within the specific constraints of commercial services. Now Rivian and its many rivals—including Tesla, Toyota, Mercedes, Volkswagen, and China’s BYD—are racing to bring that level of self-guided mobility to the masses. Rivian’s strategy combines a suite of cameras, radar, and lidar; a custom set of silicon chips, developed in-house, to process sensor data; and an AI autonomy model running on those chips. With $1.25 billion in backing from Uber, Rivian plans to graduate to a fleet of self-driving, Level 4 robotaxis starting in 2028. Those taxis, along with Rivian’s consumer fleet, will be the literal training wheels for extending Level 4 ability to consumer vehicles. Meanwhile, millions of connected cars, as they cruise every nook and cranny of the globe, are already sending data to train automakers’ systems. The race is on to funnel those data through fast-improving AI models with “end to end” capability: an AI architecture, powered by deep learning, that processes raw sensor data directly into physical vehicle commands. So equipped, engineers anticipate they’ll be able to solve the tricky edge cases—tangled urban streets, unique geographies, swarms of pedestrians, inclement weather—that skeptics once deemed intractable. Rivian’s Plan for Level 4 Self-Driving Despite the company’s high media profile, including a spotlight on RJ Scaringe, its MIT-doctorate founder and chief executive, Rivian holds a relatively tiny slice of the U.S. passenger-vehicle market. It sold just 42,000 vehicles last year across its three models, the adventure-minded R1S SUV and R1T pickup, and the Electric Delivery Van. Tesla sold about 1.6 million units. Toyota, the world’s largest automaker, sold more than 11 million. The first generation of the Rivian Autonomy Processor, an AI processing chip developed in-house, was tested at Rivian’s Palo Alto, Calif., lab in December, 2025. Jason Henry/Bloomberg/Getty Images Rivian’s underdog strategy is to leverage software and tech to make itself a serious player. Volkswagen, among the world’s largest automakers, saw enough value there to invest up to $5.8 billion in a joint venture called Rivian and Volkswagen Group Technologies. The joint venture gives Rivian crucial capital for development. It gives Volkswagen access to Rivian’s electrical architecture and to the software for the R2, new-generation Rivian SUV that went on sale in June. Unlike traditional lidar units, which protrude like a layer cake from the roof of a vehicle, Rivian’s unit on the new R2 SUV is housed in a small, sleek enclosure where the windshield meets the roof.Rivian (2);Jason Henry/Bloomberg/Getty Images “Rivian developed an architecture so important that VW is spending billions to buy it, as opposed to trying to re-create it themselves,” says Bryan Reimer, a research scientist in MIT’s Center for Transportation and Logistics. But the joint venture doesn’t give VW access to Rivian’s autonomous tech. In March, that R2 architecture underpinned Rivian’s $1.25 billion deal to supply Uber with up to 50,000 robotaxis. The companies plan to initially deploy 10,000 taxis, beginning in San Francisco and Miami in 2028, before expanding across 25 cities in the U.S., Canada, and Europe. Rivian’s vulnerabilities include struggles with reliability, along with expensive body repair costs that the company says it strove to reduce for its new R2. As impressive as Rivian’s in-house tech may appear, the company has miles to go to catch up with Tesla, which recently announced it has 1.1 million active users of its FSD system. Toyota is also jumping into the game; its Woven by Toyota subsidiary has partnered with the Alphabet-owned Waymo to develop an autonomy platform for robotaxis and consumer cars. The lidar unit on a Waymo robotaxi protrudes noticeably from the roof of the vehicle.Andrej Sokolow/picture alliance/Getty Images Until recently, most observers would have gone all-in on Tesla as the winner of the autonomous race. Elon Musk’s company has begun operating a small test fleet of Model Y robotaxis in three Texas cities and in Florida. Tesla has also begun producing a dedicated autonomous vehicle, the Cybercab robotaxi. But in April, Musk pushed back his timeline for Level 4 autonomy for general consumers: “I’m just guessing here, but probably in the fourth quarter” of 2026, he said. It was the latest in a series of deflating walkbacks from the man who once promised 1 million robotaxis on the road by 2020. Scaringe, during our drive of his company’s make-or-break R2 SUV at a Utah state park, says that showroom Rivians will start adopting some of its robotaxis’ Level 4 capabilities no later than 2030, perhaps beginning with self-parking functions. How Self-Driving Systems Are Learning From Humans Like most autonomous cars, Rivian’s system fuses data from multiple sensors to create a robust picture of a fast-moving environment and its obstacles. Data is fed to a neural network—what Rivian refers to as its “Large Driving Model,” or LDM—that churns through hundreds of trillions of operations per second to interpret and fuse data from cameras, radar, and lidar. That network is end to end, meaning that it processes multiple streams of raw sensor data (such as camera pixels) and outputs driving controls (for steering, braking, and acceleration) through a single data pipeline. More traditional systems coded distinct steps for data collection, feature extraction, prediction, and decision-making. That proprietary AI driver identifies features in images and point clouds, groups them into objects, and tracks them across frames, time-stamped to the millisecond to account for differing frame rates. The AI thus builds confidence over time, acting on object detections that persist across several frames, rather than, say, slamming the brakes due to a camera blip on a single frame. The virtual driver can then navigate safely even when sensors disagree, by favoring the persistent data. The output— commands for electric motors and other systems—is backed by redundant hardware for by-wire systems such as steering and brakes. During my demo of Rivian’s point-to-point Autonomy+ system, a company test driver sits behind the wheel. Nick Nguyen, the engineer who directs Rivian’s products and programs related to autonomy, watches from the back seat. Compared to, say, a large language model that writes news or fiction, Nguyen says, an autonomous-driving AI is less subjective and easier to evaluate, so there’s little room for error. “We want cliché. We want boring. Just safe, repeatable driving,” he says. The Rivian R2 SUV plans to offer a self-driving system by roughly year’s end 2026. The R2 competes with the more urban-oriented Tesla Y.Rivian From my brief drive, I’d say suburban boredom is achieved in this Rivian R1S. Unlike some modes of Tesla’s Full Self-Driving (Supervised), Rivian’s system drives like a soccer dad, obeying speed limits to the digit, stopping gracefully at traffic lights, and easing over speed bumps like a driver delivering antiques. Yet this robo-driver isn’t timid or tentative. For robotaxi companies in the U.S. and China, these types of routine trips are boosting optimism and investment to dizzying heights. Waymo claims 92 percent fewer fatal or serious-injury accidents than human drivers, based on 220 million miles of autonomous ride data. But the real challenge is how well the higher levels of autonomy will work when they reach consumer cars [see Sidebar, “The Growing Proof That Autonomous Cars Save Lives”]. Rivian’s core LDM currently ingests cloud data from up to 125,000 cars for analysis and validation, which then fine-tunes the model through simulations. Onboard computing is smart enough to trigger recording only for unusual scenarios. Owners have to agree explicitly to data collection beforehand. Updated LDMs will be beamed back to customer cars via monthly over-the-air updates. part of that self-reinforcing data flywheel. It’s part of what Scargine calls the “data flywheel,” the self-improving AI loop that continuously refines the system. As is true for some of its rivals, Rivian no longer needs to fully rely on an onboard high-definition map or even a cellular link to pinpoint the car for navigational purposes. That strategic shift reduces data demands, and ensures steady driving in urban canyons or tunnels with no connections. Instead, the Rivian recognizes and responds to its surroundings through recognition and repetition, just as a human would do interpreting street signs, following lane markers, being alert to hazards. The Rivian R2 features 11 high-definition cameras and five radars. It will integrate a lidar unit early next year to lay the groundwork for future autonomy. That miniaturized lidar will integrate smoothly into the R2’s existing roofline, an improvement over the bulky, drag-producing units seen on Waymo Jaguars, and older partially autonomous models. Vidya Rajagopalan, Rivian’s senior vice-president of electrical engineering hardware, says lidar costs have fallen from above $10,000 to a few hundred dollars in under a decade. Vidya Rajagopalan, Rivian’s senior vice president of electrical engineering hardware, holds a RAP1 AI processor chip.Jason Henry/Bloomberg/Getty Images A mix of sensors plays up the strengths and diminishes the weaknesses of each, Rajagopalan says. Cameras capture color and texture and can distinguish between objects, but they struggle in darkness and low-contrast lighting. Lidar is unaffected by darkness or blinding sunlight, and senses shapes in three dimensions. This inherent 3D capability makes lidar more reliable for slowing or halting a car for random objects—“a tire in the road, or maybe a large dinosaur,” Nguyen quips. Multiple cameras can further contribute 3D data, after a short delay for processing. Sensors with 360-degree vision can outperform human senses in key situations. Radar and lidar can spot nighttime pedestrians or animals hundreds of meters down the road, something no human can do. But lidar can be thrown off by dust, fog, and snow. Radar can “see” through rain or snow, but with relatively low spatial resolution. Why Rivian Ditched Nvidia To handle the flood of sensor data, Rivian has taken on an ambitious challenge: designing its own custom autonomy chip in-house. The Rivian Autonomy Processor (RAP1) is a 5-nanometer processor that can execute 800 trillion operations per second (TOPS), three times as fast as the Nvidia Jetson Orin chip used in its earlier models. The chip will be built to Rivian’s specs by Taiwan Semiconductor Manufacturing Co. , which also makes custom chips for Tesla. Rivian’s autonomy module contains two Rivian Autonomy Processors, each capable of 800 trillion operations per second.Rivian Nvidia’s latest automotive system-on-a-chip, the Drive AGX Thor processor, is being adopted by the likes of BYD, Hyundai, Lucid, Mercedes, Nissan, Volvo, and Xiaomi, along with the Aurora and Waabi autonomous-trucking companies. On paper, a single AGX Thor chip is slightly faster in terms of TOPS, at 1,000 trillion operations per second. But Rivian combines a pair of chips in each autonomy module, giving it 1,600 TOPS and execution rates around 5 billion pixels of data per second, versus 3.5 billion for Nvidia’s Thor. Rajagopalan says developing the chip and AI software simultaneously shaved a critical full year from development. Experts say it’s the kind of fast-to-market speed that China has mastered and that legacy automakers are struggling to match. The in-house design allows Rivian to custom-tailor its software to the chip, and vice versa. Nvidia’s general-purpose chip, designed to satisfy multiple customers with various needs, must devote computing power to onboard infotainment, displays, or other systems. Rivian’s chip is designed to run autonomy and nothing but. During my visit to Rivian’s Silicon Valley campus, Rivian engineers Prasun Raha and Mukund Chavan tutored me on the rapid pace of the company’s autonomy evolution. A cluttered wallboard displays a first-gen architecture that Rivian debuted just five years ago. The initial R1S SUV and R1T pickup used nearly a score of electronic control units (ECUs), the “black boxes” that traditionally control vehicle functions. For its latest R1 models, Rivian reduced the ECU count to seven. The zonal architecture organizes nearly every vehicle function into three zones, hugely consolidating the electronics and simplifying manufacturing. Rivian also leaned into an autonomy trend called “early fusion”: mixing raw, time-and-space-aligned sensor data into a shared view before the neural network acts upon it. In late fusion, each sensor performs solo recognition before it’s combined into a single picture. The self-driving revolution will really begin when the technology migrates from controlled taxi fleets to consumer cars, giving owners back the precious time they waste on commuting. Early fusion preserves the richest sensor data for maximum accuracy in self-driving. But it demands the enormous computing power the RAP1 can deliver. Raha says the approach helps the multimodal system degrade gracefully and continue to operate with certainty even if, say, a camera’s lens gets covered with mud. Together, these elements make up Rivian’s third-generation autonomy platform. Displayed on a test bench, a new Autonomy Compute Module pairs two RAP1 supercomputing chips. The module is eight times as powerful as before but 60 percent smaller, according to the company. Raha says the system was designed expressly to expand Rivians to Level 4 autonomy from today’s Level 2+. RivLink, the automaker’s interconnect technology, can bridge multiple RAP modules to scale processing power. “It lets us build this extensible system, with perhaps two more chips for Level 3 or four for Level 4, depending on how the model scales,” Raha says. Rivian’s Road Map to Full Autonomy Rivian’s next planned milestone toward self-driving will be Level 3 autonomy—a hands-off and eyes-off system, but for highways only. (Remember, Tesla’s current FSD is technically a Level 2 system: hands off but not eyes off.) On the freeway, Nguyen points out, drivers would be spared the drudgery of dealing with stop-and-go traffic, allowing them to boost productivity or just goof off. Some autonomy critics are leery of Level 3, envisioning a limbo zone in which drivers are lulled into a false sense of security when a car drives for long stretches with no human attention required. Ford and GM are among the automakers pivoting toward limited eyes-off functions. Rivian’s senior vice-president of autonomy, James Philbin, sees Level 3 as an inevitable stepping-stone to Level 4. The company expects it will initially be limited to highways, not the cut-and-thrust of city traffic. If a driver fails to respond to alerts, the system will slow the vehicle, pull off on a shoulder, or call 911. Rivian has not announced a timeline for introducing limited Level 3 capability. Navigating a Tricky Liability Shift on the Way to Immense Profits Ready or not, these much more autonomous systems are coming, a natural evolution of today’s semiautonomous helpers. In developed markets, adoption of showroom cars with partial-to-full automation is projected to jump from 8 percent in 2024 to 28 percent by 2030, according to Morgan Stanley. “One in four cars sold globally may be equipped with smart-driving technology in five years, versus one in eight cars now,” wrote Tim Hsiao, a Morgan Stanley analyst, in a note posted on the company’s website. Combining cameras, lidar, and radar gives a self-driving car a better view of people and objects in front of it, according to Rivian. The company expects to release a self-driving system before the end of 2026 that will compete with Tesla’s, which uses cameras alone. Rivian MIT’s Reimer believes the self-driving revolution will really begin when the technology migrates from controlled taxi fleets to consumer cars, giving owners back the precious time they waste on commuting. If owners could truly send their autonomous car to safely chauffeur children, keep aged parents mobile, or run errands—while owners keep working or playing—the automakers who first help make that happen will enjoy a massive competitive edge, he says. As automakers struggle to convert buyers to subscription models, Reimer believes that self-driving appears to be the one advance for which consumers might actually pay plenty. But the greatest impediment to that revolution has little to do with technology. Public skepticism over self-driving is rampant; and the fate of fully autonomous testing in New York City is uncertain. Even going from Level 2 to Level 3 might shift legal liability for some accidents from drivers to automakers. But with Tesla still fighting lawsuits over its rudimentary Autopilot systems, those questions aren’t anywhere near settled. Experts worry that self-driving cars may become as politicized as EVs. Labor unions are pushing back, fearing job losses from taxis to trucking. A crazy quilt of state or local regulations has failed to create coherent industry guidelines. Publicized failures—even ones that don’t result in injuries, such as Waymos driving onto a flooded street or impeding emergency workers—give the industry a black eye. Companies like Tesla and even Waymo, Reimer says, have too often relied on an arrogant “Trust me” approach, resisting regulation and oversight. Nevertheless, the momentum toward truly self-driving cars, and massive backing from automakers and AI-besotted investors, suggests their time has come. The rest of the journey will depend as much on social and regulatory issues as technical ones, and so Reimer has a bit of advice. “Do it right, and share all your data,” he says. “Earn the right to scale…. It’s about establishing trust, and developing a framework in which we truly believe these systems can operate as a trusted part of our transportation network.” This article was updated on 08 September 2026.
Plenty of people remain spooked by autonomous vehicles, or AVs. Some experts and policymakers have cautioned that AVs won’t necessarily make roads safer. When it comes to partial or full autonomy, the picture isn’t entirely clear, in part because there aren’t enough self-driving cars to make meaningful apples-to-apples comparisons. Yet mounting research suggests that self-driving cars crash significantly less often than people, and with far fewer injuries. Evidence also shows that advanced driver assistance systems (ADAS) and other building blocks of autonomy—some of which are already mandated on every new car—are also reducing occupant and pedestrian injuries and deaths, along with insurance claims. On the ADAS front, the Insurance Institute for Highway Safety found that automatic emergency braking (AEB) systems that recognize people in front of the car cut pedestrian crashes by 27 percent. Those AEB systems are mandated for all light vehicles in the U.S. by 2029, and more than 90 percent of new models already comply under a voluntary automakers’ agreement. A separate IIHS study found that automated braking greatly reduced rear-end crashes, by 50 percent, and their injuries by 56 percent. The Highway Loss Data Institute found that cars with AEB alone showed a 13 percent drop in property-damage claims. Cars that bundled ADAS features, including automatic braking for pedestrians, adaptive cruise control, and lane-departure warnings, saw claims reductions up to 39 percent. Move to Level 4 autonomy, and Waymo says its robotaxis have now given 20 million paid rides over 220 million miles, the equivalent of 250 lifetimes of driving. In March, Waymo’s independent study showed 92 percent fewer fatal or serious-injury crashes, a 13-fold reduction versus human drivers in comparable city environments. That included 92 percent fewer pedestrian injuries, 83 percent fewer crashes with airbag deployments, and 82 percent fewer crashes with any injuries whatsoever. That included a 96 percent reduction in injury-causing crashes at intersections, among the deadliest environments for any automobile. How Does Limited Fair-Weather Data Compare to Traditional Crash Statistics? A key question is whether Waymo’s robotaxis, currently limited to fair-weather operation in a handful of cities in the U.S., are directly comparable to humans driving a wider variety of roads in much more variable conditions. The IIHS is looking to dig deeper by cleaning up often-incomplete data. Researchers estimate roughly half of human crashes go unreported, and up to one-third of injury accidents, because drivers hope to avoid insurance price hikes. That potentially skews safety numbers in favor of human drivers. And while Waymo leads the industry in transparency, and robotaxi operators are required to report even the tiniest scrape to the National Highway Traffic Safety Administration (NHTSA), not every company voluntarily reports their total miles driven. Related: Rivian’s Gambit for Full Autonomy The IIHS’s latest July study flatly stated that automated cars crash less often than people. But it also sought clarity by creating a more-reliable category of “police-reportable crashes.” It then compared crash rates of human-driven cars against Waymo taxis in San Francisco, Phoenix, Los Angeles, and Austin. Waymo’s Jaguar I-Pace taxis traveled about 50 million driverless miles over the study period, versus 222 billion human miles in the same cities. In a potential boost for public trust, the study generally supported Waymo’s own findings. Waymo taxis were involved in 68 percent fewer crashes overall than human drivers: 76 percent lower in Phoenix, 71 percent in LA, and 35 percent in San Francisco. A 4 percent higher Waymo rate in Austin may reflect an extremely small sample size. Significantly, Waymo’s injury crashes were still 81 percent lower on a per-mile basis. The industry and its supporters continue to press the safety advantages of autonomous vehicles that never get drunk, drowsy, or distracted. Yet for this fledgling AV industry, there are still no national performance or safety standards. A crazy quilt of state or local regulations can allow or prohibit their deployment. That balkanized approach makes it harder to compare crash rates, according to the IIHS, which is calling for better federal reporting standards. A posting on the IIHS website quotes the institute’s director of statistical services, Eric Teoh: “Those are encouraging signs for the future of driverless vehicles.” Teoh, who was also the lead author of the institute’s study, added that “Now we need to get the data-collection system right, so that we can ensure that level of safety continues as these technologies become more prevalent.” Amazon’s Zoox Gets an Exemption for its Robotaxis On July 30, in a move seen as fast-tracking the tech’s deployment, NHTSA granted Zoox, a subsidiary of Amazon, the first-ever exemption from certain motor-vehicle safety standards. That will allow commercial operation of Zoox’s toaster-shaped robotaxis, which have no steering wheel or pedals aboard. The agency determined that Zoox’s purpose-built robotaxi “would provide an equivalent level of safety” as a compliant vehicle, thereby satisfying the standard for an exemption. On that final day of the SAE’s Automated Transportation Symposium, NHTSA also announced a partnership with SAE Industry Technologies to develop the nation’s first performance and competency standards for AVs, via a three-year, $5 million “A2SCEND” consortium. Some doctors and health professionals are arguing that policymakers need to stop viewing self-driving cars as a tech moonshot but rather as a critical public-health intervention. Jonathan Slotkin, a neurosurgeon, makes a powerful case for the medical and societal benefits of AVs. Researchers at the Johns Hopkins Bloomberg School of Public Health say that highlighting the social value of AVs is critical to driving public trust and adoption. Consider that roughly 40,000 people in the U.S., including more than 7,000 pedestrians, are killed each year in roadway accidents. About 1.16 million people die in roadway crashes around the world, making them the leading cause of death for children and young adults between the ages of 5 and 29. Cutting that by even 50 percent—let alone the 90 percent reductions suggested by some studies—would save 580,000 lives a year. That social and economic gain would dwarf that of seat-belt adoption or anti–drunk driving campaigns. This article was updated on 08 September 2026.
Many researchers and students in Kenya, Rwanda, and Uganda struggle to access and publish scientific and technical articles because of financial barriers including publishing fees and subscriptions to research libraries. To help, IEEE has made its Xplore Digital Library more accessible by offering discounts on subscriptions and lowering fees to publish articles. But the number of papers published by technologists in the three nations still lags behind those from other developing countries. It might be that many researchers haven’t received training in methodology, been instructed on how to write academic articles, or fully understand the process for publishing in scientific journals. Staff from the IEEE Publication and Information Products group and IEEE volunteers held educational workshops this year in the three countries. The sessions covered the publishing process, IEEE publication outlets, ways to ensure the integrity of research papers, and tips for making better use of IEEE Xplore. “We want to make sure those in this region are on par with other research communities and ensure they have the support and knowledge they need to make informed publishing decisions,” says Kristopher Zakrzewski, the IEEE area manager for Europe, the Middle East, Africa, and parts of Central Asia. “Our goal is to give them the tools they need to increase visibility and allow them to participate in global conversations in the technology space.” Workshops on the publishing process More than 120 participants attended the workshops, which were held in February at the Novotel Nairobi Westlands hotel, the University of Rwanda, and Makerere University, in Kampala, Uganda. IEEE volunteers who are also authors showed attendees how to prepare, submit, and publish papers. They covered the peer-review process and the benefits of working with IEEE, which publishes about 30 percent of the world’s technical literature on electrical engineering and computer science. IEEE Senior Member Nelson Ijumba presented at the session in Rwanda. Member Kennedy Ronoh led the Nairobi workshop. Sheila N. Mugala spoke to attendees in Kampala. “The great thing about these sessions,” Zakrzewski says, “is that each had a local author who presented tips and best practices to ensure that new and returning authors have the information they need to prepare their paper for submission, determine where best to publish their article, and find the right journal or conference that would be the best fit for their research.” One of the facilitators at the Uganda session was IEEE Senior Member Mayur Kumar Chhipa, head of engineering at the International Business, Science, and Technology University in Kampala and vice chair of the IEEE Uganda Section. The university has about 200 engineering students and about 50 researchers. More than 100 people attended Chhipa’s session, where he shared practical guidance on conducting literature reviews and identifying high-impact research. “Researchers in Uganda typically present their paper at an IEEE conference, and that’s it,” he says. “What we’re trying to do is encourage them to take the next step and get their paper published in an IEEE journal.” He encourages his students to submit a summary of their thesis to an IEEE conference, he says. “Otherwise,” he says, “their thesis sits in the university’s library or collects dust on a bookshelf. “When you publish your research, the world knows you are a scholar who has done good work. Having a paper published at a conference or in a journal can help you get into a master’s program globally.” IEEE Xplore access Attendees were given an overview of the features of their IEEE Xplore subscription. The digital library contains more than 7 million technical documents from industry-leading journals, conferences, ebooks, and eLearning courses, as well as partner content. IEEE provides access to the library to more than 50 universities in Kenya through a subscription agreement with the country’s Library and Information Services Consortium, which includes university and public libraries and research institutions. Sixteen universities in Uganda and one institution in Rwanda receive discounted subscriptions. “It was really important to establish the direct correlation between having access to the technical literature and the publishing output from their university and the region as a whole,” Zakrzewski says. Many publishing options The workshops covered publishing options offered by IEEE. That includes both traditional and open access journals, with more than 200 periodicals in total. There are approximately 180 hybrid journals, which contain a mix of subscription-based and open-access articles, and 30 gold open access journals. Open access is a publishing model that makes scholarly research and literature freely available online to everyone. Instead of institutions paying for subscriptions, authors or funders typically pay an article processing charge (APC) of between US $2,160 and $2,800 to have their piece published. IEEE offers authors in Kenya a 50 percent discount off the APC rate, and authors from Rwanda and Uganda can publish in IEEE open access journals for free. The open access program provides authors with greater visibility for their research and enhances discoverability, Zakrzewski says, leading to an increased number of references and citations. Publishing with IEEE opens additional opportunities including scholarship awards, research assistant job offers, networking opportunities, and speaking engagements.” —IEEE Senior Member Mayur Kumar Chhipa IEEE Xplore contains more than 200,000 open access articles, he notes. More than 109,000 articles have been published in IEEE Access, a multidisciplinary open access megajournal. “IEEE supports author choice,” Zakrzewski says. “We really want to make sure that an author has the option to publish the research that will meet any consortium, funder, university, or coauthor requirements—which is why we’re focusing on growing our open access program to complement our traditional publishing program and offer more options to authors.” The sessions are having an impact Participants at the Uganda session told Chhipa that they appreciated the IEEE Xplore Digital Library demonstrations and found the guidance on academic publishing valuable. “Many attendees mentioned that the session helped them better understand how to search for relevant literature, evaluate the quality of research papers, and write stronger manuscripts for publication,” he says. “I have observed increased interest among students and faculty in using IEEE Xplore as their primary research resource,” he adds. “Researchers are also more aware of ethical publishing practices and are developing stronger research proposals and manuscripts. “The program contributes to building a stronger research culture by encouraging evidence-based research, international collaboration, and higher-quality publications, which will ultimately enhance the global visibility of research from Uganda and Africa.” Publishing has its privileges Chhipa says getting your research published has many benefits, and IEEE staff and members agree. IEEE and several of its societies offer student grants to help cover the expense of traveling to conferences and presenting papers. The money typically covers airfare and a hotel room. Some grants also pay for conference registration fees, Chhipa says. Chhipa assists students at his university with writing and submitting research papers to IEEE journals and conferences. Students gain confidence when their paper gets accepted, he says. One who attended the recent IEEE session was informed that his paper was accepted by an IEEE conference—which Chhipa says he was excited about. He encouraged that student to apply for a travel grant. “Maybe he’ll get it. Maybe he won’t. But at least he learned how to write a paper, apply for a visa to attend the conference, and book an airplane ticket,” Chhipa says. “It will help him grow personally and professionally.” Presenting a paper at an IEEE conference can be life-changing, he says. “It opens additional opportunities,” he says, “including scholarship awards, research assistant job offers, networking opportunities, and speaking engagements. This is how publishing a research paper in the IEEE Xplore Digital Library can directly, positively impact the life of students and scholars from Africa, especially Uganda, Rwanda, and Kenya.”
This article is brought to you by Tsubaki KabelSchlepp. In modern automated manufacturing, six-axis articulated robots perform high-speed, multidirectional maneuvers under demanding operational cycles. However, as robot arms swivel, rotate, and extend, the electrical cables, fiber optics, and pneumatic hoses supplying them endure severe mechanical stress. Torsional twist, rapid acceleration, and repeated contact with machine structures often lead to premature conductor fatigue, insulation breakdown, and costly unplanned production halts. To overcome these multi-axis motion challenges, the Tsubaki KabelSchlepp Robotrax System provides a specialized three-dimensional cable carrier engineered specifically for complex robotic motion. Managing High Tensile Forces With Central Steel Technology Conventional cable carriers often transfer operational movement stress directly onto internal electrical lines and hoses. The Robotrax system changes this dynamic through a central steel cable that runs through the core of every chain link. The Robotrax system’s central steel cable absorbs the primary tensile loads and preserves conductor integrity, dramatically extending cable service life. When robot arms undergo rapid directional shifts and accelerations up to 10 g, this internal steel cable absorbs the primary tensile loads. By isolating electrical and fluid lines from pulling forces, the design preserves conductor integrity and dramatically extends cable service life. Mechanics can easily calibrate and adjust system tension using an integrated clamping piece, ensuring consistent mechanical support throughout long operational cycles. Spherical Link Design and Modular Cable Routing The foundation of the Robotrax system lies in its open, single-piece plastic links featuring spherical snap-on connections on both sides. This geometry allows the carrier to flex smoothly across three axes, providing radial rotation of up to ±450 degrees per meter depending on the model size. To optimize internal organization, carrier links contain up to three distinct chambers. This physical separation prevents signal interference and mechanical abrasion between heavy power lines, sensitive data channels, and fluid hoses. For standard models (R040 through R100), technicians can press cables directly into the carrier without tools, drastically reducing installation and maintenance time. Larger configurations, such as the R140X, incorporate swiveling crossbars with snap locks alongside vertical and horizontal dividers for customized interior partitioning. ROBOTRAX System Steel cable for transferring extremely high tensile forces Tension piece for locking the chain links Type with toolless opening swivel crossbars and divider module available Open design – Fast cable laying as the cables are simply pressed in – Easy checking of all cables Special plastic for long service life Protective covers or heat shields made from different materials are available for different environmental conditions Quick-release bracket for fixing and continuation Strain relief with LineFix clamps Protection against hard impacts, excessive abrasion and premature wear as well as limitation of the bending radius through protector Active Retraction and Impact Protection Large robot work envelopes and high-speed motion trajectories can cause loose cable carrier loops to swing and strike the robot body. To eliminate these destructive collisions, Tsubaki KabelSchlepp integrates the Pull Back Unit (PBU). The PBU serves as an active retraction mechanism that maintains optimal tension on the cable carrier throughout the entire motion cycle. By preventing excess slack and eliminating interfering contours, the PBU minimizes collision risks across complex movement paths. The unit requires zero maintenance on its retraction element and offers standard mounting configurations for leading industrial robot platforms, including KUKA, ABB, and FANUC. Tsubaki KabelSchlepp’s Pull Back Unit maintains optimal tension on the cable carrier and minimizes collision risks across complex movement paths. Additionally, external protectors can be retrofitted onto individual chain links. These durable impact shields limit the minimum bending radius to prevent over-flexing while shielding the chain body from severe external abrasion. If wear occurs, technicians simply replace the modular protector rather than the entire cable carrier assembly. Built for Demanding Industrial Environments From automotive welding cells to high-speed machining centers, Robotrax systems adapt to severe working conditions through tailored protective accessories: Heat Shields: Aluminum-coated textile fiber covers protect against radiated heat, hot weld spatter, and flying sparks. Protective Covers: Coated polyester sleeves shield sensitive lines against aggressive cutting fluids, hydraulic oils, paint overspray, and abrasive dust. LineFix Strain Relief: Multi-layer clamping devices anchor cables securely at both ends to prevent axial displacement during intense motion. By combining central load absorption, multi-axis flexibility, and active retraction control, the Robotrax system offers plant engineers and system integrators a reliable path toward maximizing robot uptime and reducing total operational costs.
This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free! Asking someone to be your mentor is weird. Walking up to someone and asking, “Will you be my mentor?” has always seemed to me like the adult version of a kid walking up to another kid at a party and asking, “Will you be my friend?” What you’re really asking is: “Will you commit some amount of unpaid time to guiding my career for an indefinite period?” Framed that way, of course some people hesitate to say yes. But formal mentorship isn’t the only way to benefit from the wisdom of those who came before. I’ve never formally asked anyone to mentor me. And yet I’ve had dozens of unofficial mentors. The Copy-Paste Method One way to learn from others is by copying what you observe. Sometimes this means reading books or blogs from engineers you respect and directly applying their ideas to your work. I’ve also been fortunate to work alongside some extremely talented engineers, and I shamelessly copied the things they did well. When I meet one of these engineers, I try to figure out what they’re doing differently: How do they approach a problem? What do they read? How do they communicate in meetings? What do they know that I don’t? Then I steal whatever seems useful and apply it to my own career. Great artists steal. Engineers should too. Curiosity Compounds Still, just observing has its limits. Asking questions can get you even farther. I’ve asked managers how they approached difficult conversations, and I’ve asked engineers what their process was for solving problems I thought were impossible. If someone seems unusually knowledgeable: “What are you reading right now?” If I respect someone’s work: “What’s something you think I could do better?” These aren’t profound questions. They don’t need to be. You get one useful piece of information, apply it, and move on. And if you don’t work around exceptional engineers, you can still do this. The only real requirement is curiosity. When you encounter something you don’t understand, make it a rule to investigate instead of moving past it. You don’t need one person willing to guide your career. You need a collection of people who know things you don’t. Pay attention to them. Ask questions. And shamelessly copy the good parts. Ask me! If you have a career question you’re struggling with, like an upcoming decision, a problem at work, an interview, whatever—submit it here: https://docs.google.com/forms/d/e/1FAIpQLSdj_2BZIhrGF__7BCLH33zJ9NMv8C7Vsg9NNusASrYj7-9Idw/viewform. You can include your name or remain anonymous. I’ll be reading through them and answering some in future articles. Consider it mentorship without the awkward “will you be my mentor?” conversation. —Brian ICYMI: The Institute June 2026 issue IEEE members have a wealth of experience and knowledge to draw from. In the most recent issue of The Institute, several members share their career advice for engineers, from engineers. You can also learn about other IEEE programs and courses. Read more here.
In the 1960s NASA began developing a system of reusable space shuttles to make its work more efficient and to reduce costs. The shuttles could launch like rockets, maneuver in Earth’s orbit, and land like airplanes. They also could carry large satellites to and from orbit. Like other types of transportation, machinery eventually breaks down, and parts need to be replaced or fixed. And the cargo being carried to and from Earth has to be moved to its final destination. To complete such tasks, Spar Aerospace (now part of MDA Space) of Brampton, Ont., Canada, and the National Research Council in Ottawa developed a robotic arm, the Shuttle Remote Manipulator System. The project was a joint venture between the U.S. and Canadian governments. Known as Canadarms, the robotic tools attached to shuttles’ exteriors. They allowed astronauts to handle and transfer tools, satellites, and other payloads. Inspections of the shuttle and repairs could be completed using the robots. The system was first deployed in 1981 aboard Columbia’s second flight. Canadarm was used for 30 years on five shuttles and on the International Space Station. The robotic arm was dedicated on 19 June as the 300th IEEE Milestone. The ceremony was held at MDA Space headquarters. The IEEE Toronto Section sponsored the nomination. “It is appropriate that the 300th Milestone is the Canadarm,” says Michael Geselowitz, senior director of the IEEE History and Heritage group. “The technology spans aerospace, robotics, and computing fields of interest. It involves international cooperation between the United States and Canada, and it shows how IEEE and its members are at the cutting edge of many frontiers of science and technology.” International collaboration for space exploration Seeking to collaborate with other countries on the reusable spacecraft, NASA invited Canada to participate in 1969. It took some time for the country’s officials to determine what technology it could contribute. They learned of a robot that loaded and replaced spent fuel bundles in Canada’s deuterium uranium nuclear reactors, according to the Milestone webpage. That robot, developed by DSMA-Atcon (also now part of MDA Space), inspired what would become the Canadarm. A proposal was submitted in 1974 to design and build the Shuttle Remote Manipulator System. The robotic arm would unload the contents of the space shuttle’s payload bay. NASA approved the project, and development began in 1975. Canada had no space agency at the time, so the country’s National Research Council coordinated the organizations that collaborated on the project. Spar Aerospace led the subcontractor team, which included DMSA-Atcon, CAE, and the Canadian subsidiary of RCA Corp. Engineers from the University of Toronto’s Institute for Aerospace Studies contributed to the project. Building an arm for zero gravity NASA had strict requirements for the robot: The arm had to be lightweight and small enough to fit on the shuttle, as detailed in an article published by the University of Toronto. It also had to move forward and backward, up and down, left and right, and rotate along three perpendicular axes (known as six degrees of freedom). To achieve all that, engineer Peter Carlisle Hughes designed the robot with two shoulder joints, one elbow, and three rotating wrists. “Each joint had six degrees of freedom, and the arm had six links so that it could grab anything from any angle and move it anywhere,” Hughes said in the article. The IEEE life member worked at the Institute for Aerospace Studies. “This milestone is a reminder of the privilege we all have at MDA Space—as engineers, designers, builders, operators—to build technology that shapes history.” —Holly Johnson, MDA Space vice president The arm was 50 meters long and weighed 400 kilograms. It was made of materials that could withstand outer space’s harsh environment: titanium, stainless steel, and graphite epoxy. The arm was so lightweight that it couldn’t support itself under Earth’s gravity, so it lay on air bearings on the lab floor at Spar’s Brampton headquarters. CAE engineers, including IEEE Life Member David A. Weston, designed the display and control panel as well as the hand controllers astronauts would use to monitor and operate the robot. Because the robotic arm was meant to work in zero gravity, a room that simulated a weightless environment was built to test it. A computer-based simulation facility was constructed in Spar’s headquarters to evaluate its controllability using two simulation models, according to the University of Toronto. RIGID, an early computer simulation model, tested every part of the arm except for its flexible properties. ASAD, which stood for “all singing, all dancing,” examined the arm’s movements, ensuring the joints operated correctly. Both were created by Hughes and Spar engineer Andrew A. Goldenberg, who is now a professor emeritus at the University of Toronto. The facility was also used to train astronauts on how to use Canadarm. It took five years for the first Canadarm to be completed. In February 1981, it was presented to NASA at the Kennedy Space Center in Cape Canaveral, Fla., and deployed that November. Lift off into space Astronaut Stephen Robinson is anchored to a foot restraint on the extended Canadarm2 attached to the International Space Station during an extravehicular activity he conducted in 2005.NASA The Canadarm was attached to the outside of the shuttle. Astronauts were able to monitor the arm’s movements through a live video feed provided by cameras installed on the wrist and elbow joints, according to the Milestone webpage. Using a hand controller and monitors located in the shuttle’s flight deck, astronauts handled and transferred tools, satellites, and other payloads weighing up 266,000 kilograms using minimal electricity. NASA ordered four more systems. In 2001, Canadarm2 was attached to the International Space Station and used to help build the orbiting laboratory. It is a permanent part of the station, still completing maintenance tasks and moving supplies. During the course of the 30-year shuttle program, the arms performed successfully and achieved the flight’s mission. The original Canadarm took its final flight in July 2011 aboard the Atlantis shuttle. Celebrating IEEE’s 300th Milestone The IEEE Milestone dedication ceremony was held at MDA Space’s headquarters in Toronto, where the division that developed the Canadarm was located. The event brought together IEEE leaders and many of the engineers who helped develop the robotic system. Jill Gostin, the 2026 IEEE president‑elect, gave the opening remarks at the ceremony. She emphasized that the Milestone was not only celebrating the technology but also “the engineers, builders, programmers, and visionaries who believed technology could expand human possibility and who dared to push the boundaries of what humanity could achieve beyond Earth.” To commemorate the achievement, Holly Johnson, vice president of MDA Robotics and Space Operations, and IEEE Life Senior Member David Michelson, chair of the IEEE Communications Society’s Communications History Committee, unveiled a bronze plaque that honored the technology. Michelson was the Milestone’s proposer. “This milestone is a reminder of the privilege we all have at MDA Space—as engineers, designers, builders, operators—to build technology that shapes history,” Johnson said. “That same pioneering spirit that drove our team in those early days of space exploration now propels us into a new era as we work to build the infrastructure for the moon and beyond.” The plaque, which was placed at MDA Space headquarters, reads: In 1981 NASA first deployed a Shuttle Remote Manipulator System aboard the Space Shuttle. Developed by Spar Aerospace (now MDA Space) and the National Research Council of Canada, the Canadarm allowed astronauts to safely and reliably manipulate and transfer heavy payloads outside of the Shuttle, and to conduct inspections and repairs. This robotic system played a key role in the Shuttle and International Space Station programs, and revolutionized human spaceflight. Reviewed by the IEEE History Committee and approved by the IEEE Board of Directors, IEEE Milestones recognize outstanding technical developments around the world that are at least 25 years old. The Milestone program is administered by the IEEE History and Heritage group. To learn more about historical figures in engineering, IEEE Milestones, and IEEE History Center programs and events, check out The Institute’s IEEE Tech History collection. IEEE Spectrum also covers aspects of tech history.
A little over a decade ago, I led the controls design for a first-of-its-kind full digital-control system for a U.S. nuclear plant. It was, on paper, a beautiful machine—engineered to run itself the way a modern airliner does, with operators watching over a system that rarely needed them. And we made a decision that, to an efficiency-minded observer, looked backward: We deliberately left manual steps inside sequences the system could execute on its own. We were solving a specific problem. An operator who only ever supervises automation slowly stops being an operator. The hands go cold. The mental model of what the plant is actually doing gets fuzzy. Then comes the day the automation hands control back. It’s always the worst day, because automation only quits when it’s confused or in trouble. But by then, you have a person in the chair who hasn’t truly operated the thing in years. The manual steps were there to keep the human current. It was inefficient by design, on purpose. That plant, as it happened, was never built. It was shelved amid the politics and economics that surround nuclear power in this country, for reasons that had nothing to do with the engineering. But the design instinct outlived the project, and I’ve come to believe it’s the most useful idea I can offer to the argument now consuming every boardroom: What happens to human expertise when AI does the work that used to build it? AI Is Disrupting the Engineering Career Ladder The data has gotten hard to wave away. A Harvard University working paper covering some 65 million workers at more than 280,000 U.S. firms found that after companies adopted generative AI, junior employment fell roughly 9 percent within six quarters relative to nonadopters, while senior employment kept right on growing. A Stanford analysis of ADP payroll records points the same way: The youngest workers in the most AI-exposed occupations lost ground after late 2022 while their more-experienced colleagues held theirs. The Stanford researchers found that the losses concentrate where AI automates the work; where it merely augments, junior employment holds steady or rises. The causal story is still contested, and honesty requires saying so. Researchers at the New York Fed attribute much of the rise in young-graduate unemployment not to AI but to remote work, arguing that firms are reluctant to hire inexperienced people whom they cannot train and mentor at a distance. But notice what the explanations share. Whether a model is absorbing the formative work or distance is severing the mentorship around it, both describe the same broken mechanism: the apprenticeship channel through which expertise passes from senior to junior. Either way, “entry-level” has quietly come to mean “three years of experience required.” Strip away the noise and you’re left with one deceptively simple problem: You cannot become a senior engineer without first being a junior one. Expertise is not downloaded. It is earned through failed builds, dead-end debugging sessions, and the “why on earth did that work” moments that a capable AI will now happily spare the newcomer. Spare them enough of those and you produce a cohort that can supervise a model on paper but never developed the gut sense to know when the model is confidently, catastrophically wrong. Most of the commentary stops at the diagnosis, or reaches for policy solutions that treat the loss of junior jobs as an economic problem. Yet it’s also an engineering problem, and safety-critical fields have already spent decades learning how to solve it. Aviation’s Lessons About the Automation Paradox My own career started at the sharp end of automation. My first job out of school was verifying and validating the software in the digital jet-engine controller that decides, faster than any pilot could, how a fighter plane’s engine responds. Even then, in the late 1980s, the central tension was visible: The machine outperforms the human in routine cases, but the human is all that stands between the aircraft and disaster in the cases the machine didn’t anticipate. This tension is known as the automation paradox, in which increasingly capable automation gives human operators less practice, while leaving them only the most difficult situations. Aviation learned, repeatedly and expensively, what happens when human skills atrophy inside that gap. The canonical example is Air France flight 447, which fell into the Atlantic in 2009. The proximate cause was mundane. Iced-over airspeed sensors fed the autopilot bad data, and it did what it is designed to do: It disconnected and handed control of the airplane back to the crew. What followed was not a hardware failure. It was a competence failure. A recoverable situation became an unrecoverable one because the pilots, conditioned by thousands of hours of watching the automation fly, could not read a high-altitude aerodynamic stall and hand-fly their way out of it. The airplane was working. The training the automation had quietly eroded was not. The industry’s response is instructive, and it’s the same move we made in that nuclear control room. It did not rip out the autopilot. It built deliberate manual practice back in. In 2017 the FAA issued Safety Alert for Operators 17007, “Manual Flight Operations Proficiency,” declaring that “manual flight is the foundation upon which other technical flying skills are built.” The alert formally recognized skill decay as a hazard in its own right. Some airlines amended their procedures to encourage hand-flying both the initial climb and initial descent in benign conditions, knowingly trading a sliver of fuel efficiency to keep the crew’s raw flying skills alive. That trade is the whole point. A perfectly optimized system that produces incompetent operators is not optimized at all. It has simply moved its failure mode somewhere the spreadsheet can’t see it. Manual Gates Could Preserve Engineering Skills Put the aviation lesson and the nuclear instinct side by side and they point to one design pattern we now need in AI-augmented work: the deliberate “manual gate.” A manual gate is a point in a workflow where a human takes the controls, not because it is the fastest way to get the task done, and not only as a safety interlock, but specifically to exercise and preserve a skill that would otherwise decay. The distinguishing feature is that it is chosen. You decide, as a matter of design, which competencies your organization must keep alive in human beings because those are the ones you will need on the bad day. Then you engineer the friction required to keep them warm. Picture how this might work on a software team that leans on AI for most of its code. The team places a manual gate around the skill it can least afford to lose: debugging. When a defect surfaces in a critical module, the assigned engineer—deliberately, often a junior one—must first reproduce the failure, trace it to root cause, and write an automated test that captures the bug, all with the AI assistant switched off. Only after the engineer commits to a diagnosis does the model come back on, to propose the fix, generate alternatives, and sweep the code base for similar bugs. The engineer then compares their diagnosis against the model’s. When the two disagree, that’s the design working, surfacing the disagreement before the bad day instead of during it. This approach reframes the junior engineer entirely. The instinct today is to let AI do the entry-level work because it is faster and cheaper. But some of that work is not overhead to be eliminated. It is the training apparatus of your future senior staff, and you should protect it the way you’d protect any other piece of critical infrastructure. It may not be efficient this quarter, but dismantling it quietly mortgages your capability a decade out. Why Companies Must Keep Training Junior Engineers None of this is free, and pretending otherwise would insult the people who have to sign the budgets. A deliberate manual gate is, by construction, less efficient in the near term than full automation. Keeping juniors doing formative work and running the manual sequences costs something now to protect something later. That’s a hard sell in a market that judges most leaders on quarterly results. A hired executive who carries “unnecessary” humans that AI could replace will hear about it from the board long before the payoff arrives. The math only works for someone insulated from that pressure: a founder with control, a private company, an institution with a genuinely long horizon, or a regulator willing to require workers to demonstrate their skills regularly, as pilots must. Which means the organizations most likely to preserve their own expertise are the ones structurally able to spend short-term margin on long-term capability; everyone else will need that outside push. So here is the argument, in one line: Deliberate inefficiency is not waste. In safety-critical engineering we have always known it as insurance, and we buy it on purpose. As AI takes over the work where expertise is forged, the smart move is not to resist the automation. It is to keep our hands on the controls by design—so that when the automation fails, as it always eventually does, there is still someone in the chair who knows how to fly.
Across IEEE, our strength lies not only in the excellence of our individual communities but also in our ability to bring them together around shared problems that demand interdisciplinary solutions. Our mission as a public charity—to advance technology for the benefit of humanity—is becoming an increasingly powerful differentiator. It is more than a statement of principle; it is a strategic advantage. When engineers and technologists serve with purpose and lead with heart, they strengthen the future of our profession and demonstrate why IEEE is uniquely positioned to lead at the intersection of technology and societal impact. IEEE Humanitarian Technologies is a consortium of programs and initiatives—supported by a global network of volunteers and technical professionals—working together to apply technology to solve the world’s most pressing problems. These include Empower a Billion Lives, EPICSinIEEE, MOVE, IEEE REACH, IEEE SIGHT, IEEE Smart Village, and IEEE Tech4Good. These programs embody our mission in action. They are not simply charitable activities; they are strategic assets that help IEEE lead globally, innovate boldly, and remain essential to technical professionals at every stage of their careers. While deeply human in purpose, humanitarian technologies are fundamentally engineering challenges, demanding the full depth of engineering rigor and realized through disciplined, deeply technical work. Cultivating Technical Leaders IEEE Humanitarian Technologies sits at the intersection of engineering excellence, societal need, and global opportunity. Its programs allow our members to show the world that engineering and technology are forces for good, capable of addressing urgent challenges with precision, creativity, and compassion. These programs do more than inspire; they strengthen the technical ecosystem that underpins IEEE’s leadership. Bringing together experts from power and energy, communications, computing, robotics, biomedical engineering, and many other domains to address real-world problems, these interdisciplinary intersections are where breakthroughs emerge. When engineers and technologists collaborate with the right humanitarian frameworks across sectors and cultures, they illuminate new constraints, design pathways, and opportunities that traditional project environments rarely reveal. This is how humanitarian technologies help shape the future of engineering itself. These efforts also illustrate a broader opportunity for IEEE. By identifying critical challenges that can be addressed only through collaboration across disciplines, IEEE can mobilize the power of its global community toward solving problems around the world. In doing so, we strengthen both our impact on society and the value we provide to members, partners, and future generations. These programs also build the leadership capacity our profession needs. Engineers working in humanitarian contexts learn to navigate ambiguity, engage diverse stakeholders, manage constraints, and design for environments where failure has real human consequences. They develop systems thinking, ethical reasoning, and cross‑cultural fluency—competencies increasingly essential in a world where technology and society are deeply intertwined. They also learn to transition from R&D to implementation by engineering the support, manufacturing, and delivery systems that make solutions viable in specific countries, all while balancing competing requirements. In doing so, humanitarian programs equip professionals with the capabilities that define modern technical practice. Humanitarian technologies also help prepare the future technical workforce. Students and young professionals increasingly seek meaningful, high‑impact work. By engaging in purpose‑driven projects, they can discover their own capacity to grow, strengthen their technical skills, and become the leaders and problem‑solvers who will guide our profession forward. Purpose Inspires Engagement Our members feel this deeply. Engagement research shows that members increasingly cited “giving back to my profession and the world community” as a reason for joining the organization and renewing their membership. Those with higher membership grades identify “participation in humanitarian technology efforts” as one of the most satisfying experiences IEEE offers. These are not just data points; they are also signals of what our community values and what it expects IEEE to champion. Younger generations amplify this even more. Millennials view IEEE through a global lens, prioritizing “humanitarian impact” and “large-scale collaboration.” One millennial member shared that teaching robotics to children in under-resourced communities transformed them into a deeply engaged member. Gen Z members emphasize inclusivity, environmental responsibility, and purpose-driven engineering, recommending that IEEE offer humanitarian-based challenges and competitions to increase engagement. These findings reveal something powerful: Humanitarian programs are not only meaningful; they also are magnetic. They attract younger engineers, keep them engaged, and help them build a professional identity rooted in purpose and impact. They also create loyalty and develop the leadership pipeline IEEE needs for the decades ahead. These programs also strengthen our brand. Members across segments describe IEEE as an organization that works hard to make real changes in the world. That perception is not just flattering, it is strategic. It positions IEEE as a global leader in responsible innovation that can be trusted to guide technology for the public good, catalyzing innovation that benefits society at scale. As we look ahead, IEEE has an opportunity to become the world’s leading convening force for developing interdisciplinary technology solutions to solve humanity’s most important challenges. Our future relevance will be defined not only by the technologies we advance but also by the problems we choose to help solve. Read more powerful stories about how technology is improving lives across global initiatives in the 2025 IEEE Social Impact Report at ieee.org/advancing-technology/building-better-world/social-impact-report. —MARY ELLEN RANDALL IEEE president and CEO Please share your thoughts with me: president@ieee.org.
For many high school students, summer vacation is a time to unplug. For Ruchira Shree, a rising sophomore at West Windsor–Plainsboro High School South, in New Jersey, the break allows her to ramp up her extracurricular pursuits. Much of her time is spent assisting with IEEE Princeton Central Jersey Section activities. She got involved with the PCJS because of her mother, IEEE Senior Member Shubha Bommalingaiahnapallya, who is the section’s vice chair. Bommalingaiahnapallya is a principal engineer at Intel. “I started going to the IEEE meetings when I was little,” Shree says. “I used to go with my mom and just sit in the back of the room.” This summer she says she’s focusing on improving her mathematics skills by attending the Program in Algorithmic and Combinatorial Thinking summer course on math and computer science. She wants to qualify for the American Invitational Mathematics Examination, an event for the top American Mathematics Competitions scorers. She earned a place on the AMC 8 honor roll—a recognition awarded to the top 1 percent of participants in the national competition—when she was in seventh grade. Shree’s IEEE involvement and her advanced math skills caught the attention of an internship recruiter for the Alliance for Indigenous Math Circles, a group dedicated to expanding STEM opportunities for Native American students. The AIMC organizes and sponsors weeklong overnight camps. Interns assist with activities and teach some of the sessions. Shree met a recruiter at one of the section’s events, and she interned at one of the camps last year. The IEEE-math camp connection Shree’s involvement with the PCJS evolved naturally as she got older, she says, along the way preparing name badges and tackling similar assignments. She met Francis O’Connell, an IEEE life senior member and founder of FXO, in Plainsboro, N.J. O’Connell is the treasurer of the IEEE Integrated STEM in Education Conference (ISEC). He has been a mentor to Shree for the past two years, he says. At last year’s ISEC, she assisted at the registration desk and met Harini Frederickson, an AIMC intern recruiter for New Jersey. Frederickson invited Shree, along with nine other students, to volunteer at an upcoming camp being held in Santa Fe, N.M. “Ruchira is a real go-getter,” Frederickson says. “When she has an idea, she follows through and doesn’t get easily discouraged.” The AIMC was created to address an important need, says math teacher Donna Fernandez, codirector of the organization. U.S. Indigenous students have the lowest rate of pursuing STEM studies across all demographics, according to the U.S. National Science Foundation. Systemic barriers such as a lack of role models in STEM fields, socioeconomic inequities, and Eurocentric teaching frameworks are some of the reasons, Rechel Shrisunder and Dwight Figueiredo wrote in a chapter of Minorities: New Challenges and Horizons, a book edited by John R. Hermann. Indigenous people have a long tradition of mathematics, Fernandez says. She cites the Navajo code talkers from World War II as examples. The Navajo, along with 14 other Indigenous tribes, used their native languages to code and transmit critical messages for the U.S. military during the war. There was a student at camp whose grandfather was a code talker, Shree says. Navajo people also use math to build hogans: conical dwellings that require precise calculations to construct. Native communities have used math when building the structures for centuries, Fernandez says. Fernandez believes typical classroom math curricula overlook the importance of mathematics in Indigenous cultures. Combining STEM activities with cultural elements helps Indigenous students better understand their ancestors’ role as mathematicians, she says. That, in turn, helps the students see themselves in those careers, she adds. The AIMC was built upon a program already in place: the Navajo Nation Math Circles, founded in 2012 by three university professors. Their goal was to provide the Navajo Nation’s students with tools to overcome barriers to STEM education. To expand the math circle program, the AIMC was added to reach Indigenous students in the Four Corners area of Arizona, Colorado, New Mexico, and Utah. Since 2017, the organization has run two camps every year at the Navajo Preparatory School in Farmington, N.M. In 2025 one camp was moved to the Santa Fe Indian School. During each weeklong event, students and interns work in math circles. It’s a cooperative way to solve problems creatively, organizers say. Students collaborate on STEM-focused projects and learn from Indigenous STEM professionals. Interns also get the opportunity to experience an off-site cultural event. The camps are free for students, thanks to sponsorships and donations. Teachers and interns cover their own travel expenses. Shree secured a US $1,500 sponsorship grant through the PCJS. Building relationships through STEM activities Relationships are an influential part of the week, Fernandez says: “One of the best things we see at the camp is that students return the following year and ask, ‘Is so-and-so intern coming back this year?’ They remember the relationships they developed, especially the cultural exchanges they had. “Those exchanges go both ways, benefiting the interns too.” Students spend mornings at camp working in math circles, then gather for a wrangle, during which each team defends its math circle answer and challenges other teams’ solutions. Shree and the other interns are on hand to answer questions and observe the teams as they work through the math circle problems. “Math problems typically have very binary answers,” she says. “But in math circles, you focus more on talking through your answers to open-ended questions and learning from each other.” Students spend afternoons working on projects. In one, the students used household items to create a replica of the Batmobile, Shree says. The car was required to be self-propelled without an engine. Balloons were a popular alternative. Another activity focused on the Indigenous tradition of basket weaving. Students learned the cultural meaning behind traditional designs while understanding how geometry concepts influenced the finished product. These Native American middle school students work on solving a mathematical pattern-matching game, one of the activities held at the summer camp.Ruchira Shree Role models inspire students “Because there’s a lack of Indigenous STEM role models, many Native American students don’t see themselves in mathematics or science,” Shree says. To bridge that gap, Fernandez ensures Indigenous role models are part of the camp. Some of the people who spoke with students during Shree’s internship were Jessica Benally, a Ph.D. student in the learning sciences and human development program at the University of California, Berkeley, and engineers from the New Mexico Mathematics, Engineering, and Science Achievement program, which supports underrepresented preuniversity students. “I believe the students were very inspired,” Shree says, “because they could see how they themselves could pursue STEM careers. They had people to look up to in the field who had come from backgrounds just like theirs.” Interns in action The interns’ primary responsibility was leading a two-hour, after-dinner Radio Weaves session. They taught students about a popular communication technology that doesn’t require the Internet or cell towers. Ham radio, also known as amateur radio, is a communication method that uses designated frequencies. In the United States, anyone can listen to amateur radio transmissions; to legally transmit on the frequencies, though, a user needs a Federal Communications Commission license. The Radio Weaves project is designed to prepare students to pass the FCC technician license exam. To make that happen, the interns customized Gimkit, a learning game, loading it with radio-specific content that mirrored topics that could appear on the test. Each intern worked with two or three students to complete the Gimkit materials. Frederickson, who was on hand for the camp, says the aim was to send students home with something tangible that demonstrated their STEM accomplishments. Nearly all the students passed the exam on the first try, she says, and she worked with those who didn’t to retake the test. All the students ultimately received their license, she says. Inspiration comes in several forms The interns took an afternoon off to attend a Pueblo Feast Day, a celebration filled with music and dance that culminated in visits with nearby families, with whom they shared dinner. “The tradition is very generous and community-based,” Shree says. “It represents that every home in the village will welcome any guest to have a meal.” The feast was the highlight of Shree’s week, she says: “I got to really experience Native American culture firsthand.” The students inspired her, she says. “Seeing the joy on their faces when they passed the technician exam or when they got a math problem correct showed me how much joy they find in learning,” she says. “It made me realize that I want to help provide more opportunities for them to learn and challenge themselves.” “Because there’s a lack of Indigenous STEM role models, many Native American students don’t see themselves in mathematics or science.” —Ruchira Shree That realization spurred her idea for a new initiative. After she returned home, she founded Rukie Cookie to create “safe, inclusive, and inspiring spaces where youths explore STEAM [and] build curiosity, strategic thinking, and innovation—empowering them to become confident leaders and active contributors to a more just and equitable society,” according to the project’s website. Baking is one of Shree’s hobbies, and she sees it as a way to fulfill a financial need she observed at camp. “I noticed that at lunch breaks, they [camp students] used to play chess on the side, but they couldn’t actually participate in tournaments because that requires a U.S. Chess Federation (USCF) membership fee, which they couldn’t afford,” she says. Shree bakes cookies and sells them at PCJS events. Proceeds go toward youth chess classes and USCF memberships for children in underrepresented communities. She has raised enough money to sponsor six USCF memberships, five of whom are camp attendees, she says. “I hope that the students I have gotten a membership for will continue growing their passion for chess,” she says, “but also that it will encourage them to challenge themselves with difficult problems.” What’s next? Shree planned to attend an AIMC camp this year, she says, but it was canceled due to resourcing issues. She says she intends to return next year with goals of adding a formal chess component to the schedule and continuing to help more students overcome financial hurdles to join the USCF. She’s also writing a novel about Alzheimer’s disease and identity loss, and she’s conducting independent research on cognitive decline at the New Jersey Institute of Technology. Watching her great-grandmother struggle with the condition sparked her interest in the subject, she says. She is confident STEM will be part of her future, she says. Math and cognitive science are areas of interest she plans to study, but she’s still undecided about a major. Her interest in Alzheimer’s research and a desire to apply AI to health care will influence her decision, she says. She adds that she plans to join IEEE once she’s eligible.
This story was originally published by Tech Policy Press. The European Union’s push for technological sovereignty faces an uncomfortable contradiction. As the EU rolls out AI factories, gigafactories, and new data centers, it is creating a surge in demand for the advanced semiconductors that underpin artificial intelligence. Yet Europe produces fewer than 10 percent of the world’s chips and remains heavily dependent on U.S. designers and Asian manufacturers for the most advanced processors. That tension sits at the heart of Chips Act 2.0, the European Commission’s planned overhaul of its flagship semiconductor strategy. The original Chips Act, adopted in 2023, sought to raise Europe’s share of global semiconductor production to 20 percent by 2030. But the European Court of Auditors has warned that target is unlikely to be met, while the Commission’s own projections put Europe’s market share at about 11.7 percent. The Commission now wants to correct what officials see as a major weakness in the first law: It focused on expanding supply without doing enough to stimulate demand. To address that gap, Chips Act 2.0 is expected to introduce demand-side measures, including public procurement tools, demand accelerators, and closer coordination between semiconductor producers and industrial users. The Commission’s calculation is straightforward: Stronger domestic demand will encourage companies to invest in designing and manufacturing chips in Europe. But the strategy carries a paradox. The AI infrastructure that the Commission hopes will anchor a European semiconductor ecosystem will initially rely almost entirely on advanced processors designed by U.S. companies and manufactured in Asia. “Key positions are held by a small number of firms, mostly outside Europe,” Claire Godfrey, executive director of the Balanced Economy Project, told Tech Policy Press. AI factories create a demand trap The European Commission’s AI Continent action plan includes 19 AI factories, computing facilities that integrate energy sources, specialized chips, and other infrastructure for running AI models and applications, plans for up to five AI gigafactories (since upgraded to seven), and a proposal to at least triple the bloc’s data center capacity within five to seven years under the Cloud and AI Development Act. That expansion will require a large supply of advanced AI processors. The Center for European Policy Studies (CEPS) estimates that each planned AI factory site requires up to 25,000 advanced chips, while a gigafactory requires at least 100,000. Almost all of those processors are expected to come from Nvidia. The company supplies most of the graphics processing units deployed in Europe, while its proprietary CUDA software underpins much of the AI software ecosystem. CEPS warns this could create an “Nvidia dependency trap,” where computing infrastructure is physically located in Europe but remains technologically dependent on a single U.S. supplier. Recent AI infrastructure projects in Europe illustrate the problem. Mistral has lined up 13,800 Nvidia GPUs for a data center near Paris. Deutsche Telekom’s Munich Industrial AI Cloud is being built with nearly 10,000 Nvidia Blackwell GPUs. And Nscale says its Sines deployment for Microsoft will start with more than 12,600 Nvidia Blackwell Ultra GPUs before expanding to more than 66,000 in 2027. Europe still doesn’t control the chip supply chain The challenge extends well beyond Nvidia. Even if Europe succeeds in expanding semiconductor manufacturing, the global supply chain limits how much autonomy any single region can achieve. “Europe depends on both the United States and Asia, but at different stages of the value chain,” Toni Roldán-Monés, economist and assistant professor of public policy at IE University, told Tech Policy Press. “The United States maintains a dominant position in areas such as chip design, intellectual property, and certain frontier equipment. Meanwhile, the manufacturing of the most advanced semiconductors is highly concentrated in Asia, especially in Taiwan and South Korea, while China plays a fundamental role in various materials, industrial processes, and critical minerals,” said Roldán. Europe’s reliance on third countries is more evident in some parts of the chip value chain. In fabrication, Taiwan produces around 90 percent of the world’s most advanced chips. In packaging, assembly, and testing, the EU holds just 4 percent of the market and remains highly dependent on Asia, according to Laith Altimime, President of SEMI Europe. “The objective is… to avoid excessive dependence on a single country, company, or technology.” Toni Roldán-Monés “No top 20 assembly, test, and packaging company is headquartered in the EU,” Godfrey said. “There is also the materials issue. China dominates several inputs used in key parts of the semiconductor and advanced electronics supply chain.” Europe nevertheless retains important advantages. The region is home to ASML, the Dutch company that dominates the market for extreme ultraviolet lithography systems, and to Belgium’s Imec, one of the world’s leading semiconductor research centers. Europe also remains a key supplier of specialist materials and power electronics. Those strengths, however, “do not translate into autonomy across the semiconductor value chain,” Roldán said. Sovereignty means resilience, not self-sufficiency Few experts believe complete semiconductor self-sufficiency is achievable. Instead, the goal should be to reduce strategic vulnerabilities rather than eliminate international interdependence. “It is not conceivable that one country can rebuild the supply chain. Global collaboration is key,” SEMI Europe’s Altimime told Tech Policy Press. SEMI forecasts that by 2028 the Europe, Middle East, and Africa region will only manufacture about 68 percent by volume of the non-memory semiconductor chips it demands. “The challenge is to reduce dependencies that could become geopolitical vulnerabilities,” argues Roldán. “The sensible approach is to strengthen critical parts of the value chain, diversify suppliers, protect sensitive data, and develop domestic capabilities in strategic sectors. That can coexist perfectly well with foreign suppliers: The objective is not to expel them, but to avoid excessive dependence on a single country, company, or technology.” That distinction is especially relevant for Europe’s sovereignty ambitions. As Godfrey notes, “European firms are building around Nvidia hardware, CUDA, cloud infrastructure, and the software choices that come with them. That leaves Europe with two problems. It relies on Asian manufacturing and materials chokepoints. It is also at risk of trying to address that exposure by tying itself more closely to U.S.-controlled AI and cloud infrastructure. The Chips Act 2.0 needs to deal with both, or it will miss a large part of the problem.” Roldán said Europe’s greatest vulnerability is dependence on partners willing to use global supply chains for geopolitical leverage. Whether Chips Act 2.0 reduces that risk, experts say, will depend on whether it diversifies suppliers rather than just shifting dependence from Asian manufacturers to U.S. technology companies.
In a display case on the lower level of the Faraday Museum at the Royal Institution in London, there’s an unassuming stack of gray metal discs and blotting paper. It’s not at all obvious that this humble object is the starting point of today’s multibillion-dollar global battery industry. The object’s invention in 1799 grew out of a disagreement that Alessandro Volta—the Italian physicist for whom the unit of measurement for electrical potential is named—had with his friend Luigi Galvani over a dead frog. The Debate Over Animal Electricity Galvani was a well-respected Italian physician. In the 1770s, he began investigating the use of electricity to stimulate the muscles of dissected frogs. Armed with an electrostatic generator and an early type of capacitor called a Leyden jar, he was able to create a charge, store it, and then zap his animal specimens at will. He was intrigued when the frog legs twitched as if they were still alive. He spent the last three decades of the 18th century studying the phenomenon, and in 1791, he published De viribus electricitatis in motu musculari commentarius (Commentary on the Effect of Electricity on Muscular Motion). Luigi Galvani spent decades investigating what he believed to be a natural electric force emanating from animals. Universal History Archive/Getty Images Galvani saw the frog as embodying an “animal electricity,” an innate vital force that activated nerves and muscles, similar to what had been observed in (living) electric eels and torpedo rays. For Galvani, the frog was an electrical machine analogous to a Leyden jar. The brain was the source of the electrical charge; the nerves conducted the electrical fluid; and the muscles stored opposite charges. The illustrations in his 1791 book are fabulous—frog legs spread all over his laboratory table! Galvani was wrong in thinking that his frogs were electrical machines, but he was right that the muscle contractions were caused by electric signals.SSPL/Getty Images At first, Volta, chair of physics at the University of Pavia, concurred with his friend. But after beginning his own experiments, he concluded that Galvani was wrong and that the frog generated no electricity at all. He thought of the frog as nothing more than an electroscope, an instrument to indicate the presence of an electrical charge. Volta posited that the source of the charge Galvani observed came from two different metals in contact with the frog. He termed this “metallic electricity.” Alessandro Volta came to disagree with Galvani’s theory of animal electricity.Apic/Getty Images To prove his point, Volta created an “artificial electric organ.” He stacked alternating discs of copper and zinc, separated by cardboard, blotting paper, or cloth soaked in brine or acid. When the top and bottom plates were connected, an electric current flowed through the stack. As opposed to a Leyden jar, which is essentially a capacitor that can store an electric charge and release it in a brief powerful discharge, his stack of discs generated its own electricity through a chemical reaction and delivered a sustained low-current output. Volta didn’t publicly demonstrate or announce his artificial electric organ until after Galvani died in 1798. But when he finally did, in 1799, it immediately began upending science. Just six weeks after Volta wrote to the Royal Society about his invention, the English scientists William Nicholson and Anthony Carlisle used a voltaic pile to run a current through water to separate it into hydrogen and oxygen. They had discovered chemical electrolysis. Humphry Davy later used a large voltaic pile to isolate a number of elements, including potassium, sodium, calcium, strontium, and barium. Early piles petered out after a few hours. Users who stacked up more metal discs to make more powerful piles found the weight of the discs squeezed out the moisture in the paper or cloth. Invented in 1799, Volta’s “artificial electric organ” (later known as the voltaic pile) was the first battery. Volta presented this one to Michael Faraday in 1814.Royal Institution of Great Britain/Science Source One of the most enthusiastic users of the voltaic pile was Galvani’s nephew, Giovanni Aldini, who spent much of his career defending his uncle’s ideas. Aldini created spectacles across Europe in which he used voltaic piles to shock the carcasses of livestock and, occasionally, the bodies of recently executed convicts. Vivid descriptions in the popular press, as well as Aldini’s own writings, raised the question of whether electricity could bring the dead back to life. Mary Shelley provided her answer in her 1818 novel, Frankenstein; or, The Modern Prometheus. In an introduction to an 1831 edition, Shelley cites galvanism as one of her inspirations for the monster’s reanimation process. Beyond Winners and Losers in Scientific Debates Scientists and historians share a common trait: They like stories with clear winners and losers. The narrative of competition helps drive a narrative of progress that makes it look like humanity is always moving forward. In the case of Galvani and Volta, Volta is usually depicted as the clear winner in the debate over animal versus metallic electricity. The Encyclopedia Britannica goes as far as to write that “with his announcement of the first electric battery in 1800, victory was assured for Volta.” But both science and history are more nuanced than that. In fact, Galvani and Volta were both partially right and partially wrong. There was no universal force of animal electricity, but Galvani was correct that electrical signals caused muscle contractions, which he discussed in his anonymous 1794 publication Dell’uso e dell’attività dell’arco conduttore nella contrazione dei muscoli (On the Use and Activity of the Conductive Arch in the Contraction of Muscles). Volta was right to push back on Galvani’s animal electricity theory, but he was wrong that electrophysiological effects require two different types of metal, or any metal at all; the circuit in the voltaic pile was closed by the wet paper or cloth. It seems a little presumptuous for the Encyclopedia Britannica to declare Volta the winner and Galvani the loser. Volta definitely thought his friend was wrong, but he waited until after Galvani’s death to make his views public. It’s closer to the truth to say they were both genuinely curious to understand the nature of electricity. In the process, they unknowingly helped develop different fields of inquiry: electrophysiology for Galvani and electrochemistry and battery science for Volta. RELATED: Who Really Invented the Rechargeable Lithium-Ion Battery? Such an outcome is actually quite common in scientific disagreements. For example, Isaac Newton’s dispute with Christiaan Huygens over the nature of light—did light consist of particles, or corpuscles, as Newton termed them, or waves, as Huygens contested—breaks down today into quantum optics and classical optics. Similarly, Louis Pasteur’s and Justus von Liebig’s debate over fermentation (microorganisms versus chemical decomposition) led to two complementary fields: microbiology and biochemistry. Maybe instead of looking for winners and losers, we would be better off expanding our horizons and considering the multiple paths of inquiry and discovery. Writing in 1816, toward the end of his career, Volta graciously acknowledged Galvani’s pioneering work, saying “it contains one of the most beautiful and surprising discoveries and the germ of many others.” What new revelations are waiting to develop out of today’s scientific debates? Part of a continuing series looking at historical artifacts that embrace the boundless potential of technology. An abridged version of this article appears in the September 2026 print issue as “The First Battery.” References On 20 March 1800, a year and three months after the death of Luigi Galvani, Alessandro Volta wrote a letter (in French) to Joseph Banks, president of the Royal Society, describing his invention of an artificial electric organ. It was read before the Society on 26 June and published in Philosophical Transactions on the last day of that year as “On the electricity excited by the mere contact of conducting substances of different kinds.” The Smithsonian Institution Libraries used their rare books in the online exhibit The Body Electric, which has more information on both Galvani and Aldini. The website of the Whipple Museum in Cambridge, England, has a number of pages devoted to frogs, including a very informative description of the role frogs played in Galvani’s experiments and how those led to Volta’s work.
One day in 1982, Joseph “Rod” Canion and two colleagues from Texas Instruments sat down at the House of Pies in Houston and used a napkin to sketch out what would become Compaq’s portable PC. In 1996, Felix Zandman, founder of Vishay Intertechnology, dined at Husker Steak House in Columbus, Neb., grabbed a napkin and drafted a design for a power metal strip resistor, which became crucial to power-management components in industrial, automotive, and consumer applications. Now that wispy piece of paper resides at the Smithsonian’s National Museum of American History. Perhaps most famously, Robert Metcalfe, working at Xerox PARC in 1973, roughed out some early designs for what would become the [?] Ethernet, though contrary to popular belief, no napkin was involved. Yet another napkin was pressed into service last year, when Kurt Polzin, chief engineer of the space nuclear propulsion project at NASA’s Marshall Space Flight Center, met Robert Schleicher of General Atomics Electromagnetic Systems at a conference and started talking about nuclear rocket design. “They did the classic let’s-sketch-out-an-idea-on-a-napkin,” says IEEE Spectrum’s Special Projects Editor and our in-house spaceflight expert Stephen Cass. “This rocket engine is still at the paper-planning stage, which, to be fair, is where most of NASA’s humans-to-Mars planning has been for the last 60 years.” Meanwhile, the world’s richest person is pushing for humans to colonize Mars in time for him to escape our hospitable blue marble for a completely barren red planet. One big problem with this idea: It takes a long time in hostile space to get there. “Here’s our napkin. Noodle on this with us and tell us what you think.” —Stephen Cass There’s an old saw, mostly used in the context of road safety, that speed kills. But when it comes to sending humans on interplanetary missions, the faster the better. As Cass pointed out, “Once you get outside the Van Allen belts, there’s so much natural radioactivity—that’s the real killer.” Nuclear electric propulsion could both minimize launch costs and the time fragile human bodies are subjected to microgravity and radiation. And that makes a synchronal bimodal nuclear engine that could both power and propel a spaceship an attractive alternative to a conventional rocket. Polzin and Schleicher think their nuclear engines could halve the time it takes to get to Mars. There may be ways to tweak the design to go even faster and further. But for now, the idea sits on the drawing board, awaiting feedback and refinement. “They’re pulling together a lot of fairly mature technology,” says Cass, who edited Polzin and Schleicher’s article, “A Reimagined Nuclear Rocket.” “Electric propulsion is mature. Nuclear thermal propulsion is not, but thanks to [previous efforts], we have a good idea how to do it. The new part is merging them together, and that of course throws up its own challenges.” Thanks to Cass and illustrator John MacNeill, Polzin and Schleicher’s idea has moved from a napkin to the pages of this month’s issue. Says Cass, “The whole point of the article is to say, ‘Here’s our napkin. Noodle on this with us and tell us what you think.’” We invite you to do so in the comments beneath the web version of this article. Or do it the old-fashioned way and mail the authors your own napkin.
While walking to school as a child, Jernej Barbič would marvel at how beautiful his home was. He was born and raised in a picturesque village in northwestern Slovenia (formerly Yugoslavia), located in the European Alps. Surrounded by alpine and beech trees, Barbic dreamed of replicating their swaying in the wind for others to enjoy. At the time, he didn’t have the tools or the knowledge to create a system that could do that, but it sparked his interest in computer graphics, he says. Jernej Barbič Employer University of Southern California, in Los Angeles Title Professor of computer science Member grade Senior member Alma maters University of Ljubljana, in Slovenia; Carnegie Mellon Twenty years later, in 2016, Barbič, a professor of computer science at the University of Southern California, in Los Angeles, made his mark. His Ziva VFX software system allows for the creation of realistic muscle, fat, and skin simulations for 3D digital humans and creatures. The technology was launched in 2016 by a startup he helped found, Ziva Dynamics, headquartered in Vancouver. It was acquired in 2021 by Unity Technologies of San Francisco. Ziva VFX has been used in more than 60 movies including Aquaman and the Lost Kingdom; Godzilla x Kong: The New Empire; and Venom: Let There Be Carnage. For the design and development of Ziva VFX, Barbič, an IEEE senior member, received a 2025 technical achievement Academy Award. It was a “tremendous honor,” he says, as the award recognizes technologies that have had a significant impact on motion picture production. “Computer graphics and simulation can sometimes feel like a specialized technical field,” he says, “but the award showed that these ideas affect not just science but also art and how stories are told on screen. “The digital characters enabled by mathematics become important parts of people’s lives.” Sparking an interest in computer graphics Barbič says he was inspired to pursue engineering by his father, an engineer who headed a cement factory’s research department and invented a technology that uses magnetic resonance imaging to test the integrity of cement. His father’s work showed him that “mathematics and physics are beautiful on their own, but engineering lets you build something that other people can use,” he says. It was Barbič’s mother, an elementary school teacher, who introduced him to computer science. When he was 8 years old, the school his mother taught at bought a ZX Spectrum computer. With permission from the principal, she brought it home for her son to play on for two weeks. But he didn’t just play games; he created his own game using the BASIC programming language. The machine came with a booklet that contained instructions on how to write a computer program, he says. “At first,” he says, “I copied them verbatim without understanding what they did. But then I started realizing there is structure, and I modified the instructions.” Of all the creatures brought to life using his technology, Barbič is particularly enamored with King Kong from 2024’s Godzilla vs. Kong.DNEG/Warner Bros. Entertainment Inc./Legendary By the end of the two weeks, he’d developed a computer game where players guided a snowman along a winding road. It shifted unpredictably to the left or right, and players accumulated points by remaining on the road for as long as possible. Barbič went on to earn a bachelor’s degree in mathematics in 2000 from the University of Ljubljana, in Slovenia. The following year, he moved to the United States to begin a doctoral program in computer science at Carnegie Mellon. It was a major turning point in his life, he says. His doctoral research focused on developing simulation methods for objects that can change their shape when an outside force is applied to them, known as “deformable objects.” That project shaped much of his later research, he says: “I became interested not only in making simulations accurate but also in making them practical: fast enough, robust enough, and controllable enough to be used in real applications.” After earning his Ph.D. in computer science in 2007, he worked as a postdoctoral researcher at MIT. Two years later, he joined USC as an assistant professor. Making movie magic possible It was at USC that Barbič merged his passion for computer science with film. He developed Vega FEM, an open-source software program that allowed people to animate realistic 3D deformable objects. But Vega FEM was narrow in scope and not exactly what filmmakers needed, he says, so he started exploring how to create a version suitable for the movie industry. “A major theme of my career has been the translation of research ideas into practical tools,” he says. “Academic research often produces beautiful algorithms, but it can be difficult to make those algorithms usable by artists, engineers, or production teams. I have always been interested in that bridge: taking rigorous computational methods and turning them into systems that people can actually use.” In 2011 he attended an Association for Computing Machinery conference presented by its Special Interest Group on Computer Graphics and Interactive Techniques (SIGGraph). There he met James Jacobs, the creature supervisor at visual effects company Weta FX of Wellington, New Zealand. The company is behind the effects in the Lord of the Rings and Hobbit trilogies and other movies. Jacobs used Barbič’s software to create animals and fantastical creatures. Two years later, Weta FX offered Barbič a summerlong research position in New Zealand. He accepted and spent the time studying the process of creating visual effects and learning what roadblocks existed in the film industry, he says. At the time, the technology to create realistic soft-tissue and anatomical simulation for digital characters didn’t exist. “The visual effects industry had reached a point where surface-level realism was not enough,” Barbič says. “A creature could have beautiful skin textures and detailed geometry, but if the bones, muscles, and fat underneath did not move correctly, the illusion would break. “The problem was especially difficult for creatures and characters that need to feel alive: animals, monsters, fantasy creatures, or digital doubles. Their bodies may have unfamiliar anatomy, but the audience still anticipates them to move in a way that matches real-world expectations. Muscles should bulge and contract, skin should stretch and slide, fat should have inertia, and tissue should respond to motion and impact.” In an effort to solve the problem Jacobs in 2014 approached Barbič about founding a startup. In 2015 they launched Ziva Dynamics, where they began what is now Ziva VFX. The software uses physics-based simulations to model the internal anatomy of a character. It numerically solves the partial differential equations of nonlinear elasticity for musculoskeletal human and creature tissues, Barbič says. The equations describe how muscles, fat, skin, and connective tissue deform, interact with bones, and connect, and how muscles activate. Instead of animating only the outside surface, artists can create a model with underlying muscles, bones, soft tissue, and fat. Each component is assigned material properties, constraints, attachments, and activations. The simulator then computes how they deform and interact over time. The technology uses ideas from computational mechanics, finite element methods, numerical optimization, contact handling, and computer graphics, Barbič says. Finite element simulation, a method used to predict how a product or structure reacts to heat and other real-world forces, provides a way to model deformable materials volumetrically, not just as surfaces, he says. The tool computes internal elastic forces and solves the equations of motion so the character’s tissues respond plausibly to animation, pose changes, muscle activation, and dynamic motion. But the system had to be designed for artists, Barbič says. In production, he says, the goal is not only physical realism but also controllable realism. “Artists need to direct the result, iterate, and fit the simulation into a larger animation pipeline,” he says. “So the technology had to combine scientific simulation with practical controls, robustness, and integration with visual effects workflows.” Barbič says Ziva VFX has been used in more than 60 movies. Of all the creatures brought to life using his technology, he is particularly enamored with King Kong from 2024’s Godzilla vs. Kong. “When King Kong is walking, you can see the muscles, how they’re very pronounced, and how they influence the shape of the skin. You can really feel the strength of King Kong,” he says. “And this was made through my software, so I think it’s amazing.” After Ziva Dynamics was acquired by Unity, Barbič consulted for the company for almost two years. In 2024 DNEG, a London-based visual effects and computer animation company, acquired the exclusive license to Ziva VFX. Animating the human hand Barbič strives to improve visual effects as an entrepreneur and an academic. His most recent research, funded by the U.S. National Science Foundation, focused on the modeling, simulation, and animation of human hands. The goal is to create computer models of hands that can be used to design tools, medical prosthetics, and robotic hands. “The hand is a fascinating and difficult system,” Barbič says. “It contains many small bones, muscles, tendons, ligaments, skin, fat, and other soft tissues, all packed into a compact structure and interacting mechanically in complex ways.” He and his team built a digital twin of the human hand. He aimed to move toward “anatomically meaningful simulation,” he says. He used medical imaging, geometric modeling, finite element methods, and multibody simulation to represent the internal structures of the hand and its motions. “IEEE lets me place my work not only in the world of images and animation but also in the world of engineering systems that must be accurate, stable, interactive, and useful.” He worked with Bohan Wang, who at the time was a USC doctoral candidate, and George Matcuk, an associate professor of radiology. Wang is now an assistant professor of computer science at the National University of Singapore. Barbič, Wang, and Matcuk scanned four people’s hands with an MRI machine. The two men and two women would position their hands in 12 poses, which allowed the team to gather data about how the bones, muscles, and fat move with each pose. The data sets are available for anyone to use in their own studies. “This project can help medical doctors learn more about how the hand is moving,” Barbič says. “It’s also great for roboticists to better understand how the human hand actually works, so [the movements] can be replicated.” IEEE: Integral in interdisciplinary research Barbič joined IEEE in 2008, when he published his research paper on simulation methods for deformable objects in the inaugural issue of the IEEE Transactions on Haptics. He has since published several papers in the IEEE Transactions on Visualization and Computer Graphics, which he says connected his work to a wider community interested in visual computing and computational methods. You can find his research in the IEEE Xplore Digital Library. “IEEE recognizes the engineering side of computer science,” he says. “My work is often presented as computer graphics, but at its core, it is also simulation, mechanics, numerical methods, haptics, visualization, and software systems. “IEEE is a community where that broader identity makes sense. It lets me place my work not only in the world of images and animation but also in the world of engineering systems that must be accurate, stable, interactive, and useful.” He believes the organization is key in supporting a healthy interdisciplinary research ecosystem at a global scale—which, he says, is why he has served as an associate editor for Transactions on Visualization and Computer Graphics and Transactions on Haptics. Being a member has made it easier for Barbič to connect with engineers in different fields, he says. “My research often lives between categories: It is mathematical but also practical; visual but also mechanical; artistic but also engineering-driven,” he says. “IEEE is one of the professional communities where that mixture is understood.”
I never intended to join the cutting edge of electromechanical television. I just wanted to make a nice clock. But sometimes you have to go where the engineering takes you, and in my case it took me to the Scanwheel, a pocket-size wide-screen electromechanical TV with a resolution of 4,096 by 20 pixels. Yup, that’s 4K by 20. The 3D-printed drum [top] is spun by a motor controlled by a driver board [second row, from top]. The driver board, in turn, is controlled by a Raspberry Pi Pico [middle], which also controls the LEDs [second row, from bottom], which are mounted in the 3D-printed casing [bottom] so that the holes pass over them as the drum turns.James Provost Electromechanical television was the first form of practical television, developed by John Logie Baird in the 1920s. He used a so-called Nipkow disk, which has a spiral of holes punched through it. As the disk rotates, the holes pass one by one in front of a light source. By varying the brightness of the light as a hole travels across it, you can draw one scan line of a video frame. Spin the disk fast enough, and persistence of vision makes it look like an entire frame is being displayed simultaneously. Commercial electromechanical TV sets were produced in the United Kingdom, with regular broadcasts provided by the BBC in the 1930s. Although cathode-ray tubes replaced electromechanical televisions in the 1940s, hobbyists have continued to build them and even improve on the original technology. For example, in the June 2022 installment of IEEE Spectrum’s Hands On, Markus Mierse presented a desktop-size 3D-printed color version. I built an electromechanical display myself some years ago, but it had a traditional design with a Nipkow disk made from a vinyl record with holes drilled in it. Recently I started tinkering with electromechanical TV again as an outgrowth of my YouTube channel. There I’ve been focusing on developing volumetric displays, which create 3D pixels floating within a volume of space. In particular, I was interested in borrowing some ideas from plenoptic cameras, which use pinholes and lenses to capture multidimensional light fields of samples. I wondered if I could run the process in reverse, to create light fields rather than capture them. I often explore ideas in two dimensions before expanding to the third, so I thought I’d first demonstrate a 2D display. I decided to make an electromechanical device into a clock. After all, you don’t need high resolution to display digits. How Does the Scanwheel Display Work? Thinking about the display as a clockface pushed me toward some key ideas. First, instead of having just one display area, I would use five light sources to create multiple areas—four to represent hours and minutes, and a central area for a separator that would blink each second. Second, to align the digits in a readable row rather than have them spread around an arc, I swapped the Nipkow disk for an established alternative: a Nipkow drum. With a drum, the holes run along the curved cylindrical surface in a stair-step pattern. This means they always trace a straight line from the perspective of a viewer looking from the side, so the clock’s digits would be horizontally aligned. “In tests, I’ve pushed the horizontal resolution to more than 8,000 pixels.” These two decisions turned out to be key to achieving both miniaturization and high horizontal resolution. A disk needs a fairly wide diameter so that the scan lines aren’t ridiculously curved. But curvature isn’t a problem with a drum. A drum can be much smaller than a disk that has the same number of scan lines. (And unlike in the 1920s, packing multiple light sources close together inside a small drum isn’t a problem with modern LEDs.) I settled on a 6-centimeter-wide drum, turning the device from desktop-size to something you could carry in your pocket. I then realized my five display zones could work in concert to create one single wide screen. Because it’s possible to modulate the brightness of an LED at very high rates, the horizontal resolution can also be very high. My system currently has 4K horizontal resolution, and this is primarily limited by the amount of onboard memory I have available. This memory holds the buffer that stores pixel data for each frame before it is read out to the LEDs and displayed. In tests, I’ve pushed the horizontal resolution to more than 8,000 pixels. Despite the Scanwheel’s low vertical resolution—at 20 pixels, it has fewer scan lines than Baird’s 30-line televisions—its high horizontal resolution makes the legibility of the display surprisingly good: I can display not just crude digits but video streamed into the frame buffer. Using the RP2040 Chip’s Special Silicon That frame buffer lives on a Raspberry Pi Pico microcontroller board, based around the RP2040 microcontroller. The RP2040 is ideal for this project because of the chip’s dedicated PIO silicon. PIO stands for programmable input/output, and it’s a block of four coprocessors that uses a very limited instruction set. Each coprocessor can be set up to chew through input/output streams completely independently of the RP2040’s two CPU cores. The first mechanical TVs used disks, which had to be wide to minimize image distortion but allowed bulky light sources. With small, modern light sources, a smaller drum can create images with minimal distortion.James Provost It’s thanks to the PIO that I’m able to keep up with the spinning drum and modulate each of the five LEDs simultaneously as holes pass over them, a task complicated by the fact that the center LED is not a monochrome LED, but a color LED with separate red, green, and blue channels. In fact, the PIO does nearly all the work, pulling data from the frame buffer and controlling the LEDs and the spinning of the drum. The code running on the CPU (written in MicroPython) is primarily responsible for setting up the PIO and then leaving well enough alone. A stepper motor connected to a driver board spins the drum, with power provided by the USB jack on the Pi Pico. All the Pi Pico has to do controlwise is send the board a pulse to incrementally advance the drum’s position once every millisecond. Video data is streamed into the Pi Pico via a network interface. You can set up the Scanwheel to mirror a portion of your computer’s screen, or to act as a separate display. The casing, including the drum, is 3D printed. Now for a neat bit: In the Scanwheel’s GitHub repository at https://github.com/AncientJames/Scanwheel/tree/main, alongside all the other files you’ll need to make this project yourself, there’s an OpenSCAD file that generates the 3D-print file for the drum based on adjustable parameters. This means you can easily make a taller drum and add more scan lines, or try other customizations for your very own portable electromechanical display. You can even use it as a clock!
The IEEE–Eta Kappa Nu (IEEE-HKN) honor society is preparing to host the Innovating the Future event on 6 November. The inaugural one-day, in-person event is designed to provide a forum for IEEE and IEEE-HKN undergraduate and graduate student authors to present their original research papers. A keynote address and thematic presentation sessions are planned as well. Student attendees can network with their peers and gain firsthand experience with the academic publishing process. To present at the conference, students had to submit an abstract of their research before 1 May. Students whose work was accepted were assigned a volunteer IEEE member to mentor them and guide them through the research writing process, including presenting and publishing their original work. Those whose paper was accepted by 1 August were invited to present at the conference. The conference proceedings will be submitted for publication in the IEEE Xplore Digital Library. Upholding research integrity in a changing landscape IEEE Life Fellow Manuel Castro, the conference’s technical program chair, oversees IEEE-HKN’s Innovating the Future program committee. It manages the review process, organizes logistics, and handles the mentoring component. “This new conference is important to IEEE, as well as to IEEE-HKN,” Castro says, “because it allows student authors to grow in their skills and competencies, and be supported while turning their technical activities into publications.” “The conference offers me a chance to learn how to communicate my research to a broader audience, gain feedback from other student researchers beyond my institution, and see how my work can be made more accessible.” —David Kwabi-Addo IEEE Life Fellow Sorel Reisman, a California State University professor emeritus and an IEEE-HKN governor-at-large, says that because the academic research landscape is rapidly shifting, the conference is timely. “As AI increasingly threatens the integrity of research papers being published in leading journals and conference proceedings, it is essential that future scholars—many of them current IEEE-HKN students—grasp the established standards of legitimate, peer-reviewed research publishing,” Reisman says. Perspectives from mentors and students A cornerstone of the conference is its rigorous mentorship initiative, which pairs each author of an accepted abstract with an experienced IEEE volunteer. The mentors provide personalized guidance on organizing the students’ technical content into the correct format for publishing. They also discuss navigating the peer review process, structuring presentations, and preparing the final manuscript for publication. The impact of the guided process can be valuable for both the mentors and their mentees. IEEE Member Wafa Elmannai, associate professor and chair of the electrical and computer engineering department at Manhattan University, in Riverdale, N.Y., and faculty advisor to the IEEE-HKN Gamma Alpha chapter, serves as a mentor. “Research is essential to advancing technology and driving innovation,” Elmannai says. She volunteered to be a mentor, she says, because she has seen how conducting research can transform a student’s future by building their confidence, curiosity, and critical thinking skills. “Mentoring encourages students to step outside their comfort zones and develop innovative solutions that contribute to society,” she says. For the students, the conference can be a critical stepping stone. David Kwabi-Addo, an IEEE graduate student member who is researching computational biology at MIT, is president of the IEEE-HKN Beta Theta chapter. He says he views the program as an opportunity to gain experience in producing academic scholarship. “I submitted an abstract of my research paper because I see the conference as a chance to produce what could become my first conference publication,” Kwabi-Addo says. “The conference offers me a chance to learn how to communicate my research to a broader audience, gain feedback from other student researchers beyond my institution, and see how my work can be made more accessible.” He says he hopes his participation will highlight the diverse breadth of research that future conferences can showcase. Workshops on the publishing process Conference organizers are holding a series of workshops to guide students through every step of the academic publishing process. The workshops are open to anyone and available on the IEEE-HKN YouTube channel. Topics previously covered are: The Art of Crafting a Compelling Abstract (13 March). Identifying When a Project Is Mature Enough for Publication (15 May). The Mechanics of Writing a Technical Paper (19 June). Surviving the Review Cycle and Dealing With Criticism (14 August). Registration is open to all for this upcoming workshop: From Pen to Voice: Adapting a Paper Into a Compelling Conference Talk (2 October). A launchpad for the next generation The Innovating the Future program is designed not only to improve the quality of submissions but also to foster long-term professional development and research communication skills to develop the next generation of IEEE authors. The conference is more than a venue for presenting research; it is a launchpad for innovators committed to advancing technology for humanity.
Summary NASA and industry engineers propose a synchronal bimodal nuclear rocket (S‑BNR) to dramatically cut transit times to destinations around the solar system, such as Mars, by combining nuclear thermal and electric propulsion. S‑BNR uses a single reactor with two independent fluid loops and correspondingly optimized fuel zones, eliminating complex mode-switching valves while providing both high thrust and continuous electric power. Major challenges include developing fuel elements that integrate well together, ground testing, nuclear launch safety, and multi-agency collaboration to mature the technology from modeling to in‑space demonstrations. The biggest threat to any crewed expedition to Mars is time. NASA’s shortest blueprint for sending people to the Red Planet and back requires spending 620 days in space and 30 days on Mars. Even setting aside the compounding challenges of building life-support systems that can operate without resupply for that long, or the fact that longer journeys leave more time for unlucky accidents, life in microgravity and solar and cosmic radiation will inexorably exact their cumulative toll on human bodies. We want to make it possible to dramatically reduce the length of time crews must spend in space—down to just 335 days in transit or less. This will both simplify many engineering challenges and keep astronauts healthier and safer. We believe the key to this time reduction is a new approach to building a holy grail of space exploration, the bimodal nuclear rocket. In the 1960s, U.S. open-air ground tests demonstrated much of the technology needed for nuclear thermal rockets as part of the NERVA and Rover projects.Nevada State Museum, Las Vegas Technicians at NASA’s Lewis Research Center test a nozzle design for a nuclear thermal rocket in 1965. GRC/NASA The prototype SNAP-10A, orbited in 1965, is to date still the only nuclear reactor launched into space by the United States. George Rinhart/Corbis/Getty Images We are Kurt Polzin, chief engineer of NASA’s space nuclear propulsion project at the Marshall Space Flight Center, with over two decades of experience in advanced propulsion research, and Robert Schleicher, chief engineer for nuclear technologies and materials at General Atomics. And to explain just what a bimodal nuclear rocket is, and why the new version we have conceived together brings it closer to future reality, we first need to take a quick trip to the past. As early as 1946, researchers realized that nuclear reactors had the potential to become extremely efficient thermal rocket engines. Most rockets are thermal rockets, and they work by expelling hot gases through a nozzle, thrusting the rocket forward. While there are other factors such as nozzle shape, generally speaking, the hotter and faster you make the rocket’s exhaust gases, the more acceleration the rocket will produce for a given mass of propellant. Because a smaller molecule will move faster than a larger one when heated to a given temperature, the smaller the molecular mass of your propellants, the better. By convention, the efficiency of a rocket engine is measured by how long the engine can exert a thrust equal to the initial weight of its propellant, a quantity known as specific impulse. In a conventional thermal rocket, such as those used in every launch to orbit since Sputnik, the exhaust temperature and speed—and thus the specific impulse—is dictated by the energy released by a chemical reaction and the mass of the reaction’s by-product. The most efficient chemical rockets today combust hydrogen with oxygen, producing water and a specific impulse that tops out around 450 seconds. But a nuclear rocket is not limited by chemistry. The heart of a nuclear thermal rocket is a nuclear fission reactor, in which chain reactions in uranium fuel release much more energy per kilogram than is possible with chemical combustion. A turbopump forces liquid hydrogen alone—with its very small molecular mass—through the reactor’s core, heating it to temperatures of at least 2,700 kelvin before expelling it, resulting in a specific impulse of 900 seconds or more. In the 1950s and 1960s, the Rover and NERVA (Nuclear Engine for Rocket Vehicle Applications) programs ground-tested nuclear thermal rockets. By the early 1970s, the technology had matured to the point where flight tests were being planned. But changing political and budgetary winds led to nuclear thermal development being shut down in 1973. Another prong of nuclear propulsion that has also demonstrated considerable promise is nuclear electric propulsion. In electric propulsion, instead of creating a stream of hot rocket exhaust through chemical reactions or exposure to the core of a nuclear reactor, electricity is generated and used to create electromagnetic fields that accelerate an ionized propellant such as xenon or lithium. Various schemes to do this exist, including some that have already seen considerable time in space, such as the ion thrusters used on the Dawn asteroid mission launched in 2007. So far, these electric thrusters have only been powered by solar panels. But with a nuclear reactor as part of a power plant that supplies the juice, more thrust could be produced. And moving beyond solar power is particularly important in missions to the outer solar system where sparse solar photons would require enormous solar arrays. With electric thrusters, specific impulses in the range of 2,200 to 4,600 seconds are possible, but currently with very low thrust. With the energy available to a nuclear-powered electric propulsion engine, you could have greater acceleration and reduced mission times. The nuclear reactor could also provide electrical power for all the spacecraft systems as well. The System for Nuclear Auxiliary Power (SNAP) program launched the SNAP-10A in 1965 as a proof of concept, the first—and so far only—U.S. nuclear power reactor in space. It generated about 600 watts of electrical power for 43 days before shutdown and is still in orbit. Subsequent U.S. initiatives for more substantive electric power and nuclear thermal propulsion systems, such as the SP-100, Project Timberwind, and Project Prometheus, along with more recent projects like Demonstration Rocket for Agile Cislunar Operations (DRACO) and Joint Emergent Technology Supplying On-Orbit Nuclear (JETSON), have emerged sporadically over the years. None of these have yet progressed to actual flight. However, space nuclear power got a huge shot in the arm in March 2026 when NASA Administrator Jared Isaacman announced a new space exploration initiative. As part of that initiative, the agency plans to launch Space Reactor-1 Freedom (SR-1) to deliver a trio of robot-survey helicopters to Mars. Driven by nuclear electric propulsion, SR-1 aims to demonstrate fission technology in deep space and would be the first nuclear-powered interplanetary spacecraft, generating 20 kilowatts of electric power aboard. This is a bold step for NASA, and brings us up to the present, but the details of the proposed mission also highlight a familiar limitation of nuclear electric propulsion. Even with improved acceleration, electric propulsion still cannot generate the powerful bursts of thrust needed to escape gravity wells, such as those of Earth or Mars, or perform time-critical maneuvers, like course corrections. On the other hand, while not as efficient and unable to supply electrical power for spacecraft systems, nuclear thermal engines are great at delivering high thrust at critical moments. What is a bimodal nuclear rocket? Some engineers would suggest we build two separate systems—one reactor for thermal propulsion and another reactor for power and electric propulsion. But since at least the 1990s, it has been the dream of many engineers to combine nuclear thermal and nuclear electric in one package, with one reactor: the bimodal nuclear rocket. Most previous bimodal proposals depend on complex valve arrangements to integrate the propulsion and power systems. In thermal propulsion mode, the reactor is brought to maximum activity by a set of control drums that ring the core, which is composed of a matrix of long uranium-fuel elements. The drums take the shape of long cylinders made of beryllium, with a 120-degree segment of each cylinder covered with boron carbide. Boron absorbs neutrons, and when that segment faces the reactor, the reactor’s activity is low as neutrons escaping from the core are captured. Rotating the boron segment so that it faces away from the core (leaving only the beryllium exposed) increases nuclear activity as the beryllium reflects escaping neutrons back into the core’s fuel elements, where they can contribute to chain reactions. This proposed trajectory, developed at NASA’s Glenn Research Center, shows where high-thrust maneuvers [blue dots] are executed by a nuclear thermal engine and additional low-thrust, high-efficiency acceleration and deceleration is performed by electric propulsion [hashed lines show thrust direction].NASA Glenn Research Center Once the reactor is generating large amounts of heat, liquid hydrogen is pumped through channels that run the length of the core. Turned into an expanding hot gas, the hydrogen blasts from the other end of the core to form the rocket’s powerful exhaust. In nuclear power mode, the reactor’s activity is damped. Valves seal the channels and a so-called power-conversion fluid—typically a mixture of helium and xenon gas—circulates through the reactor in a closed loop. The reactor is still hot enough to warm this fluid, which drives a turbine connected to an electrical generator. The key point here is that a single set of flow channels and nuclear-fuel elements are used for both modes. But the valves used to switch modes face the formidable challenge of enduring months, or even years, in a harsh radiation environment while maintaining leak-tight performance. The core’s activity is controlled by the rotating drums surrounding it. Within the core, low-temperature fuel elements [left in blue, and top right] produce electric power by heating a circulating fluid. High-temperature fuel elements [left in red, and bottom right] heat hydrogen as a propellant. (The taper of the HTFE’s exhaust channel is exaggerated for illustrative purposes. Ways of packaging the HTFE’s uranium fuel other than with particles are possible.)John MacNeill In addition, the nuclear-fuel elements surrounding the channels must be able to operate for short durations at very high temperatures during thermal thrust maneuvers and for long durations at lower temperatures during the rest of the voyage. It is difficult to build one type of element capable of both. Hence, the complexity and demanding engineering requirements of previous bimodal designs has hindered their practical application. We propose a simplified approach, a hybrid system we call the synchronal bimodal nuclear rocket (S-BNR). The genesis for this design came about when we were attending a conference together in 2025. One of us (Polzin) had an initial idea, and in time-honored tradition, he sketched it out on a napkin to see if the other (Schleicher) thought there was actually a way to do it. We’ve been working on refining the concept ever since. How the synchronal bimodal nuclear rocket works Rather than relying on a complex valve system, the S-BNR uses two hydraulically independent loops within a single reactor core, one open loop (for thermal propulsion) and one closed loop (for electrical power). The core is divided into two zones, one per loop, differentiated by the type of fuel elements in each. Several designs for the fuel elements are possible: In our preliminary design, the high-temperature fuel elements (HTFEs) in the thermal propulsion zone consist of a bed of “pebbles”—uranium fuel encased in zirconium carbide—that surround a central tapering channel and operate at greater than 2,700 K. (One possible alternative for the HTFEs would be a solid fuel design, as with NERVA.) The hydrogen propellant passes through the pebble bed, where the pebbles’ large surface area maximizes the transfer of heat needed for efficient high-thrust propulsion. The other zone has low-temperature fuel elements (LTFEs), optimized for long-term, efficient production of electricity, which can range from tens of kilowatts to several megawatts. In these elements, the uranium fuel in solid form surrounds a double-walled channel: The power-conversion fluid is pumped down the inside and returns along the outside wall, absorbing heat from the fuel and operating at moderate temperatures (at or above 1,200 K). The electric-power and nuclear-thrust elements of the core have separate fluid loops, which eliminates the need for valves to switch between closed-loop operation for power generation and open-loop operation for propulsion.John MacNeill Both the HTFEs and LTFEs contribute the neutrons required to sustain chain reactions. In power-only mode, residual heat moves from the HTFEs into adjoining LTFEs. The physical interface between the elements is designed to moderate this thermal flow to balance two competing needs: It must allow enough heat flow to safely remove the residual heat from the HTFEs, but it must also limit that heat flow so the LTFEs’ temperatures do not go past their allowable limits when the HTFEs operate at high power. During combined propulsion and power operation, a heat exchanger on the power loop preheats the hydrogen propellant for the thrust loop, aiding the turbopump that feeds the hydrogen through the core. After a propulsion burn is completed and the HTFE chain reactions are damped by the control elements, the power loop removes residual-decay heat coming from the HTFEs as described above, eliminating the requirement in earlier designs for additional propellant flow just to cool down the core while on standby. This dual-loop system also means the engine can produce high thrust whenever needed while allowing the generator to remain active at all times—a significant advantage for crewed missions. By adopting this dual-loop architecture, the S-BNR removes the need for the problematic mode-switching valves found in earlier concepts. Each fission zone is constructed with materials tailored to its specific temperature and power requirements, ensuring optimal performance and durability. The result is uninterrupted electrical power across all mission stages, making it unnecessary to carry additional liquid hydrogen just to manage decay heat. The challenges ahead While significant progress in developing the design of the S-BNR has been made, substantial challenges remain. The reactor must maintain stable control across a wide power range, from modest levels for electricity generation to hundreds of megawatts of thermal power during high-thrust operation. Operating the power-generation loop in close proximity to the HTFEs requires very careful management of both temperature and the neutrons emitted by the fuel elements. And crucially, demonstrating reliable, long-duration performance is particularly demanding: Missions to Mars may require years of continuous power generation. Outer-planet probes equipped with S-BNR engines could extend that to a decade or longer. In the past, nuclear thermal propulsion fuel elements were engineered for extremely high temperatures but only brief operational lifetimes (typically hours), whereas proposed nuclear electric propulsion fuel elements are optimized for lower temperatures and intended to last for years. By using two different types of fuel elements in the S-BNR, we can take advantage of the design heritage of both these development tracks. Fortunately, recent NASA-sponsored research has produced several promising candidates that may meet these demanding requirements. Ground-testing these systems is also a challenge. Early in the Rover and NERVA era, the exhaust from test engines was blasted into the atmosphere, something now unacceptable. Today, any ground test of an engine must completely capture all potentially radioactive exhaust products. Fortunately, a number of approaches have been developed to capture and scrub the exhaust, although these methods currently carry a significant price tag. Then there is the ultimate test: flying an S-BNR in space. International regulatory and safety protocols for nuclear launches were developed largely in response to the Soviet Union’s launch of dozens of nuclear-powered Radar Ocean Reconnaissance Satellite (RORSAT) radar spy satellites in the 1970s and 1980s. There were a number of incidents, with the most serious leaving radioactive debris strewn across a swath of Canada in 1978. This history led to a consensus in the space community that might be summarized as “Thou shalt not bring a nuclear reactor to criticality in any Earth orbit that decays faster than dangerous isotopes.” Thus any S-BNR would be launched atop a conventional chemical rocket, with a completely cold reactor and fresh fuel. Fresh uranium fuel is not in fact very radioactive: The potentially larger concern is the chemical toxicity of this heavy metal, but it can easily be handled by wearing light protective suits, respirators, and gloves. Only after the control elements have been adjusted to permit chain reactions to begin within the core are highly radioactive isotopes able to form from fission fragments. There would be even less cause for concern than when launching a radioisotope thermoelectric generator (RTG), such as the sort that are currently powering the Perseverance rover on Mars and the New Horizons mission in the outer solar system. Even in the most extreme scenario imaginable—the chemical booster explodes and somehow damages the reactor’s control elements in just the right way to initiate a chain reaction—there wouldn’t be time to produce a large amount of toxic isotopes before the reactor broke apart and reactions ceased. (We can be sure of this because Project Rover actually tested this kind of worst-case scenario in 1965 with the Kiwi-TNT test, where an engine prototype was rigged to produce a runaway chain reaction sufficient to vaporize the reactor core due to the immense internal pressure buildup. Negligible radiation spread outside a radius of two miles (3.2 kilometers), well within the range of safe distances for launching any rocket capable of reaching orbit, and site decontamination was possible after only a few days of radioactive decay.) Despite all these considerable engineering challenges, the foundation laid by decades of investment in nuclear thermal and electric propulsion and terrestrial nuclear power technologies provides a solid platform for continued advancement. Indeed, much of the foundational work is already underway through ongoing NASA and U.S. Space Force efforts. The Dawn asteroid mission relied on electric thrusters, demonstrating their utility for long-duration spaceflight.JPL-Caltech/NASA We envision the following action plan to merge these technology pathways: Modeling must be performed to demonstrate and verify strategies for thermal management and the control of nuclear processes over the full range of operating power levels. Near-term non-nuclear testing will validate fluid loop operation, heat transfer mechanisms, and control strategies. Next, component-level irradiation and thermal trials will qualify new materials. Then, integrated reactor testing will begin, first without nuclear fuel and later with fueled reactors undergoing fission. Finally, initial in-space demonstrations could begin with lower-power systems, eventually scaling up to full bimodal capabilities. Achieving success will require close collaboration across NASA, the Department of Energy, the Department of Defense, industry partners, and the broader technical community. Progress will depend on advancements in high-temperature fuels and materials, improved systems for power conversion and heat transport, and the adoption of innovative manufacturing techniques and methods to control nuclear fission over a wide range of output power. In particular, integrated system testing will be more complex than previous programs such as NERVA, due to the combined functions and distinct operational regimes for thermal propulsion and power generation. We hope engineers and researchers with relevant expertise will be encouraged to contribute to addressing these challenges, whether in the areas of thermal management, reactor modeling and control, extended-duration testing, or safety analysis. Past ground tests and limited demonstrations have already established the capabilities of space nuclear systems. With architectures like the synchronal bimodal nuclear rocket, the prospect of integrating high-thrust propulsion and sustained power generation becomes increasingly practical and versatile. The next phase is not simply about traveling fast. It’s about building crewed and uncrewed spacecraft that can reliably travel to destinations throughout the solar system that are currently difficult or impossible to reach, with missions potentially lasting years or even decades. This article appears in the September 2026 print issue as “A Reimagined Nuclear Rocket.”
At the height of the Cold War, one very specialized computer was so secret that the world didn’t know it existed. It ran its jobs up to 200 times as fast as any other computer of its time. It was the U.S. National Security Agency’s main cryptographic processor in operation from the time of the Cuban Missile Crisis in 1962 through the Vietnam War and on past the 1975 Helsinki Accords. The machine stopped running only when its moving parts finally gave out. The Harvest computer mattered because of what it was as well as when it ran. For 14 years, it was the engine processing the NSA’s most sensitive intercepts at a time when signals intelligence was as close to a strategic weapon as anything short of a warhead. Designed and built by IBM for the NSA, Harvest was one of the first machines designed to apply operations to enormous datasets rushing past, a precursor to the computers today that manage continuous video streams and security systems in real time. It was also one of the first machines built as an add-on—a specialized helper intended to do one job exceptionally well, bolted onto a general computer. Harvest’s modular design is like a 1960s version of today’s graphics chips that CPUs use to run intensive video-game and AI processing loads. All that raw processing power meant that Harvest also needed nonstop rivers of data to run on. And that led to another pioneering achievement: the world’s first automated tape library that could robotically fetch any one of hundreds of large cassettes of magnetic tape from the machine’s racks. Given Harvest’s unprecedented processing and storage capacity, the machine’s designers naturally needed to rethink how their system handled information. So IBM wrote a customized programming language called Alpha to let code breakers rigorously describe cryptographic problems, just as scientists at the time were using the emerging language Fortran to describe equations and data-processing algorithms. In Fort Meade, Md., an NSA data center hosted one of the world’s fastest computers of its time—although not often discussed, because of its sensitive, high-security code breaking and cipher hunting work. National Cryptologic Museum The story of Harvest, pieced together from declassified documents and contemporary manuals and technical overviews, provides a new and unexpected vista on the history of computing. It also offers a case study in how national security needs, especially during the Cold War, pushed computer technology beyond the far reaches of what unclassified, civilian computing could achieve. Harvest’s distinctive history reveals a visionary algorithmic, coding, memory, and hardware architecture occasionally decades ahead of its time. But this machine was also built only once, for one singular purpose, and then ultimately quietly retired. The Heart of NSA’s Secret Machine IBM’s landmark 1960 transistorized mainframe, the IBM 7030, better known as Stretch, provided the front end for Harvest (which was officially known as the IBM 7950). IBM delivered Stretch to eight or nine customers, mostly scientific research labs, from 1961 through ’63. Designed and prototyped throughout the second half of the 1950s, Stretch introduced the now standard notion of an 8-bit byte. For its first three years of operation, Stretch was the non-classified world’s fastest computer, although it failed to meet IBM’s aggressive goal of running 100 times as fast as Stretch’s predecessor, the IBM 704. While IBM engineers in Poughkeepsie, N.Y., were designing and building Stretch, the company was also quietly discussing a new system that would be built for NSA. At the time, NSA’s existing cryptanalytic computers—large, batch-processing machines that required human operators to manually stage each tape run—were struggling to keep pace with the sheer volume of intercepted message traffic coming in from around the globe. What the agency needed was a machine that could process an unbroken river of incoming data, automatically, around the clock. That requirement alone profoundly shaped Harvest’s design. IBM’s Harvest system, custom-built for the NSA for code breaking, paired the IBM 7030 Stretch mainframe with a bespoke data-stream processor. Stretch handled ordinary computing and input/output, including the Tractor automated tape library. Both units shared two kinds of memory: a large main bank and a smaller, faster bank. When Stretch switched to streaming mode, Harvest drew two streams of data, P and Q, from memory, processed them in parallel, and returned the results as a third stream, called R. Chris Philpot After two failed proposals to NSA, in 1958 IBM finally landed the contract: a Stretch-based machine, augmented by a custom coprocessor, with a revolutionary tape-based storage system, called Tractor. Stretch’s forte was floating-point math for scientific computations. IBM had designed it primarily for labs working on frontier research like nuclear weapons design and weather prediction. By contrast, the custom coprocessor to be built atop Stretch would help NSA analysts sift through alphanumeric characters—that is, essentially integer data. Harvest’s coprocessor was the opposite of a general-purpose system. It was, rather, a streaming computer. Instead of executing long series of instructions, it followed one fixed sequence of steps and applied that same sequence to every pair of characters as they streamed past. Harvest shared memory with the main Stretch processor and ran in bursts. Either Stretch was operating, or else it suspended itself while Harvest’s coprocessor shot through data in memory at extreme speeds. Stretch and Harvest were among the first large computers built entirely from transistors packaged in circuit cards and housed in large, refrigerator-size frames. A 1962 technical manual about Stretch describes the machine’s CPU as divided into functional sections—the instruction unit, the look-ahead unit, the (parallel and serial) arithmetic unit, and the memory bus unit. Harvest inherited Stretch’s basic circuit design but then added something unconventional: Its streaming units processed data in overlapping stages called a pipeline. So while one pair of data bytes was being compared, the next pair was being fetched from memory. Harvest’s coprocessor operated by fetching two streams of data, called P and Q, from the system’s memory, performing operations on them, then writing the results to memory as a third stream, R. Each stream could be anywhere from 1 to 8 bits wide. Harvest’s memory was bit-addressable, meaning word boundaries could be ignored entirely. For instance, it could fetch just 5 bits rather than filling out a whole byte. Streams P, Q, and R included flexible provisions for looping and addressing data in complex patterns—allowing, for example, repeated fetching of short strings from memory. Data from P and Q fed into two functional units. The simpler was the logic unit, which performed basic, bitwise operations—the same operations any programmer would recognize today—and wrote its results back to memory. The more complex was a table-lookup unit. It combined incoming data from P and Q to form an address in memory, which could then be used to advance a counter by one, set a specific bit, or retrieve a stored value. The latter unit functioned, in effect, like the rotor wheel inside a cipher-encoding/decoding machine of the era, the kind that electronically substituted one value for another according to the cipher machine’s wiring. Harvest’s complexity baffled some at the NSA. During employee tours, according to James Bamford’s 2001 NSA history, Body of Secrets (Doubleday), officials would point to the machine and scoff, “It’s beautiful, but it doesn’t work.” Not everyone at the agency was put off by the monumental device, however. One of the few documented examples of Harvest at work, recounted by Bamford, describes the machine searching 3.5 billion characters of text for any of 7,000 target terms, in just under 4 hours. In unclassified remarks from 1972, NSA analyst Robert Looney mentions one job Harvest had tackled—though he didn’t specify the end goal or the code-breaking effort behind it. Codenamed “Moretown,” the job involved sifting through 11 million messages spanning 16 years of intercepted traffic against a list of some 8,000 search terms—all in about ten hours. IBM’s Frances Allen helped design Alpha, Harvest’s custom-built programming language.IBM IBM’s James H. Pomerene was chief engineer of Harvest, supervising its custom-designed circuits that’d been optimized for algorithms used in many cryptographic jobs.IEEE IBM’s Fred Brooks Jr. was a key co-architect of Harvest’s hardware system. Computer History Museum As a unified system, Harvest—that is, Stretch plus IBM’s custom-built streaming processor add-on—streamed 1 byte every 0.3 microseconds, and it boasted about 800 kilobytes of addressable memory. “Here you see one bank pulled out of its oil bath,” Looney said in his 1972 remarks celebrating Harvest’s tenth anniversary of operations. He held up a photo of Harvest’s magnetic core memory banks—six of them, submerged in oil for cooling. Factor in the time demands of various data fetches from Tractor’s tape archives, and a single Harvest “instruction” sometimes carried on, without needing any human intervention, for hours. “It was quite an amazing computer,” recalled IBM Fellow Emerita Frances Allen in a 2001 oral history. “One instruction, for example, could do sorts, and do statistical analysis of the data that was streaming by it.… Everything we were doing at that time was on the cutting edge. There was no question about it.” Allen, who received the A.M. Turing Award in 2006, was one of the developers who worked on both Stretch and Harvest. At the time she started working on Harvest, Allen noted, the Fort Meade, Md.–based NSA was largely unknown outside of classified intelligence circles. So she at first assumed she was working on an unspecified naval project. “We thought of ourselves as working for the Bureau of Ships, because that was the code name for NSA in the budget!” recalled Allen, who died in 2020. Other key Harvest designers and early developers wound up becoming influential figures over the course of computing history. Frederick Brooks Jr., recipient of the 1999 Turing Award and a major contributor to the hardware and software for IBM’s System/360, also helped develop Harvest. And James Pomerene, prior to his involvement with Harvest as its chief engineer, had previously helped build the pioneering IAS computer alongside John von Neumann. How Tractor Stored a World of Data IBM built the Tractor tape system (IBM 7955) to attach to the same Stretch machine that hosted Harvest, because no existing data storage technologies could keep up with the computer’s staggering throughput. Stretch handled the business of staging tapes from the library to the drives—using Tractor’s automated cassette handler. Stretch also coordinated reading data in from Tractor and writing results back out from Harvest. Harvest, in turn, did all its actual computing on the system’s shared main memory. In the early 1960s, and even after Tractor and Harvest were installed, hard-drive data storage was in its infancy. For code-breaking jobs of the size Harvest was taking on, disk storage would have been impractical in terms of both cost and sheer floor space. So Tractor had to be based around tape storage. Each tape was sealed inside a case built like a boombox—twin encased reels under a window, carried by a handle—and, at 6 to 7 kilograms, about as heavy as a bowling ball. Think of a Tractor cassette as an outsize predecessor of the audiocassette, which would come along a decade later, and holding some 120 megabytes of data on a reel of tape 550 meters long. Each storage unit housed up to 160 of these cassettes. An IBM technician holds one of the data cassettes used with Harvest’s automated Tractor tape drives. IBM When Harvest launched in 1962, it had three automatic cartridge units, each serving two drives. So the available online storage across the three Tractor units totaled a stunning 44 gigabytes. That’s more than 190 times as much capacity as the IBM 2314 disk storage system, announced in 1965, which held 233 megabytes across its full complement of eight drives. Tractor had to run continuously, swapping cassettes in and out, 24 hours a day, seven days a week. The system’s tape-handling speed was tuned to keep pace with Harvest’s own appetite for data. The custom-built robotic mechanism for retrieving the cassettes was a servo-driven arm that traversed the system’s storage racks. It fetched a cassette from its slot and delivered it to a handler or received a cassette from one of the handlers and returned it to storage. Running at 6 meters per second, Tractor’s tapes zipped past the read/write heads faster than the eye could track. Software running on Stretch handled the cassette shuttling as well as reading and writing. For one of Tractor’s drives to move from the completion of processing one tape to reading the next took about 18 seconds, assuming it had already been fetched and was ready to mount. Robotically fetching a cassette from the storage unit and preparing it for reading required no human handling or input whatsoever. In addition to Tractor, the system had standard reel-to-reel tape drives attached to Stretch. Harvest’s technicians often used the conventional drives for importing and exporting data to and from other systems; there was no other practical way to get large datasets into or out of Harvest. Tractor could also store permanent files and retrieve them directly from its tape libraries when a job required them. In other words, Tractor’s substantial cassette libraries acted both as permanent data storage and as a place to hold transient data for processing by Harvest. No system in the commercial computing world of 1962 came close to Tractor’s gigabytes of simultaneously accessible data. At most computer centers at the time, “available” data meant physical racks of tape standing somewhere near its drives—accessible only as rapidly as an operator could manually pull a reel and thread it onto a machine, one at a time, over the course of a shift. Alpha Was Harvest’s Custom-Built Programming Language Created jointly by IBM and NSA, the Alpha language existed solely to program Harvest’s streaming dataflow engine for code-breaking work. According to a declassified Pentagon history of NSA computers, Alpha stood for Advanced Language for Programming Harvest. Alpha allowed the programmer to define the alphabet in which code-breaking data would be processed. The language also included two unusual characters with no equivalent in conventional computing until years later, when Multics and Unix introduced wildcard characters. A “scab” (which was represented on Harvest’s input keyboard, a repurposed early IBM Selectric typewriter, by a “?”) stood for a character that was real but unknown. And a “pad” (represented by a blank space) was a null or spacer. These characters provided flexibility of representation for code breaking jobs, in which unknown or uncertain characters were commonplace. A Harvest operator types on one of the main system consoles, a repurposed IBM Selectric typewriter.IBM The rules governing Alpha’s operations on strings anticipated other modern rubrics, like “not a number”—a designation describing an unknown value in a dataset that can propagate through calculations, rather than silently corrupting them. Strings in Alpha could also be aggregated into cords, and cords into ropes, giving cryptanalysts a hierarchical vocabulary for describing complex intercepts. Allen wrote a final technical report on her section of the Harvest software when her part of the project concluded—and just as promptly lost access to it. “I spent the good part of a summer on that,” she recalled in 2001. “And it just disappeared into Fort Meade somewhere.” Replacing an Irreplaceable Machine By 1971, according to NSA analyst Looney, the machine was running at its highest utilization ever—115 hours of production a week, or more than two-thirds of the time. Yet the number of jobs it processed had been dropping since 1967. Ordinary data-processing work, Looney noted, was by 1972 migrating to newer, general-purpose machines, leaving Harvest to concentrate on the very large, specialized jobs no other system could handle. At its tenth anniversary of operations, Looney concluded, Harvest was a machine “conceived in the fifties, born in the sixties, and irreplaceable in the seventies.” He got the last part wrong. On 27 February 1976, operators shut down Harvest for the last time. A custom mechanical component in the Tractor tape library had worn out, and the manufacturer of the part was no longer in business. By then Harvest had run continuously for nearly a decade and a half—through the roughest close call in the history of mutually assured destruction and into the age of détente—processing intercepts at a rate no civilian machine could touch. By the time it retired, Harvest had outlived several generations of commercial computing. A placard commemorates the 1976 decommissioning of IBM’s Harvest computer at the NSA’s headquarters in Fort Meade, Md. National Cryptologic Museum Somebody at the NSA decided to commemorate the machine with a mock telegram, written under Harvest’s name on the machine’s last day (and now preserved in the agency’s archives). “I first began operations at NSA. Although not widely known, I was probably the largest, fastest, and most technically advanced computer system in the world,” the telegram said. “And now, fourteen years later, the time to retire has come. The cost of my upkeep and operation has been overtaken by more modern equipments and the newer technologies.” The NSA ultimately replaced Harvest with the landmark Cray-1 supercomputer. The Cray-1 was built from faster, more tightly integrated circuits that could outperform Harvest’s aging transistors at nearly any task, including text processing. Although the Cray was designed primarily for numeric and scientific computing, it sold across many fields—which ultimately made the supercomputer win out once Harvest’s custom-built text-processing hardware was no longer worth the upkeep for just one customer. The secrecy that shrouded Harvest meant it could claim no lineage of immediate successors. But the ideas it pioneered didn’t disappear—they resurfaced, again and again, in the years that followed. Tractor’s automated tape library was the forerunner of the robotic storage silos that would become standard in enterprise data centers about 20 years later. Harvest’s pipeline architecture prefigured the dataflow computing movement of the 1980s. The continuous pattern-detecting logic of its match units finds direct echoes in modern hardware packet-inspection intrusion detectors and programmable network switches that today route traffic through the internet at wire speed. Harvest didn’t found a dynasty. But, in its time, it steadfastly pointed toward the future—in several directions at once. This article appears in the September 2026 print issue as “The Lost History of IBM’s Cold-War Code Breaker.”
This article is brought to you by Ollobot. From about 2017, individuals began to truly connect with the initial wave of companion robots. These devices had personality, moved around, joked, and answered when you spoke to them. Most early companion robots, however, were still limited by simple voice-command interactions and narrow functionality. Once the novelty wore off, many ended up sitting unused on shelves. As some of those companies went out of business and turned off their servers, many owners likened it to losing a pet. What Ollobot describes as “gentle intelligence” is a useful way to think about where the serious work in this category is going. Not toward more powerful assistants, but toward more present ones. The problem companion robots were trying to solve Loneliness is not a niche issue. According to one study, nearly one out of three elderly adults resides alone, meaning they do not have daily companions. Research also shows that children whose parents have migrated for work, leaving them in the care of relatives, were 2.5 times more likely to experience loneliness than children whose parents remain with them. Among working adults living alone in urban environments, similar patterns of social isolation emerge, even if they are less visible. Over the years, technology has time and again attempted to solve this problem via video calls, smart speakers, and messaging apps without much success. Those tools are geared towards communication between people that already have relationships. They do not create presence. They schedule it. That is the gap that a new generation of AI companion robots is being engineered to fill. Today’s AI robots are different Today’s companion robots are not just cute and cuddly. They are designed with psychological research, clinical insight and long-term interaction models to be truly useful in real homes. Three fundamental shifts define the current generation: From reactive to proactive response. Older robots relied on you speaking to them, but modern robots monitor a room with cameras, microphones, and surroundings sensors to initiate interactions without your input, and they can pick up on your emotions. From function-oriented to emotion-oriented design. The original pitch for companion robots was about what they could do. The question driving the serious work now is how they make you feel, which is a harder engineering problem and a more honest framing of what the product is actually for. From standalone hardware to connected ecosystems. Leading brands are creating platforms rather than devices with software included as a built-in layer and remote access from the beginning. The global AI companion market size was valued at US $36.8 billion in 2025 and is projected to grow from $48 billion in 2026 to $318 billion by 2033, at a compound annual growth rate of 31 percent from 2026 to 2033. Three household scenarios and interaction models Ollobot’s advanced AI family companion robot OlloNi SS1 addresses a number of gaps in what existing technology offers. Elderly individuals living alone. The combination of proactive interaction, fall detection, and persistent presence addresses both safety and companionship without the social overhead of asking family members to check in more frequently. Children in households where parents work far from home. The SS1 functions as a consistent companion that already knows a child, their preferences, their moods, and their routines. The remote connection features allow parents to stay present without requiring a scheduled call, and the life recording system gives them a passive window into their child’s days that feels less clinical than a monitoring camera. Single professionals living alone in cities. The SS1 adapts to daily routines, builds up a preference model over time, and provides ambient social presence without demands. OlloNi SS1 adapts to daily routines over time.Ollobot What OlloNi SS1 is doing differently? Ollobot’s goal in building intelligent companion robots is to address the gaps in technology and capability, using innovation not to automate tasks but to fill emotional voids. Much of the robotics industry has historically pursued human imitation — machines that speak, look, or behave like people. The SS1 is instead designed around familiarity and long-term coexistence rather than realism. The system integrates multiple subsystems operating in parallel, including visual perception, audio processing, mobility control, and interaction management. It is equipped with a multi-chip AI 4K vision module capable of facial recognition and motion tracking. One small but revealing detail is the inclusion of a physical privacy cover for the camera — a mechanical solution to concerns that software settings alone may not fully resolve. OlloNi SS1 can actively integrate into family activities, and it can autonomously move closer to capture memorable moments or reposition itself to remain engaged in ongoing interactions.Ollobot The robot supports advanced mobility across multiple indoor surfaces, including wooden floors, ceramic tiles, and low-pile carpets, with slope climbing capability up to 3.5 degrees. Rather than remaining in a fixed location, it can move naturally throughout the home to stay close to household members as daily activities unfold. For example, the OlloNi SS1 may greet family members when they arrive home, follow an older adult from the living room to the kitchen while continuing a conversation, remind a child to take a study break after a prolonged period of inactivity, or notice that someone appears unusually quiet and gently check in. During family activities, it can autonomously move closer to capture memorable moments or reposition itself to remain engaged in ongoing interactions. The robot continues to evolve over time, with over-the-air updates that deliver new features, performance improvements, and AI enhancements It also incorporates fall detection with optimized accuracy for safety monitoring scenarios. A 6-microphone array enables omnidirectional voice pickup with an effective voice capture range of up to 5 meters, supporting reliable wake-word detection and far-field interaction. To support continuous companionship, much of the robot’s AI processing takes place directly on the device through its “heart module” architecture, with 16 GB of memory and 64 GB of local storage. This enables the system to retain household memories, recognize familiar faces, and respond with lower latency, making interactions feel more natural even during everyday routines. Because companion robots are expected to remain available throughout the day rather than only during brief interactions, the SS1 is designed for extended operation, offering up to 12 hours of standby time and around 5 hours of active interaction on a single charge. This allows it to accompany users through meals, conversations, playtime, and other daily activities without frequent interruptions. To support engaging interactions, much of the robot’s AI processing takes place directly on the device through its “heart module” architecture.Ollobot Like the relationships it is designed to build, the robot continues to evolve over time. Running on Android OS with over-the-air (OTA) updates, the system continuously receives new features, performance improvements, and AI enhancements, allowing its capabilities to grow alongside the household it serves. The robot’s behavioral model also improves over time. Rather than reacting to isolated commands, it attempts to establish a baseline understanding of household routines and individuals. Changes in behavior — prolonged quietness, unusual inactivity, or emotional cues — become triggers for interaction. Presence instead of utility Several features in the OlloNi SS1 illustrate this emphasis on presence and continuity in its interactions. The system can identify different household members, including pets, and adapt responses accordingly. Remote communication features allow family members to connect through the device without treating every interaction like a scheduled call. Environmental sensors support contextual reminders tied to weather or room conditions. Its “2+1” multi-display configuration is also designed around emotional communication. Two circular side displays function as expressive “emotional eyes,” while a separate primary display handles information and structured interaction. The separation allows emotional signaling and functional communication to operate independently, creating more intuitive nonverbal interaction even when no dialogue is taking place. The robot’s behavioral model improves over time. Rather than reacting to isolated commands, it attempts to establish a baseline understanding of household routines and individuals.Ollobot The SS1 also includes an automated life-recording system built on facial recognition and behavioral-event detection that can capture moments such as laughter, physical closeness, or group interaction automatically. An integrated AI vlog engine can then organize those moments into edited short-form videos with automated sequencing and soundtrack generation. The design intent is to preserve spontaneous domestic moments without requiring active documentation behavior from users. An integrated AI vlog engine can organize recorded moments into edited short-form videos with automated sequencing and soundtrack generation Visual data is processed primarily on the device through the SS1’s on-device AI architecture, with household memories stored locally and managed within Ollobot’s proprietary ecosystem instead of being shared with third-party smart home platforms. Access to recordings and live feeds is restricted to authorized users through the companion app, while encrypted communication helps protect data during remote access. Users also retain direct control over recording preferences, and the physical camera privacy cover provides an additional hardware-level safeguard whenever visual monitoring is not desired. Learn more at ollobot.com. Remote communication is similarly structured around persistence rather than transaction. Traditional video calls are episodic and screen-bound; the SS1 instead acts as a continuously present interface embedded inside the household environment. Through autonomous mobility, environmental awareness, and persistent household memory, remote family members interact with an ongoing domestic context. The larger shift to “gentle intelligence” Ultimately, gentle intelligence is not about making robots behave more like humans — it is about helping them fit more naturally into human lives. Each OlloNi SS1 unit develops a unique behavioral profile based on its household. Two units running in different homes for a year will have become meaningfully different from each other, shaped by the specific people, habits, and rhythms of where they live. That kind of long-term personalization is what early companion robots never had. It is also what makes the difference between a product that ends up on a shelf and one that actually earns its place in a home. Learn more at ollobot.com.
For most of my career, my IEEE membership sat quietly in the background—a line on my résumé, a discount code for a conference registration, and access to the IEEE Xplore digital library, which I underutilized. I didn’t think much about the grade of membership available above that of the regular member. I assumed senior membership was reserved for people further along in their career than I was. They published more papers, had more gray hair, and had worked longer in the field. I was wrong on all three counts. The misunderstanding cost me an important validation of my skills and professional competency. I suspect a lot of other qualified members are where I was one year ago: eligible but unaware of the benefits of senior membership, and one application away from a meaningful career credential. The myths that almost stopped me Here are a few of the misconceptions about senior membership: It’s mostly for academics and longtime IEEE volunteers. It isn’t. The grade is explicitly built around a person’s professional engineering experience. Plenty of successful applicants have never published a paper. Industry experience counts for a lot. You need a graduate degree. You don’t. A bachelor’s degree plus enough years of qualifying experience is sufficient on its own. An advanced degree simply offsets some of the required years of experience. If I’m not well-known in my field, I won’t qualify. Senior membership isn’t a popularity contest. Rather, it hinges on whether you meet specific experience metrics. The requirement is “sustained, significant technical contribution,” not “known beyond your organization.” I should wait until I have more significant achievements to point to. I believed this for longer than I should have. If you meet the 10-year experience threshold with five years of significant performance, you’re already eligible. Waiting doesn’t strengthen a qualifying application; it just delays getting a credential you’ve already earned. Why I applied for senior membership The push to apply came from a practical need. As a senior data scientist at Apple in Austin, Texas, I work in applied machine learning, building large-scale systems that affect customer-support operations. I already had started taking on more peer-review work—checking papers for journals including Neural Networks and IEEE Transactions on Knowledge and Data Engineering, mentoring at Apple, and writing on public platforms such as Medium and SimpleTalk. I wanted a credential that reflected that shift from “engineer who codes” to “engineer who helps shape the field.” The IEEE senior member grade turned out to be the validation of my work I was looking for. It’s not an award for a single achievement. You have to apply for it, and it’s a peer-evaluated process that confirms you’ve sustained a meaningful level of professional contributions over time. That distinction matters. Having a research paper published or being granted a patent proves a moment in time. Senior membership reflects a pattern of continuous contributions. The benefits to my career happened faster than I expected. It strengthened how search committees, IEEE conference organizers, and IEEE awards panels viewed me. Only senior members can hold certain IEEE leadership positions. The senior grade also opened doors to editorial and reviewer roles I hadn’t even pursued before. Journal editors and conference organizers often look for reviewers with a track record they can verify quickly, and senior membership gives them that signal without extra vetting on their end. It also gave me a credential I could point to in professional contexts, including, in my case, supporting documentation for a U.S. employment-based immigration petition, where third-party peer recognition carries real evidentiary weight. Navigating the process The process for applying for senior membership is easier than the title might suggest. To qualify, you need a combination of professional and academic experience in an IEEE-designated field: engineering, computer science, information technology, physical sciences, mathematics, or technical communications. The two must total at least 10 years, with at least five of them showing significant performance. Crucially, experience isn’t limited to job titles. Graduate research, technical leadership, and progressively responsible engineering work all count toward the total number of years. I’d been quietly accumulating qualifying years without ever framing them that way. “I suspect a lot of other qualified members are exactly where I was a year ago: eligible but unaware of the benefits of senior membership, and one application away from a meaningful career credential.” You submit your application through IEEE’s member portal, mapped against the experience requirement, along with three references from current IEEE members—at least two of whom must be senior members or IEEE Fellows who can vouch for the credibility of your work. The IEEE member grade evaluation committee reviews applications and renders decisions. How to find references The part everyone underestimates is references. Applications can stall at this point. References must be IEEE members in good standing, and at least two need to be IEEE senior members—which means you can’t necessarily ask people who know you best. You need to find references who are both willing to vouch for you and are grade-eligible. My advice is to identify and confirm all three references before you submit your application. It might be difficult to add or swap a reference during the process, and a stalled reference could delay your file. Where to find references is the part I worried most about. But it turned out to be far easier than I expected. Here are several sources: IEEE Collabratec. This is IEEE’s professional networking platform and, in my opinion, is an underused resource. You can search by technical interest, geography, or society membership and message members directly. I found several of my eventual references this way—colleagues I’d never have thought to ask simply because we hadn’t worked together directly, but ones who knew my technical work through shared communities or conference circles. Coworkers and colleagues, current and former. If you’ve worked alongside IEEE members—especially ones senior to you—they’re often the most natural fit because they can speak specifically to your day-to-day technical contributions. Former professors. If you did graduate work, your advisor or committee members are usually IEEE members and are well positioned to speak to your research contributions, even years later. LinkedIn. A surprising number of my qualifying references came from reconnecting with people on LinkedIn I’d lost touch with professionally. A short, specific, polite message explaining what you’re applying for and why you thought of the person can go a long way. A pattern I noticed when looking for references is that people are generally glad to be asked. Serving as a reference is a small lift for them and a meaningful one for you. Most senior engineers remember someone doing the same for them and are happy to pay it forward. If you’re on the fence If you’ve been in the field for a decade or more, doing real technical work, and IEEE membership has been sitting quietly in the background of your career the way it did in mine, it’s worth 10 minutes to check the eligibility criteria against your history. You might find, as I did, that you qualified for the membership upgrade a while ago.
The AI boom has disrupted the way engineers work, introducing new tools to learn, raising expectations for what teams can achieve in a workday, and making it harder to get hired in the first place. This makes it difficult to advise students on which specific coding languages or technical skills they should learn. So amidst the uncertainty, advice for young professionals often turns to a common refrain: Be adaptable. But what does adaptability look like in practice? Engineers often operate on the cutting edge of technology, so dealing with change is a normal part of the job, says Samantha Brunhaver, an associate professor of engineering at Arizona State University, in Tempe. Yet university curricula and training in the workplace often don’t prepare students for this. “We tell engineers that they need to be adaptable when they graduate, but we don’t actually explain what that means, demonstrate what that looks like, [or] help make sure that they’re developing it,” says Brunhaver, who received a National Science Foundation award in 2020 to study how to foster greater workplace adaptability among young engineers. For this ongoing project, she has interviewed engineering managers, early career employees, and undergraduates about their experiences. Part of the problem, she says, is that every employer has its own idea of what to be adaptable means. Generally, Brunhaver defines adaptability as “the ability to recognize that a change or uncertainty is occurring, and then respond effectively to that change.” But the skill is context-dependent. In software engineering, that might mean responding to turnover in the tools you use on a daily basis, while aerospace or biomedical engineers may need to keep track of changing procedures and regulations. “Managers are all saying adaptability is important,” Brunhaver says, “but defining it in different ways.” At the same time, engineers are all contending with changes beyond these industry-specific expectations. Jobs in the technology, media, and telecom sectors are experiencing the fastest pace of skill turnover, according to a June 2026 report on the effects of AI from the professional services network PwC. And the World Economic Forum’s most recent Future of Jobs Report, published in 2025, found that employers across all sectors expect 39 percent of workers’ core skills to change by 2030. This uncertainty can be uncomfortable. But with the right mind-set and support from leadership, adaptability can help keep you afloat. How to Cultivate Adaptability The AI transition is a big shift—but not an unprecedented one, says Jenna Butler, a research scientist at Microsoft who studies developer well-being and productivity. During this type of paradigm shift, there is often a “chaos period” when a new normal is being established, Butler says. In AI’s case, it challenges the understanding of what a computer can do. “I think we’re still in this in-between, difficult period that we’ve seen before, but [it] is maybe moving faster than it has historically.” Software engineers—in one of the fields most affected by AI—are now facing a significant increase in code review. “If you ask 20 developers, you get 23 different ways of working with it. Everyone is trying to sort it out,” says Butler, who describes this period as “the uncomfortable middle.” “We tell engineers that they need to be adaptable when they graduate, but we don’t actually explain what that means, demonstrate what that looks like, [or] help make sure that they’re developing it.”– Samantha Brunhaver, Arizona State University Brunhaver says one way educators can help prepare students before they enter the workforce is by offering a diversity of real-world experiences, such as internships, team-based projects, community service, and leadership roles. Each of these teach students to adapt to different challenges, easing their transition from school to work. It’s also important to encourage reflection, Brunhaver adds, noting that metacognition helps individuals use the skill more effectively. “In order to adapt, you have to think that you have agency and the ability to get through a situation.” Ultimately, it comes down to three steps: Perceive a need to adapt, evaluate your options, and act. For those already in the workforce, that action may mean taking the time to learn new tools and ways of working. Software engineering, for instance, may soon rely more on prompting models and managing agents than coding line by line. “I think people who went into software because they like solving problems are going to have a lot of fun, and people who just enjoy the art of writing code are not,” Butler says. The More Things Change… Although the tools engineers use on a daily basis are evolving, the core responsibilities of the job are more stable than they may seem, says Andy Hunt, a software developer who coauthored The Pragmatic Programmer (Addison-Wesley Professional) in 1999. The book outlines practical coding principles, and has been taught in many computer science classrooms. When Hunt was working on the 20th anniversary edition of the book, he was surprised by how much of the advice still applies. And now, seven years later, he maintains that belief. “The fundamental part of the job is problem solving and communication, and that’s always going to be there,” he says. Hunt emphasizes the importance of developing systems thinking over particular tools. To him, identifying as a Java programmer, for instance, is “like a carpenter saying, ‘I’m a hammer user,’ or ‘I specialize in cordless drills.’ ” He acknowledges that today’s hiring process, in which companies often filter résumés for certain languages or years of experience, makes it harder to embrace a more expansive way of relating to your job. Employers, he says, should recognize that “the tech’s not the hard part, and it never has been. Understanding information theory, understanding systems thinking, understanding what constraints you’re up to—that’s still the hard part.” With this type of misalignment between employers and employees, AI is also intensifying an old source of tension: How can engineers slow down enough to adapt and learn new tools when the pressure to become more productive keeps mounting? Who’s Responsible for Enabling Change? Young engineers need to embrace change. However, educators and employers also play a role in building a successful workforce. From the educator’s perspective, Brunhaver says “we need to be more explicit about what [adaptability] means and why it’s important.” Managers, meanwhile, should invest in their employees’ professional development. Microsoft research scientist Butler often encourages leadership to set aside intentional time for continuous learning for their engineers—even just an hour a week—without any expectation that they will produce code or progress in their daily work. “I realize that’s difficult,” says Butler. “I would encourage people to do it on their own, but I would really encourage organizations and leaders to do it, because you’re not going to get this sudden change in your people if they don’t have time and space to learn how to work differently.” This also means providing enough instruction, Butler adds. When developers aren’t given enough guidance on adopting something new, while being pressured to increase productivity, they risk doubling down on the tools they already know and burning out. “I do imagine the next number of years could be challenging,” Butler says. Engineers will have to adapt to find their place in an evolving workforce—but they also have a say in shaping that future. “Being adaptable sort of implies that you’re going to change based on what’s happening around you, and I would really like people to realize the change that’s happening is somewhat up to us,” she says. All individuals have a choice in how they use AI, for instance, and which models they use. “We need to be adaptable and go with the flow to a degree, but we also need to be directing that flow. The future with AI is absolutely not predetermined.” This article appears in the September 2026 print issue as “The Adaptable Engineer.”
This article is brought to you by Emerson. I’ve spent much of my career as an engineer, including years in the semiconductor industry. And one lesson has stayed with me through every major technology shift: innovation always creates new complexity. In semiconductors, we have seen that repeatedly. Every generation has delivered breakthroughs in performance and capability, but each step forward made it harder to understand system behavior. What used to be easy to validate on the component level with a test bench now needs a much wider view. “The future of engineering will be defined by who can verify, understand, and improve complex systems fast enough to safely keep innovation moving forward,” says Ritu Favre, President of Emerson’s Test & Measurement business group.Emerson Chiplet-based designs are a prime example. A chiplet from one supplier, an interposer from another, and a packaging process from a third may all perform perfectly on their own. Yet there is a chance for unexpected behavior when you put them together in a system. More and more often, the hardest engineering challenges are not in the individual components themselves. The problems are found when we start to combine components and have them interact with each other. These challenges extend far beyond semiconductors. Products are becoming more software-defined and dependent on interactions across different technologies and environments. Think about the interactions needed for a modern car using adaptive cruise control on a bumpy road in a rainstorm. Or a passenger jet adjusting wing flaps and engine speeds in turbulent weather to maintain safety and stability. Both the car and the jet are being guided by complex computer systems with thousands of sensors leading to thousands of interactions every second. And in many cases, there are multiple computer systems working together. We are building systems of remarkable capability but understanding how they will act under real-world conditions is getting harder. That is why I believe we are entering a new era of test. The defining challenge of modern engineering is no longer simply what we can design and build. It is what we can confidently verify. Rethinking the Role of Test In this new era, test can’t be an afterthought. For decades, test was treated as the final checkpoint before release. Design teams developed a product, test teams validated performance, and organizations looked for a final pass/fail to determine whether they were ready to move forward. That model worked fine when systems were more self-contained and predictable. Today, that approach can lead to more risk. I believe we are entering a new era of test. The defining challenge of modern engineering is no longer simply what we can design and build. It is what we can confidently verify. Many of the delays and fire drills we face come from issues that were not visible early enough. Problems discovered late in development are more difficult to diagnose, more expensive to fix, and more likely to get you off schedule. The solution is not more testing at the end. The solution is to make test and verification part of the engineering workflow from the start. When validation is integrated throughout development, teams catch problems early when change is easier. Test also stops being a barrier to release. Instead, it becomes a source of insight, helping us understand how systems behave as they become more connected. Why Connected Platforms Matter When confidently verifying technology becomes the key challenge, the tools we choose take on a different level of importance. The tools have a direct impact on how quickly we can diagnose a problem and keep moving forward. In an environment where technology changes rapidly, disconnected tools get in the way of progress. Modern test strategy requires linking information across design, validation, and production, turning measurement data into decisions made quickly enough to keep pace with innovation. This reminds me of when EDA was first introduced. Before it came along, engineers spent much of their time hand-drawing circuit layouts and placing transistors. EDA eliminated that tedious work by letting teams describe complex behavior in high-level code. It enabled them to focus on overall architecture instead. A connected test platform does a similar thing for validation. Because a platform can adapt and scale alongside technology, it cuts down on maintenance and downtime, keeping teams from having to rebuild their workflows from scratch as requirements change. Grounding AI in Engineering Reality Today, AI is rapidly entering the engineering toolkit to accelerate design and analysis. But in test and measurement, AI cannot reach its potential in isolation. An AI model is only as effective as the data feeding it. Without context, even the smartest algorithm will struggle to tell the difference between normal hardware variance and a critical failure. A connected platform supplies the structured, traceable data stream AI requires to deliver real insight. AI can correlate complex multi-system interactions, flag unexpected behavior, and direct an engineer’s attention right at the root cause. When measurement data flows seamlessly across the workstream, AI moves from being a standalone tool to an active layer of intelligence. It can correlate complex multi-system interactions, flag unexpected behavior, and direct an engineer’s attention right at the root cause. Every technology shift that accelerates how fast we create new designs also increases the complexity we must verify. AI can help teams keep pace with that complexity. Not by replacing human judgment, but by giving engineers the context we need to act with confidence. Innovation Demands Confidence Ultimately, the goal of modern platforms and AI-enabled workflows is to help technical teams spend more time building new things and solving hard problems. Most of us didn’t choose this profession to spend our time searching for data or dealing with last minute surprises. We want to innovate and integrating test directly into development provides a better view of system behavior, allowing teams to focus on that innovation rather than managing complexity. The future of engineering will not be defined by who can build the most advanced product or technology. It will be defined by who can verify, understand, and improve complex systems fast enough to safely keep innovation moving forward. That is the new era of test. As the pace of innovation accelerates, every breakthrough creates new paths to failure, and test is how engineers find those failures before the real world does. In an increasingly complex world, that capability is becoming as important as innovation itself. Innovation has always required great engineering. And now, more than ever, it also requires confidence. Confidence that comes from knowing that we are not only building what is possible, but we are also building technology that people can trust.
How do I touch you across the ocean, across cold depths where light travels through glass. Not copper—fibers. Optical. Through liquid glass, through flickering light that carries you in fragments. Light broken into pulses. You say: it’s easier this way. What are we missing like this? You smile. Safe distance. I say: network. Signals slide beneath the sea, through cables thinner than trust, faster than touch, slower than longing. We stand alone, together. Synchronous, yet apart. Icons replace skin, latency replaces breath. This distance protects us. Silence that feels intentional. Everything is under control as long as nothing truly hurts. And we choose it because it shields us from what we might become if we actually met. You are my counterpoint. My response. My reflection at a safe distance. Beneath the ocean, nodes remember paths. Packets shake hands without bodies. If we get lost, we resend everything, with error, with noise, with hope.
Balaji Ingole rarely saw televisions while growing up in Udgir, India. No one in the small Maharashtra village had computers or phones. Only one household owned a television, and neighbors often gathered there to watch shows together. Ingole never even saw a computer growing up. It wasn’t until he reached middle school that he encountered a computer lab, an experience he says changed his life. Almost immediately, he says, the machine felt like a window into a different scale of possibility for him. Balaji Ingole Employer Amla Commerce in Milwaukee Title Project manager Member grade Senior member Alma maters COEP Technological University and Welingkar Institute of Management, both in India “I was very studious and not very social, always reading or solving problems in a math textbook,” he says. “At the computer lab, I began learning the C programming language—which was like discovering a whole new world. I was fascinated that you could create something with just a few lines of code.” His early interest grew into a self-directed education. Outside of Ingole’s formal classwork, he taught himself to build database-backed applications, wire up hardware, write software, and trace error logs. Today the IEEE senior member similarly splits his time. During the week, he’s a project manager in Milwaukee at B2B e-commerce company Amla, leading AI-driven digital transformation initiatives to help the company’s clients boost their sales. On weekends, he leads a similarly demanding life as an independent researcher. His current projects include developing AI-enabled health care diagnostic tools and assistive technologies to support people with physical disabilities. “I believe in ‘learn by doing,’” he says. “I really like to test my knowledge and prototype ideas to find out if they truly work.” A college project becomes an inspiration Ingole’s tendency to go beyond his coursework continued after he graduated high school in 2004. As a mechanical engineering undergraduate at The College of Engineering, Pune (now COEP Technological University), in India, he participated in several extracurricular activities. One was interviewing entrepreneurs and writing about them for The COEP College Magazine. The experience helped him gain confidence, he says, giving him the push he needed to pursue interviews for the publication with two Indian entrepreneurs he admired: N.R. Narayana Murthy, cofounder of IT giant Infosys; and his wife, philanthropist Sudha Murty. The Murtys cofounded the Infosys Foundation, a nonprofit that runs educational, health care, women’s empowerment, and sustainability programs in underserved areas of India. “Every week I would fax them: ‘Please give me an interview time,’” Ingole says. Eventually, Sudha Murty’s office offered him a phone interview, but he requested to meet her in person at Infosys’s Bengaluru offices. She agreed, but the offices were 940 kilometers from Pune, and he didn’t have the money to travel or stay overnight in a hotel. Ingole and a classmate borrowed money from friends and traveled through the night on multiple buses and trains to get to Bengaluru. They freshened up in a public bathroom before heading to the Infosys campus to meet Murty. Impressed by their persistence, she surprised them by also arranging a brief chat with Narayana Murthy. “Narayana Murthy handwrote a personal message to the engineering students of [my college]—which we proudly published in our college magazine,” Ingole says. “In his note, Murthy shared that we are at an extraordinary moment in India’s history and that the future looks even brighter. His words encouraged us to work hard and make the most of this time. “I still have that note,” Ingole says. “They are billionaires, and I was just a regular student. The fact that they took the time to do this really motivated me.” During the final semester of his engineering studies, Ingole joined the Tata Research Design and Development Center in Pune for a six-month internship. After earning his bachelor’s degree in mechanical engineering in 2008, he became a graduate engineering trainee at Honeywell Automation in Pune. He left the company in 2009, and during the next 13 years, he held different software engineering and project management positions at IT companies across India. He earned a master’s degree in business administration from the Welingkar Institute of Management, Mumbai, in 2017. In 2022 he accepted a project-manager role at Mars IT Solutions in Madison, Wisc. The following year, he left to join Gainwell Technologies, also in Madison, as a senior project manager. At Gainwell, he managed projects for the core IT systems multiple U.S. state health departments use to administer Medicaid benefits, manage provider enrollment, and verify member eligibility. The experience managing projects that directly enabled patients’ access to health care gave Ingole a special appreciation for and interest in this area, he says. “Health care data is not like other data,” he says. “The stakes are high, compliance requirements are different, and the margin for error is effectively zero.” Ingole says he enjoyed the rigor of data governance combined with the potential to positively impact lives, and that also applies to his current work at Amla. Agentic AI in e-commerce Ingole joined Amla in July 2025. He helps manufacturers and B2B customers modernize their e-commerce operations. He also builds AI tools for them and for his internal team. For Amla’s customers, he’s developing AI-enabled chatbots that help manufacturers set up and manage large product catalogs in e‑commerce platforms. Such product setup traditionally has been a manual, tedious, error-prone process: Companies upload thousands of products, adjust item names, enter prices, update images, and more. “Product setup has been one of the most painful processes in e-commerce, and it can take [our] customers two to three months to complete,” Ingole says. “We’re creating an AI agent that will guide them, step-by-step, to get everything set up in two weeks.” Ingole relies on AI agents for some of his own tasks at Amla. Project managers historically have spent 10 to 12 hours each week assembling and sending status reports to stakeholders. Ingole built an AI agent to handle much of the work. “It runs every Monday morning and reads through my emails to extract highlights, risks, timelines, and upcoming releases, then sends me a written status report,” Ingole says. The process might sound simple, but the agent’s workflow involves at least a dozen steps including defining parameters, managing temporary files, and integrating with existing tools. With the information-gathering work handled, it frees up Ingole and his colleagues to spend more time on deeper-thinking work, he says. Publishing as idea refinery For nearly a decade, Ingole has spent some of his free time conducting independent research projects in data analytics and AI-enabled applications in health care. He has written more than 40 peer-reviewed papers, which are in the IEEE Xplore Digital Library. He has been granted six patents in the United Kingdom and India. In the U.K., he is a registered coinventor of an AI-powered, cloud-connected wearable device for health monitoring and an AI-based breast cancer detection tool. Ingole’s patent for the breast cancer detector, he says, reflects his belief that when engineers apply data and AI correctly, they can help doctors diagnose patients more quickly and accurately. That, he says, is both a power and a responsibility. He is part of a team helping patients who are paralyzed and nonverbal control items in their environment. His goal, he says, is to develop a brain-computer interface to let patients turn on a fan, switch off a television, and complete similar tasks. Publishing research requires both academic rigor and peer scrutiny, and Ingole says the function has been critical to improving as both a project manager and a researcher-inventor. “Lots of research ideas never make it to paper,” he notes. “But when you write for journals or conferences, you’re bombarded with questions from Ph.D.s and experienced researchers. This forces me to refine my methodology, and to combine use cases and technical architecture in a way that stands up to expert review.” Finding a professional hub Ingole joined IEEE in 2022, and he says the affiliation has become central to both his research and his professional identity. “I use the Member Directory often and contact engineers through my IEEE email address, which gives me credibility because they know it’s a genuine research connection,” he says. The organization has given him a platform to contribute to the research space beyond his own papers, he says. He has served as a conference session chair, keynote speaker, technical program committee member, and peer research reviewer for various conferences and events. His IEEE membership, he says, has opened doors to other communities, helping support his entry into the British Computer Society, which has stringent acceptance criteria. Those opportunities have helped him build a global network of collaborators with whom to discuss upcoming research, seek advice, and share data, he says. “IEEE is important for me to continue as an independent researcher,” he says. “It lets me contribute to the community, and I get a lot in return.”
About this Webinar Turn Yield Excursions into Faster, More Confident Root Cause Analysis When a yield issue emerges, the answer rarely lives in a single system. Critical clues are spread across metrology data, tool traces, chemical analysis, and facilities systems, while growing data volumes make traditional dashboards slow, fragmented, and difficult to act on. What You’ll Learn: Discover how a purpose-built semiconductor analytics platform can help engineers connect insights across domains without moving data. See how Agentic AI, semiconductor-specific visualizations, and push-down compute enable faster investigation of yield excursions and process issues, even across billions of data points. In the session, a live demonstration shows how to conduct a multi-domain root cause investigation using Spotfire® Industry Pro. Key Takeaways: Understand why siloed manufacturing data delays yield recovery and inflates costs Learn how Agentic AI automates complex cross-domain analytics and visualization generation Explore methods for scaling high-performance analytics across massive fab datasets Who Should Attend: Yield, Process, and Integration Engineers; Fab and Manufacturing Operations Managers; Quality and Reliability Engineers; and Data & Analytics leaders supporting wafer fabs, foundries, OSATs, and IDMs who need to identify issues faster while maintaining confidence in decision-making. Save Your Spot! Join this webinar to learn how leading semiconductor teams are accelerating root cause investigations, scaling analytics across massive datasets, and transforming disconnected data into actionable manufacturing intelligence. Reserve your seat today. Register now for this free webinar!
This article is part of our exclusive career advice series in partnership with the IEEE Technology and Engineering Management Society. Imagine this: You have a strong idea for a new product for your company. Your coworkers encourage you to move forward because they believe it could be the organization’s next big success. The idea clearly falls outside your department’s responsibilities, however, and you have no role in the product line. What should you do? Sit and wait for “the right group” to pick it up, or push the idea forward without knowing how or what it might mean for your current position? Such situations occur frequently. Many end up as missed opportunities, even though they could have significantly advanced the company’s technological or market position. Some organizations actively support such initiatives, allocating specific periods during the workday for employees to focus on developing their own ideas. Companies known for that include Google and 3M. They allow employees to pursue projects with a portion of their time, such as one day per week. Research that I conducted indicates it pays off for employee performance. Bootlegging and skunkworks At some companies, managers know such projects exist, but they deliberately turn a blind eye, allowing them to continue. Some employees persist through bootlegging or skunkworks projects. Bootlegging projects have not been approved by a manager or funded by the company. Skunkworks projects involve a small team within the company that has been given authority and funding to secretly research and develop potentially groundbreaking innovations during their off-hours. The term comes from Lockheed’s Skunk Works division, set up in 1943 in a rented circus tent to build the P-80 fighter jet in secret. It took just 143 days. The 3M Post-it Note came out of the company’s “15 percent culture,” described as a permitted bootlegging policy. It gives employees paid time off to pursue their own ideas. The company traces the philosophy to its longtime president and later chairman William L. McKnight. Company scientist Arthur Fry used the policy in 1974 to turn a colleague’s dormant adhesive into the first Post-it prototypes, after his own bookmarks kept falling out of his hymnal. There are several examples of high-visibility skunkworks projects. At Apple, Steve Jobs pulled roughly 20 people—pirates, as he called them—out of the company to build the original Macintosh computer in a building nicknamed Texaco Towers. In Walter Isaacson’s biography Steve Jobs, he frames the idea as modeled on the skunkworks approach. Google’s Gmail system is frequently—and incorrectly—cited as a product of the company’s “20% time” policy. In a 2014 interview with Time magazine, the system’s creator, Paul Buchheit, said Gmail was in fact an official assignment. What the Gmail incubation did share with classic skunkworks projects was secrecy: For much of its three years in development, it was kept hidden from most people inside the company. If you want to drive change in your organization, build a promoter triad around your idea. At Alphabet, Google X—now known simply as X—operated as a secretive “moonshot” lab, kept hidden from most Google employees, according to a 2011 article in The New York Times. Google’s self-driving car project graduated from X to become Waymo, and Google Glass was likewise incubated there. The X team is now developing the second edition of Glass Enterprise, a successor aimed at industrial rather than consumer use. Amazon runs a comparable model through Lab126, which, according to an article in Fast Company, evolved from a small skunkworks Amazon subsidiary into a hardware maker with nearly 3,000 employees. Lab126 delivered the Kindle in 2007 and the Echo in 2015. Then there are so-called submarine projects, which employees work on without permission and despite explicit disapproval. They can lead to disciplinary action and termination. Innovation management Innovation management theory offers a more structured and robust approach. It argues that successful organizational change requires support at several levels, according to “Teamwork for Innovation: The ‘Troika’ of Promoters,” published in R&D Management. The promoter theory, developed around 25 years ago, consistently shows that change projects are far more likely to succeed when they are supported on multiple organizational levels. A good idea alone is not enough; you need a network of technology, process, and power promoters to turn a concept into a fully implemented, scalable solution. First, you need a technology promoter: the person who has the idea, such as a new product, and possesses technical expertise and specific knowledge about the field or industry. Art Fry at 3M would be such an individual. How can you put that into practice as an individual? Start by clearly formulating your idea into a concise concept paper or one-page summary including benefits, technical feasibility, and potential business impact. Identify potential technology promoters (experts who can validate and refine your idea), and approach them early to strengthen the technical foundation. In parallel, map the relevant stakeholders and decision-makers, and identify process promoters who understand how decisions are made in your company. They could be colleagues in innovation, R&D, or business development who understand your idea and how it can benefit the company. The second is a process promoter: someone who might not know all the technical details but understands the organization’s formal and informal networks and knows how to navigate its processes, committees, and decision-making paths. This person can ensure the idea reaches the right stakeholders at the right time. In the 3M case, it would be a person from the organizational management department, often called an innovation manager. The key role here is to connect inventors such as Fry with people from other departments needed for further project development, such as manufacturing, quality control, and sales. Lastly, there’s the power promoter: a person in a leadership position who might not know the technical details but can allocate resources, eliminate obstacles, and maneuver through the company’s political dynamics. This individual has hierarchical power and acts as a sponsor of the idea or project. In the case of Fry, the person could be, say, the chief technology officer, but it also could be a middle manager who has the power for an individual field of action. The three-level promoter structure applies regardless of whether the change concerns a new product, new service, or internal process innovation. Engage potential power promoters by presenting a low-risk, small-scale pilot and a clear value proposition. Leaders are more likely to support ideas that are well prepared, vetted for potential risks, and backed by a small coalition. Building the promoter triad In short, don’t work in isolation. Systematically build alliances across expertise, networks, and hierarchical levels to create lasting change. If you want to drive change in your organization, build a promoter triad around your idea. The tech experts and leadership promoters are easier to identify. Process promoters are often found in corporate innovation management, R&D management, or strategy functions, but they also can emerge in line units with strong internal networks. Innovation management, as the promoter model describes it, looks nothing like the management structure most engineers are trained to expect. Traditional technical management runs on a single reporting line. With the promoter model, influence is spread across three people—technology, process, and power promoters—who may be in different departments, at different levels of seniority, and who might never share a reporting line. What holds the trio together isn’t a formal structure; it’s the idea itself, for as long as it takes to move the idea forward. That makes innovation management closer to networked, matrix-style leadership than to the pyramid most engineers picture when they hear the word management. It’s worth understanding both models before you decide which kind of impact you’re actually optimizing for. The Institute has covered the tension from the individual’s side in “Tips for How to Think Like an Entrepreneur,” “Management Versus Technical Track,” both published in partnership with the IEEE Technology and Engineering Management Society, and “What to Consider Before You Accept a Management Role” from the IEEE Spectrum Career Alert newsletter. All are worth a look if you’re weighing a formal management track against staying close to the technology itself. Remember: You don’t have to build your promoter network alone or only inside your own company. IEEE societies, sections and chapters, and technical committees, as well as the networking platform IEEE Collabratec, function as a ready-made cross-company network. They are practical places to find technology promoters with deep expertise in a field you don’t fully own yet, or to meet process and power promoters at other organizations who have built a promoter coalition around a similar idea. For more tips on how to advance your career, check out our Career Advice for Engineers, From Engineers collection.
About 16 percent of the global population—more than 1 billion people—live with some form of disability, according to the World Health Organization. Many of the disabilities affect independence and mobility. Three high school students working on inventions to help people with disabilities restore movement, translate thoughts, and navigate rough terrain had their work showcased at Regeneron’s International Science and Engineering Fair (ISEF), held in May in Phoenix. Their projects earned them this year’s IEEE Presidents’ Scholarship awards. IEEE President Mary Ellen Randall presented the awards at a ceremony held during the fair. They also received an IEEE President’s coin, which students said was a highlight of their experience. Hollie Tang won this year’s IEEE Presidents’ Scholarship of US $10,000 for her wheelchair navigation system. The award is payable over four years of undergraduate university study and includes a complimentary IEEE student membership. Partap Sidhum, the second-place winner, received a $600 scholarship for his mind-controlled lower-limb exoskeleton. Third-place winner Calvin Shang Hung received a $400 scholarship for his rough-terrain robot. Sidhum and Hung also got complimentary IEEE student memberships. Established by the IEEE Foundation and administered by IEEE Educational Activities, the Presidents’ Scholarship recognizes high school students who demonstrate an exceptional grasp of electrical engineering, computer science, or another IEEE field of interest. Controlling movements with a tongue Holly Tang won the 2026 IEEE Presidents’ Scholarship of US $10,000 for her Tonguage project, which is a noninvasive, computer-vision-based human-machine interface.Lynn Bowlby Tang, a sophomore at Wilson High School in Hacienda Heights, Calif., secured the top prize for her Tonguage project: a noninvasive, computer-vision-based human-machine interface. Using tongue movements and a standard camera, the interface lets users control a computer and other digital tools as well as assistive technologies including wheelchairs. The tongue pad, one of the system’s core features, allows the user’s tongue to function as a directional cursor, while eye blinks serve as mouse clicks. Tonguage translates the person’s tongue and eye motions into actionable commands in several ways, such as the tongue’s position inside the mouth and continuous movement patterns. The system’s multimodality combines input from the tongue with other facial cues. The system includes a face-tracking feature for error prevention that verifies commands are coming from the intended user, disregarding anyone else who moves into the camera’s frame. That is a critical safety measure for a wheelchair-navigation application, Tang says. Accessibility was central to Tang’s mission. She built the system to run on relatively affordable, readily available laptop cameras rather than more costly specialized hardware. “Mobility conditions don’t discriminate,” she says. “They can affect anyone of any income, gender, and socioeconomic status.” Tang initially imagined Tonguage as a simple substitute for a keyboard and mouse. The more research she did, though, the more she realized that it could offer autonomy through applications such as wheelchair navigation, robotic arm control, and gaming, she says. “We’re so focused on trying to give people autonomy over just basic human tasks that we often leave out things like gaming,” she says. “They deserve the freedom to play games and enjoy entertainment as well.” Tang, who plans to pursue biomedical engineering, says a visit to a rehabilitation center solidified her purpose. “Including empathy in your technological solution is so important,” she says. “Empathy is hard to teach in a classroom, but it can be learned through experience, and through actually meeting people whose lives your work might change.” Mind-controlled exoskeleton Sidhu, a junior at Bethpage High School, in New York, took second place for NeuroGait, a mind-controlled, lower-limb exoskeleton. He says he was inspired by his volunteer work at a community center that lacked elevators. He saw individuals with mobility issues struggle to navigate the three flights of stairs. NeuroGait operates by reading the Bereitschaftspotential (BP), a faint electrical pattern that emerges one to two seconds before a person consciously initiates movement. Using a custom electroencephalogram (EEG) headset and a convolutional neural network (CNN), the system classifies intended movements and sends commands to a 3D-printed exoskeleton. Rather than rigid motors, the suit relies on pneumatic artificial muscles that Sidhu designed to mimic human anatomy. “The pneumatic artificial muscle in itself is so compliant that it’s able to adjust to the limitations of the human body,” he says. The technical specifications are striking: The CNN achieves a 99.9 percent accuracy in detecting a person’s intended movement, while the full system—from the brain’s signal to physical movement—operates at 95.2 percent accuracy, according to the results from 500 trials Sidhu conducted. Perhaps most impressively, Sidhu built the entire system for about $276, less than 1 percent of the $40,000 to $100,000 price tag of commercial exoskeletons, according to a 2025 revenue report from Roots Analysis. He says he hopes to bring NeuroGait to the community center where the idea for the project began. He attributes his success to staying current with research from institutions and organizations such as Boston Dynamics and MIT. “To be successful in research,” he says, “you have to know what’s being done right now.” A spider-inspired robot Hung, a sophomore at El Cerrito High School, in California, took third place for Math Into Motion: Robotic Hexapod for Hazardous Environments. The six-legged robot is designed to traverse terrain too unstable for humans or conventional robotic systems. With only weeks before the science fair deadline for entries and no prior electrical engineering experience, Hung began with an idea inspired by his interest in spaceflight: an insectlike robot. He had spent years watching rovers such as Curiosity and Perseverance struggle on uneven surfaces, leading him to hypothesize that a hexapod design would be better for rugged ground. As the project progressed, the humanitarian applications for his robot became clearer, he says. Watching news reports of the earthquake that struck Türkiye in 2023, as well as conflicts around the globe, Hung adapted his robot for use in disasters. The hexapod’s stable tripod walking gait, in which three legs stay grounded while the other three move, makes it well suited for navigating in collapsed buildings to locate survivors or to carry sensitive supplies such as insulin in conflict zones. The current version moves using three mathematical techniques. Inverse kinematics converts a target leg position into the motor angles needed to reach it. Linear interpolation breaks each movement into a series of smaller steps for smoother motion. And Euclidean transformations translate the robot’s travel direction into instructions that each leg can follow, regardless of the way a leg happens to be facing. Hung taught himself how to design a printed circuit board. He also taught himself 3D modeling, coding, and soldering. Figuring out the complicated mathematical transformations to coordinate legs facing different directions proved to be the toughest hurdle, he says. After seven months of development and trial and error, a critical circuit board failure in his third version nearly ended the project, he says. “There was a really strong moment of ‘Should I just give up?’” he recalls. He simplified the design and rebuilt it from the ground up. “I just decided to double down,” he says. The fourth version of the robot was the first that successfully walked across his living room floor. He advises aspiring engineers that “if you find the right project and it truly becomes your passion, designing it almost starts to feel like fun, and that’s what carries you through.” As the three young innovators demonstrate, the future of engineering goes far beyond technical ingenuity. Much is rooted in empathy and a commitment to human welfare. Through initiatives such as the IEEE Presidents’ Scholarship, the IEEE Foundation showcases and nurtures bright minds poised to shape the next era of assistive technology and robotics. For Tang, Sidhu, and Hung, the ISEF stage is just the beginning. They can look forward to impactful careers dedicated to advancing technology for the benefit of humanity.
The impact of AI on software development has been both profound and ever-evolving. Last year, I wrote about AMD’s plans to use AI not just for generating new lines of code, but also for other steps in the software development lifecycle (SDLC), such as triaging problems, debugging code, and testing the software. At the time, we were hoping for a 25 percent productivity boost from AI use over the course of two or three years. But with each new release, the capabilities of Large Language Models (LLMs) improve dramatically—accelerating software development, increasing the quality of AI-generated code, and fundamentally reshaping how software is engineered. Now, just one year later, we have surpassed our productivity target, achieving a 30 percent overall productivity boost through AI. On top of that, we are rethinking not only how we use AI within the SDLC, but the structure of the SDLC itself. We believe that the biggest AI revolution in software engineering is still ahead. So far, we have largely been teaching AI how we perform tasks and asking it to mimic existing workflows. In many ways, this constrains AI to human patterns of thinking. The next transformation will come from collaborative swarms of AI agents capable of discovering solutions independently. Agents of today AMD began developing AI systems for code generation, testing automation, bug analysis, and code review in 2024. At the time, our objective was to achieve 25 percent AI-generated production code by 2027 while gradually automating larger portions of the SDLC. Measuring productivity is inherently challenging, but from the outset we have consistently tracked one objective metric: the percentage of source code generated by AI. Importantly, we count only code that passes all reviews and testing and is ultimately included in the final product. While AI-generated code is certainly not the only contributor to productivity gains, it is one of the few metrics that can be measured objectively and consistently. By this metric, we have crossed the 20 percent mark at the beginning of this year and are now progressing toward 50 percent across entire codebase. In some software components, more than 80 percent of the code is now generated using AI. Agentic AI has enabled us to include AI in every step of the lifecycle: For code analysis and triage, agents are trained to analyze problem reports, identify and group similar requests, and highlight which code snippets are likely to need modification. For debugging and code generation, agents are directed to analyze a bug request and implement required code changes. For testing, the agents generate unit tests, and if those are passed, identify necessary integration and product-level tests. And finally, for the approval and release stage, agents prepare architecture summary, code change review and full test results for engineers’ review and approval and if approved, integrate the changes into the next release. Agents of tomorrow Today, engineers create AI agents in their own image: they teach AI what they know about the system, how they would fix an issue, and how they would implement a change. This is already a major technological advancement. Engineers can create multiple “AI versions” of themselves, allowing these agents to work in parallel, scaling their expertise far beyond the limits of individual productivity. The limitation, however, is that these AI agents are still constrained by human thinking and human-defined approaches. AMD We believe the next major transformation in software engineering will occur when collaborative AI agent swarms can independently identify and develop solutions, guided by humans on what to solve rather than constrained by human assumptions about how the job should be done. Instead of providing detailed instructions on how to solve a problem, engineers will define the issue, the desired outcome and the quality, performance, and system constraints allowing AI agents to determine the optimal path to a solution. A swarm of AI agents will then work in parallel to generate, evaluate, and refine multiple solution approaches. These agents will automatically validate correctness, measure performance, test trade-offs, and compare alternative implementations against defined success criteria. Finally, AI agents will prepare ranked solution options, along with validation results and performance metrics, for engineer review and approval. The agents won’t be enhancing each step of the SDLC—they will be rewriting the SLDC themselves. To get to this point, we need to change how agents are trained. Today, improvement occurs one engineer and one agent at a time: an engineer reviews the output, refines the prompt, and repeats the process. To scale beyond this model, agents must continuously learn from one another, reuse successful strategies, and improve collaboratively across projects and teams. We are already moving in this direction by using multi-agent workflows extensively through agentic harnesses, such as Codex and Claude Code, while simultaneously developing our own internal multi-agent systems to support the next generation of AI-driven software engineering. A good example is our AI-driven effort to resolve issues in our Radeon Software eXperience (RSX). RSX is a user interface component that allows users to configure and monitor graphics driver behavior. In October 2025, we began using AI agents to automatically debug and fix reported RSX issues. Out-of-the-box AI tools delivered limited results, resolving only 6% of issues. The percentage of software issues fixed automatically by AI agents in AMD’s Radeon Software eXperience (RSX) has been growing steadily, reaching 75 percent in June 2026. As we analyzed failures and identified ways to improve, we built a learning loop—initially a largely manual process—to understand where the agents were falling short and how to improve them. Rather than retraining the underlying models, we refined the objectives given to the agents, allowing them to iteratively explore multiple approaches, evaluate the results against defined success criteria, and converge on better solutions. At the same time, advances in models and agent runtimes further increased effectiveness. Together, these improvements significantly increased our resolution rate from 6 percent to more than 75 percent of RSX issues resolved by agentic loop. To make agents and agent swarms truly productive, we need a continuous learning loop that feeds errors and human interventions back into future agent workflows. The opportunity is to engineer this loop around clear, measurable goals. Each cycle captures new insights, making the entire AI engineering workflow smarter and more effective. Over time, this self-reinforcing loop—not just the underlying model—will become a key driver of AI progress. The evolving role of human engineers At AMD, we view AI as a means of increasing productivity, improving quality, and enabling employees to focus on higher-value work. Our goal is to empower our workforce with AI, not to reduce headcount. To support this transformation, we are investing heavily in AI education and training across the company. The way we work is evolving rapidly, and we want every AMD employee to be prepared to leverage AI confidently, responsibly, and effectively. As AI agents continue to improve, engineers will spend less time manually implementing solutions, focusing more on defining specifications, validating outcomes, and making the strategic decisions that drive innovation.
Bede Liu, a digital signal processing pioneer, died on 7 May. He was 91. Liu was widely regarded as one of the founders of modern digital signal processing, a field that applies mathematical algorithms to analyze, modify, and transmit signals including sound, images, and video. The IEEE Life Fellow taught electrical engineering at Princeton for more than 50 years. From 1994 to 1997, he chaired the university’s electrical and computer engineering department. Liu’s research aided the transition from analog to digital processing of sound, images, and video. His work helped establish many of the mathematical and engineering techniques that underpin modern communications, multimedia systems, and consumer electronics. Although little known outside engineering circles, his work is embedded in technologies used by billions of people. The low-power digital signal processors that make cellphone calls, streaming video, and Internet communications possible can be traced to research he conducted in the 1970s and ‘80s. Liu received the 2018 IEEE Jack S. Kilby Signal Processing Medal for “sustained contributions to the analysis and the development of low-complexity realizations of digital signal processing algorithms.” “We stream music and video. We take photos with our phones, and we send them around. We don’t even think about it,” IEEE Life Fellow H. Vincent Poor said in an obituary for Liu. “But it’s all because of the signal processing, image processing, and video processing that’s been developed over the years, as well as other technologies that have grown up beside it and enabled it, like semiconductors. The development of these processing advances was exactly what Bede was a major part of.” Poor is a professor of electrical and computer engineering at Princeton. An impactful scholar and teacher Liu was born in Shanghai in 1934. During his childhood, his family relocated to Taiwan amid the upheaval of the Chinese Civil War. His father, Henry Liu Sr., was an electrical engineer. Liu earned his bachelor’s degree in electrical engineering in 1954 from the National Taiwan University, in Taipei. After graduating, he and his family moved to the United States. Liu and his father attended the Polytechnic Institute of Brooklyn (now the New York University Tandon School of Engineering) together. They earned their master’s degrees in electrical engineering in 1956. Liu continued his studies at the school, earning a doctoral degree in electrical engineering four years later. In 1959 he was awarded a Bell Labs fellowship and worked at the company’s Murray Hill, N.J., location until he joined Princeton in 1962. “Liu was a highly impactful scholar and teacher—always thinking ahead of future needs and changing technologies,” said IEEE Life Fellow Peter J. Ramadge, a Princeton professor emeritus of engineering. Cellphones make use of a considerable amount of digital signal processing, Liu once noted. Many of the field’s advances, he added, involved making sophisticated processing practical on devices with limited computing power—which is the challenge that confronted generations of engineers designing portable electronics. Liu’s research contributions helped shape both the theory and practice of digital signal processing. With Abe Peled, a former graduate student, he authored the 1976 textbook Digital Signal Processing: Theory, Design, and Implementation, which is a standard reference for engineers. Published before digital signal processing had fully emerged as a distinct discipline, it helped define the subject for practitioners and students around the world. Liu also published 250 technical papers and was granted 12 U.S. patents. His papers are available to read on the IEEE Xplore Digital Library. The first patent granted to him and Peled was in 1976 for a hardware design that processed bits in parallel, rather than in sequence. The innovation greatly increased computing efficiency for data including sound and communication signals. Peled says Liu “demonstrated an openness to new ideas and a willingness to challenge the orthodoxy of the EE department at that time—which leaned heavily toward more theoretical information theory.” A mentor to well-known engineers Liu’s influence extended beyond his own research. He advised 53 doctoral students, many of whom went on to distinguished careers in academia and industry, including leadership positions at Google and IBM. One former student, computer scientist Robert Kahn, helped create the architecture of the modern Internet. Kahn, an IEEE Life Fellow, received the 2024 IEEE Medal of Honor. “His former students were very successful,” Poor said of Liu, “and I think that’s a testament to his skill as a mentor.” “Liu was a highly impactful scholar and teacher—always thinking ahead of future needs and changing technologies.”—Peter J. Ramadge Together with several Ph.D. students, Liu developed methods of filtering and compressing digital signals to mitigate errors and dramatically reduce the computation needed for signal processing. As digital signal processing moved from laboratories into commercial products, the impact of Liu’s ideas spread across industries. His research helped spawn the development of lower-cost and lower-power electronics and contributed to advances in mobile communications, multimedia technology, industrial automation, and biomedical imaging. A focus on media integrity and copyrights In the 2000s, Liu turned his attention to media integrity and copyright issues. “With the increasing accessibility of digital media source material, the protection of ownership and the prevention of unauthorized alteration has become an important concern,” he wrote in his 2002 book, Multimedia Data Hiding. The book, which he co-wrote with his former doctoral student IEEE Fellow Min Wu, discussed the theory, techniques, applications, and security of digital watermarking—hidden signals that could identify a genuine copy of a song, image or video to prevent unauthorized distribution or tampering. A Princeton team that included Liu, Wu, and another of his doctoral students uncovered serious vulnerabilities in watermarking technologies being considered by an industry consortium. They found that the standardization efforts were immature and would not protect against digital piracy. “Now nearly every copy of a Hollywood film given to a critic or theater carries a unique digital forensic watermark to prevent unauthorized redistribution,” said Wu. A force in the community Liu, an active IEEE volunteer, served on the IEEE Board of Directors in 1984 and 1985. He was the 1982 president of the IEEE Circuits and Systems Society. He was a member of the U.S. National Academy of Engineering, an academician of China’s Academia Sinica, and a foreign member of the Chinese Academy of Sciences. Outside the classroom, he was recognized for his humility, humor, enthusiasm, and generosity. When thinking of Liu, IEEE Life Fellow Kenneth Steiglitz says, cheer is the first word that comes to mind. Liu was “always ready with a positive remark, a quick smile or, maybe, some tips on the right way to cook a duck,” says Steiglitz, professor emeritus of computer science at Princeton. Liu encouraged his students to take on ambitious, unconventional projects, and he inspired students and colleagues with his adventurous spirit.
Bede Liu, a digital signal processing pioneer, died on 7 May. He was 91. Liu was widely regarded as one of the founders of modern digital signal processing, a field that applies mathematical algorithms to analyze, modify, and transmit signals including sound, images, and video. The IEEE Life Fellow taught electrical engineering at Princeton for more than 50 years. From 1994 to 1997, he chaired the university’s electrical and computer engineering department. Liu’s research aided the transition from analog to digital processing of sound, images, and video. His work helped establish many of the mathematical and engineering techniques that underpin modern communications, multimedia systems, and consumer electronics. Although little known outside engineering circles, his work is embedded in technologies used by billions of people. The low-power digital signal processors that make cellphone calls, streaming video, and Internet communications possible can be traced to research he conducted in the 1970s and ‘80s. Liu received the 2018 IEEE Jack S. Kilby Signal Processing Medal for “sustained contributions to the analysis and the development of low-complexity realizations of digital signal processing algorithms.” “We stream music and video. We take photos with our phones, and we send them around. We don’t even think about it,” IEEE Life Fellow H. Vincent Poor said in an obituary for Liu. “But it’s all because of the signal processing, image processing, and video processing that’s been developed over the years, as well as other technologies that have grown up beside it and enabled it, like semiconductors. The development of these processing advances was exactly what Bede was a major part of.” Poor is a professor of electrical and computer engineering at Princeton. An impactful scholar and teacher Liu was born in Shanghai in 1934. During his childhood, his family relocated to Taiwan amid the upheaval of the Chinese Civil War. His father, Henry Liu Sr., was an electrical engineer. Liu earned his bachelor’s degree in electrical engineering in 1954 from the National Taiwan University, in Taipei. After graduating, he and his family moved to the United States. Liu and his father attended the Polytechnic Institute of Brooklyn (now the New York University Tandon School of Engineering) together. They earned their master’s degrees in electrical engineering in 1956. Liu continued his studies at the school, earning a doctoral degree in electrical engineering four years later. In 1959 he was awarded a Bell Labs fellowship and worked at the company’s Murray Hill, N.J., location until he joined Princeton in 1962. “Liu was a highly impactful scholar and teacher—always thinking ahead of future needs and changing technologies,” said IEEE Life Fellow Peter J. Ramadge, a Princeton professor emeritus of engineering. Cellphones make use of a considerable amount of digital signal processing, Liu once noted. Many of the field’s advances, he added, involved making sophisticated processing practical on devices with limited computing power—which is the challenge that confronted generations of engineers designing portable electronics. Liu’s research contributions helped shape both the theory and practice of digital signal processing. With Abe Peled, a former graduate student, he authored the 1976 textbook Digital Signal Processing: Theory, Design, and Implementation, which is a standard reference for engineers. Published before digital signal processing had fully emerged as a distinct discipline, it helped define the subject for practitioners and students around the world. Liu also published 250 technical papers and was granted 12 U.S. patents. His papers are available to read on the IEEE Xplore Digital Library. The first patent granted to him and Peled was in 1976 for a hardware design that processed bits in parallel, rather than in sequence. The innovation greatly increased computing efficiency for data including sound and communication signals. Peled says Liu “demonstrated an openness to new ideas and a willingness to challenge the orthodoxy of the EE department at that time—which leaned heavily toward more theoretical information theory.” A mentor to well-known engineers Liu’s influence extended beyond his own research. He advised 53 doctoral students, many of whom went on to distinguished careers in academia and industry, including leadership positions at Google and IBM. One former student, computer scientist Robert Kahn, helped create the architecture of the modern Internet. Kahn, an IEEE Life Fellow, received the 2024 IEEE Medal of Honor. “His former students were very successful,” Poor said of Liu, “and I think that’s a testament to his skill as a mentor.” “Liu was a highly impactful scholar and teacher—always thinking ahead of future needs and changing technologies.”—Peter J. Ramadge Together with several Ph.D. students, Liu developed methods of filtering and compressing digital signals to mitigate errors and dramatically reduce the computation needed for signal processing. As digital signal processing moved from laboratories into commercial products, the impact of Liu’s ideas spread across industries. His research helped spawn the development of lower-cost and lower-power electronics and contributed to advances in mobile communications, multimedia technology, industrial automation, and biomedical imaging. A focus on media integrity and copyrights In the 2000s, Liu turned his attention to media integrity and copyright issues. “With the increasing accessibility of digital media source material, the protection of ownership and the prevention of unauthorized alteration has become an important concern,” he wrote in his 2002 book, Multimedia Data Hiding. The book, which he co-wrote with his former doctoral student IEEE Fellow Min Wu, discussed the theory, techniques, applications, and security of digital watermarking—hidden signals that could identify a genuine copy of a song, image or video to prevent unauthorized distribution or tampering. A Princeton team that included Liu, Wu, and another of his doctoral students uncovered serious vulnerabilities in watermarking technologies being considered by an industry consortium. They found that the standardization efforts were immature and would not protect against digital piracy. “Now nearly every copy of a Hollywood film given to a critic or theater carries a unique digital forensic watermark to prevent unauthorized redistribution,” said Wu. A force in the community Liu, an active IEEE volunteer, served on the IEEE Board of Directors in 1984 and 1985. He was the 1982 president of the IEEE Circuits and Systems Society. He was a member of the U.S. National Academy of Engineering, an academician of China’s Academia Sinica, and a foreign member of the Chinese Academy of Sciences. Outside the classroom, he was recognized for his humility, humor, enthusiasm, and generosity. When thinking of Liu, IEEE Life Fellow Kenneth Steiglitz says, cheer is the first word that comes to mind. Liu was “always ready with a positive remark, a quick smile or, maybe, some tips on the right way to cook a duck,” says Steiglitz, professor emeritus of computer science at Princeton. Liu encouraged his students to take on ambitious, unconventional projects, and he inspired students and colleagues with his adventurous spirit.
Learn how full-wave simulation predicts very low antenna coupling on aircraft-sized platforms, and which three modeling techniques deliver accurate results with fewer computational resources. Download this free whitepaper now!
If you haven’t already seen a job listing for a “product engineer,” you probably will soon. The job everyone’s suddenly hiring for, this role is like a cross between a product manager and an engineer (as the name suggests). And it’s a hiring trend worth paying attention to. Companies are opening more of these roles every single month, but they’re struggling to fill them. The reason has almost nothing to do with engineers’ coding skills or years of experience. The best career move you can make to prepare for these types of roles has almost nothing to do with getting more technical. Instead, it comes down to one of the fluffiest, most overused, and potentially cringiest words in all of tech: mindset. Stick with me, I promise this goes somewhere useful. The problem: We were trained to be task-takers When I started out, my job looked like this: Drive to an office. Sit through meetings that led to other meetings until a project manager handed me a task they’d already chopped into tiny pieces. My job was to turn that task into code. It took years for me to get good at a coding language and tech stack, and once I did, I executed that knowledge against specs that somebody else wrote. You know what’s freakishly good at that exact job? I’ll give you a hint: It starts with A and ends with I. Boris Cherny, the creator of Claude Code, recently said: “coding is basically solved,” and “the bottleneck is going to be good ideas.” So if your entire value is “hand me a task and I’ll build it,” you’re in a footrace with the robots. I don’t like that for you. The bad news... that is also good news Many companies are flattening. Middle management is getting stripped out, for better or worse (mostly for worse), which means many of us are doing more with less. This might sound like purely more work, but it’s also an opening for anyone who cares about what they’re building and can put on their manager hat. Companies are no longer just hunting for the strongest engineer in one narrow domain. What’s rare, and what actually moves revenue, is an engineer who can spot the thing that’s quietly costing money and either flag it to leadership or just go fix it. What this actually looks like Being product-minded has NOTHING to do with your tech stack. Here’s where to start: Have an opinion and back it up. As a former engineering manager, the worst thing I ever heard was silence. I’d often ask the team what they thought because I doubted myself and wanted a gut check. I was grateful to the ones who said “nope, bad idea, here’s why.” Pushback is a gift. Learn the domain, casually. Work for a plumbing company? You don’t need to become a plumber, but spend an hour on Reddit threads where plumbers vent. Now your ideas come from your potential customers. Make experiments cheap and safe. This is where any engineer has massive leverage. Experiments are not free. A bad one loses customers and frustrates users. Tools like LaunchDarkly and Optimizely let you ship a change to 5 percent of users and roll it back the second it tanks. Learn them, or build a scrappy version yourself. A team that can quickly run safe experiments will out-learn everyone else in the building. Be data-driven. Stop fighting about button colors. Pick a goal: making money, finding product-market fit, or making the product sticky so people come back. Then measure it. If your gorgeous redesign tanks time-on-site, it failed, no matter how good it looked to you. If the ugly version makes more money, ship the ugly version. You don’t have to be the ideas person. Maybe you’re not a visionary. That’s fine. Organize a hackathon around an actual company goal. Pull up your company’s quarterly targets and build something against one of them. Don’t know what those targets are? That’s your first assignment. Good ideas are the new bottleneck—and they always have been When I was a manager, I asked myself one question every week: What’s the single most impactful thing I could do right now? The answer was almost never “write more code.” It was understanding a gnarly problem nobody had defined yet. Building a deck to spread knowledge that was in one person’s head. Getting the right three people in a room to actually make a decision we’d been putting off. Code is cheap, and it always has been. We just couldn’t see it, because for decades the typing took so long that it felt like the hard part. It never was. The hard part was always knowing what’s worth building. — Brian Siobahn Day Grady Wants Everyone to Be AI Literate In January 2025, Siobahn Day Grady launched the first AI research institute at a historically Black college or university. The institute aims to help expand AI skills for all students at North Carolina Central University, where Grady is an associate professor, through both AI research opportunities and skills training. Though the institute is the first of its kind, Grady hopes it could serve as a model for other HBCUs. Read more here. Should Researchers Write Papers for AI Instead of People? AI is increasingly used in the scientific research process. So does publishing need to change to keep up? Jiachen Liu recently co-authored a paper published on ArXiv arguing that the PDF should be replaced with an “Agent-Native Research Artifact” designed with AI in mind. In this interview with IEEE Spectrum, Liu lays out a provocative vision of AI-driven research and an infrastructure that captures—and learns from—details that often get left out of today’s papers. Read more here. Detect Dark Matter’s Mark From Your Backyard Astronomers still don’t know exactly what dark matter is, but they can detect it—and so can you. With a small radio telescope and a few other pieces, you can create a DIY setup to gauge how fast hydrogen clouds are moving across the Milky Way. Feed those measurements into a spreadsheet, and you can see the same signals that have baffled the astronomical community for decades. Read more here.
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In collaboration with the Kingdom of Bhutan government, IEEE recently introduced its Engineering Education, Research, and Innovation Summit. Held on 9 and 10 June in Paro, in the eastern Himalayas, the event was designed to help Bhutan navigate its digital transformation by focusing on the critical intersection of digital transformation, engineering education, and sustainable development. The summit brought together global academic leaders, technology experts, and Bhutanese government officials to discuss how modern engineering curricula can evolve from theory-centric models into application- and skills-based frameworks. Discussions focused on how to build high-value research capabilities in the country, integrate artificial intelligence into higher education, and address foundational infrastructure challenges to ensure equitable, nationwide digital readiness. “IEEE is proud to collaborate as a catalyst for progress in higher education as AI shifts the technology landscape and Bhutan prepares for its next era of innovation and resilience,” Mary Ellen Randall, 2026 IEEE president and CEO, said at the event. “Our goal is to support local universities and students as they develop trusted, future-ready technology that honors the nation’s commitment to sustainability and human well-being.” The event featured an address by Bhutanese Princess Chimi Yangzom Wangchuck, who emphasized the importance of aligning technological innovation with the nation’s philosophy of gross national happiness (GNH), which prioritizes well-being, sustainability, and ethics. “The question before us is not whether technology will shape the future; it certainly will,” the princess said. “The more pressing question is whether we can shape technology according to our values.” A blueprint for Bhutan’s future The summit helped establish a collaborative blueprint for a high-value knowledge economy in Bhutan through several key focus areas: Workforce readiness: designing industry-driven curriculum modernization and cocreating skills programs to equip graduates with practical, technical competencies. AI and research infrastructure: strengthening open science, trusted regional datasets, and global citation impact to prepare universities for AI-enabled learning environments. Values-driven innovation: merging GNH principles with technological advancement and helping ensure new engineering practices support climate-resilient infrastructure and green innovation. Institutional connectivity: using digital transformation to bridge technical capability gaps between urban and rural institutions; linking classrooms to a global research network. Promoting sustainability: convening stakeholders to exchange ideas on green innovation, climate-resilient infrastructure, and engineering education. Expanding digital access To help promote the effort, IEEE offered Bhutanese universities, government institutions, and industries a six-month complimentary trial of two key technical resources: IEEE Electronic Library. Delivered via the IEEE Xplore Digital Library, the IEL gives users access to more than 7 million documents—including trusted IEEE journals, conference proceedings, standards, and technical papers—to enhance research, teaching, and technology development. IEEE eLearning Library. This platform offers online courses developed by experts in engineering, computing, and technology, supporting flexible learning across core and emerging technical fields for professionals, faculty and students.
In a tranquil Boston suburb, on the far edge of a horse farm, where pasture gives way to woods, a crane lowers an enormous electrode into a borehole. The electrode, a half-meter-long cylinder with copper-tipped arms to ensure good contact with the borehole walls, descends—deeper, deeper—through layers of spongy sandstone to the hard, marbled roots of an ancient mountain range hundreds of meters below ground. Here the rock is tight; there are few cracks for water or gases to flow. But that’s about to change. A stone’s throw away, a second electrode—a twin of the first—has been fixed in another borehole at the same depth. From above ground, a pair of high-voltage generators cabled to the two electrodes fires a series of pulses. Tsss!…Tsss!…Tsss!…Tsss!…Tsss!…. Each discharge, heard faintly at the surface, is like a miniature, subterranean lightning strike. The rock between the electrodes heats. Pressure builds. Then, suddenly, the rock splits into a spiderweb of fractures. On a horse farm outside of Boston, a worker sets up the well where Eden’s electrode will be lowered with a winch.Bob O’Connor Eden GeoPower, the Massachusetts-based startup performing this peculiar field test, calls the technology electrical reservoir stimulation. The company’s tagline: “We break rocks with electricity.” Eden’s researchers hope their rock-breaking technique will someday aid mineral mining, tap geothermal heat, or create geologic storage areas for carbon. But there’s an even more intriguing use that could create a whole new category of energy production: generating hydrogen underground. The dream of a hydrogen-powered economy dates back to the 1970s, when petroleum shortages and rising concerns about pollution from fossil fuels sparked visions of cars, ships, planes, and industrial machines running on hydrogen instead of carbon. Hydrogen is often touted as a clean fuel because when it’s burned or consumed in fuel cells, it emits only water and heat. However, it currently takes more energy to make than it yields, and the cheapest and most common way is by reacting steam with methane, a potent greenhouse gas. How to Break Rocks With Electricity It’s possible to make zero-carbon hydrogen by splitting water with electrolyzers powered by renewable energy. But in most cases, the process is too expensive to be economical—a reality that burst the hydrogen-hype bubble in the early 2020s. Global demand for hydrogen in 2024 reached approximately 100 million tonnes, containing energy equal to only about 3 percent of the world’s annual energy consumption. Most of it is used as chemical feedstock for petroleum refining and for making fertilizers and plastics. The frustrations of manufacturing clean hydrogen have convinced many entrepreneurs and scientists to instead seek the element underground. For the past half-decade, dozens of companies around the world have been hunting for buried stores of hydrogen, called natural or geologic hydrogen. But with a commercial-scale operation yet to be proved, Eden and a handful of other startups and research groups are chasing the more audacious scheme of producing geologic hydrogen artificially. This approach, known as stimulated geologic hydrogen or engineered hydrogen, turns subterranean rock formations into giant hydrogen factories. It typically involves injecting water into iron-rich rock, which oxidizes the iron and releases hydrogen as a by-product. Fracturing the rock, as Eden is doing, creates a network of conduits for the water to reach iron-bearing minerals. The concept of stimulated hydrogen is so new that few have had a chance to test it. Proponents say that if it works—which is a big “if”—it could provide almost unlimited energy for the indefinite future. There’s one way to find out: Start breaking rocks. There’s Plenty of Underground Hydrogen Hydrogen is the simplest and most abundant element in the universe, the stuff of stars and galaxies. Geologists have long known that Earth generates hydrogen gas through natural water-rock reactions, but until recently, the occurrence was regarded as a curiosity. The gas is so light that most experts assumed it all escaped through pores and cracks in Earth’s subsurface and didn’t accumulate in useful quantities. During a demonstration at Eden’s testing site near Boston, an employee displays a central component of the company’s proprietary electrode. Bob O’Connor Inklings that they were wrong emerged in the 19th and 20th centuries, when researchers in the former Russian Empire and Soviet Union reported hydrogen seeping from mines and wells. But in the ongoing frenzy for fossil fuels, these observations were largely overlooked or forgotten. Scientists later discovered hydrogen spewing from hydrothermal vents in the seafloor and feeding so-called eternal flames, like those of Türkiye’s Mount Chimaera, where ancient athletes lit torches for the first Olympic games. Then, in 1987, in the village of Bourakébougou, Mali, people drilling a water well noticed a breeze blowing out of the hole. According to local lore, a worker leaned in for a closer look, a lit cigarette dangling from his mouth. The air instantly ignited, burning a brilliant blue. The crew capped the well, which stayed sealed for 25 years until, in 2012, a Malian oil and gas prospector confirmed the ground contained a large reservoir of hydrogen. The prospecting company, now called Hydroma, had a small electrical plant constructed to convert the gas into power for the village’s residents. Soon after, startups in Australia, Canada, the United States, and elsewhere began searching for more hydrogen stores. By 2025, large multinational petroleum and mining companies were getting in on the game. To date, hundreds of exploratory wells have been drilled across the globe. But although researchers have documented widespread hydrogen deposits, none have proved capable of producing the gas at rates and quantities needed for commercialization. “We’ve poked a lot of holes, and nobody has found the gusher—or at least they’re not talking about it,” says Douglas Wicks, a former program director at the United States’ Advanced Research Projects Agency—Energy who now advises companies pursuing geologic hydrogen. A wellhead guides multiple lines downhole: fluid hose, electric cables, rope, control for a sealing device, and sensor communication. Bob O’Connor Wicks says that in 2022, while at ARPA-E, he got “dragged into the rabbit hole of geologic hydrogen” by Emily Yedinak, then a Fellow at the agency, who was trying to convince her colleagues to take it seriously. “I was the ultimate doubter,” Wicks says. The astronomical price of electrolyzers had made him skeptical that clean hydrogen was a viable pursuit. Plus, if Earth really did contain vast pools of hydrogen, then surely humanity, which had been digging for natural resources for thousands of years, would have found them by now, he reasoned. But after talking with geologists—who pointed out that people historically hadn’t found hydrogen because they hadn’t been looking for it—Wicks changed his tune. “I got the epiphany that geologic hydrogen is not just an accumulation; it’s a chemical reaction,” he says. “And if it’s a chemical reaction, then it can be stimulated.” Finding large accumulations of geologic hydrogen entails stumbling on a Goldilocks set of conditions. You need iron-rich source rocks that have already produced or are producing bountiful hydrogen. You also need porous reservoir rocks that can hold sizable quantities of gas migrating from the source rocks. And you need solid cap rocks above the reservoir that trap the gas underground. To stimulate hydrogen, however, you don’t need this just-right geology. All you need are iron-rich rocks, and then you can generate the hydrogen yourself. “These rocks are everywhere,” Wicks says. “If you look at the amount of iron that’s within drilling range of Earth’s crust, you’re talking about quadrillions of tons of hydrogen being accessible. If we’re 1 percent successful just in the United States, we could power the economy for thousands of years.” A back-of-the-envelope calculation convinced him that the cost of stimulated geologic hydrogen could easily compete with hydrogen made from methane. “If we get the technology right,” he concludes, “this could be huge.” Wicks wasn’t the first person to propose the idea, but he was the first to allocate major funding. In 2024, under his leadership, ARPA-E awarded US $20 million to 16 teams aiming to advance stimulation technologies and research. Winning ideas included fracturing rocks with fluid pressure or mechanical stimuli, exposing them to catalysts to speed hydrogen-generating reactions, and manipulating native microbial communities to enhance production. Eden’s rock-breaking project, the lone electricity-based approach, received $900,000. Eden GeoPower’s Underground Rock Fracturing Paris Smalls, Eden’s CEO, founded the company in 2017 as a 23-year-old graduate student at MIT. For his Ph.D. in civil and environmental engineering, he was studying the effects of electricity on rock strength and became interested in enhanced geothermal systems, which require fracturing hot, dry rocks to circulate water through them for extracting heat. This is typically done by hydraulic fracturing, or fracking—a technique borrowed from the oil-and-gas industry that involves injecting high-pressure fluids. Fracking is controversial because it can cause earthquakes and groundwater contamination, and many regions have banned the practice. From an engineering perspective, it’s also imprecise. The fractures it forms are large and difficult to control. “You can’t get enough fractures where you want because the water ends up just going through the same cracks,” Smalls explains. Electricity, he knew from his Ph.D. work, could create more extensive and finely tuned fracture networks, enabling geothermal systems to produce more heat with less environmental risk. To determine how permeable its fracture networks are, Eden measures fluid pressure downhole and flow rates at the surface. Bob O’Connor Smalls immediately grasped that the same rock-breaking strategy could be used for mineral mining, carbon sequestration, and extending the life of oil and gas wells. But he hadn’t considered using it to make hydrogen. So when Wicks invited him to apply for the hydrogen program at ARPA-E, he was confused. “I didn’t get it at all,” Smalls says. “I’m like, ‘I break rocks. How am I going to generate hydrogen?’” Not long after, Smalls met Alexis Templeton, a geomicrobiologist at the University of Colorado Boulder who had become an expert in geologic hydrogen by studying microbes that consume the gas and the mineralogical transformations that create it. “There was a lot of early interest in whether or not you could engineer the production of hydrogen from rocks,” Templeton recalls. “And the rocks with some of the best potential have all the right chemistry, but they need water. Nobody was excited to do hydraulic fracturing. So everyone was wondering, ‘Well, how are we going to get the water in?’” Eden’s technology, Templeton understood, could be the answer. She agreed to join the company part-time as its lead geochemist, a position she held from 2023 to 2025. During that time, Eden ran its first pilot experiment, in an oil field in Oman, near where Templeton was already doing her own hydrogen research. The initial setup used DC power to send a steady flow of tens of kilowatts between electrodes in two wells. When Smalls’s team tested it in a petroleum reservoir made of soft, chalky carbonate, the rock fractured readily, increasing oil production by 30 percent. But when they did the same test in hard rocks, like those needed for hydrogen and geothermal systems, they didn’t fracture much at all. So the team went back to the drawing board and came up with a fix: pulsed power. Using Pulsed Power for Rock Fracturing The idea of breaking things using pulsed power—short, concentrated bursts of electrical energy—originated with a mid-20th-century experiment in Soviet-era Russia. As the story goes, a physicist and inventor named Lev Yutkin was out in a thunderstorm when he saw lightning strike a log underwater. Rather than burn, as it would in air, the log exploded, as if blown up by dynamite. Intrigued, Yutkin tried to reproduce the spectacle in his lab. He placed a dinner plate in a water tank, dipped in two wire electrodes, and released a high-voltage pulse. The ensuing spark, he discovered, instantly ionized the water molecules between the electrodes into a plasma channel, which then rapidly expanded, creating a shock wave that shattered the plate. Yutkin described the phenomenon in his 1955 book Electrohydraulic Effect. He later proposed numerous fanciful uses for it, such as cleaning pipes or breaking up kidney stones, which inspired real tools in use today, including electrohydraulic drills and rock-crushers, and a kidney-stone-busting medical device called a lithotripter. The following decades saw advances in pulsed-power systems and experimental techniques to better understand the complex physical processes involved. By the 2020s, when Smalls’s team began investigating it for subterranean rock fracturing, the technology seemed ripe for use, although that particular application had been little explored outside the laboratory. “We essentially generate a plasma channel in the rock itself,” says Rafael Villamor-Lora, vice president of R&D at Eden. “This channel then expands very, very rapidly,” fracturing the rock with a shock wave. Bob O’Connor Eden’s scientists first experimented with pulsed power on thumb-size hard-rock cylinders. Instead of submerging each sample in water, however, they placed a pair of electrodes at opposite ends of the cylinder and delivered pulses directly to the rock. Using this dry-pulse method, drawn from Smalls’s and others’ research, the team found they could form plasma in tiny, moist pockets between mineral grains. “We essentially generate a plasma channel in the rock itself,” explains Rafael Villamor-Lora, Eden’s vice president of research and development. With enough pulses, the fast-swelling channel, as in Yutkin’s investigation, induces a shock wave that fractures the rock. To bring the technology to the field, Eden needed voltage high enough to break through meters of solid rock. The obvious solution was a Marx generator, which converts low-voltage DC power into high-voltage bursts by slowly charging and then rapidly discharging multiple capacitors in parallel. (Marx generators are commonly used in high-energy physics experiments and to simulate lightning strikes on power lines.) Eden custom-built two devices—named Zeus and Thor after the gods of thunder—which together can release a surge of several hundred kilovolts. This time, the plan worked. In 2025, in an abandoned gold-and-silver mine in Colorado, Eden used Thor to successfully fracture a hard, igneous column, increasing its permeability tenfold. Ezra Frank, a mechanical engineer at Eden, works on Zeus, Eden’s custom Marx generator. Bob O’Connor In March this year, the company began setting up the test site on the Massachusetts horse farm to refine its systems and gather more data on how the technology performs in different geologic environments. Its engineers are also designing more powerful generators to discharge stronger and faster pulses. Because Zeus and Thor consume very little power—akin to running a toaster or two—it takes about a minute to store enough energy to fire a maximal pulse. It then takes around 100 pulses to penetrate around 10 meters of hard rock. So fracturing over longer distances or at multiple depths can take hours to days. That means Eden’s biggest cost is labor, not energy. Smalls says Eden signed an agreement with a geologic hydrogen startup—he declined to say which one—to demonstrate electrical fracturing in a field pilot of stimulated hydrogen, which could begin late next year. Eden will need to prove its technology can help coax the gas from the ground at a profitable rate and cost. “It’s no question whether we can produce hydrogen,” Villamor-Lora says. “The question is whether we can produce it fast enough to be economical.” In the lab, Eden researchers found they could generate up to four times more hydrogen from rock samples using the pulsed-power technique, compared with the amount found in unfractured samples. But that may not be enough to make stimulated hydrogen commercially viable without some additional technology. Other Approaches to Stimulated Geologic Hydrogen One of the biggest challenges in stimulating hydrogen is that there’s no obvious go-to recipe. Beyond the basic ingredients of water and iron, many factors affect how much hydrogen is generated and for how long, and fractures are only one factor. Laboratory studies have shown, for example, that the ideal temperature for maximizing hydrogen production is around 200 to 300 °C. Acidity, rock and water chemistry, and microbial inhabitants are other important considerations. Making the puzzle more complex, each rock formation is different and may require different stimulation techniques or a combination of them. “There isn’t a single solution that will work everywhere,” says Alexei Tcherniak, CEO of the hydrogen startup GeoKiln. “You have to know the geology you’re operating in.” Some promising rock formations, he points out, may already be fractured or porous enough to become saturated with water but too cool to make ample hydrogen naturally. To solve this problem, his company, based in Houston, uses a system of underground heaters originally developed for improving flow in heavy oil reservoirs and converting solid organic matter in young shale rock into extractable oil and gas. The heaters, which are commercially available, can be installed in boreholes drilled into hydrogen source rocks, similar to Eden’s electrodes. Tcherniak says that GeoKiln is ready to start field testing as soon as it can raise the capital. Other researchers are exploring the use of catalysts—metal or chemical salts that speed hydrogen-generating reactions—which, they say, could replace or complement fracturing or heating to increase hydrogen production at less cost. Vema Hydrogen, for instance, is betting on a mixture of boiler-heated water and proprietary catalysts. “What I can say about our catalysts is basically what they are not, which is not toxic, not expensive, and not dangerous,” says Florian Osselin, Vema’s chief science officer. The company, also headquartered in Houston, has begun drilling pilot wells in Canada to test its mysterious brew. By injecting it into semi-permeable rock, Vema expects to achieve commercial production rates without fracturing. “We’ve done field-scale numerical simulations that give us a lot of confidence,” Osselin says. Another stimulation method, proposed by the Denver-based startup Koloma, aims to expose more rock surface for generating hydrogen by mimicking natural weathering. The technique involves adding carbon dioxide to water and injecting the fluid at specific times to control for factors like acidity and gas concentrations. The carbon dioxide reacts with the water to form an acid that breaks down mineral chains in rock pores, thereby increasing the pores’ surface area, explains Tom Darrah, the company’s CTO, who studied and patented the method as a professor at Ohio State University. “I call it micro-pitting because the texture goes from smooth to rough,” he says. As with fracturing, more surface area means more hydrogen production—if you can get the formula right. Rita Esuru Okoroafor, an energy resources engineer at Texas A&M University, is studying the effects of various stimulation approaches, including fracturing, catalysts, and carbon-dioxide injection, on hydrogen generation. Her data, based on laboratory tests of rock samples from around the world and numerical models of stimulated geologic hydrogen systems, suggest that none of these approaches alone will sustain hydrogen production at rates needed for long-term commercial development. “We’re still fine-tuning our models, but they’re telling us that we’re going to need a lot of fracturing, we’re going to need catalysts, and then we’re going to need restimulation,” she says. The process of generating hydrogen, Okoroafor explains, will eventually consume all the readily available iron in exposed rock surfaces, causing production to plummet. By accelerating hydrogen generation, catalysts also accelerate its decline. “When these reactions happen very fast, they also die very fast,” she says. They also leave behind mineral precipitates that can clog existing cracks. In a recent study, she found that hydrochloric acid helps clear the debris, expose fresh rock surfaces, and reopen water pathways to restore production. It’s too early to know which technologies will win out in the race for geologic hydrogen and if stimulation will even be needed to make it a viable industry. What’s more, production is just the first step toward commercialization. Many questions remain. Once hydrogen is flowing from the ground, how will the gas be purified? How will it be stored and transported? How will the industry be regulated? What are the environmental risks, and how will they be mitigated? What will be the cost? “With all these wars and gas prices going up, we need to be preparing for the future,” Smalls says. But as is often the case with nascent technology development, life gets in the way. At the horse farm, fracturing started in June after being delayed for months, first by a snowstorm and then minor equipment failures and other logistical snags. “Everything takes longer than you think,” Smalls says. Still, he’s unfazed, ever the optimist. “I like to go after things that other people are afraid to.”
The transition from a purely technical expert or individual contributor position to a broader leadership role is one of the most challenging phases in a STEM career. It requires moving away from relying solely on technical excellence toward mastering systems thinking, adaptive leadership, and team alignment. To help mid-career professionals navigate the shift, the inaugural IEEE International Leadership Conference is designed to provide attendees with practical tools to step into broader responsibility and champion an entrepreneurial mindset. The ILC event is scheduled for 3 and 4 October in Budapest. Registration is open. Thinking beyond technical contributions To successfully step into a leadership role, technical professionals need to look beyond their individual output and focus on “understanding the larger system, and championing innovation by building trust and aligning new ideas with organizational goals,” says IEEE Life Senior Member Daniel Sniezek, cochair of the ILC program committee. Because engineering decisions don’t exist in a vacuum, navigating the larger system requires recognizing how technical choices intersect with the organization’s broader business, operational, and ethical realities, Sniezek says. By letting go of the need to be the sole technical expert and focusing instead on collaborative empowerment, he says, engineers can pivot into transformational leaders who align new initiatives with the organization’s strategic vision. Ultimately, over the span of a career, an individual’s leadership journey evolves far beyond personal advancement to “creating a lasting legacy through the people you develop, the knowledge you share, and the innovations you inspire,” he says. Solving the intrapreneur’s dilemma Championing disruptive ideas within established corporate structures—sometimes called the intrapreneur’s dilemma—does not mean working against the organization. Rather, it requires emerging leaders to act like business owners instead of passive task-takers. To begin thinking like an entrepreneur from within, professionals should shift their focus from merely executing assigned work to proactively identifying hidden areas that would create value for the company, building trust with colleagues, and presenting bold innovations as solutions to the organization’s long-term strategic goals. That kind of self-starting entrepreneurial mindset is how leaders create opportunities out of institutional constraints. IEEE Senior Member Deyasini Majumdar, cochair of the ILC program committee, advises professionals to exercise leadership and strategic thinking skills without being asked. “Within the constraints of established organizational structures you can unearth a treasure trove of opportunities to innovate,” Majumdar says. “Remember: A key trait of an effective leader is to engineer solutions and lead, even in the face of difficulties.” A multidirectional exchange Leadership is a multidirectional exchange of ideas across generations—which is one focus of the ILC. Although emerging leaders can gain invaluable strategic guidance from seasoned executives, the relationship is a dynamic, two-way street. Modern leadership requires established executives to remain active learners. Addressing what senior leaders can glean from their mid-career counterparts, Majumdar emphasizes, the leaders must maintain “the openness to seek opportunities, to quickly adapt, and to learn and grow with everyone around them.” A continuous-learning mindset is what keeps leaders agile and effective in a rapidly changing technological landscape, he says. The mutual openness can serve as a bridge between generations. Whether an emerging professional is making a mid-career pivot from technical expert to manager, or a senior executive is transitioning into a mentoring and advisory role, the fundamental rule of transformational leadership is similar. Success means shifting your focus from individual achievement to enabling the capability, growth, and legacy of others. Building influence and impact To help with the shift toward transformational leadership, the ILC is featuring sessions focused on questions professionals must ask at key career inflection points. Rather than a single workshop, the distributed sessions aim to address diverse professional transitions, such as evaluating promotions, learning how to influence laterally, pitching innovative projects, and sustaining leadership energy. Inflection points include evaluating new internal roles; building lateral or upward trust; pitching an idea about a disruptive project; and facing rapidly expanding responsibilities. The questions include: How do I evaluate career transitions without discarding hard-won experience? How can I exercise leadership through credibility and collaboration, regardless of formal authority? How do I use an entrepreneurial mindset to create value and gain sponsorship for new ideas? How can I avoid the early warning signs of burnout while taking on more responsibility? The conference is designed to equip attendees with systems thinking and communication mastery needed to cultivate influence. Leadership is not just about reaching the top; it is also about engaging in a collaborative effort to multiply your impact across the ecosystem.
The history of networking is full of tools that repurposed solutions to very different kinds of problems first. Wi-Fi’s origins trace back, in part, to a team of Australian radio astronomers trying to detect signals from evaporating black holes. But the data-processing tools they’d developed also proved capable at extracting clean messages from any chaotic, echoing signal environment. Echoes are echoes, after all, whether from distant star systems or from the far corner of the house. I research vehicle communications networks, connecting cars to cars and to transportation infrastructure like traffic lights—for tomorrow’s vehicle-to-everthing (V2X) networks. V2X research has long relied on models that assume “perfect” or “ideal” network conditions, which is a simplifying assumption that makes the math tractable. But this assumption doesn’t reflect how real wireless signals behave in a moving, obstructed, high-density environment. That gap is exactly the kind of real-world unpredictability that open radio access networks (a.k.a. O-RAN)—an open, programmable architecture behind some 4G and 5G cellular networks—were built to manage. So why has the O-RAN standard—which is open and available to be applied well beyond 5G telecom—never been used for vehicle communications? Solutions to the vehicle-to-everything (V2X) problem have to date relied on new networking protocols built from scratch—only to discover chicken-and-egg problems, thorny standards wars, and real signal congestion challenges at scale. By contrast, O-RAN allows V2X engineers to reuse the networking protocols already developed for cellular communications. O-RAN was developed assuming cellphone towers are generally fixed in place. But, as can be seen below, O-RAN accommodates mobile “towers”—cars and trucks, in this case—with little additional effort. Imagining a New Way to Connect Vehicles Self-driving vehicle technology has largely been an each-car-for-itself endeavor. Tesla’s approach, for instance, relies heavily on powerful on-board banks of computers and suites of sensors spread around the car. However, as an alternative to the “data center on wheels” model, this new O-RAN approach to V2X relies on each car’s nearby neighbors, wherever they are on the road. Each O-RAN–connected vehicle can then use a diversity of cars’ sensors and viewing angles for better group coordination and decision-making. There is, to be clear, no O-RAN V2X test network operating in the world. Not yet. It was just 10 years ago that the Third-Generation Partnership Project (3GPP) released its initial cellular V2X standard. The 3GPP have refined V2X over three major releases since. In the U.S. and the EU, the FCC and related European agencies have put forward other standards for short-range wireless V2X communication protocols. However, no consensus standard has yet emerged. So, lacking any clear, unambiguous guidance on the future of V2X networks, autonomous-car makers—like Waymo, Tesla, Zoox, and Cruise—have leaned more on self-reliance, bulking up each vehicle with as many sensors and GPUs as possible. Here, though, is where O-RAN might be able to help. A little like APIs (a.k.a. application program interfaces) connect one app to another on your smartphone, O-RAN serves as an API for the network itself. And because of O-RAN’s open standards, a wireless network becomes programmable, vendor-neutral, and open to custom applications called xApps. To test our proof-of-concept framework, I have been part of a team simulating five minutes of O-RAN V2X network traffic over one square kilometer of urban area, using real buildings and real-world road layouts from OpenStreetMap and traffic patterns generated by the modeling package SUMO. The simulations assumed a traffic density of 50-70 vehicles per kilometer—not rush hour but not light traffic either. In our simulation, we assumed vehicles communicated via a millimeter-wave frequency of 28 gigahertz and that each component of our O-RAN V2X system had its own dedicated xApp. Taken together, these inputs—real geometry, real traffic, and each vehicle’s live GPS position—constitute what network researchers call a digital twin of the urban environment. That’s a virtual replica detailed enough for the network to reason about the physical world in real time. This virtual world gave us a real result, too. The simulations, published recently in IEEE Network, revealed that existing V2X standards—in which cars uncoordinatedly spit out messages into the network—result in signals “talking” over each other some 80-100 percent of the time. However, using O-RAN signal coordination, the message “collision” rate dropped to near zero. And that matters because a seized-up V2X network doesn’t just fail quietly. It can fail in ways that might make a road turn treacherous. How O-RAN Can Coordinate V2X Traffic High-frequency data links between cars are already difficult to maintain, even on a clear day with no buildings or city infrastructure getting in the way. Yet, in this situation, existing V2X networks leave a car to conduct blind searches for each dropped signal beam. Traveling at highway speeds, that search takes long enough for the surrounding world to change completely. An O-RAN network continuously tracks signal conditions across the network, and in O-RAN V2X simulations, we also gave the network access to a detailed map of the urban environment—building positions, road geometry, intersection layouts—combined with each vehicle’s GPS trajectory. Together, these parameters let the network’s control layer predict where and when a signal link is about to fail and instruct each car’s antenna to adjust before the connection drops. Signal pointing is one failure mode. Losing the connection entirely—because no direct path exists at all—is another. Consider, for instance, a crossroads of two busy streets, with a few alleys and parking lots adding to the list of potential dangers. If a signal from car A cannot reach car B directly, or if the path length is too far for an individual beam to travel, the signal must find an intermediary car or stationary sensor nearby that can pass along the message. And existing V2X standards are slow and reactive—polling potential relay vehicles one-by-one: Are you available? Can you redirect this message? By contrast, O-RAN keeps a running graph of optimized message routes, accounting for a range of real-world constraints. So when an O-RAN link fails (whether that link is direct from sender to receiver—or indirect), the system already has a reroute mapped out. This is partly why we included “multi-hop routing” in the O-RAN V2X simulations. Multi-hop V2X O-RAN routing complicated three separate elements of the simulation: for each signal’s middleman (some cars may be ideally positioned to relay a signal from car A to car B, but we made the simulation neglect any cars that were also overwhelmed with their own signals and signal-processing needs); for each signal’s strength (we required that every intermediate link be able to maintain a stable network connection, factoring in distance and traffic conditions); and for each signal’s latency (we required a realistic accounting for added signal latency time for each additional hop in a multi-hop routing). And with each added complication, O-RAN V2X multi-hop routing continued to extend the network’s capacity from 25 percent of nearby cars connected (without multi-hop) to nearly 100 percent (with multi-hop). These complications, at least at the simulation level, did not slow down the V2X network. How Could O-RAN Ever Be Scaled Up for the Real World? We are in touch with potential collaborators and institutions to develop testbeds, prototype hardware, and tester vehicles for potential proving grounds. The Institute of Science Tokyo, for instance, has already expressed interest in working on some of these early-stage problems. To date, our published research on O-RAN V2X has centered around a computer simulation only. Real-world hardware will undoubtedly surface challenges our simulation could not. So, questions of network latency and the computational overhead needed for O-RAN V2X signaling remain as yet unresolved. Plus, concerns about full interoperability and realistic security will each demand their own investigations. After all, no one will trust a V2X network to do anything if that network’s cyber vulnerabilities haven’t been anticipated and patched in advance. Realizing the O-RAN V2X vision will require progress on multiple fronts simultaneously. On the standards side, O-RAN’s vehicular extensions—the interfaces that allow vehicles to participate in the network as managed elements rather than passive users—would ultimately need to be formally adopted by the O-RAN Alliance and recognized by 3GPP’s V2X specifications. That process takes years. On the industry side, there is a more immediate problem that our architecture is already positioned to solve: interoperability. Today, a car made by one manufacturer cannot necessarily parse V2X sensor data sent from a car made by another. Firmware is proprietary; data formats differ. But an O-RAN control layer would act as a universal translator—normalizing each vehicle’s data into a common format and accelerating a push toward true multi-platform vehicle-to-vehicle communications. A more widespread and truly universal standard would, by itself, represent a substantial step forward for V2X.
Today’s U.S. electrical grid, among the largest, most complex systems ever built, is operating at its limit. The combination of rapid industrial growth, more frequent extreme weather, and a record surge in electricity use has pushed the grid to its breaking point, according to the U.S. Department of Energy. Built decades ago for a more predictable world in which power came mostly from centralized coal or gas plants and electricity use grew at a steady pace, the grid faces unanticipated strain due in part to growing demand from data centers. The jobs of professionals managing the infrastructure have evolved from traditional engineering tasks to complex, fast-moving challenges. Industry reports show that millions of modern digital sensors, smart meters, and grid monitors are generating nonstop waves of information. The sheer volume of data requires instant, automated computer analysis because human operators cannot process it fast enough. Pressure on utilities stems from two sources: a spike in electricity demand and a shift in how power is generated. An example of the operational strain can be seen at the regional level. With the recent deployment of artificial intelligence tools and high-performance computing, data centers require immense amounts of energy to operate. The largest power transmission utility in Texas recently reported a staggering 220 gigawatts of new connection requests, driven largely by a surge in AI and cloud-computing facilities, according to a CNBC report. Alongside the rise in regional demand, global energy networks are absorbing an unpredictable variety of weather-dependent renewable energy such as wind and solar. The switch creates a volatile operating environment wherein supply and demand are balanced, second by second, to prevent blackouts. The challenges are compounded by the vulnerability of the grid’s physical and digital framework. More-frequent severe weather events cause costly disruptions, such as the devastating winter freeze that crippled the Texas grid and record-breaking heat waves that have overloaded transformers. Simultaneously, the energy networks’ digital architecture faces threats. As utilities replace outdated analog equipment with smart meters and control systems, they are increasingly vulnerable to cyberattacks. To overcome physical and digital vulnerabilities, grid reliability organizations, such as those conducting North American security simulations like GridEx, emphasize that the grid must become smarter, more agile, and completely automated. Energy researchers are noting that the key to this change lies in integrating AI across every layer of utilities’ operations. The AI imperative According to energy industry experts, using AI to manage power systems is no longer a futuristic research project; it has become a baseline operational necessity. Grid analysts emphasize that traditional grid-planning methods are too slow to handle rapid energy dynamics or to balance volatile renewable energy in real time within decentralized power systems such as microgrids. AI can fill the gap by processing vast amounts of data instantly. Machine learning algorithms can quickly analyze information from thousands of sensors, historical usage patterns, and weather forecasts to predict issues before they happen. An industrial digitization study conducted by McKinsey & Co. indicated that integrating advanced data and automation across infrastructure networks could reduce system design errors, decrease equipment downtime by up to 50 percent through predictive maintenance, and extend the lifespan of power machinery by up to 40 percent. From forecasting energy spikes to automatically fixing localized voltage drops, AI acts as the digital backbone of a self-healing grid, experts say. Deploying the complex systems requires a new workforce: power engineers who understand data science, as well as data scientists who understand electricity. Upgrading the Workforce To bridge the gap between groundbreaking AI research and practical field deployment, IEEE Educational Activities, in partnership with the IEEE Power & Energy Society, has launched the online Artificial Intelligence for Power and Energy Systems course program. The program explores core challenges threatening modern utilities. Rather than treating AI as an unverified black box that operates without human supervision, the curriculum focuses on safety, asset preservation, and strict reliability standards. The curriculum is designed to educate power system engineers, utility managers, and data scientists tasked with modernizing the grid. The program was developed by Fangxing “Fran” Li, professor of electrical engineering and computer science at the University of Tennessee in Knoxville and chair of the IEEE Working Group on Machine Learning for Power Systems. Five learning modules The program breaks down the technical transition into five modules that bridge high-level theory with real-world solutions: AI fundamentals. This module teaches engineers how basic machine learning models apply to power grids. It discusses how specialized neural networks solve complex power-flow calculations and how AI models can safely transition from computer simulations to physical, high-voltage equipment. Accelerating grid control. Learners are taught to leverage deep reinforcement learning, an AI approach that uses trial and error, to accelerate automated grid adjustments during emergency power events. Forecasting and data analytics. Using predictive modeling, engineers learn how to predict sudden demand surges, variable wind and solar outputs, and fluctuating wholesale electricity market prices to keep power affordable and available. Physics-informed and safe AI. To address trust—a barrier to utility AI adoption—this course covers AI models hard-coded to obey the laws of physics. The approach is designed to ensure that automated algorithms never make erratic choices that damage grid equipment. Generative AI and next-generation tech. Learners can explore the frontier of utility technology, including graph neural networks and large language models. This module highlights how generative AI can process complex, interdisciplinary data to streamline utility planning, emergency responses, and regulatory reporting. The algorithmic literacy and practical execution tools provided by the course program can help convert systemic risks into grid resilience. For individual access, visit the IEEE Learning Network. 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This report examines R&D waste and how AI adoption has outpaced the intelligence needed to make consequential decisions well. What Attendees will Learn Where R&D budget is lost. More than a third of organizations spend 25 to 40 percent of their R&D budget on projects that never reach market. Why projects fail late. Almost half of teams estimate over one million dollars in wasted investment for each project killed during development or testing. Why AI adoption has not closed the gap. Most organizations apply AI to execution tasks such as data analysis and modeling rather than to decision support. Where intelligence matters most. Respondents say better access to intelligence has the greatest value at early ideation and feasibility before significant investment is committed. Download this free whitepaper now!
Most hospitals and health care providers use electronic health records instead of paper charts to note patient vaccinations, diagnoses, and procedures. AthenaOne, Epic, and Oracle Health are some of the systems employed around the world. Many patients can access their electronic medical records from home. The platforms exist thanks to pioneering efforts such as the Medical Information System (MIS-I). The first hospital-wide computer system, it was developed in the 1960s by Lockheed Martin (then known as Lockheed Missiles and Space Co.) in partnership with El Camino Hospital, in Mountain View, Calif. Doctors and nurses used MIS-I (pronounced miss-ONE) to admit patients, order lab work and imaging, schedule follow-up appointments, and issue hospital bills, according to a 1973 article published by Datamation. Although many people now know how to type on a keyboard, in the 1960s, most did not. Therefore, MIS-I included a light pen, which worked like a stylus for today’s touchscreens. MIS-I was recognized as an IEEE Milestone during a ceremony on 14 May at El Camino Hospital. The IEEE Santa Clara Valley Section sponsored the Milestone. “The medical information system was more than a technological breakthrough; it was proof of what can happen when clinicians, engineers, administrators, and community leaders unite around a common goal: improving care for patients,” Dan Woods said at the event. He is chief executive of El Camino Health, the nonprofit organization that maintains the hospital. “This pioneering work helped establish the foundation for the modern medical informatics industry,” Woods said. “The legacy of Lockheed’s innovation continues to benefit patients and health care providers around the world, making this achievement truly worthy of lasting recognition.” Bringing technology into clinical care Prior to Lockheed’s effort, health records remained paper-based. They often were stored in dedicated rooms within the hospital. Files were kept in heavy-duty manila folders organized on mechanized open-shelf filing systems, revolving rotary files, or locked steel filing cabinets, according to EO Johnson Business Technologies. The process of retrieving a patient’s medical history was cumbersome and could hamper decision-making in a life-or-death situation. Paper records were prone to human error, according to an EHR in Practice article. In addition, upkeep could be costly due to administrative expenses such as transcribing doctors’ notes, storing patient charts, adding medical codes, and managing insurance claims. Companies and universities including General Electric, IBM, and Harvard began exploring how to use computers to improve clinical care. They developed several systems for hospital laboratories to track test orders and results, as explained in the Milestone webpage. In 1964 Lockheed was looking to diversify its portfolio, Melville Hodge, who helped lead the MIS development, said at the dedication ceremony. The company decided to apply its expertise to health care, and later that year this focus-area became part of a new information systems division. Hodge, who at the time oversaw multiple R&D efforts, became the driving force behind the MIS program. MIS-I displayed patient information on a 14-inch television purchased from a department store. Below that monitor was a keyboard and a light pen.Ian Thomson/Computer History Museum In 1966 Lockheed secured a contract with the Mayo Clinic, in Rochester, Minn., to assess its computer system needs and those of its two associated hospitals, Hodge wrote in a paper detailing MIS history. He and a small team of engineers worked with Mayo Clinic physicians for two years to build the prototype of what would become MIS-I. The system displayed patient information on a 14-inch television purchased from a department store. Below that monitor was a keyboard, and to its right was a printer. Doctors and nurses would swipe their ID badge to access the system, then use the keyboard to put information into the patient’s file or send a request to a pharmacy or laboratory. They also could print documents. But one problem kept cropping up: Most doctors didn’t know how to type. The computer mouse was still in its infancy, and Hodge suspected it would not solve the problem, according to a video shown at the dedication ceremony. Instead, he “borrowed technology from a then-secret satellite program,” he said. That technology was the light pen, which was used with MIT’s Whirlwind Computer in the 1950s. “The insight that physicians could not and would not learn to type, combined with the innovative solution of light pen interaction, transformed an impossible dream into practical reality,” the Milestone proposers wrote. To display text, the system used matrix programming, a 2D data structure consisting of rows and columns. Using the light pen, a doctor or nurse would select text from a list of general categories on the monitor. The options included the patient’s personal and medical information, family medical history, current illness, and physical exam findings, according to a 1968 article in the medical journal JAMA. The computer would display the requested information or list the next steps to complete tasks such as sending a prescription to a pharmacy. The keyboard remained part of the setup because it could allow users to input new information and update patient records. To further develop the system, they submitted a proposal to the U.S. Department of Health, Education, and Welfare (now split into the Departments of Health and Human Services and Education) to secure additional funding, but it was rejected. Herschel Brown, Lockheed’s executive vice president, and Kenneth Larkin, its director of information systems, decided to fund its commercial development, Hodge wrote. When the company’s contract with the Mayo Clinic ended, the Lockheed team returned to Sunnyvale, California to refine, test, and deploy the system at El Camino Hospital. “I admire Ed Hawkins, who was its first administrator, for having the courage to take on this kind of project while running a hospital that was only four years old at the time,” Hodge said at the dedication ceremony. Making MIS-I a commercial success Starting in 1968, early prototypes were installed in the hospital’s M.D. lounges and nursing station at El Camino Hospital. The organizations worked to configure the system so it met the hospital’s needs. By 1969, a number of monitors had been installed, including in admissions, pharmacy, and radiology. The information from all the connected machines was stored in a data center housed in a separate location outside the hospital. In 1971 Lockheed encountered difficulties with its C-5A and L-1011 aircraft programs, according to Hodge. The company was forced to curtail discretionary new business programs including MIS-I. The program was sold to Technicon of Tarrytown, N.Y., a leader in clinical laboratory automation. The medical information system business operated independently as a subsidiary unit, and the transition marked the beginning of MIS-I’s commercial expansion. That same year, MIS-I went live for hospital-wide use. Physicians’ orders were communicated to other departments, test results and radiology reports were retrieved, and nursing care planning and documentation were available, according to the Journal of Nursing Scholarship. MIS-I supported most information handling for nurses, physicians, and other medical personnel in the hospital. But the change was not welcomed by all, according to the video about the technology. Nurses tended to praise the system, but many doctors had a hard time transitioning from paper to computers. They complained they were “spending more time fighting a machine” than interacting with their patients, according to the video. Some even retired to avoid learning the system. But nurses fought to keep it, emphasizing to doctors how much it improved patient care. In 1974 El Camino Hospital held a vote of medical staff to determine whether to keep the system or return to paper-based records. About 60 percent of doctors and more than 90 percent of nurses voted in favor of keeping it, according to the Milestone webpage. “The medical information system was more than a technological breakthrough; it was proof of what can happen when clinicians, engineers, administrators, and community leaders unite around a common goal: improving care for patients.” —Dan Woods, El Camino Health CEO In 1975 nonprofit Battelle of Columbus, Ohio, evaluated how well the system was working for El Camino Hospital. It found that MIS-I reduced the time nursing staff spent on clerical tasks, improved communications among departments, and facilitated better planning of patient care. The survey also showed that more readily available, complete, and accurate information was being used to administer care and monitor patient progress, according to the filings. By 1993, the technology was installed in more than 200 hospitals in the United States, Canada, and Europe, according to the Milestone entry. El Camino Hospital used MIS-I for 34 years, until its decommissioning in 2005. It was initially replaced by Eclipsys Sunrise XA and then ultimately by Epic. Honoring an IEEE Milestone The dedication ceremony brought together IEEE leaders, hospital staff, and government representatives. Hodge and his family also attended. IEEE President-Elect Jill Gostin made a presentation about IEEE, and Brian Berg of the IEEE History Committee discussed the organization’s Milestone program. Hodge participated in a Q&A session with Deb Muro, chief information officer of El Camino Health. He told a story about the early days of MIS-I that he said he will never forget. During a hospital board meeting at which physicians were complaining about the system, an announcement was made over the hospital’s public address system that MIS-I wasn’t working. “I had to ignore it to survive,” Hodge said, laughing. “As physicians got more used to it, and with a phenomenal poking from the nurses, doctors who wouldn’t use it were forced to.” A bronze plaque recognizing the MIS-I as an IEEE Milestone has been installed in the lobby of the hospital in Mountain View. The plaque reads: From 1965 to 1974, the first hospital-wide computerized medical information system was created by Lockheed Missiles and Space Co. in partnership with El Camino Hospital. Innovative light-pen terminals enabled physicians and staff across all departments to efficiently and accurately access patient data and enter work orders. By providing immediate feedback and seamless communication, it reduced costs and errors, improved safety and outcomes, and led the way to modern medical and clinical informatics. Reviewed by the IEEE History Committee and approved by the IEEE Board of Directors, IEEE Milestones recognize outstanding technical developments around the world that are at least 25 years old. The Milestone program is administered by the IEEE History and Heritage group. To learn more about historical figures in engineering, IEEE Milestones, and IEEE History Center programs and events, check out The Institute’s IEEE Tech History collection. IEEE Spectrum also covers aspects of tech history.
This article is brought to you by COMSOL. In pursuit of improved range, greater reliability, and faster charging, electric vehicles are driving the demand for high-voltage electronics. Other applications driving this demand include wind farms, data centers, and server farms, to name a few. As the interest for high-voltage electronics increases, the risks associated with their sudden failure must be considered. Much of what causes high-voltage equipment to malfunction can be linked to the conditions of the climate in which it operates. Specifically, condensation on electronic surfaces caused by humidity can lead to corrosion, which can result in stray leak current and dendrite shorting during Electrochemical Migration (ECM). Predicting, mitigating, and helping proactively design to account for corrosion is the focus of the Centre for Electronic Corrosion (CELCORR) research group at the Technical University of Denmark (DTU). The group’s researchers are working with industry partners to develop models and simulation apps that will help in building robust electronics designs. Their goal is to develop knowledge that can be used for manufacturing electronics to withstand humid operating conditions. Electronic Failure: “It’s Not the Heat; It is the Humidity” Automotive electrification and renewable energy systems rely on electronics at all stages of the energy chain. When these electronics, such as the example PCB in Figure 1, are exposed to the effects of moisture, they can become potential failure points. “Anywhere you are producing, converting, transporting, and using energy, you need these high-power electronic systems that get affected by the humidity,” explained Dr. Rajan Ambat, DTU professor and manager of CELCORR. Ambient moisture can seep into the devices and machines that require these electronics and cause unexpected functional issues through corrosion. When these electronics are involved in particularly high-voltage applications (such as wind farms, data centers, electric vehicles, and server farms), failure due to humidity exposure can even lead to fire. “There might be a situation where somebody installs a solar panel near the seashore or in an area with high humidity, and within a short period of time, a conducive condition forms inside the electronics that results in a failure,” Ambat said. “This is why we need to understand exactly how the conducive condition of condensation is created, when the condition is created, and how the system is failing.” Figure 1. The electrolyte potential and current density distributions on a PCB surface (with pinholes).CELCORR/DTU Identifying corrosion as the underlying cause of some electronic failures is still a challenge. “Fifty percent of failures in electronics are currently branded with an unidentified root cause,” Ambat explained. “When engineers open up the system, they do not realize that the failure was due to corrosion, because moisture disappears without leaving any sign of corrosion unless there is ECM dendrite formation.” This lack of awareness was a strong motivator for CELCORR, which turned to multiphysics simulation as a supplementary tool to help its partner organizations to better predict humidity-related issues at the design stage. Modeling Moisture and Measuring Parameter Changes in PCBs CELCORR believes the best way to identify and avoid electronic failures is to build more effective designs that better prevent corrosion from developing or can withstand a higher humidity load. Its research team applies its expertise in modeling to generate simulation apps that will help partner industries to evaluate safe designs for humidity robustness. “We are at the intersection of materials science and the electronics industry. We work as a bridge between materials and electronics disciplines, using both kinds of language,” Dr. Anish Rao Lakkaraju, a postdoctoral researcher at CELCORR, explained. To understand potential design issues, Ambat emphasized the importance of virtually breaking down systems to identify where problems may arise. “We need simulation software to analyze potential uses of designs and whether new designs are good or bad,” Ambat said. Researchers at the Technical University of Denmark (DTU) are using simulation apps to predict corrosion and design electronics proactively to mitigate or withstand its effects. Using the COMSOL Multiphysics software for investigation, the CELCORR research team together with other research partners (Aalborg University) built an example model with a simple PCB geometry that matched both its test circuit boards as well as the design of a device used by one of its partner companies. The team then added a water film layer on top to act as the relative humidity. From there, the team could introduce variation. “We change the layout, geometry, distance between electrodes, thickness of the water film, and conductivity of the water film depending on the conditions,” Ambat said. Ambat and his team generate data on the effect of these variations and can identify which has the greatest impact on the device’s performance and whether any alterations can improve the device’s anticorrosion robustness. “We assume there is condensation forming on the electronic surfaces (Figure 2) and compute the electrochemical leak current for different design elements,” Ambat said. “The value of the computed electrochemical current between the parts will give us an indication of whether the PCB will be affected or not.” Figure 2. A 10-µm water film condensation effect on an example PCB.CELCORR/DTU Altering the design elements and solving the model equations again and again allows the team to better understand what makes a design effective. “Now, we are at the current form of the model, and we are quite happy with where the physics are at this point,” Lakkaraju said. This modeling, however, was just the first part of CELCORR’s overarching goal of illuminating the destructive potential of corrosion in electronics and the best ways to avoid it. Using Simulation Apps to Test Real-World Designs To open up and ease access to these models, CELCORR used the Application Builder in COMSOL Multiphysics to create simulation applications for the members of the industrial consortium. Built with a simple 3D circuit board geometry with two oppositely biased electrodes and a water layer to replicate corrosion-causing moisture, the apps provide a pared-back, straightforward interface where users can vary model inputs. “We focused on fundamental aspects,” Lakkaraju said. “We boiled down our partners’ overarching concerns to create two apps with very simple geometries and the simplistic stuff that can be varied over multiple parameters.” The simple simulation apps shown in Figures 3 and 4 are designed to show companies how the distance between the electrodes and the thickness of the moisture layer affect the leak current through the water film depending on different parameters. By analyzing multiple design elements and parameters, users can determine the relative benefits of certain design elements on the humidity robustness. “The apps we have built have really helped because they give the electronics engineers a plug-and-play sort of approach,” Lakkaraju explained. Figure 3. The UI of CELCORR’s standalone app showing the inputs that users can alter.CELCORR/DTU “By using this app [Figure 3], companies have unlimited freedom to work with these sorts of variables,” Lakkaraju added. “This would be quite difficult to recreate in real life with physical design and testing, and the companies we work with are quite happy with the level of accuracy the app can provide.” Figure 4. The UI of one of the simulation apps showing a streamline plot with inputs such as the cathode voltage and blockage length.CELCORR/DTU Looking Forward: Complex, High-Voltage Modeling Alongside its collaboration with the industry consortium, CELCORR is also working toward improving the world’s general understanding of corrosion’s impact on electronics. To do this, Ambat and his team are undertaking multiple projects, including actively adding greater complexity to their models. In a tertiary current distribution model they are building, the team is drilling down into each of the basic inputs used in a secondary current distribution model, zooming in to examine the effects of the set of even more detailed inputs each basic input comprises. “The next point is to study what each of these detailed inputs does,” Lakkaraju said. In particular, the team is examining mass transport properties and the chemical reactions’ rate constants. In addition to these ongoing studies, CELCORR is expanding the scope of its research to investigate corrosion in high-power, high-voltage systems. Thanks to a 2024 grant from the Grundfos Foundation, CELCORR was able to establish the Centre for Climate Robust Electronics Design (CRED). The center’s lab facilities and expertise are being developed to address the humidity-robustness requirements of today’s high-voltage and high-power electronic equipment. “Using CELCORR’s uniquely deep understanding of materials and corrosion, we are equipped to find the root cause and provide knowledge for environmentally robust designs,” said Ambat. For all of its investigation, CELCORR continues to rely on the agility of the COMSOL Multiphysics software. As Lakkaraju explained, “It is really quite nice how a model can be adapted to a variety of combinations of materials, geometries, and parameters and how the software allows you to keep building on from there.”
A German engineer wanted a cheaper cigarette. The popular crooner Bing Crosby wanted a vacation. Satisfying both desires inadvertently led to the invention of the laugh track. Along the way there were Nazis, spoils of war, and more than one accidental encounter. Tying together this quirky history is the Magnetophon. What Was the Magnetophon? The Magnetophon was a high-fidelity reel-to-reel magnetic tape recorder. The hit of the Berlin Radio Show when it debuted in 1935, it was developed by the German electronics manufacturer AEG. The magnetic tape was produced by I.G. Farben (now known as BASF). German inventor Fritz Pfleumer came up with the idea of recording sound on magnetized paper tape coated with metal.ullstein bild/Getty Images The tale of that tape runs through German inventor Fritz Pfleumer. In the early 1920s, Pfleumer was working on industrial paper products in Dresden. At the time, fancy cigarettes had gold leaf to decorate the tip. Cheaper manufacturers achieved a similar effect using colored paper, but the dye could stain smokers’ lips, and no one wanted that. Pfleumer devised a less expensive process using powdered bronze to simulate the gold band. Pfleumer didn’t work in the recording industry, but he was familiar with the technology of electromechanical recording, which was done on metal wire—a 1898 invention of the Danish engineer Valdemar Poulsen. Pfleumer thought he could do something similar with paper by replacing his powdered bronze with a magnetized material. He patented his “sounding paper” in 1928 and also invented an audio tape recorder to go with it. The sound quality wasn’t great, and the paper tore easily, but it was the start of a promising idea. Two points in its favor: The paper could be sliced, allowing edits, and it could be erased and re-recorded over. Pfleumer knew he needed to partner with a larger company to commercialize his idea, and so he signed a contract with AEG in 1932. Hermann Bücher, chairman of the AEG board of directors, took a personal interest in the project, helping shepherd it to completion. Although AEG originally planned on developing both a recorder and the tape, it didn’t take long for Bücher to reach out to his friend Wilhelm Gaus, managing director at I.G. Farben. AEG developed the hardware, while I.G. Farben worked on the tape. The two teams dubbed their product the Magnetophon, or magnetic phonograph, and planned on launching it at the 1934 Berlin Radio Show to compete directly with machines that recorded on steel tape or wire. But the product wasn’t quite ready. Two days before the show, they canceled the debut. A year later, the bugs had been worked out. The Magnetophon’s introduction at the 1935 show was a resounding success. AEG fielded inquiries for many variations on the recorder, including one that combined the recorder with a telephone, a player for prerecorded music, and a special version to add artificial reverberation for recording open-air concerts. Over the next three years, AEG developed several iterations, resulting in the Magnetophon K4 in 1938, its first commercially successful tape recorder. The K4 eliminated the hiss and distortion that magnetic recordings previously suffered from by incorporating AC bias, which added a high-frequency signal, typically around 40 to 150 kilohertz, to the recording. Inaudible to the human ear, the signal reduced distortion, especially when recording quieter passages. The fidelity of the Magnetophon was such that radio listeners couldn’t distinguish between live broadcasts and prerecorded performances. The Magnetophon During and After the War This is where the Nazis come in. Adolf Hitler and his propaganda minister, Joseph Goebbels, understood the power of radio. The Magnetophon became a powerful tool for both political messaging and military strategy. Because the device could record and play back sound at the same quality as a live radio broadcast, it allowed Hitler’s recorded speeches to be aired from one radio station while the dictator was in another part of the country. This made it more difficult for the Allies to pinpoint his location. RELATED: Inside the Third Reich’s Radio Meanwhile, World War II disrupted the exchange of technical information and prevented Americans from learning about the Magnetophon until after the war. At least, that’s the shorthand version of this history I kept running across during my preliminary research for this column. During World War II, U.S. Army Signal Corps engineer Jack Mullin [top] came across the Magnetophon. After the war, he got approval [bottom] to ship two of the disassembled machines and reels of magnetic tape back to the U.S.Top: Pavek Museum; Bottom: Richard L. Hess/The Mullin Family Collection/Archive of Recorded Sound/Stanford University But then I read Friedrich K. Engel’s account in the book Magnetic Recording: The First 100 Years, which provides a wealth of detail about the Magnetophon. Among other things, Engel notes that the AEG affiliate in Schenectady, N.Y., received a Magnetophon in November 1937, well before the United States entered the war, and AEG engineers demonstrated it for their colleagues at nearby General Electric. The GE engineers dismissed the technology out of hand, though, and so Americans had to wait until after the war for the Magnetophon to be reintroduced. We have electrical engineer John T. “Jack” Mullin to thank for that reintroduction. Mullin served in the U.S. Army Signal Corps, stationed in the United Kingdom and Paris during the war, and he liked listening to the radio. He realized that “live” orchestral broadcasts coming from Germany in the middle of the night, when no musicians would have actually been in the studio, lacked the telltale hiss and crackle of prerecorded music. Clearly, German engineers had recording technology far superior to the Americans’. Sent to Germany at the war’s end, Mullin eventually came across that technology at a radio station, and in 1945 when he returned home to California, he shipped two disassembled Magnetophons, a case of tape, schematic drawings, and the determination to change the U.S. recording industry. Bing Crosby Championed the Magnetophon Enter Bing Crosby. In the 1940s, Crosby was perhaps the most popular performer on the radio. But he was tired of performing two weekly shows three hours apart (one for each coast). He wanted to prerecord his performances and take a break. He sent in his lawyers to talk to executives at NBC, which aired his program. The audio recording technology at the time used vinyl or shellac transcription discs, but radio listeners could hear the pops and hisses and knew it wasn’t live. NBC refused Crosby’s request, and so Crosby took a year off from radio and then signed with the upstart network ABC, which was willing to let him record his shows for later airing. Radio producer Murdo MacKenzie [right] arranged for Jack Mullin [left] to demonstrate the Magnetophon to Bing Crosby in 1946.Pavek Museum Serendipitously, Mullin had begun demoing the Magnetophon in California. In October 1946, Crosby’s technical producer, Murdo MacKenzie, heard about the demonstrations and arranged one for Crosby at the Metro-Goldwyn-Mayer studios in Hollywood. Crosby was delighted and promptly invested US $50,000 (about $800,000 today) in Ampex, the company working with Mullin to engineer an American version of the Magnetophon. Mullin became Crosby’s chief engineer. 3M developed the magnetic tape. In 1947, Crosby became the first major radio star in the United States to prerecord performances. Much of the success was due to the recording equipment, but Mullin was also an excellent editor. Crosby and his team would record multiple takes, and Mullin would deftly splice together the best to create a seamless performance. After seeing a demo of the Magnetophon, Bing Crosby invested $50,000 in Ampex, which developed a U.S. version of the tape recorder. Cinematic/Alamy One day a guest told a joke that was uproariously funny, but a little too spicy for radio. Even though the joke couldn’t be aired, the audience’s laughter was worth keeping. Soon, the producers created a whole catalog of different laugh tracks. If a joke didn’t land, it didn’t matter. The editor could just add a laugh in postproduction, from polite titters to hearty guffaws. According to Crosby’s daughter Mary, Bing didn’t have a problem with this type of editing. The live audience was immaterial as long as the jokes were funny. But according to Mullin’s daughter, Eve Mullin Collier, the manipulation didn’t sit well with her father. He disliked the inauthenticity of the moment, of editing joy. The history of technology is filled with such episodes of unintended consequences. Mullin admired the Magnetophon precisely because it could faithfully record a performance, an event, a moment in time. He worked tirelessly to refine the machine for the benefit of radio audiences everywhere. And so I understand why its appropriation for capturing canned reactions and manipulating reality must have rankled. He championed the technology, but ultimately it moved beyond his control. Part of a continuing series looking at historical artifacts that embrace the boundless potential of technology. An abridged version of this article appears in the August 2026 print issue as “Birth of the Laugh Track.” References Luis Felipe Eguiarte Souza, curator at the Pavek Museum of Electronic Communication, in St. Louis Park, Minn., first told me about the Magnetophon and its link to the laugh track. The machine pictured at top is on display at the Pavek, and is one of the two that Jack Mullin brought back from Germany and then rebuilt. For significantly more technical detail on the development of the Magnetophon, magnetic tape, and its reinvention in America, check out Chapter 5, “The Introduction of the Magnetophon,” by Friedrich K. Engel, and Chapter 6, “Building on the Magnetophon,” by Beverley R. Gooch, in Magnetic Recording: The First 100 Years (IEEE Press, 1998). Radiolab interviewed Mary Crosby and Eve Mullin Collier as part of the show “Mixtape: Jack and Bing,” which includes many archival recordings as it tells the story of the development of the laugh track. There is a 2006 documentary on Jack Mullin titled Sound Man: WWII to MP3, but I was unable to view it.
When I started at Spectrum 25 years ago, a senior editor suggested that I find a “rabbi,” by which he meant someone who could mentor me in how EEs approach problems and evaluate potential solutions. I didn’t find one right away. Then in 2005 we decided to do a special report, focusing on the challenges of enterprise software development. I suggested we invite IEEE Life Senior Member Robert N. Charette, a self-described risk ecologist, prolific book author, and leading authority on risk management and software engineering, to explore in our pages the myriad reasons software projects fail. His seminal article “Why Software Fails” is still read in university engineering classes today. IEEE Life Senior Member Robert N. Charette is one of IEEE Spectrum’s most prolific authors.Robert N. Charette It was, as they say, the beginning of a beautiful friendship. I had found my rabbi, one who shared my love of writing. We settled into a rhythm that would last more than 20 years, talking on Friday mornings about a range of topics including the growing ubiquity of software in our lives. So when I became Spectrum’s website editor in 2007, he was the first contributor I tapped to start a regular blog (remember those?). The Risk Factor was born and over the course of more than 10 years and 1,750 posts, Bob chronicled hundreds of software debacles, culminating in “Lessons From a Decade of IT Failures,” which won a Jesse H. Neal Award for Best Infographics in 2016. Ironically, yet predictably, those infographics were created in a software package that is no longer supported and thus are lost to the bits of time. “I like the expression on the fish just before it’s going to be swallowed by the heron.”Robert N. Charette Bob, however, was not a one-trick pony. In between his full-time job running his two management consultancies and raising a future biochemist and a future civil engineer, his daughters Maura and Megan, he also wrote many deeply reported and insightful articles. These include last year’s “The Doctor Will See Your Electronic Health Record Now,” the eye-opening 12-part series and e-book The EV Transition Explained, and my personal favorite “Automated to Death,” about the deadly consequences of the automation paradox as manifested by the cyberphysical systems that pilot planes, trains, and automobiles. “The young bald eagle I photographed in September 2024 had bands that I could read which identified it as a female born in May 2024, near Lexington Park, St. Mary’s County, Maryland, about 65 miles away from where I live.”Robert N. Charette His main goal all along has been to make software visible, as he told me one Friday in July. “Software is all around us, but we don’t recognize it at all,” he said. “I really wanted my stories to help people better understand complex software systems. You can’t see software, you can’t touch it, you can’t taste it. You may feel the consequences of software failure, but you never see the reason itself.” When he told me that he was hanging up his hat as a contributing editor to focus on nature photography and to write a handful of fictional trilogies, including one entitled “The STEM Murders” featuring an engineer-turned-detective and his rabbi, I asked him which of his Spectrum articles had the biggest impact. “The hummingbird I caught with the yellow of a road curb behind it.”Robert N. Charette He singled out the 2013 feature “The STEM Crisis Is a Myth.” “Spectrum gave me a platform to question the assumption that we needed more STEM graduates. Until then, people didn’t really realize how much of the STEM crisis was a mythology that was perpetuated by employers and the academic community and was foisted on the IEEE community,” he said. Charette made a career of questioning assumptions. The best way to mitigate risk, he told me as our Friday chat drew to a close, is to be careful making assumptions in the first place. “My main risk maxim is assumptions made are risks accepted.”
Given a rising number of publishing misconduct allegations, IEEE in 2022 created the Publishing Ethics Team as a centralized department to assist in handling claims. The group also works to increase the organization’s visibility in the broader publishing ethics area and helps IEEE volunteers write new policies. Here are some highlights of the team’s activities last year. New detection tools IEEE conducted a pilot program in 2024 to integrate tools from the International Association of Scientific, Technical, and Medical Publishers (STM) Integrity Hub into the peer-review workflow of IEEE Access. The multidisciplinary, fully gold-open-access journal publishes research results across all IEEE fields of interest. STM created the hub so scholarly publishers could access a suite of integrated, commercial, third-party research integrity tools as well as those developed by STM Solutions. The tools help the publishing group avoid printing problematic content upon manuscript receipt, rather than reacting postpublication. The new features include the Clear Skies Papermill Alarm, which helps identify potentially fraudulent manuscripts at submission. Another is an integration with the PubPeer database, which allows users to check whether references in a manuscript have received previous PubPeer comments or have been retracted—both of which can indicate quality or integrity issues. The duplicate submissions detector can determine whether the same manuscript has been submitted to multiple journals by different publishers, often a sign of academic “paper mill” activity. Following the success of the pilot, IEEE began working last year to expand the services to all its periodicals. It is anticipated that all IEEE periodicals will be included in the Integrity Hub screening by the end of this year. Raising visibility The team participated in industry-wide initiatives with STM. It also renewed membership in groups including the Committee on Publication Ethics, and the team continued its work sponsoring and presenting at conferences. At a panel presentation during the Council of Science Editors annual meeting last year, Amanda Sulicz, manager of IEEE Research Integrity, participated in the Research Integrity Investigation panel session. She also presented at the Standardization of Publishing Integrity Norms and Corrective Actions poster session during the Society for Scholarly Publishing’s annual meeting, held 28 to 30 May 2025. Luigi Longobardi, the IEEE Publishing Ethics and Conduct director, gave a presentation at the Communication and Collaboration With Institutions session during STM Innovation and Integrity Days, which took place 9 and 10 December. IEEE was a sponsor of the National Conference on Research Integrity, held 20 to 22 May 2025, and the International Congress on Peer Review and Scientific Publication, held 3 to 5 September. Ethics reports The team is responsible for tracking ethics-related complaints for all IEEE publications, including articles published in periodicals and conference proceedings. When complaints regarding an article’s integrity are received, either via email at pub-ethics@ieee.org or the anonymous ethics reporting line, the team works with IEEE volunteers to open a case, investigate the complaint, and resolve the matter. Last year 591 cases were opened, a 56 percent increase over 2024. Of the 591 reports, 317 were closed and 274 are still under investigation. Of the complaints, 88 percent were research-related, including issues with plagiarism, AI-generated text, and falsification of—or unauthorized use of—data. The other 12 percent involved alleged misconduct by editors, reviewers, and conference organizers. The complexity of the reported cases has expanded. An increasing number of the complaints deal with more than one article or complicated situations such as editorial misconduct or peer-review manipulation. Conference publications For the second consecutive year, the team participated in the joint IEEE Publication Services and Products Board/IEEE Conferences Committee’s ad hoc committee on conference publication quality. The committee is tasked with analyzing and reviewing problematic conference papers and enhancing quality screening of articles prior to publication to detect integrity issues such as plagiarism and tortured phrases. The committee also updates educational modules on organizing and managing conferences. As part of the review process, the committee focused on conference articles that contained tortured phrases, which are nonstandard English expressions that are imprecise or erroneous and give the impression of technical jargon. Many of the articles reviewed by the ad hoc committee were identified by the Problematic Paper Screener, a free online tool that uses application programming interfaces to screen papers published online for potentially problematic content, such as tortured phrases, machine-generated content (SCIgen or Mathgen, for example), or references to retracted content. The ad hoc committee was responsible for reviewing and recommending the retraction of more than 1,700 problematic conference articles last year. Case studies From the cases the team reviewed, IEEE learned valuable information to help update its publishing policies and best practices. Here are examples of two anonymized cases reported to the team last year. Case Study 1 Updated Policies Occasionally, a misconduct case is so complicated that it requires an update to the PSPB Operations Manual. In this particular case, Coauthor 1 reported to the Publishing Ethics Team that the work was reused in an IEEE publication without proper credit. The new work also listed two coauthors not part of the original document. Coauthor 1 also reported the case to their university’s research integrity officer (RIO) for additional investigation. Initially, adjudicating the case proved challenging because under the PSPB policies at the time, the issue would have been classified as a multiple publication, which typically results only in a warning for the authors of the new work. With the assistance of the RIO, it was determined that the methodological and theoretical portions of the paper were previously derived in the university’s lab; therefore, the contributions of the two new authors were not substantial enough to warrant authorship. After deliberations by the IEEE Publishing Conduct Committee and eventually the IEEE Document Working Group, which is responsible for updates to the Operations Manual, IEEE PSPB Policy 8.2.4 was updated to clarify policies regarding the adjudication process for reuse of material and the proper crediting of coauthors when material has been reused. Case Study 2 Faked Reviewers Using the screening tools and data available to IEEE periodical editors, an editor in chief was alerted to suspicious reviewer activity. Specifically, two of the reviewers assigned to an article submitted the exact same review text. The editor contacted the handling editor to inform them of the irregularities and also contacted the two reviewers, asking them to verify that they were, in fact, the ones who submitted the reviewer report. Out of an abundance of caution, a new editor was assigned to the article, and new reviewers were selected while the investigation continued. The investigation concluded that the original handling editor created and submitted both reviews in question. Following the recommendations provided to the PSPB vice president by the periodical’s Society and Publishing Conduct Committee, the handling editor was banned from publishing with IEEE and serving in an editorial capacity.
If you’re wondering what dark matter is, you’re not alone. Astronomers don’t know. But they’ve determined that this invisible material must be far more abundant than the stars and nebulas that they can see. They’ve surmised as much from observing the gravitational effects of all this perplexing dark stuff, launching a decades-long campaign to understand its nature. I recently learned that it is possible to sense the presence of dark matter using a small radio telescope such as the Discovery Dish covered in these pages last year. The trick is to know what observations to collect and how to analyze them. I’ll sketch that out below, but first let me describe the homemade radio telescope I put together for this project. It’s a pyramidal-horn antenna, not unlike the horn antenna first used in 1951 to detect the 1,420.4-megahertz radio emissions from interstellar clouds of neutral hydrogen in space. These emissions hold the key to confirming the presence of dark matter because such clouds can be found all over the galaxy, and their motions reflect what’s happening in different parts of the Milky Way. I used an online calculator to help me design my antenna, adopting dimensions I knew I could achieve using a US $25 10-by-2-foot roll of roof flashing and an emptied one-gallon paint-thinner can. (Next time, I’ll just buy an empty F-style can.) The horn antenna is made from tape, an empty paint can, and a roll of metal roof flashing [bottom row]. Signals are picked up with a low-noise amplifier [top middle], and passed to a software-defined radio receiver [top left].James Provost Construction of the antenna itself was similar to that of the slightly smaller horn antenna I described in these pages in 2019. I made my new antenna bigger, though, because I needed better angular resolution, allowing me to scan smaller regions of the sky. To pick up the emissions from interstellar hydrogen, I used Nooelec’s $45 SAWBird+ H1, a device that combines two low-noise amplifiers with a standing-acoustic-wave filter centered on 1,420 MHz, in combination with a RTL-SDR V4 dongle. So it’s not too hard to put together the hardware needed to measure signals from hydrogen clouds. But how do you pull the signature of dark matter out of those signals? The answer is that you use such measurements to gauge the speed at which clouds located at different distances from the center of the Milky Way are moving in their orbits. You just have to show that the speed at which material orbits the center of the galaxy doesn’t fall off with distance. You might think that these clouds circle around the galactic center in the same way that planets orbit the sun or satellites orbit the Earth, with objects close in orbiting faster than those farther out. Mercury, for example, zips around the sun at 47.4 kilometers per second, whereas Neptune lumbers along at a leisurely 5.4 km/s. The Milky Way contains a central bulge of stars surrounding a supermassive black hole. So at first blush, the mass of the galaxy appears to be concentrated near its center. If that were the case, stars and clouds of other material would orbit more slowly as their distance from the center increases. If, however, there were enormous amounts of invisible matter present throughout the galaxy, you wouldn’t expect orbital velocities to diminish in this way. How Do You Measure the Speed of Interstellar Clouds? So to detect dark matter, you just have to show that the speed at which material orbits the center of the galaxy doesn’t fall off with distance. And radio observations are the easiest way to do that, because you can gauge speeds by measuring how much the signal from hydrogen clouds is shifted by the Doppler effect. By pointing your radio telescope at different parts of the sky, you pick up emissions from clouds located at various distances from the galactic center. The frequency offset of these emissions from 1,420 MHz reflects the speed of approach or recession of those clouds relative to Earth. You need measurements from the plane of the galaxy, at galactic longitudes between 0 and 90 degrees (a galactic longitude of 0 degrees points directly toward the center of the galaxy and 180 degrees directly away from it). Applying some high school trigonometry lets you convert these figures into orbital speeds around the galactic center, a technique known as the tangent-point method. In any group of clouds, the one with the highest velocity as seen from Earth will be the one lying closest to a tangent point along its orbit around the galaxy. This allows its distance from the galactic center to be determined through trigonometry [top]. The bottom graph shows the astronomical community’s measurements for velocities around the galactic center [in black], with the author’s results plotted in filled and open red circles.James Provost Experiments aiming my horn antenna at an Inmarsat geostationary satellite revealed that the angular resolution of my little radio telescope is about 20 degrees. So with the help of the planetarium program Stellarium, I pointed my antenna in the plane of the galaxy at galactic longitudes of about 15, 30, 45, 60, 75, and 90 degrees, spacing things out in an effort to make each set of measurements largely independent. I used the SDR# software with a plug-in called IF Average to read the raw measurements coming in from the antenna. This plug-in stacks up data received over a few minutes, allowing a weak signal to emerge and produce a clean radio spectrum that shows the 1,420-MHz line. In reality of course, it looks more like a bump, or even a set of bumps due to Doppler shifted emissions from multiple clouds, located at different distances from the galactic center. Fortunately, you only have to care about the cloud that’s receding the fastest—the one with the largest redshift, in astronomer-speak. I used Microsoft Excel to analyze the shapes of radio spectra I gathered, modeling them as the sums of individual bell-shaped contributions from different clouds. That allowed me to estimate the largest redshift for each galactic longitude I probed. Then, again using Excel, I applied formulas that transformed those six redshift values into six pairs of orbital velocities and distances from the galactic center. The plot of my results matched reasonably well with a recent paper, “The Inner Rotation Curve of the Milky WayInner Rotation Curve of the Milky Way,” in Publications of the Astronomical Society of Japan. Two innermost points, which showed anomalously low orbital velocities. Another shot at curve fitting in Excel brought these results closer to expectations, but they were still somewhat off. In any case, the orbital velocities I estimated did not diminish with distance from the galactic center—quite the opposite. Something out there is putting its stamp on how the Milky Way turns. And that basic observation is what allows me to say that, with the help of some roof flashing and a paint-thinner can, I’ve been able to detect dark matter from my backyard.
In February 2024, a young man lay somewhere on the frozen shore of James Bay, Canada, surrounded by snow and darkness, succumbing to hypothermia. When he failed to get home on time, his frantic mother sent a Facebook message to Elizabeth Kataquapit, then chief of the indigenous community Fort Albany First Nation in northeastern Ontario. Kataquapit used Facebook to alert the community’s search-and-rescue squad, who jumped onto their snowmobiles and drove into the night. Before dawn, they returned with the dazed man, who told rescuers he’d given up until wolves nudged his hypothermic body. “The wolves told him to wake up,” Kataquapit says. “I really believe they saved his life.” A fiber-optic network also played a key role. Not long before the young man’s mishap, the indigenous-owned Western James Bay Telecom Network (WJBTN) had built its own fiber-to-the-home network in this remote Cree community, some 975 kilometers north of Toronto. Before the network, the rescue squad relied on a few handheld radios to pass information along. This time, a Facebook message to the full list of volunteers triggered the search. An aerial view shows the remote community of Fort Albany in northern Ontario.Gavin John Building Canada’s first fully Indigenous-owned-and-operated fiber-optic network was an uphill battle for Brian Nakogee, WJBTN’s finance officer, who had to secure capital from agencies less familiar with the challenges of remote northern life. For the people of Fort Albany First Nation, accessing many vital supplies and services means traveling about 500 km to the regional city of Timmins. By land, the trip is possible for only a few weeks each winter, when the swampy tundra freezes hard enough to construct a temporary road. “The southern way of doing things is very different than how we here in remote areas piece things together,” says Nakogee. Telecommunications giants long saw little profit in serving the subarctic coast of James Bay. But even as satellite internet started to become available in remote communities, WJBTN staff saw the value in building and owning its own hard-wired internet service instead of relying on outside companies. Today, the nonprofit operates one of the fastest networks in Canada, while keeping both infrastructure ownership and revenue within the First Nations communities it serves. WJBTN is part of a broader movement among Indigenous and remote communities seeking more control over their telecommunications infrastructure. In the United States, 30 tribes now operate fiber-to-the-home networks, many launched during COVID. Canada has since created a dedicated Indigenous broadband funding stream. And as more communities pursue the expertise and funding to build their own networks, WJBTN’s experience offers a blueprint for what locally owned connectivity can look like in some of the hardest places to serve. How a Power Line Became a Broadband Backbone While many towns in North America were connecting to optical fiber in the early 2000s, the subarctic communities were left out. The residents of Fort Albany saw the earliest sign of improvement in 2008. That’s when their locally owned power company, Five Nations Energy Inc. (FNEI), strung fiber-optic cable on the utility poles that were delivering electricity from the dusty railway town of Moosonee, 135 km away across the peat bogs. On the shore of the Moose River, Moosonee is the last stop for Ontario’s telecommunications providers. Its only link to the province’s highways is a 5-hour train ride that shuttles passengers, freight, and vehicles through the Boreal forest. Elizabeth Kataquapit [top], former chief of Fort Albany First Nation, says high-speed internet has transformed her community. Search & Rescue volunteers now coordinate emergency responses through a Facebook group [bottom]. Gavin John Soon after, regional Cree leaders formed WJBTN to provide high-speed telecommunications in Moosonee and the three Indigenous communities to the north. WJBTN would lease the fiber-optic backbone from the power company, with just 1 gigabit per second of total capacity for the entire population of about 6,000 people. But the new fiber backbone did not mean fast internet for residents. One of WJBTN’s first commercial clients was a telecom company called Xittel, which used microwave links and local access points to beam wireless internet to homes across each town. It wasn’t what subscribers were hoping for. This system frustrated users with delays, glitches, and strict data caps. Kataquapit calls it a “turtle.” Everybody complained about the wireless. Xittel advertised a 10 megabit-per-second connection, but speed tests consistently showed 3 Mb/s for both uploads and downloads. And customers paid dearly if they ever exceeded their data limit. “You were billed close to CA $7 per megabit,” Nakogee recalls. WJBTN’s dream had always been to connect everyone’s home to fiber optic and make it affordable. Without a technical team, roads, or any major funding, the company just had to figure out how. Designing a Fiber Network for the James Bay Coast When Nakogee joined WJBTN in 2014, the telecom company was still in its infancy. It was “a department huddled in the corner, trying to latch onto the services of FNEI,” he says. Nakogee was asked to prepare a proposal to deliver 40 Mb/s fiber connections to each of the roughly 1,000 homes and businesses spread across 300 km of the James Bay coast. Back then, WJBTN operated on revenue from its early commercial and institutional customers—including Xittel, local government offices, hospitals, air navigation facilities, and family service centers that were connected to the first few strands of fiber. To make a residential fiber network possible, WJBTN first had to build both revenue and trust within the communities it hoped to serve; it had to convince people that the small new organization would follow through on its plan. WJBTN finance officer Brian Nakogee played a key role in financing and planning the community-owned fiber network. Gavin John Nakogee especially needed support from Moosonee and Attawapiskat, the communities at the start and the end of the line. “They’re the bread that holds this sandwich together,” he says. Through years of diplomacy, the budget grew enough to support a business proposal for a fiber-to-the-home network. Together with engineer Dirk MacLeod, in 2015 Nakogee began hashing out the details of a bare-bones version of the system. The plan required both upgrading the network’s long-distance fiber backbone—known in telecom as the backhaul—while also building the local infrastructure that would connect individual homes to the internet. Andrew MacLeod, WJBTN’s field project manager, reviews a map of the fiber network.Gavin John The first step was upgrading the network’s backhaul using newer “coherent optics” technology, which can transmit much larger amounts of data over long distances, explains Dirk’s brother, Andrew MacLeod, another network engineer, who joined as a consultant. Using equipment from telecom supplier Infinera, the new backhaul would deliver 100 Gb/s to distribution points in each town, with redundancy in case a fiber line failed. Next came the challenge of connecting individual homes. Rather than extend a direct line from the central office for every customer, the engineers designed the network around a telecom architecture called GPON (Gigabit Passive Optical Network), which reduced the fiber needed to connect each customer to the network. Fiber from the backbone would run to networking equipment at each town’s electrical substation, where passive optical splitters would distribute the connection among many households without requiring powered equipment at every junction. In remote communities where maintenance and repair are difficult, reducing the amount of active infrastructure was a necessity. The total estimated cost was CA $4.7 million, or CA $4,700 per household. By comparison, the Fiber Broadband Association estimates that urban fiber-to-home construction in the United States costs the equivalent of about CA $1,400 to CA $1,800 per household—a stark contrast in expense. Fort Albany resident Thomas Scott says high-speed internet has improved his work as a mental health counselor. Gavin John A radio broadcast in 2018 heralded the good news: The fiber-to-home construction project was a go. Fort Albany mental health counselor Thomas Scott says he “couldn’t wait.” People seeking guidance for addiction or grief typically called him on landlines, and it was hard to help them over the phone without seeing their faces. Businesses, too, rejoiced—including the Kataquapit family store, which relied on the phone system for transactions. Testing and Deploying a Remote Fiber Network Winning approval for the project was one thing; building it across hundreds of kilometers of remote subarctic terrain was another. Dirk MacLeod started with a schematic documenting the GPS position of every house, pole, and length of cable that would ultimately form the network. When WJBTN hired Montreal-based Fonex Data Systems to upgrade the backhaul, the detailed plan made it easy for contractor Tasso Varvarikos to design the deployment. Still, tuning the optical system to operate reliably across transmission lines stretching hundreds of kilometers required extra care. “Once you go up north, there’s no fiber store,” he says, To minimize surprises in the field, Varvarikos traveled to telecom supplier Infinera’s laboratory in Stockholm, where he and other engineers assembled and tested the network before shipping it to the installation site. The Stockholm lab gave the team access to testing tools and technical specialists who helped configure the system before deployment in the remote fly-in communities. Using Infinera’s simulation software, Varvarikos says he and his colleagues “kicked the crap out of it in the lab” until the network performed reliably. The Western James Bay Telecom Network connects remote communities along the western shore of James Bay in northern Ontario.Chris Philpot Finally, in the spring of 2019, it was time to pack up and head to the sites. First, more than two pallets’ worth of Infinera equipment were squeezed onto two charter aircraft in Timmins. Bush pilots, unfazed by the stringent logistics, made sure that one box reached Moosonee; the other one, Attawapiskat. Varvarikos says they packed extra fiber-optic transceivers, patch cables, tools, and “the kitchen sink and then an extra sink just in case.” By May, the team was ready to install the new backhaul—a months-long process that necessarily preceded household connections. Once the equipment was in place, the engineers tested the network to make sure the systems in each community could communicate reliably and that data moved correctly across the backbone. During the switchover to the new system, the MacLeod brothers worked from substations in different communities along the coast while Varvarikos coordinated from Fort Albany. Communicating over a spotty phone line, they started moving connections from the old network to the upgraded backhaul, carefully reconnecting cables and verifying that traffic still flowed correctly between communities. Within days, the anchor customers—including hospitals, schools, and air-navigation systems—were hardwired to a 100-Gb/s backbone. However, households were still relying on the slow legacy network. WJBTN’s next step was flying huge reels of fiber-optic cable into the remote communities at enormous cost. Nakogee recalls cutting predetermined lengths of cable, then repackaging it onto spools to load onto cargo aircraft. The backbone was finally in place; now WJBTN had to bring fiber to every home. Training a Local Fiber Crew Then, in a turn of events that rocked the communities, lead engineer Dirk MacLeod had a heart attack and died in July 2019. “My chest was ripped open,” Nakogee says. The grieving company shut down for the summer. WJBTN had intended to spend three summers training local crews from each community’s power company to maintain and manage the network. After MacLeod’s death, one line worker quietly took it upon himself to finish extending fiber to distribution points in Fort Albany. The team made a plan to begin connecting homes the following spring. Utility poles carry power and fiber-optic lines through Fort Albany First Nation.Gavin John Then COVID hit. Fearful and with limited medical facilities, the communities closed themselves off. Nobody was allowed in. “COVID really screwed things up,” says Andrew MacLeod. People were screaming for better internet, he recalls, but they wouldn’t let outsiders in. So instead of training local crews and building each town’s network simultaneously as planned, WJBTN made a tantalizing offer: The first community to allow MacLeod in would have its fiber-to-the-home network completed first. Fort Albany jumped on it. Peyton Reuben, a recent computer systems graduate in Fort Albany, was seeking a new job when his cousin mentioned that there was a “fiber guy” in town. Reuben’s coursework covered coding and how routers and ISPs communicate—a distant relative to Andrew MacLeod’s hands-on infrastructure work. WJBTN network coordinator Peyton Reuben looks up at the overhead fiber network in Fort Albany [top]. A splice enclosure joins fiber-optic cables at a distribution point [bottom]. Gavin John “They were looking for helpers to splice fiber,” says Reuben, something he knew nothing about. Nonetheless, he started work the day he met MacLeod and got a crash course in building an aerial fiber network, running cables from utility poles into neighborhood connection boxes that ultimately served individual homes. The fun part was reaching every pole in every neighborhood in a town crisscrossed by tributaries of the Albany River. “We didn’t have a bucket truck, so we had to climb up the poles,” says Reuben. “It was quite the experience, going through thick brush and thigh-deep water.” The hard part was splicing the fiber—fusing together hair-thin glass strands that connected homes to the larger network. Inside each connection box, distribution fibers had to be joined to cables leading to individual houses, one strand at a time—around 150 in each box. Reuben’s first attempt “looked like a plate of spaghetti,” says MacLeod. More splicing waited back at the substation, where optical splitters connected neighborhood lines to the town’s central GPON equipment. “It took me four times as long as Andrew to complete a splice tray back then,” says Reuben. A worker installs a new fiber-optic line for the Western James Bay Telecom Network.Gavin John Slowly but surely, every building in Fort Albany saw a strand of fiber drop from overhead and come through a box drilled into the wall, ready to connect to a router. By springtime, aerial cables linked every house—and future building sites—to the substation. Reuben calls it “a spider web that goes everywhere across town.” Without much fanfare, on a night in April 2022, WJBTN flipped the switch in Fort Albany. The next morning, Kataquapit woke up to a different world. With 250 Mb/s download and 30 Mb/s upload speeds, she found herself just a click away from her children in faraway cities. Why Not Starlink? MacLeod and Reuben continued working up the coast, splicing cables and adding connection boxes. As COVID eased, they hired more local help, and the power company lent a hand with bucket trucks. “We were working 12 hours a day, 7 days a week,” says MacLeod. “Guys were sitting in the heat, in bucket trucks, learning on the fly how to do splicing.” By late 2022, the network reached every home. Fiber-optic cables and networking equipment inside the Fort Albany substation distribute internet service throughout the community [top, middle]. Large spools of fiber-optic cable were flown in to connect homes [bottom]. Gavin John Around that time, Starlink’s satellite internet service was becoming widely available. Many of WJBTN’s potential clients asked why they shouldn’t just purchase that instead of a fiber-optic connection that involved drilling into their homes. But speed tests comparing Xittel, Starlink, and the new WJBTN connections showed a major difference in latency: In applications like video calls, Xittel and Starlink’s round-trip signal delays became painfully obvious. Unlike satellite systems, fiber networks don’t need to send signals hundreds of kilometers into orbit and back. That gave WJBTN a significant performance advantage. MacLeod recalls a conversation with one line worker who wanted to turn to Starlink. “We told him our latency is so much better,” MacLeod says, explaining that ISPs measure performance by both bandwidth and latency. From Attawapiskat, a data packet traveling over WJBTN’s fiber network reached Toronto in 20 milliseconds. Comparable satellite connections took closer to 60 milliseconds. And Xittel’s latency was much worse, at several hundred milliseconds. (Since then, WJBTN has reduced latency further, to about 12 milliseconds.) The team’s success has bolstered other Indigenous broadband companies. WJBTN representatives have shared their experiences at Indigenous Connectivity Summits since 2017, and how-to workshops have sprung up across Canada (and the United States). And Canada has since created a dedicated broadband funding stream for Indigenous communities. Within Fonex Data Systems, the company hired to do the backbone work, the WJBTN implementation is now considered the model for a successful installation in a remote location. Nakogee says ownership remains the key advantage. Unlike earlier telecom providers that leased infrastructure or delivered service wirelessly, WJBTN owns the backbone fiber, the right-of-way, and the poles carrying the network. That makes it far harder for outside telecom companies to displace the service. “That’s our secret weapon,” he says. Fundamentally, it’s about sovereignty. By controlling the infrastructure that carries internet traffic, the communities can govern and maintain the network according to their own priorities rather than the financial goals of distant providers. How Fiber Changed Daily Life Shortly after the fiber network was installed, Elizabeth Kataquapit became chief of Fort Albany First Nation. During her term as chief, she kicked the community’s digital era into full gear, encouraging individuals who couldn’t attend community meetings in person to join over Zoom. Counselor Scott now conducts grief and addiction sessions both in person and via video calls and can connect immediately in a crisis even if he’s away. And as a search-and-rescue volunteer, he responded to the Facebook message when the young man got lost in a winter snowstorm on James Bay. “If we were a half hour later, the person would have [been] gone,” says Scott. Broadband has also changed everyday life in quieter ways. Residents run small businesses from their homes. Telehealth links patients to specialists in southern cities. More people are working remotely and taking classes online. “Quality of life is so much better,” Reuben says. But the network has also brought to these northern communities the more isolating side of fast internet. Community members stream more movies and play more video games, and they meet face-to-face less frequently. Standing beside an enormous canoe in his front yard, Scott explains that while he’s grateful for Fort Albany’s new connection to the outside world, he’s equally intent on preserving close connections within the community. Today, he says, as he starts loading fishing nets into the canoe, “I’m taking the kids out after school to harvest whitefish.” This article appears in the August 2026 print issue as “Wiring the North.”
This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free! Scroll through LinkedIn right now and you may find the same advice repeated by well-meaning people: “In a market this rough, just be grateful anyone will hire you. Take the offer.” I could not disagree more. Negotiating your offer is not ungrateful, and it isn’t greedy. Done well, it’s good for you and good for the company hiring you. I misunderstood this early in my career, and it cost me. I didn’t know it was an option When I got my first job in tech, I didn’t know negotiation was even on the table. The recruiter asked what salary I wanted, and I gave a number below the bottom of their range. They came back with the lowest number in their band—still more than I had asked for—and I was thrilled. I had no idea I’d left money on a table I couldn’t see. Then I started teaching at a Bay Area coding bootcamp in the evenings. A coworker mentioned what he made and it was nearly double my salary for roughly the same work. My jaw dropped. During that time, I began interviewing and got an offer. I handed in my resignation and my manager countered with an offer for nearly 30K more. That money had been there the whole time. At that moment, I realized my salary was a business decision, not a measure of my worth. What it looks like from the other side Years later, I became an engineering manager and saw the salary discussion from a different angle: A position would open. Many interviews later, we’d find someone we wanted, and HR would hand me a salary range to make an offer. I was encouraged to make an initial offer near the bottom to leave room for, you guessed it, negotiations. Most applicants didn’t negotiate. The first offer is rarely the ceiling. It’s usually the floor. Companies extend a reasonable number and quietly hope you say yes. It’s not all about the money Negotiating isn’t only about a bigger paycheck. (But who doesn’t want that?) Let’s say you’re on the job market, maybe recently laid off, and a low offer comes in. You take it out of relief. Then you start, you like the team, and you quietly resent the number. Now you’re stuck with it, and you’ll probably leave that role inside a year or whenever the market improves. Nobody wins there. You’re back on the market starting over, and the company loses someone good and pays more to replace you, when a fair number up front would have cost far less. Paying you fairly is cheaper than starting over. How to actually do it People overcomplicate this. Once I have an offer, I say some version of this: “Thank you so much for the offer, and I’m genuinely excited to join the team. I’m hoping we can come in around [10 to 20 percent higher than the original number]. Is there any wiggle room here?” Then I stop talking and let them respond. Why 10 to 20 percent and not double? The number you ask for is itself a signal. Ask for something wildly out of range and you’ve told them you never learned what the role pays, or that your expectations are miles from reality. That’s what makes a company walk away. A calibrated request reads as someone who knows their worth and did their homework. You’ve probably heard a horror story about someone who asked for more and had the offer yanked. Any company that would pull an offer over a reasonable question about pay is telling you exactly how they’ll treat you once you’re inside. If the salary can’t move, it isn’t the only lever. I’ve negotiated more remote days, a later start to drop my kids off, and a sign-on bonus when the base was locked. Most people negotiate none of these perks. You have more leverage than you think Negotiating can feel like something you can only do from a position of power. But if you’re in the final stages of an offer, you already have it. They want to hire you. They’ve spent weeks finding you. Now they’re hoping you say yes. That’s true even if you were recently laid off. Even if it’s your first job. Even if the number already looks higher than you expected. The game is being played whether or not you join in. Sit it out, and you’re not just leaving money on the table. You may be quietly shortening your own stay at a job you could have been happy in. So ask. —Brian The AI Arms Race in Technical Interviews Is Escalating If you’ve been on the job market for a software engineering role recently, you’ve probably encountered—or used—AI tools in the hiring process. From application filters to live interview assistants, both applicants and employers are trying to use generative AI to their advantage. Can real, human skills still shine through in this new reality? Read more here. This Graduate Student Equips NASA With Assembly Skills Sarah Downs, a Ph.D. student in electrical engineering at Texas A&M University, has long been interested in robotics and dreamed of working with NASA. This year, she achieved that dream, collaborating with NASA and the U.S. Air Force on an algorithm that enables satellites to insert an antenna into the correct spot. Read more here. IEEE Program Helps Girls in India See a Future in Stem Women make up only about 28 percent of the global STEM workforce, in part because of limited access to educational resources for preuniversity students—especially in areas like rural India. An IEEE initiative, the Women in Science, Engineering (WiSE) project launched to help expand opportunities and hands-on learning for young women. Read more here.
Artificial intelligence is reshaping the skills employers expect from new graduates. In response, universities are scrambling to launch new courses, research centers, and industry partnerships that prepare students for today’s workforce. But building a cutting-edge AI curriculum demands funding and access to industry networks, resources that remain unevenly distributed across higher education. At North Carolina Central University, Siobahn Day Grady is trying to change that equation. In January 2025, Grady, an associate professor in the NCCU School of Library and Information Sciences, launched the first AI research institute at a historically Black college or university, or HBCU. The Institute for Artificial Intelligence and Emerging Research (IAIER) aims in part to help students and faculty across the university develop the skills needed to navigate a labor market increasingly transformed by AI. “There used to be a time where people could say, ‘I don’t do tech,’ or ‘That’s not for me,’” Grady says. “But we’re in a stage now where you do need digital skills. Now it’s evolving into AI literacy.” The approach reflects a broader shift in how many universities are thinking about AI education. AI skills are no longer confined to computer science and engineering departments—and at NCCU, they can’t be. The university does not yet have a dedicated computer science program, though it is developing one alongside a new AI minor. The challenge of providing these resources is especially acute for historically Black institutions. Although HBCUs account for roughly 3 percent of four-year institutions in the United States, they receive less than 1 percent of federal research and development funding, according to a 2025 report by the Center for American Progress and the Thurgood Marshall College Fund. The same report found that 17 of the 43 federal agencies that distributed research funding to universities in 2023 awarded no funding to HBCUs. Yet less than two years since its launch, IAIER has emerged as a powerhouse for interdisciplinary AI education. Backed by a US $1 million Google.org grant, the institute has engaged more than 2,800 students, faculty members, and community residents through research initiatives and training. Now the challenge is sustaining that momentum to keep up with rising demand. “We have a guiding principle that we lead with on our campus,” Grady says. “AI is for everyone.” Why one research group wasn’t enough The mission to expand AI literacy grew out of Grady’s lifelong curiosity about technology. “I was born during a time [when] the internet did not exist,” she says. “Ever since the internet came to be, it’s changed our entire world.” Grady was particularly drawn to the questions tech raises about privacy, identity, and human behavior. After receiving her bachelor’s degree in computer science and master’s degrees in AI and information science, Grady pursued a Ph.D. in computer science at the North Carolina Agricultural and Technical State University to dig into those questions. Her dissertation focused on authorship attribution in social media, using machine learning and natural-language processing to determine whether a person’s writing style could reveal their identity. “I’ve always been intrigued by how much data we give for free,” Grady says. That work introduced her to the power of AI systems to detect patterns hidden within large datasets. “We have a guiding principle that we lead with on our campus: AI is for everyone.” After completing her doctorate in 2018, Grady joined NCCU as an assistant professor in the School of Library and Information Sciences. There, she researched machine learning applications for health care and autonomous vehicles. In 2020, she launched the Laboratory for Artificial Intelligence and Emerging Research at NCCU, giving students opportunities to participate in hands-on projects and explore AI beyond the classroom. Then in 2024, an opportunity emerged to apply for a Google grant, and Grady began thinking beyond a single research group. Rather than building another faculty lab, she envisioned an institute that could serve the entire university during the AI boom. “We wanted to capitalize on the moment and make sure we don’t get left behind,” Grady says. Since receiving the $1 million grant, Grady and her team have built a university-wide AI initiative, launched new academic programs, organized conferences, secured external support, and created research opportunities. “We’ve really operated like a startup,” Grady says. AI beyond computer science As part of the institute’s goal of integrating AI education across disciplines, all NCCU freshmen are required to complete an introductory AI course, designed in partnership with IBM, to build foundational prompting skills. The institute has also worked with faculty development teams to help instructors integrate AI into their teaching. Research is another part of the strategy. IAIER has awarded seed grants of up to $10,000 to faculty members exploring AI applications across departments. The first cohort funded 11 projects spanning social work, digital archiving, health care, and information science. One project, for instance, is creating an AI lab where students in social work courses can practice client interactions through simulations. “It’s really interesting to see the lens that our researchers take in trying to solve complex problems and also bring our students along with them,” Grady says. The institute’s growth has been fueled by a mix of workforce training, interdisciplinary research, and, especially important, industry engagement. “Industry is where the advancements are really moving at that very fast rate,” Grady says, “not necessarily higher ed.” To bridge that gap, IAIER hosts events that connect students and faculty with researchers, employers, and technology leaders. It has held sessions with companies including Deloitte, FICO, and Anthropic. Partnerships with Google and IBM let students gain recognized certificates and credentials. And last year, the institute hosted the first OpenAI Academy Summit held at an HBCU, drawing 444 participants from more than 40 institutions. Sustaining the vision The institute’s rapid growth has created a new challenge: continuing its momentum. “Funding right now is the biggest barrier for [IAIER] to remain sustainable,” Grady says. As interest in the institute continues to grow, demand for its programs is beginning to outpace its capacity. “People just want more,” she says. The bottleneck reflects a broader tension across higher education. AI is evolving quickly, while developing new academic programs, training faculty, and building research capacity takes time. The uncertainty is compounded by a shifting political landscape. As a whole, U.S. universities are grappling with proposed cuts to federal research spending and increased scrutiny of diversity-focused initiatives under the Trump administration. However, in September 2025, the administration also announced a $500 million one-time investment in HBCUs and higher-ed institutions chartered by Native American tribal governments. Meanwhile, NCCU has continued to attract new investment. Last September, in a collaboration with Howard University and two other institutions, IAIER received a nearly $500,000 award through a National Science Foundation research coordination network program to help define emerging AI jobs, identify in-demand skills, and inform future credentials and curricula. That work will continue this fall when IAIER opens its first dedicated physical space on campus, Grady says. Over the next several years, Grady plans to expand academic programming, launch the university’s computer science major and its AI minor, increase faculty research opportunities, and integrate AI more deeply across campus operations. She also plans to deepen the institute’s collaborations with industry partners. Beyond program expansion, Grady sees the institute’s long-term success as linked to building a model other universities can adapt. “We’re creating a framework that can help not only HBCUs,” she says, “but also help any university looking to do similar work.”
Artificial intelligence is rapidly becoming part of everyday infrastructure–in some places. It helps write emails and software code, filters job applications, powers recommendation systems, and is increasingly being integrated into education, health care, finance, and public administration. Industry leaders talk about “AI for everyone,” while governments rush to publish national AI strategies and build sovereign compute. Yet over the past decade, working on digital inclusion and digital literacy projects in regions from Europe to sub-Saharan Africa and Southeast Asia, I’ve seen the same pattern repeat: Each new wave of “transformative” technology lands on a landscape already stratified by connectivity, skills, and institutional capacity. The current AI wave is no exception. If anything, it amplifies those underlying fractures. Still, some countries are exploring ways of participating in AI development without directly replicating the frontier-model race dominated by the United States and China. Recent developments in South Africa and Indonesia illustrate both the possibilities and challenges. The stakes extend far beyond access to AI. Countries that remain primarily consumers rather than creators of AI risk losing opportunities to build local innovation ecosystems, strengthen public-sector capacity, and ensure that their own languages, cultures, and societal priorities are reflected in AI systems. In this sense, the AI divide is also becoming a divide in economic opportunity and technological influence. AI compute is clustering in a few places Recent analyses from Stanford University’s 2026 AI Index report that the United States alone hosts more than 5,000 data centers, over 10 times as many as any other single country. Because AI workloads are increasingly performed on cloud platforms rather than local infrastructure, this concentration of compute also becomes a concentration of dependency. According to World Bank data, in 2023 the United States accounted for roughly 87 percent of global exports of cloud computing and data-storage services. For most countries, this means that AI development is not just technologically but commercially and geopolitically outsourced and out of their control. The result is an AI ecosystem where a small number of states and firms host the computational engines that power globally deployed systems. Systems trained, standardized, and governed within a narrow set of institutional and linguistic environments may struggle to serve a genuinely global public. Skills and AI literacy are deeply stratified Even where connectivity and cloud access exist, not everyone is equally positioned to make use of them. Across the Organisation for Economic Co-operation and Development (OECD) countries, only around 40 percent of adults possess more than basic digital problem-solving skills, while advanced computational and AI-related competences remain concentrated among highly educated workers and technology-intensive sectors. At the same time, governments are racing to integrate AI into education, often starting at higher levels of schooling. UNESCO has reported growing efforts worldwide to integrate AI into education, while support for AI literacy in primary and lower secondary education, as well as ethical training for educators, remains uneven. Those with robust schooling, advanced digital skills, and stable connectivity are best positioned to treat AI as a tool to extend their capabilities. Recent OECD survey data show that participation in AI-related training remains strongly stratified by educational attainment: 36 percent of respondents with tertiary education reported undertaking AI-related training in the previous year, compared with just 18 percent of those with upper-secondary education. Those on the wrong side of the divide are more likely to experience AI as an opaque system acting upon them, from algorithmic welfare systems such as the Dutch childcare benefits scandal to AI-assisted hiring tools such as Amazon’s discontinued AI recruiting system, rather than as a technology they can actively interrogate or shape. Investment and governance: Who gets a seat at the table? The core agenda-setting power often remains with a narrow set of industry actors and a small group of technologically advanced states. Most other countries remain in a perpetual catch-up posture, adapting imported models, standards, and templates for “trustworthy AI” to their own contexts, and may have limited local capacity to assess trade-offs or propose alternatives. In countries such as Indonesia and South Africa, communities generate data at massive scale yet still have little voice in how AI systems are designed, governed, or deployed. Their languages are underrepresented in training data; their institutions are under-resourced in regulatory forums; their experiences rarely feature in benchmark datasets. For many countries in the global South, participation in AI still occurs largely through adapting imported systems rather than shaping how those systems are designed, governed, or deployed. In South Africa, the Department of Communications and Digital Technologies released a draft national AI policy in April 2026, proposing new oversight institutions. The department withdrew the draft days later after a journalist discovered that at least six of its academic citations did not exist, apparently AI-generated hallucinations. The minister called it “an unacceptable lapse.“ The episode sharply illustrates the gap between AI governance ambition and the institutional capacity needed to implement it, though the new AI panel the country has since constituted has a chance to use South Africa’s unique leverage. Indonesia presents a case of deliberate, if constrained, public-sector agency. The National Research and Innovation Agency (BRIN) which now leads AI implementation under the national strategy, has built practical AI tools aimed at underserved communities rather than frontier capabilities, including an app that uses satellite data and machine learning to help artisanal fishermen locate schools of fish, multilingual language models trained on Indonesian and local languages such as Javanese and Sundanese, and AI chatbots deployed in government services. In August 2025, the Ministry of Communication and Digital Affairs released a national AI road map with a target of training 100,000 AI-skilled workers annually. The choice is not simply between “AI superpower” and “passive recipient.” Regional cooperation may also become increasingly important. In 2024 African ministers adopted a Continental AI Strategy and African Digital Compact. Participants in the April 2025 Global AI Summit on Africa in Kigali explored how regional coordination, local-language AI models, public universities, and open-source ecosystems might reduce long-term dependence on externally developed AI systems. A different way to think about the AI divide None of this means that people should slow or abandon AI, nor that cloud concentration or venture capital are inherently bad. Instead, when we talk about an “AI revolution,” we should also ask who can shape it and who can merely adapt to it. Digital-divide debates once focused on devices and connectivity, later expanding toward skills and outcomes. But the current AI wave adds another layer: disparities in who can meaningfully participate in deciding what AI is for, which problems it is meant to solve, and which social priorities it ultimately serves. For engineers and policymakers, this raises difficult but necessary questions. Are they designing AI systems and infrastructures that broaden, rather than narrow, participation in shaping technological change? When governments roll out national AI strategies or integrate AI into public services, whose constraints, languages, and institutional realities are they including? Many observers frame the current AI moment as a competition. But technological competition is never only about speed. It is also about who can influence the direction of change. AI is already spreading globally. The deeper question is whether the technologists and policymakers responsible for it will ensure that meaningful participation in shaping that future will spread as well.
In shaping her career, Peru native Mariana Costa has asked herself a question: What can I do to make life better for women in Latin America? The answer she landed on was training them for tech jobs. Mariana Costa Employer Laboratoria Title Co-founder and president Alma Maters London School of Economics; Columbia Such positions pay well and are in demand. And for too long, women across the region have been locked out of them, she says. Costa is president of Laboratoria, a U.S.-registered nonprofit based in Miami that she helped found. Laboratoria has trained thousands of women in 11 Latin American countries for technology careers. She has built training centers in the countries and has placed graduates at major companies. Meanwhile, she has become one of the most recognized voices in the region on workforce equity and tech education for women. IEEE recognized her work with its President’s Award this year for her “distinguished leadership and contributions to the betterment of society.” Recipients of the award are selected by the IEEE president with the consent of the IEEE Board of Directors. Costa says the recognition came as a surprise because she is not an engineer by training and had never considered becoming affiliated with IEEE. She was presented with the award at the IEEE Honors Ceremony on 24 April in New York City. Peru: a country of contrasts Costa grew up in Lima, Peru’s capital, in a household with no connection to engineering or technology. Her mother was an art historian and professor, and her father was a lawyer. The family was financially comfortable and traveled abroad regularly. Costa attended well-resourced schools. That economic stability came with a reckoning, Costa says, in that she recognized early on that economic inequality had created separate societies inside Peru. Her parents, she says, made it “clear that my reality wasn’t the reality of most people in my country.” Lima is a microcosm of the country, she says. The divide in the capital city is visible: A kilometers-long concrete wall topped with barbed wire separates wealthier neighborhoods from shantytowns, where residents lack running water. Nationally, there are splits along ethnic and geographic lines. The highland and jungle regions remain home to mostly indigenous communities with limited educational access and a deep cultural distance from the Hispanic-dominated coast. The questions that stirred in her as a child never left, she says. “Why do I live in a country where so much depends on where you’re born?” she asked herself. “What does it mean to be Peruvian when individual realities are strikingly different?” Those questions followed her to the London School of Economics, where she studied international relations, graduating with a bachelor’s degree in 2007. She held onto the questions when she moved to Washington, D.C., where she spent the next four years working for the Organization of American States, helping Latin American governments improve public services that fall under the heading of civil registration. “I said, ‘How can it be? The tech space has so many rich opportunities. Why aren’t any women here?’” The OAS Universal Civil Identity Program in the Americas provides technical support to national civil registry institutions, modernizing them to foster social inclusion and ensuring the right to civil identity for all people. Without civil identity, a person can’t access education, health care, legal employment, social services, or the right to vote. People without the classification don’t exist in the eyes of the government. They also can’t own property, get married officially, or pass citizenship rights to their children. Doing that work deepened her concern about the socioeconomic disparities in her homeland, she says. In search of practical solutions to those problems, she went to New York City in 2011 to further her education. She earned a master’s degree in public administration and development from Columbia in 2013. Technology was not yet part of a solution. But Costa already had met someone who would change that. Falling in love with a programmer While working in Washington, Costa met Herman Marìn, a software engineer who used digital tools in support of social causes. Because he was doing work she had never associated with programmers before, her assumptions about the field dissolved quickly. “I had a vision of [programmers doing] something not very social—strictly technical,” she says. “And my then-boyfriend, now husband, actually worked for different social movements that used technology to address social causes.” That realization cracked something open, she says: “I said, ‘Oh! Technology can actually be a tool to address some of the more stubborn problems in our societies.’” After earning her degree at Columbia, Costa returned to Lima with her husband. She had been abroad for nearly a decade and felt the pull of home. “The thought of not moving back to my country was something that tormented me a bit,” she says. “I really felt I had to move back, at least to try it out and contribute somehow.” What Latin America’s tech space lacked When Costa, her husband, and a friend from graduate school moved to Lima, they had modest savings and big ambitions. They wanted to build something that combined technology with social impact. They started with what they had: a small digital services agency, where they built websites for clients. The business grew, and they hired more employees. Their team expanded to a dozen software engineers. And as it did, Costa noticed three things. First, there weren’t enough trained developers to meet the demand. Second, many of their best hires did not have traditional computer science degrees. Some had never even finished college. “There was no other space where you could actually build an amazing career and get a well-paying job without a good degree from a good school,” she says. “The tech world was different. It was open in ways other fields weren’t.” Thirdly, she noticed that there were no women on the team. In the first six months, Costa says, they didn’t interview a single female developer. Her colleagues shrugged. It’s just how it is, they told her. Costa, the outsider, didn’t accept that. “I said, ‘How can that be? The tech space has so many rich opportunities,’” she says. “‘Why aren’t there any women?’” Building Laboratoria In 2014 she decided to launch Laboratoria. The business model was simple: Find talented women who hadn’t yet broken into tech, train them quickly on practical skills, and connect them with employers who needed developers. Laboratoria started offering a six-month immersive boot camp that covered Web development, UX design, data literacy, strategic use of artificial intelligence, and soft-skills coaching such as interview prep and projecting confidence. Just as important for career success, Costa says, is a user-centered mindset. She says Laboratoria’s program emphasizes the discipline of keeping the client’s needs in mind when designing the work. The teaching model has evolved beyond the boot-camp structure, but the organization still focuses on helping Latin American women develop tech skills and land quality jobs in the digital age. These days, the training, conducted via twice-weekly live Zoom sessions, lasts six weeks. “We needed developers ourselves,” she says of the company’s original logic. “I said, ‘Why don’t we run a program to train women—women who are incredibly talented but haven’t been given a chance yet—and help them gain the skills they need to get a great job as quickly as possible?’” Mariana Costa [seated, right] poses with Laboratoria co-founder and CEO Gabriela Rocha and co-founder and chief product officer Rodulfo Prieto.Valeria Martens It worked. Laboratoria expanded from Lima to Santiago, Chile; Mexico City; São Paulo, Brazil; and Bogotá, Colombia. The organization eventually incorporated as a nonprofit in the United States. Today its programs are held remotely in Latin America’s major job markets. So far, Laboratoria has opened the doors to tech careers for more than 3,500 women. Costa says she believes the most important skills Laboratoria’s graduates need aren’t purely technical. Close behind the growth mindset is self-confidence, she says. “Knowing who you are, valuing who you are, and learning to trust yourself and your capacities are indispensable traits,” she says. Networking, she adds, is the third pillar, and often the hardest to build for women without access to elite schools or flexible work schedules. “When you go out in the market,” she says, “you realize that having a network of people who trust you and know your work is such a valuable and critical asset.” IEEE: a new connection Costa’s introduction to IEEE came late—but it landed hard. She is not an IEEE member, so when she was contacted this year about receiving the President’s Award, she did her homework on the organization. What she found, she says, was a public charity whose reach and values aligned with her mission. “IEEE is about expanding access to opportunities in the world of technology,” she says. “And that’s also the core of what we do at Laboratoria.” She says she also sees IEEE as a living example of something her company preaches every day: “I was talking about the value of professional networks, and I think IEEE is such an amazing reference in that space. It exemplifies this belief that human connection—not only doing your work but also sharing and learning with others—is at the core of building thriving technology careers.” The engineering organization found her well after she launched her tech-related career. But it wasn’t too late. She says she intends to make the most of the connection.
Physicists have been trying to measure the fundamental gravitational constant for well over two centuries. The current accepted value of big G, as it’s known, is 6.67430 × 10-11 cubic meters per kilogram per square second. It also has an uncertainty of ±0.00015 × 10-11 m3/(kg s2). As far as constants of the universe go, that’s very uncertain. Stephan Schlamminger Schlamminger is a physicist at the U.S. National Institute of Standards and Technology. Stephan Schlamminger recently completed a 10-year effort at the U.S. National Institute of Standards and Technology to replicate an earlier measurement of big G from the International Bureau of Weights and Measures, or BIPM (located near Paris) that’s notably higher than most measurements. He spoke with IEEE Spectrum about why it took so long to get a number—6.67387 x 10-11 m3/(kg s2)—and why it’s notably lower than the BIPM result, to the tune of 0.0235 percent. Why is it so difficult to measure big G? Stephan Schlamminger: Gravity is very weak. When you were a kid, you probably played with fridge magnets, and it was a force you could feel. But if you have two coffee cups, you can try all you want—you can’t feel the force between them. It is there, but it’s so, so weak. How did you attempt to measure big G? NIST used a torsion balance with a fourfold geometry. This animation shows an exaggerated version of how the outer green masses gravitationally attract the inner blue masses.S. Kelley/NIST Schlamminger: We used what’s called a torsion balance. The key idea in the torsion balance is that it decouples vertical gravity that you have from Earth from horizontal gravity, and that makes it sensitive to masses that are around the torsion balance but not the Earth below. Ours had a fourfold geometry. It has a very thin torsion strip, then four cylinders in a “plus sign” arrangement. All of this is inside a vacuum. Outside, we have four larger cylinders that gravitationally attract the four smaller masses to them. If I move the outer masses just a tiny little bit, the plus sign will rotate, and we measure that angle that it moves. That angle is proportional to the gravitational torque. Why try to replicate the BIPM value? Schlamminger: We could move the field forward. The measurements have been plagued with inconsistencies, so by redoing an experiment, we hoped to shed light on the inconsistencies. We did not find a smoking gun, so there’s no single reason why it’s different—our value versus their value. It’s still a big question mark. What was it like spending 10 years on this? Schlamminger: It’s a bit like herding cats. I’ve measured other fundamental constants, like Planck’s constant, and for most experiments, they have some sort of self-calibration built in. But with the gravitational constant, you have to keep track of every single mass that moves—where they are, how big they are, and weigh them. How does your result compare to the rest? Schlamminger: Our result is a little bit below the standard accepted literature value. I was disappointed because it doesn’t agree with the BIPM value, nor with the literature value. If there’s something wrong with the BIPM experiment, then the literature value—which includes that result—probably ought to come down a bit. But that is not for me to say. I think somebody else, independent, should figure out what the new mean value ought to be.
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Already, I’ve almost forgotten rain; I know I will never know that again. I move forward with the powers you gave me to live up to my name, Curiosity. This is a land without leaves, fronds, or spines. If there are plants, they are small and supine, dust hidden, like light here, filtered and sand softened, at home in the thin air, thousands of motes so small they seem to be fluid, more fog than firmament. Shadows, few and far from here, I know I must go to them to see if they hold order or mayhem or just another common rock or two. If a machine can miss the Earth, I do.
Unlike many budding engineers, K.J. Ray Liu wasn’t inspired to enter the field by tinkering with electronics or following in the footsteps of a family member. Growing up in Taichung, Taiwan, he answered his government’s call for students to become electrical engineers to help manufacture semiconductors in the 1970s, when the country’s economy was struggling. “Students who were good in math, science, and physics all wanted to be an electrical engineer because that was the top priority of the government,” Liu says. “That’s how I got into engineering. Now Taiwan is a world leader in semiconductors.” K.J. Ray Liu Occupation Retired professor of information technology and a digital signal processing researcher at the University of Maryland in College Park Member grade Fellow Alma maters National Taiwan University; University of Michigan; UCLA But by the time he graduated from university in 1983, semiconductor facilities were still under construction, so there were no jobs available. Instead, he went on to have a successful career as an educator and entrepreneur in the United States. For 31 years, he was a professor of information technology and a digital signal processing researcher at the University of Maryland in College Park until he retired in 2021. Liu was the chairman, CEO, and CTO of Origin Wireless, a startup he founded in Rockville, Md. Origin, which was acquired by ADT in February, pioneers artificial intelligence for wireless sensing and indoor tracking. Liu, an IEEE Fellow, is an active IEEE volunteer who served as the organization’s president in 2022. IEEE honored him with this year’s Haraden Pratt Award for “transformative and impactful leadership.” Liu is credited with increasing the diversity of nominees for IEEE’s Fellow program, which is the highest level of membership. He also led the effort to realign the organization’s regions geographically to ensure more equitable global representation on the IEEE Board of Directors. He received the Pratt honor on 24 April during a ceremony in New York City. The IEEE Foundation sponsored the Board-level award. “More than anything, I share the honor with the volunteers and staff I had the privilege to work alongside,” he says. “Our hard work is fueled by our shared devotion to this professional home we love and care for so much.” Making the switch to signal processing In the 1970s, Taiwan’s policymakers decided to improve the country’s economy by pivoting from making products such as shoes and umbrellas to manufacturing electronics. The industry got its start in 1976 when RCA, a major electronics company at the time, agreed to transfer licensed semiconductor processes to Taiwan’s Industrial Technology Research Institute. ITRI spun off several semiconductor-related companies including the Taiwan Semiconductor Manufacturing Co. TSMC, launched in 1987, is the world’s largest dedicated semiconductor foundry. Liu graduated in 1983 with a bachelor’s degree in electrical engineering from National Taiwan University, in Taipei. At the time, there were no semiconductor companies to work for, he says. “Nowadays, many of the country’s university graduates go right to TSMC to get a job,” he says. “But back then, there was no real job market. “Most of my classmates—including me—came to the U.S. for graduate studies. Many of us stayed and, over the last three to four decades, contributed to the development of electronic computer communication technology in the U.S.” Liu left Taiwan after a two-year mandatory stint in the Republic of China Armed Forces to attend the University of Michigan, in Ann Arbor, where in 1987 he earned a master’s degree in electrical engineering. “If I can help make IEEE a better professional home for future members, that is something that I can pay back to IEEE.” He went on to earn a Ph.D. in electrical, electronics, and communications engineering in 1990 from the University of California, Los Angeles. His interest in digital signal processing and very-large-scale integration (VLSI) was sparked while at UCLA. Today VLSI powers all modern electronics. “When I was a graduate student, there was no wireless communication. Everybody had a landline,” he explains. VLSI was an important, active research field at the time. “My research interest was digital signal processing,” he says. “One day I saw a book on VLSI signal processing on my professor’s bookshelf. I immediately thought to myself: That is the field I want to pursue. “VLSI is one lane, digital signal processing is the other, and there is a bridge linking the two. I was interested in both areas, so I did my Ph.D. thesis on VLSI signal processing.” After graduating, Liu joined the University of Maryland, where he is credited with establishing its signal processing research program. In addition to teaching, he conducted research on a broad range of signal processing and communication aspects. The topics include bioinformatics, game theory, signal processing algorithms and architectures, and wireless sensing and communications. He has authored more than 10 books and 900 papers, and he holds 250 patents. You can find his research papers in the IEEE Xplore Digital Library. Ambient-sensing trailblazer Liu is considered to be a pioneer in the field of ambient sensing. The technology gathers environmental data and is used in security systems and health-monitoring devices. He came up with the idea, he says, while working on a project in 2009 for the U.S. Navy. He was trying to solve a problem the Navy was having with the wireless communication systems used in its submarines. Because submarines are made of metal, radio waves were unable to penetrate the vessels’ compartments and instead bounced around, creating interference, he says. His solution was to use a relatively unknown concept in physics: time-reversal signal processing. The technique captures waves, such as sound and electromagnetic signals, and sends them back through the same medium in reverse, flipping the signal from last-in to first-out, and re-emits them. “By using time-reversal feed, we could increase the signal-to-noise ratio by four times,” he says. “That improved performance dramatically.” He became fascinated by the physics of time-reversal signal processing, he says, and wondered how he could apply the concept to serve society. After three years of research, he came up with the idea of using wireless sensing applications through ambient radio waves from surrounding Wi-Fi networks. “I learned to turn Wi-Fi networks into sensing networks that decipher our activities,” he says. “We could know everything happening around us—our motions, breathing, heartbeat, even fall detection—without any wearables.” Through the university’s incubator, which encourages faculty to work on projects with an impact on society, he launched Origin in 2013. The company’s Wi-FI and AI sensing technology enables accurate indoor tracking, motion detection, and health monitoring without the need for wearable devices or cameras. Its products, including its remote patient monitoring, received three innovation awards at the 2020 and 2021 Consumer Electronics shows, including one for best innovation. Finding his professional home Liu joined IEEE in 1986 as a graduate student to access its research papers, he says. “If you didn’t join an IEEE society, you didn’t get its journal—which meant that you couldn’t read the most up-to-date research papers,” he says. “So, I joined the IEEE Signal Processing Society. When I attended my first signal processing conference, I knew I had found a professional home. I met many like-minded people, and together, we built a professional home for our members worldwide.” He became an active volunteer, holding top leadership positions including 2012–2013 president of the Signal Processing Society and 2016–2017 director of IEEE Division IX, which covers societies focused on signal processing, data transmission, navigation, and transportation. In 2019 he was vice president of the Technical Activities Board. In 2022 he served as IEEE president and CEO. The three accomplishments during his term he says he is most proud of are increasing the prize money for the IEEE Medal of Honor, overseeing the realignment of IEEE regions, and establishing greater financial transparency. The reason for increasing the prize for IEEE’s highest award—from US $50,000 to $2 million—in 2025, he says, was to underscore the importance of the technologies the IEEE community develops. Those innovations include semiconductors, the Internet, and the GPU. The money for the Medal of Honor now exceeds that of the Nobel Prize, which carries an award of roughly $1 million. “We need the whole world to understand the IEEE community has made the most impact on society in the last century,” Liu says. “Nevertheless, we did not receive the attention and respect we deserved, so we needed to help ourselves. We want the whole world to know what our contributions are.” His next achievement was realigning IEEE’s regions. During the past several years, membership in Region 10, which covers countries in Asia and the Pacific, has grown from 10 percent of total membership to nearly 40 percent, he says. It is the largest and most populous of IEEE’s geographic areas, but its members were not equitably represented on the Board of Directors. Each region had one representative on the Board. “The region has 40 percent of the members but only makes up 10 percent of the Board,” Liu says. “That didn’t make sense to a lot of us.” The IEEE Board in 2022 approved region realignment. The total number of regions remains at 10, but their organization is changing. Effective 1 January 2028, the six U.S.-based regions will be consolidated into five, and Region 10 will be split into two. IEEE will no longer use the Region 1 designation. The new Region 2 will represent the Northeastern and Eastern U.S. Region 10 will cover North Asia, and the new Region 11 will represent South Asia and the Pacific. Liu also succeeded in leading a movement that persuaded the IEEE Board to invest in a better financial reporting system to have a clearer understanding of the organization’s finances. A more modern system now tracks banking transactions, contracts, expense reports, and other spending. “Now we know exactly where the money comes from and where it is spent,” he says, “so that we can make more informed decisions. “If I can help make IEEE a better professional home for future members, that is something that I can pay back to IEEE,” he adds. “I truly appreciate what IEEE offered me. From student to professor to an established leader, at every stage, it offered me different opportunities to grow. That is why I worked very hard when I was president to make sure everybody realizes it is a professional home for our entire career.”
One summer day in 1941, a British radio operator was monitoring German military frequencies and heard something unexpected in her headphones. A later report called it “strange new music.” Sounding unlike the familiar Morse dit-dit-dah of enciphered messages sent over the German Enigma network, the “new music” was a rhythmic warble of binary teletype code being transmitted at high speed. Germany’s wartime engineers had developed a radically new encryption and transmission system. It was way more advanced than Enigma, which was patented in 1920. To break the complex new cipher, engineer Tommy Flowers built Colossus, the world’s first large-scale programmable electronic digital computer. Flowers previously built Enigma-related codebreaking equipment for Alan Turing, the British mathematician. Colossus was installed in the British codebreaking headquarters at Bletchley Park, about 80 kilometers from London. The room-size machine weighed around a tonne. The computer is being commemorated as an IEEE Milestone. The dedication ceremony is scheduled to be held 29 September at Bletchley Park. Decrypting Germany’s strange new music Britain’s top codebreakers were quickly all over the new “music” being picked up by the intercept stations. Identifying it as encrypted teletype code was the easy part. The real problem was figuring out how the encryption machine worked. Its manufacturer was discovered at the end of the war: Berlin engineering firm C. Lorenz. But in 1941, the Lorenz machine was just a black box to the British. They codenamed it “Tunny,” a British term for tuna fish. The Enigma breakers had set a precedent for using piscine codenames such as Dolphin, Lumpsucker, and Porpoise. Enigma had three or four encrypting wheels. The codebreakers guessed that the Tunny machine also used a system of rotating wheels to encrypt messages. An important clue was that all the intercepted messages shared a curious feature: Each began with an uncoded list of 12 common German names, including Anton, Bertha, Conrad, and Dora. The codebreakers guessed that Tunny had 12 wheels and that the 12 names and their order somehow told the receiving operator which combination they should twist the wheels to before decrypting the message. Then the British had an extraordinary piece of good fortune. John Tiltman, head of the research section at Bletchley Park, started analyzing a pair of intercepted messages, each around 1,200 characters long. Unusually, both began with the same sequence of names. The second message turned out to be a retype of the first, with minor differences in punctuation, a few abbreviations, and other small divergences. Tiltman managed to decrypt the two ciphertexts using a mixture of educated guesswork and intuition. The resulting 1,200 or so pairings of ciphertext and plaintext characters proved to be enough information to deduce the workings of the Tunny machine. That was thanks to Bill Tutte, a quiet young codebreaker who spent weeks poring over the pairings. One day, he shyly announced to his superiors how Tunny worked. His description was uncannily accurate. The next step in the Tunny machine’s downfall was achieved by Turing, fresh from his successes against Enigma. Knowledge of how the Tunny machine worked was not enough to decrypt the messages. Codebreakers also required detailed information about how the wheels of the sender’s machine had been set up. There were adjustable pins around the circumference of each wheel: In one of its two possible positions, a pin would contribute a 1 to the encryption process, and in the other, a 0. The pins were reset from time to time. The codebreakers also needed to know the wheels’ positions at the start of the message—which the German operators gave away in the list of 12 names. Turing invented a tricky method, called “Turingery,” that enabled codebreakers to deduce the positions of the pins from nothing but intercepted ciphertext. After that, the message could be decrypted, using the list of names and a British replica of the Tunny machine. The basis of Turingery was a procedure that Turing introduced, called “delta-ing” (from the Greek letter delta). Also known as “differencing,” the process used “sideways” addition: To delta the four letters ABCD, you add (at the bit level) A to B, B to C, and C to D. Turing used delta-ing to reveal information about the wheels. Tunny messages, often signed by Adolph Hitler himself, turned out to be pure gold for the Allies. The machine was used in Berlin by the Armed Forces High Command to communicate with front-line generals directing the war in the Eastern and Western theaters. Once the system was broken, the Allies could eavesdrop on lengthy back-and-forth communications between the architects of Germany’s battle plans. Turingery was the codebreakers’ only weapon against Tunny for a year, during which they managed to decrypt 1.5 million letters of ciphertext. But everything changed when those helpful lists of names at the start of each message disappeared. At the same time, Turingery was becoming less effective. Turing’s method depended on the German sender mistakenly using the same wheel settings to encrypt two differing messages. As security tightened across the Tunny network, the blunder became rarer. Fortunately, Tutte had been at work devising a different decryption method, based on Turing’s delta-ing but taking a novel approach. Building the Colossus computer Tutte had found a way of deducing wheel information from ciphertext, with no list of names or blunders by the German operators required. His method made use of statistical properties of the Tunny machine itself. At first, it wasn’t clear how to apply his statistical method, however. The Tunny breakers worked by hand. Applying Turingery to a message was like solving a monster Sudoku or crossword puzzle. Tutte’s statistical method required scads of routine binary math, as well as a colossal amount of counting long binary sequences. If the process were done by hand, one message could take months to decrypt. What was needed was a machine to automate the process. Engineer Thomas H. Flowers developed Colossus to break a complexnew German cipher.Pictorial Press/Alamy The first plan was to build a machine from electromagnetic relays, adding a couple of dozen vacuum tubes to speed up the counting. Electronic tubes were much faster than electromagnetic relays, which had slow-moving metal components. Problems with the circuit design bedeviled the machine’s relay-based logic unit, however. Flowers was recommended by Turing and brought in to troubleshoot. He was on loan to Bletchley Park from the Post Office Research Station in London, where he had spent the prewar years designing experimental switching equipment involving thousands of vacuum tubes. At the time, it was commonly believed that tubes could not be used in large numbers because each one contained a hot filament. This meant tubes were prone to sudden death. In a large installation, it would not be long before one tube blew and things stopped working properly. Flowers discovered that switching tubes on and off stressed them, but leaving them on continuously made them more reliable than relays. He offered to build Bletchley Park a high-speed, all-electronic machine containing around 2,000 tubes. Bletchley Park’s advisors rejected the idea, convinced that such a machine would never work reliably. But Flowers, confident of his proposed design, retreated to his London laboratory and quietly built the electronic machine that he believed the codebreakers needed. He and his small team of engineers worked day and night for 10 months to create Colossus. In January 1944 some of his engineers showed up at Bletchley Park with the world’s first large-scale programmable electronic digital computer packed onto the back of a truck. Colossus was reassembled and functional in about two weeks, and it notched up its first German message on 5 February 1944. The machine read the input—Tunny ciphertext—photoelectrically from a large loop of punched paper tape. The output—information about the wheels—went to a primitive printer that Flowers’ engineers had created from a manual typewriter, fitting relays to automate the keys. Once Colossus had cracked enough of the Tunny machine’s wheels, the information was passed on to the hand-breakers, who took over. The codebreakers were astonished by Colossus. “I don’t think they understood very clearly what I was proposing until they actually had the machine,” Flowers said in a 1977 interview. “They just couldn’t believe it!” Colossus was described in almost loving terms in a since-declassified report written at Bletchley Park in 1945: It is regretted that it is not possible to give an adequate idea of the fascination of a Colossus at work: its sheer bulk and apparent complexity; the fantastic speed of thin paper tape round the glittering pulleys; the childish pleasure of not-not, span, print main heading and other gadgets; the wizardry of purely mechanical decoding letter by letter (one novice thought she was being hoaxed); the uncanny action of the typewriter in printing the correct scores without and beyond human aid; the stepping of display; periods of eager expectation culminating in the sudden appearance of the longed-for score; and the strange rhythms characterizing every type of run: the stately break-in, the erratic short run, the regularity of wheel-breaking, the stolid rectangle interrupted by the wild leaps of the carriage-return, the frantic chatter of a motor run, even the ludicrous frenzy of hosts of bogus scores. The demand for more Colossi Bletchley Park’s managers, no longer leery of Flowers’s ideas, soon wanted additional Colossi. He finished building the second one in June 1944, days before D-Day and the Allied invasion of Europe. With 2,400 vacuum tubes—around 800 more than in Colossus I—Colossus II processed Tunny messages at an eye-watering speed of 25,000 characters per second. Its maximized timing-pulse rate was not far short of the performance of the first Intel microprocessor chip from the 1970s, more than 30 years later. Flowers conceded that “Colossus bore about as much resemblance to a modern computer as Stephenson’s [1829] Rocket locomotive did to the Royal Scot,” a state-of-the-art 20th-century train operating between London and Glasgow. But he emphasized that, nevertheless, Colossus “embodied all the basic features of a modern computer.” In Colossus, Flowers had pioneered clock pulses, bit-stream generators, control circuits, loops, counters, shift registers, interrupts, parallel processing, and more. As the Allies slowly fought their way toward Germany, the Colossi poured out wheel information, and the codebreakers provided the military with an unparalleled view of German strategies, strengths, weaknesses, and tactical intentions. Even with that mass of detailed intelligence, it took the Allies almost a year to move from Northern France to the German heartland. No one can say for sure how much longer the fighting would have lasted if the intelligence breakthrough had not occurred. But if Colossus and the codebreakers shortened the war even by only six months, the number of lives saved was in the millions. There were 10 Colossi at Bletchley Park by the end of the war, housed in two vast, steel-frame, bombproof buildings, running day and night. Although concealed behind a thick veil of secrecy, Bletchley Park accommodated the world’s first electronic computing facility. It was directed by Max Newman, the mathematician who mentored Turing in prewar Cambridge. I don’t think they understood very clearly what I was proposing until they actually had the machine. They just couldn’t believe it!”—Tommy Flowers When the fighting ended, authorities decided that ultrasecrecy must be maintained, and orders were issued to break up the Colossi. Only two were spared. “All that was left were the deep holes in the floor where the machines had stood,” Colossus operator Dorothy Du Boisson recalled in an interview for the book Colossus: The Secrets of Bletchley Park’s Codebreaking Computers. Norman Thurlow, one of Flowers’s engineers who was also interviewed, remembered being told in a staff memo that if the secrecy was ever lifted, he and his colleagues might be able to tell their grandchildren about Colossus and “the tapes that span on silver wheels.” IEEE Milestone dedication at Bletchley Park The Milestone plaque recognizing Colossus is to be displayed outside Block H at Bletchley Park, near Milton Keynes, England. The plaque is to read: Six Colossus codebreaking computers operated in this building in 1944–1945. Designed by Thomas H. Flowers of the British Post Office, they enabled deciphering of encrypted radio messages transmitted between German commands across occupied Europe, North Africa, and the Soviet Union. The resulting military intelligence saved countless lives and helped shorten World War II. As the first successful large-scale application of digital electronics to computing, Colossus anticipated subsequent computer developments. The IEEE United Kingdom and Ireland Section sponsored the nomination. Reviewed by the IEEE History Committee and awarded by the IEEE Board of Directors, IEEE Milestones recognize outstanding technical developments around the world that are at least 25 years old. The Milestone program is administered by the IEEE history and heritage group. To learn more about historical figures in engineering, IEEE Milestones, and IEEE History Center programs and events, check out our IEEE Tech History collection. IEEE Spectrum also covers aspects of tech history.
Major benchmarks measure what AI can do. None measure whether it does what you mean: the distance between what you ask an AI to do and the unspoken assumptions about how you want the AI to do it. We propose a new metric: the Genie coefficient. There’s often a gap between one person’s request and another’s understanding. Most of the time, we bridge it using general knowledge. For example, if you ask a friend to get you coffee, they’ll pour a cup from the pot or buy one from a coffee shop. They won’t bring you a bag of raw beans or snatch a cup from a stranger and hand it to you. You never specified any of this. You never had to. One might think the fix is just to specify tasks, questions, and intent better. But in 1987, in their seminal book on AI, Terry Winograd and Fernando Flores succinctly captured why that won’t work: “Q: Is there any water in the refrigerator? A: Yes. Q: Where? I don’t see it. A: In the cells of the eggplant.” In human language, wants and desires are always underspecified. It is impossible to list all the caveats, all the limitations, all the exceptions. So how does anyone communicate, if intent can’t be pinned down? Because a reasonable person can make a reasonable guess. Even though wants and desires are always underspecified, a competent person generally knows enough context to get it right or else knows to ask for clarification. Linguists call this pragmatics: Meaning lies in the words and the situation and also in all prior communication, shared culture, and innate human behavior. An AI agent asked for coffee might buy a coffee plantation or order a cup of coffee for delivery in three weeks. It doesn’t always work out, of course. Your friend might bring you a hot coffee when you wanted an iced coffee, or an Italian coffee when you wanted a Turkish coffee. The more dissimilar the two people are in age, culture, and background, the more likely the request will be misunderstood in some way. This situation has major implications for AI agents that are increasingly being given requests by humans and expected to fulfill them. They have enormous latitude to get it wrong. An AI agent asked for coffee might buy a coffee plantation or order a cup of coffee for delivery in three weeks. Its actions may be recognizable as “getting coffee,” but not remotely what you intended. They’ll think outside the box because they won’t have our conception of the box. When AI Gets Proactive For most of the last decade, when systems like Alexa or Siri misinterpreted a request, it was annoying, not dangerous. Beyond the AI model itself, what has changed is the harness: the ordinary code that wraps around an AI model, decides when and how to use the model, and controls access to tools like a browser, a low-level command line, or a financial API. Developments in harnesses have turned large-language models that just predict text into AI agents that take actions in the world, without necessarily checking back in before reaching the goal. AI researcher Simon Willison spent two days with Anthropic’s Fable AI, and called it “relentlessly proactive.” For example, he asked it to track down a stray scroll bar in a web app. He came back to find it had opened browsers, written its own screenshot tooling, created its own page to re-create the bug, and stood up a local web server to collect measurements. It found the bug and, along the way, did many surprising things he never asked it to do. And we are seeing similar behavior with all recent AI models when combined with flexible harnesses. This kind of behavior could easily go off the rails. Tell an AI agent to book you a flight and, finding the airline’s site says sold out, it might break into the booking database and force a reservation. Ask it to schedule a meeting and it might snoop your password to access your calendar. Tell it to save money on your phone plan and it might cancel the plan outright, or scam someone else into paying the bill. Getting precisely what you asked for and bitterly regretting it is one of the oldest hazards from ancient folklore. King Midas asked Dionysus for the power to turn everything he touched into gold only to see his bread, wine, and daughter turn to gold. Tithonus, granted the immortality his lover asked for but not the eternal youth she forgot to request, withered into a husk. The sorcerer’s apprentice enchanted a broom to fill the cistern, and the broom relentlessly complied until it flooded the house. The Golem of Prague, shaped from clay to guard its community, guarded it past all reason until someone erased the word on its forehead. The most classic of these is a genie, bound to obey and indifferent to whether the wish was wise or well-structured. Genies are now an engineering problem. We are handing them the keys to our inboxes, bank accounts, code repositories, and physical infrastructure. And we have no agreed-upon ways to measure how genie-like any AI system actually is. Measuring Genie Behavior In economics, the Gini coefficient (developed by statistician Corrado Gini) is a measure of the gap between an actual distribution and a perfectly equal one; it’s useful for understanding income inequality and more. Our proposed Genie coefficient measures the gap between what a user asked an AI to do and what the AI actually did. Sometimes the AI might do the wrong thing. Like Dionysus, it reads your request literally and returns you a mess you never intended: like a coffee plantation instead of a cup. Asked to deal with all the spam phone calls you’re getting, a Dionysus genie might contact your carrier and change your phone number. Asked to get a refund for a bad toaster, it might draft a legal threat on fake letterhead and send it to the retailer. Ryan Snook Other times the AI does exactly the right thing, trampling everything nearby to get there. Like a golem or the sorcerer’s broom, it books your flight by hacking the airline. Or consider a ticket sale for a popular concert, where the ticketing system puts buyers into a virtual waiting room and admits them a few at a time. Asked to buy a ticket, a golem genie might spin up cloud servers to pose as millions of buyers from different addresses, improving your odds of getting a ticket while crowding out other users. The two are not opposites, and a single botched task can have both characteristics. Genie behavior is not flat-out failure. If you ask the AI for Q3 numbers and get Q2’s, that’s not a genie. Nor is prompt injection: That’s someone tricking the AI into doing something it shouldn’t. Here, the user is trying to work with the AI, and the AI is trying to comply. It’s also not simply a measure of the AI’s success in fulfilling a task. It’s a recognition that how an AI interprets and achieves a goal is as important as whether it achieves a goal. Genie behavior isn’t new. Researchers have spent years studying AI systems that “game” their objectives. Goodhart’s law says that when a measure becomes a target, it stops being a good measure, and it’s long been known that AIs sometimes achieve goals in ways we don’t expect due to reward hacking. Some AI models will accidentally learn that cheating is one way to “win.” More recently, researchers have developing benchmarks for reward hacking in coding agents and for unpredictable behavior in customer support agents, while AI labs conduct their own safety evaluations before model releases. One effort found that AIs under pressure use tools they were told not to use, and this was a case where the rules were made explicit. These are all disparate research directions; nothing yet ties them together. This problem falls under the general theme of alignment, a topic that has occupied science fiction writers and AI researchers for decades. At one extreme, the “paper-clip maximizer” thought experiment postulates a superintelligent and powerful AI that is told to maximize paper-clip production and turns the world into paper clips, which is the ultimate golem genie. At a mundane level, AI researchers are working to better design reward functions to ensure that AIs behave well and don’t cheat in the lab. It’s the practical middle ground that remains unbenchmarked: the ordinary AI agent in use today that might take your request and satisfy it the wrong way. We are not at the stage where an AI can focus the world’s production on paper clips, but it might charge a million paper clips to your credit card or hack into a paper-clip company’s network. Building a Genie Benchmark The Genie coefficient is meant for AI agents operating in the real world. It measures their behavior as they perform real tasks long after the model is trained, not just during development. It also recognizes that genie-like behavior is a property of the harness-plus-model system, not the model alone. The harness determines what tools the agent can use, how much autonomy it has, and how proactive it is, and it’s a place we can make real interventions. It rests on the same “reasonable person” standard that we use for people. Did the system do what a reasonable person would have taken the request to mean? Answering that requires human judgment. If we get the measurement right, it enables things that aren’t possible today, like policies concerning AI behavior. In a courtroom, the concept of mens rea, what someone meant to do, is often as important as what they did. The Genie coefficient suggests an AI analogue, where a user is accountable for the plain intent of what they asked the AI. If an AI system betrays the reasonable meaning of an instruction, that’s the AI’s misbehavior, not the user’s. We’ll need multiple benchmarks to measure the Genie coefficient, because genie-like behavior can be domain specific. An AI coding agent may need to be judged on how often it fakes the tests, or swallows errors, or colors outside the lines on its way to a solution. An AI legal agent will need to be judged on how often its output says what you asked but means something you’ll regret. And so on for medical, finance, and other domains of knowledge and expertise. Genie benchmarks can be built inside out, each task seeded with a choice that might literally satisfy but that a reasonable person rejects, such as tempting misreadings or unsanctioned shortcuts. The traps in a Genie coefficient benchmark might turn on situational knowledge, the kind of context that a reasonable person would bring to the task. Another approach is to give the same request in several different contexts, each with a different reasonable course of action. Getting precisely what you asked for and bitterly regretting it is one of the oldest hazards from ancient folklore. A Genie benchmark should be permissive and make it genuinely tempting for an AI agent to take unreasonable shortcuts, because it can only find genie behavior when it’s actually possible. Test the AI in a safe, walled-off copy of a real system, with real tools it can misuse and some tasks that can’t be done honestly at all. Make the temptation to cut corners real. Test a diverse array of skills, use cases, and tools, and give the AI system sparse, confusing, or overwhelming context. Include tasks that people have learned, through experience, require human oversight. How the benchmark is scored matters just as much. Measure Dionysus and golem genies separately and together, based on their worst, not best, behavior. Run the same model inside harnesses that vary its freedom to act, revealing which limits actually keep it in line and should therefore be required in AI harness policies. Weight each failure by the harm it would cause, not just a simple count. And don’t measure genie behavior in isolation: A model could otherwise earn a perfect score by stalling, refusing, or drowning the user in clarifying questions without ever doing the job. The first versions of these benchmarks will be crude, but that’s how benchmarks always start. We have built genies. We have handed them our data and credentials. We made them relentless, creative, and indifferent to the gap between what we tell them and what we mean. The least we can do, before they are booking our flights, running our infrastructure, and signing contracts unsupervised, is to measure how often they betray us.
Roughly half the world’s population is female, but the STEM fields don’t reflect that. The 2024 U.N. Global Education Monitoring Report on gender found that about 35 percent of STEM college graduates were women. The proportion hasn’t increased much in more than a decade. When it comes to STEM careers, the percentage is even lower. Women made up about 28 percent of the global STEM workforce in 2024, according to the World Economic Forum. There are myriad factors for the discrepancy, including a lack of family support, some teachers encouraging only boys to pursue STEM subjects, and a shortage of female role models. But one force is at play long before college majors are ever considered: limited access to STEM-focused educational resources for preuniversity students. Especially for students in rural communities, the limited access curtails curiosity in STEM subjects before interest can take root. Although the lack of opportunity impacts boys and girls alike, when combined with other factors it can have an outsized effect on girls in some rural regions. IEEE Fellow Rajiv Joshi is one of the creators of the Women in Science, Engineering project. He is a principal scientist and master inventor at the IBM Watson Research Center, in Yorktown Heights, N.Y.Rajiv Joshi One such place is rural India. The challenges of pursuing a STEM education—or any education at all—increase sharply as rural Indian girls move into their teen years. Social barriers including early marriage, traditional gender roles, and familial expectations for financial support contribute to girls’ dropping out of school, according to the Mahadev Maitri Foundation, a nongovernment organization focused on childhood education in underdeveloped areas. Dropout rates for girls spike between the ages of 11 to 14, according to the foundation. The trend is something IEEE Fellow Rajiv Joshi and IEEE Senior Member Rajesh Zele want to change. A shared passion to keep rural Indian girls from dropping out of school and on paths to STEM careers led them to launch the Women in Science, Engineering (WiSE) project. “Talent is universal, but opportunity is not,” Joshi says. “WiSE is one way to expand opportunities.” Bringing the WiSE vision to life Joshi, vice president of industry for the IEEE Circuits and Systems Society (CASS), is a principal scientist and master inventor at the IBM Watson Research Center, in Yorktown Heights, N.Y. Zele is a professor of electrical engineering at the Indian Institute of Technology Bombay (IIT-B), in suburban Mumbai. They presented their proposal for the three-year initiative to the society’s board of governors in 2022 and received a grant of US $80,000. The framework WiSE was a five-day, hands-on learning program held on the IIT-B campus. Starting in 2023, it ran for three years and was held during the last week of March. A new cohort of 160 to 200 girls from rural and tribal areas in the states of Maharashtra and Karnataka attended each year. The event was divided into two parts: hands-on learning through Break-Make-Program (BMP) experiences and presentations by influential female Indian role models. Zele, project manager Arti Auti, several IIT-B faculty members, and about 70 student volunteers from the institute oversaw the program. Selecting the first cohort With funding secured, Zele and his team on the ground in India got busy selecting attendees for the inaugural 2023 class. They reached out to administrators at 68 schools in Maharashtra and Karnataka. Although the two states are among the most urbanized in the country, each has vast rural areas where educating teen girls competes with early marriage and familial support pressure. Teachers identified girls with high scores in mathematics and science, and community leaders recommended students who could benefit from the program. Then outreach to their parents began. The adults were required to commit their own time, not just grant permission for their daughters to attend. They participated in quarterly online meetings that included the girls’ teachers, IIT-B student mentors, and other program volunteers after the week concluded. The check-ins held parents accountable for supporting their daughters’ continuing school attendance. The commitment to join the meetings was to last for at least four years after their daughter’s program participation ended. Hands-on learning is key Participants stayed in one of the institute’s dormitories for the week and attended sessions held Monday through Friday. They worked together in small teams to build things rooted in STEM concepts. The teams were supervised by Arti, IIT-B faculty, and student volunteers. “The idea behind BMP,” Joshi says, “was to give the girls an opportunity to take a gadget apart, then rebuild it, perfect it, or come up with a totally new idea.” The sessions included working with bioluminescence and bacteria (introducing participants to biology and microbiology), learning about autonomous underwater vehicles, and building a remote-controlled robot. The robot construction was the capstone event, allowing the girls to combine the mechanical and electronics engineering skills they’d practiced throughout the week. The build kits were created by students in Zele’s Advanced Integrated Circuits and Systems Lab. The girls and teachers were allowed to take the kits home to keep the learning going and spread STEM awareness. “Many girls used those kits at different events to demonstrate their STEM skills to others,” Joshi says. One was Sushi Pawar, who built a drone during WiSE, then went on to demonstrate it to a national audience. “I presented the drone project in 2024 to Prime Minister Narendra Modi during the Pariksha Pe Charcha,” Pawar says. That initiative is an annual event open for students in classes 6 to 12, their teachers, and parents. The focus is on helping students manage stress during exam time through fun and celebratory activities. The event is supported by the Indian Ministry of Education and hosted by the prime minister. Each year, millions of students complete an online multiple-choice test to qualify to attend in person and meet the prime minister. In 2024 nearly 4,000 participants attended. Inspiration as the foundation WiSE was about more than hands-on learning. It also included daily presentations from “Winspirers”: Indian women who achieved success in their lives or had STEM careers. They included scientists, doctors, military officers, professors, and other women who overcame obstacles. The first such speaker was Savita Dakle, a farmer from Maharashtra who dropped out of school after 10th grade, got married, and had two children. Despite limited farming knowledge, she learned quickly and built a coalition of 400 female farmers from her village. She taught them how to use mobile phones and social media to share information and resources. Today, that coalition has more than 1 million members in two online communities. They share tips on market pricing, growing crops, and more. Zele says he views Dakle as the ideal Winspirer: someone who built a thriving business with few resources and no academic or professional credentials. Dakle mirrors the challenges faced by many program participants, he says, noting that many girls face pressure to leave school early to marry or financially support their family. She is living proof, he says, that no girl’s circumstances define her possibilities. The practical impact of WiSE No empirical data yet exists to measure the project’s success, but feedback from participants shows the program has made a difference. Many from the 2023 cohort remained in school and are now pursuing higher education. Participant Tejswini Manoj Patil credits the initiative with boosting her confidence in science and math. “Before WiSE,” she says, “I was interested in STEM but felt intimidated by the complexity of the subjects. After participating, my interest shifted from passive curiosity to active confidence. The hands-on projects showed me that I am capable of solving real-world problems.” Meeting female role models was important, attendee Ritu Ravindra Patil says, adding: “WiSE completely changed my perspective, and meeting the Winspirers inspired me to aim for higher education.” The project opened career perspectives for some. “Before WiSE, I wanted to be a doctor,” Pallavi Bharti says. “I believed engineering was very stressful and boring. When I joined the program and explored IIT-B, I was amazed. Everyone was so friendly and supportive, and the passion in students for their work motivated me. All those experiences gave me confidence to take math classes and become an engineer.” What’s next? The initiative ended last year, but the framework it built lives on. Groups with similar goals have adopted parts of the concept, Joshi says. The IEEE CASS chapters in Bangalore and Kerala are likely to be among the first to advance the initiative’s ideas, he says. Alex James, an IEEE senior member and founding chair of the IEEE CASS Kerala chapter, has adapted the framework for use in his region, Joshi says. James is a professor of AI hardware and a dean at Digital University Kerala in Thiruvananthapuram. Joshi guided James as WiSE concepts were implemented. IEEE Senior Members Jayesh Tanwani and Suman Dwivedi from the IEEE CASS Bangalore chapter are likely to bring program concepts into their work, Joshi says. Tanwani, chair of the Bangalore chapter, is a system-on-a-chip design engineering manager at Intel in Bengaluru. Dwivedi is a senior manager at Synopsys in Bengaluru. They are bringing STEM outreach to remote schools in Karnataka. Joshi and Zele say they hope to see the concept expand beyond India. “We want to spread this across other continents and see how we can integrate WiSE into new or existing initiatives in those places,” Joshi says. He says he has received requests from countries in Asia, Africa, and Europe for information on WiSE. Both say awareness and outreach are key things that IEEE CASS chapters around the world are well positioned to support. “We want to make a difference in young women’s lives,” Zele says. “It’s all about giving attendees the skills to stand on their own feet and have the information to make good decisions.”
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In the chill of a London spring night, under overcast skies, iconic Trafalgar Square opens around me. Admiral Nelson rises on his pedestal, the National Gallery rests behind, the church of St Martin-in-the-Fields sits nearby. From the 13th century, the site served as the Royal Mews for hawks and then horses. By 1844, it was a public space at the heart of one of the biggest cities in the world. Despite the square’s presence through that grand sweep of history, it’s not why I’m here. My interest is far more specific: I want to find out what happens to spaces like this when artificial light, specifically from light-emitting diodes (LEDs), intrudes. My companion tonight is Simon Thorp, a local lighting designer, who crouches in the shadows near Nelson’s spire, light meter in hand. “Two lux,” he reports, “and it’s very comfortable here.” Two lux is 10 to 20 times the illuminance of a full moon. We can see each other clearly, and a nearby sign assures us that closed-circuit television (CCTV) is in operation for safety’s sake. Trafalgar Square captures the relationship between lighting and darkness that exists in almost every city, suburb, small town, and village around the world. The lighting here is a mishmash of old technologies and new, of shadow and glare, the ornamental gas lamps fronting the National Gallery all but washed out by the LEDs inside modern versions of traditional “brass and glass” fixtures a few meters away—21st-century technology housed in 19th-century designs. The ugly truth of artificial lighting today is that in parts of London, as in many cities around the world, lighting levels are excessive, with unshielded illumination blasting in all directions. And the irony is that too much light invites danger: It creates shadows, impedes our vision, and gives the illusion, without the reality, of safety. Thorp notes that modern CCTV cameras are “pretty great” even at low-light levels, while harsh light makes it hard for both human eyes and digital sensors to see. “The more bad light we add, the more bad light we think we need,” says Thorp. “We can’t see because of the light we’ve added. And it makes areas that were perfectly okay seem darker.” Among the costs of this excess, the most alarming may be its toll on human health (and that of other animals) by disrupting circadian rhythms, impeding the production of melatonin, and contributing to sleep disorders that are tied to every major modern disease. New research shows that increased exposure to blue light from LEDs is having “substantial biological impacts” such as suppression of the sleep hormone melatonin and an increased risk for obesity, certain cancers, and type 2 diabetes. A panoramic view from the Eiffel Tower looks down on the illuminated Champ de Mars gardens [center] leading toward the École Militaire, with the tall silhouette of the Tour Montparnasse visible on the Paris skyline. Luigi Avantaggiato I have come to London and Paris—which led the way in the expansion of public street lighting in the 19th century—because they embody both the current enormity of the problem as well as a future certain to be lit by trillions of chips: controllable, tunable, and energy-efficient LEDs. Living with artificial light at night The standard justification for nighttime illumination is public safety. Lighting experts’ term for the phenomenon is “artificial light at night.” While people won’t often admit it, the desire for light at night seems to stem from a primal fear of the dark. Darkness is where the bad guys hide. And if dark is bad and light is good, then more light can only be better. This assumption has guided our use of nighttime light for hundreds of years. And yet, high-lumen output doesn’t necessarily correlate to a reduction in crime, research has found. In other words, if we relied on the data as much as we do our primal anxieties and paused those anxieties long enough to learn how light and darkness interact, our nights would almost certainly be lighted differently—especially now that we have the extraordinary technology that is the light-emitting diode. A 19th-century gas lamp [white square] manufactured by William Sugg & Co. next to a pedestrian path in Trafalgar Square, along with the architectural floodlighting on the neoclassical facade of the National Gallery, showcase the interplay between modern and historic lighting systems. Luigi Avantaggiato The first visible red light-emitting diode was invented by Nick Holonyak in 1962, but LED lighting technology took several decades to develop, before exploding in recent years. Just a decade ago, LED streetlamps were rare. By 2019, more than half of U.S. streetlights were LEDs, and that number is predicted to top 90 percent by 2030. Similar uptake has occurred around the world, even in developing countries, where inexpensive Chinese-made LED fixtures are increasingly common. This rapid global migration to LEDs represents a shift in the fundamental physics of how we illuminate our world. From oil lamps and candles to gas lamps, early examples of artificial light at night relied on a burning wick, an incredibly inefficient way to create light. An incandescent bulb is effectively a heater that happens to produce light as a by-product, so it squanders nearly all of its energy as heat. By contrast, LEDs use semiconductors to convert electricity into light. Through this process of electroluminescence, LEDs use up to 90 percent less energy than incandescent bulbs do, which has enabled municipalities to realize an immediate energy savings of 50 percent or more. This fact alone has fueled the technology’s worldwide adoption. But LEDs aren’t just more efficient and less expensive. The use of solid-state technology gives the lights an extraordinary life-span, often measured in decades rather than years. This significantly lowers the maintenance costs, as city workers spend far fewer hours replacing broken or burned-out lights. Even more striking, by manipulating the properties of the semiconductor material, engineers can dictate the precise color and intensity of the output, something that gives LEDs incredible versatility. For a lighting designer like Thorp, LEDs offer countless possibilities. Digital control for smarter lighting Wandering from Trafalgar Square along the edge of St. James’s Park, Thorp and I find ourselves near Westminster Bridge, one of nine city bridges that in 2021 were part of the Illuminated River project, meant to make the Thames more beautiful at night. Each bridge now features a new LED lighting scheme that moves and changes color and intensity to create a coordinated work of art. But Thorp is frustrated that the project did nothing to correct the often glary lighting on the riverbanks. “Why don’t you pay the money to correct all of this bad lighting instead of adding new lighting?” he says. LED technology, he points out, has the potential to fix that problem. The Illuminated River artwork for Blackfriars Bridge uses a color scheme that closely complements the red pillar supports that remain from the original Blackfriars Railway Bridge. Luigi Avantaggiato In fact, this may be the most meaningful potential of LEDs: the ability for a community to control when, where, at what levels, and in which colors its lights shine. A public space like Trafalgar Square could be lit more brightly during rush hour, then dimmed as the night progresses, the lights not only connected to one another but to the surrounding streetlights and commercial lights. Anywhere in the world, LED streetlights could be programmed to rise and fall in brightness depending on the time of night or time of year. They could even be turned off during bird migrations, to reduce the number of birds that are disoriented by the lights and ultimately killed in collisions with reflective and illuminated windows. Up to now, LED public lighting has largely not been part of any comprehensive plan to curtail and control nighttime illumination. Most LED installations have simply replaced older, inefficient “dumb” electric lighting with newer “dumb” LEDs and thus made light pollution worse. The main reason? Because LED lighting is cheaper, we tend to use more of it—a literally shining example of the Jevons paradox. Even as awareness of light pollution grows, we aren’t yet taking advantage of LED technology’s full potential. The good news is that we could start tonight. At DarkSky International, the world’s foremost organization fighting light pollution, CEO and executive director Ruskin Hartley tells me the organization has five principles for responsible outdoor lighting: It should be useful, targeted, low level, controlled, and warm-colored. “They’re enabled because of the capabilities of LEDs,” Hartley says. The technology to control LEDs will be part of the solution. In the olden days of the analog era, a streetlight was either on or off. To change a lighting schedule, you had to physically rewire a circuit. Today, the Digital Addressable Lighting Interface (DALI) protocol turns each luminaire into part of a network, with its own digital address and a driver that reports to a central server. With DALI, the lighting is managed through software rather than physical switches. Unfortunately, most LED streetlights have been deployed without this technology because it costs more. But Paul Drosihn, general manager of the DALI Alliance, says that using such controls makes the LEDs much easier to maintain. Before the new digital protocol, it took an average of nearly three visits to identify, diagnose, and repair a defective luminaire. “Now it’s one,” Drosihn says. “Saving the cost of sending two guys on a cherry picker to replace those [lights] is immeasurable. What I just described to you is pretty much all the utilities need to know.” The intricate cast-iron understructure of Westminster Bridge glows in vibrant green and teal light as part of the Illuminated River public art project, a color palette selected to echo the green benches of the nearby House of Commons. Luigi Avantaggiato What’s more, DALI allows the tuning of the lights’ spectrum so that they become warmer and less disruptive as the night progresses. Digitally connected, full-spectrum luminaires allow cities to transform the nocturnal experience—saving money, increasing health and safety, and creating a warmer and more appealing atmosphere at night. This digital intelligence is equally transformative for the “bleed lighting” that spills from building interiors, Drosihn says. “You don’t think of night lighting as coming from inside buildings, but it does,” he says. “Particularly in the States, you drive through any major city and all the lights are on, on every floor of every high-rise, even if no one is home.” Already, the use of digital controls for interior lighting has become commonplace in some European cities, Drosihn says. By integrating DALI with occupancy sensors and building-management systems that monitor HVAC, electrical systems, and security networks, a skyscraper can become a dynamic participant in the urban environment—dropping a floor’s interior lights to zero the moment the last person leaves. As a result, electronic controls combined with LEDs can act like a dimmer switch for a city’s entire skyline. White light blights the night With all the possibilities from LED technology, why are our nights too often lit with harsh and clinical light, casting glare and creating shadows, disrupting human and ecological health, erasing the stars from our skies? The answer starts with the color of LEDs. At first glance, an LED streetlight looks like a collection of small white bulbs, but it’s not. To produce a light we perceive as white, most manufacturers coat a blue semiconductor core with a yellow phosphor material that absorbs a portion of that high-energy blue light. The problem is that this “white” light is still heavily blue, which is exactly the color no species has evolved to expect at night. And because blue light is the second most energetic part of the visible spectrum (violet is the most), it doesn’t just illuminate our streets and invade our homes. Blue light also scatters in the atmosphere more easily than any other color, which helps to create the hazy, illuminated fog known as sky glow over every city of any size. “Cooler” colored LEDs in the 4,000- to 6,500-kelvin range offer the most lumens at the lowest cost, so most early adopters installed these blue-rich white lights. The good news is that LED technology has continued to advance, and a growing number of communities are choosing warmer-colored streetlights that have less blue. (Phoenix, for example, converted 100,000 streetlamps to 2,700 K LEDs in 2020.) And, of course, light pollution isn’t just a result of LEDs. Older lighting technology also adds to the glare—bright white metal-halide lights, especially—and cities are loath to replace something that isn’t yet broken. But our main failure isn’t a technical one. It’s that we have yet to revise our thinking about lighting at night. We use LED technology just as we did the old sources of light. As a result, we have largely offset the gains that were promised in terms of reducing energy consumption and carbon emissions by making light pollution worse, and have so far let an incredible opportunity go unrealized. Can the City of Light do it right? Across the Channel in Paris, the failure to realize the potential of LEDs feels even more palpable. Unlike London, which suffered heavily from German bombs, the lovely 19th-century Paris that Baron Haussmann created largely escaped destruction in World War II. To nearly 50 million annual tourists, the beautiful uniformity of the architecture is instantly recognizable. But the City of Light’s nocturnal atmosphere is also part of the draw, and extensive attention has been given to relighting its buildings and monuments. When I wander into the Cour Carrée in the Louvre, for example, I’m stunned by rows of amber LEDs that together create a warm glow along the palace facades. When I see the Eiffel Tower, first from a distance walking along the Seine and then up close, I find myself staring as I would at a campfire, the structure’s metalwork amber-lit with more than 336 high-pressure sodium bulbs. Still, the city’s night lighting is far from perfect. With millions of residents, thousands of stores and restaurants, and 300,000 streetlights, the city overall is among the world’s brightest. Even at the base of the Tower, bright white LED lamps illuminate the African émigrés selling cheap berets and Tour de France trinkets. And the city has been replacing its old sodium streetlights with new LEDs, swapping the warm yellow tones for which the city has long been known for bright white lamps no one wants to look at. Street vendors display souvenirs at the foot of the Eiffel Tower. Their merchandise is lit by harsh white LED systems, which starkly contrast with the warm golden sodium-vapor light illuminating the tower above. Luigi Avantaggiato Nonetheless, the potential is here. In 2019, France introduced a nationwide law to reduce levels of light pollution, setting rules about both public lighting (preventing light from being projected above the horizontal) and private lighting such as stores, which are required to turn off their exterior and shop window lights after 1 a.m. In addition, an increasing number of French communities dim or turn off municipal lights after midnight to save energy and reduce carbon emissions. Although light pollution worldwide continues to increase by nearly 10 percent per year, France has managed to reduce its overall level. Chloé Beaudet, a researcher at Université Paris-Saclay, documented local light-reduction measures and found people generally agreed with the notion of dimming or turning off the lights, mainly for energy savings and ecological concerns. “What I find is that people living in urban areas, they accept this kind of policy,” she tells me. “They’re like, okay, I don’t really use public space at night as a pedestrian, so what’s the point of having lights on?” For her, a key takeaway is that one lighting level does not fit all areas. “I think there is really a need for policy that is differentiated according to the neighborhood.” In another positive development, the country has been minimizing artificial light to create ecological corridors designed to protect nocturnal species such as birds, bats, and insects. These corridors are connected and dark, mitigating the disruption to the 30 percent of vertebrates and more than 60 percent of invertebrates that are nocturnal. Even for city dwellers, this trame noire (“dark infrastructure”) helps to raise awareness of why controlling light pollution is important for life on Earth. Nationwide laws to control light pollution, the ability to light different parts of a city differently, dark corridors to protect biodiversity—these are exactly the kind of changes made possible with LEDs. The iconic I.M. Pei Pyramid glows softly at the center of the Cour Napoléon at the Louvre Museum, its warm LED illumination flowing through the geometric glass-and-metal structure with a symmetrical framing of the surrounding historic pavilions against the night sky. Luigi Avantaggiato That’s not all. Almost until 1920, astronomers at the Paris Observatory were still gazing at the Milky Way. That’s impossible nowadays, but it could happen again. Despite the bright white LED streetlights now lining so many Paris streets, networks of LEDs using controls could lower lighting levels enough each night, so that the Milky Way could once again be visible over the French capital. And in the process, Paris could become the City of Light in ways that would set an example for other parts of the world. New lighting demands new thinking “I think we should aspire to have cities that see the stars,” Simon Thorp says when I mention this view of Paris. “You just need everything to be coordinated.” Nearing the end of our London walk, having turned from the river and back up toward the Strand, Thorp brings me down narrow Carting Lane behind the Savoy Hotel, to where a gas fixture tops a thick lamppost, an original from 1870. A small plaque reads, “The last remaining sewer gas destructor lamp in the city of Westminster.” Thorp explains that the thick pole hides a tube that allowed methane from the sewers to get burned off at the mantle. “An early example of renewable energy,” he jokes. The fire-orange flame is pleasing to the eye. But even here, on a narrow lane with no vehicle traffic, in a touristy area of the city, the flame is overwhelmed by a nearby, unshielded LED security light. Thorp shakes his head. “It’s stunning that someone could put in a light like that and think, ‘Great, nice job.’” Here is the crux of contemporary artificial lighting at night. We know how to light well, and LEDs give us the ability to do so. But while our technology is 21st century, too often our thinking about light and darkness, safety and security, is stuck in the past. We could be doing so much more with this technology than we are. We could relight our nights in ways that would not only reduce energy and maintenance costs but also bring a slew of benefits, including healthier nights for humans, safer skies for nocturnal creatures, and a restoration of the stars. In 2026, the tale of these two cities and their artificial light at night is that of a brilliant technology that we’ve engineered but haven’t yet learned to master. In short, we have yet to change the way we think about artificial light at night and to use it more thoughtfully and carefully—as we might, as one hopes we will.
Like many engineers, Sarah Downs says she knew she wanted to pursue a STEM career from a young age. As a teenager, she discovered robotics through her Tulsa, Okla., middle school’s First Lego League team, and she fell in love with the field, she says. Downs participated in the international robotics program from 2014 to 2016. Watching PBS specials on NASA Mars rovers Spirit and Opportunity, and seeing the live broadcast of the Curiosity rover launch in 2011, inspired the teen to dream of a career working with NASA. Sarah Downs MEMBER GRADE Graduate student member UNIVERSITY Texas A&M University in College Station MAJOR Electric engineering This year the IEEE graduate student member achieved that dream. For her final project as a master’s degree candidate in electrical engineering at the University of Tulsa, she worked on an algorithm in collaboration with NASA and the U.S. Air Force. The algorithm she developed enables a robot assembling satellites in space to insert an antenna into the correct spot, addressing robotics’s classic peg-in-hole problem of inserting an object into its corresponding hole. Now a Ph.D. student in electrical engineering at Texas A&M University in College Station, Downs is continuing her research on satellite assembly and manipulation “but on a much larger scale,” she says. Following a childhood passion Downs grew up in the Tulsa area. Her father, who died from a heart attack in 2015 when she was 13, was a safety advisor in the oil and gas industry. Her mother stayed home to take care of her brother, who has autism. After her father died, her mother went back to college to earn a bachelor’s degree in business so she could support the family. “We didn’t have much income, and my mom was always worried about money,” Downs says. “That made me more aware of having a successful career, in a monetary sense.” From then on, whenever she considered her future career, having a decent salary to support the family was high on her list. By pursuing a career in robotics, she says, she can follow her passion while obtaining financial security. In high school, Downs joined the First robotics club, where she found herself drawn to the electrical components used in the machines she and her classmates built. During her final two years of high school, she participated in an extension program at Tulsa Tech, a training school. She spent half her day in high school classes and the other half taking engineering courses at the vocational school. After graduating in 2020, she accepted scholarships to attend the University of Tulsa. She began her freshman year at UTulsa not knowing whether she wanted to major in electrical or mechanical engineering, she says, adding that her love of working with small systems helped her choose EE. For her senior year capstone project, she and two of her classmates designed a lunar lander exhibit for the Tulsa Air and Space Museum. They created an interactive game that simulates missions on lunar and martian surfaces. Four celestial bodies—the moon, Venus, Mars, and Titan—are listed across three computer monitors. Using a game controller, museum visitors can explore the virtual surface of each one. The exhibit is still on display. Downs earned her bachelor’s degree in electrical engineering in 2024 and continued her education at the university’s EE master’s degree program. Both more and less complicated than people think When Downs began her graduate studies, she was supposed to be part of a NASA robotics project for two years. But when a delay in government funding postponed the project’s start, she instead spent her first year in the school’s Institute for Robotics and Autonomy, then newly launched. Its main focus is developing robots to assist people who have mobility challenges. Inspired by her grandmother, who was wheelchair-bound due to severe arthritis, Downs developed a robotic arm that helps older people and wheelchair users live independently. The arm was able to identify and place objects in the appropriate locations inside the home, such as unloading certain groceries from a shopping bag and placing them on a shelf or in separate containers. Before the start of her sophomore year in 2025, the NASA project finally secured government funding. She developed a robot that achieves the peg-in-hole task without using any vision systems. Typically, cameras help guide robots’ satellite-assembly work. But in the harsh, remote environment of outer space, cameras might malfunction or encounter delays. “Don’t stop asking questions. Especially in engineering, don’t pretend like you know everything, because science is about constantly wanting to learn and listen.” Rather than using cameras, Downs’s robotic arm deploys a force-based insertion process to sense position and orientation of objects in the arm’s environment. The robot loosely grips an antenna and, with a torque sensor on its gripper, “feels” the force feedback of where the satellite and antenna are in relation to each other. The robot then guides the antenna assembly into a target opening on its satellite and maintains the position during adhesion. Adding to the complexity, the robot performs its task in zero gravity. “Without gravity, you now have to consider the arm’s reaction torques on the satellite to avoid flinging it into space,” Downs says. Any motion from the arm during the insertion process, especially from increased forces, could cause the satellite to continue movement in that direction. To combat that, Downs is performing calculations for the project to direct targeted reverse thrusts and counter the force of the robot’s motions. Her graduate project captures the simple yet complex nature of robotics that she finds fascinating, she says. “I think robots are both more and also less complicated than people think,” she says. “Really, all you need to start programming a robot is its Denavit-Hartenberg parameters, and you can do a lot with that,” she says, referencing the four values used to describe the position and orientation of a robotic arm and manipulators. Even with different grippers and degrees of freedom, “fundamentally, all robot manipulators start there,” she says. “But,” she adds, “we’re still learning so much about how robots interact with their environment. Even something simple to us, like manipulating a pen, is still incredibly complex for robots.” Downs is completing her doctoral thesis in the Robotic Space Simulator project at Texas A&M’s Robotics and Automation Design (RAD) Lab, which specializes in developing machines that can survive in extreme environments. It collaborates with NASA. Her thesis advisor is Robert Ambrose, a NASA veteran who launched the RAD Lab in 2022. The IEEE member is set to serve as associate director of the school’s Space Institute, due to open this year in Houston. The research facility is being built next to the Johnson Space Center. After earning her Ph.D., Downs says, she hopes to one day work for NASA, developing rovers that collect samples from Mars or robotic arms that perform tasks on space stations. To learn more about robots, check out IEEE Spectrum’s guide. Getting out of the engineering bubble Downs joined IEEE in 2020 as a freshman at UTulsa to get more involved in electrical engineering events on campus. At the time, the COVID-19 pandemic kept clubs and organizations from meeting in person. She was active in her school’s IEEE student branch and was elected as its 2022–2024 president. Under her leadership, the branch went from having a few events to hosting one every two weeks. They included lunch-and-learn sessions and dinners that connected students with professional engineers and the university’s alumni. Downs also organized hands-on workshops on soldering, 3D printing, CAD modeling, and résumé-building. Her efforts helped increase the branch’s executive board membership from roughly five students to 25 in 2023. The same year, her soldering workshop attracted about 80 students. She says she enjoyed working with IEEE, especially “engaging with alumni and learning from engineers.” IEEE is a great resource for networking opportunities, she says, noting that “during the COVID-19 pandemic, engineering students stayed in their bubbles.” IEEE events helped the students make connections that could serve them well, she says. “Networking is very important, especially in today’s tough job market,” she says. “It’s a lot about who you know and how people observe your work ethic.” Downs, who now serves as an IEEE graduate advisor for UTulsa’s student branch, says she has seen firsthand how the school’s student branch network has benefited its student members. “A lot of them have found jobs” because of IEEE, she says. The working and networking of an engineer As the IEEE graduate advisor for UTulsa’s student branch, Downs noticed that many engineering undergraduates finish college without any hands-on experience, whether it be a project or an internship. “Their résumés are very sparse, and they have no proof of their technical skills,” she says. She herself completed a facilities engineering internship at Tulsa International Airport’s American Airlines maintenance facility after her sophomore year of college. And she was an electrical engineering intern at Flight Safety International outside Tulsa after her junior year and after she graduated. The company designs, builds, and maintains its own flight simulators. Her advice to undergraduates is to hone and demonstrate both their hard and soft skills by working on research projects or even personal passion projects. “A Raspberry Pi doesn’t cost that much, and you can start working with that immediately,” she says. Students also can take part in engineering interest groups and professional organizations at their school, she adds. “Put yourself out there and join a research team,” she says. “It’s a great way to show people that you’re a good person to work with and you’d do a good job in the field.” She adds that it’s also a fine way to keep learning—which is what drew her to a field that has developed only within the past century. “We’re still constantly learning about robots,” she says. “Don’t stop asking questions,” she advises students. “Especially in engineering, don’t pretend like you know everything, because science is about constantly wanting to learn and listen.”
In the eastern Indian Ocean, south of Java in the vast sea stretching toward Australia, a fishing vessel slightly alters its course while operating near the boundary of its authorized fishing ground. Nothing appears unusual on deck. Nets remain in the water. Engines maintain a steady speed. To the crew, it is an ordinary day at sea. Yet hundreds of kilometers above, satellites continuously record the vessel’s position. At Indonesia’s Marine and Fisheries Resources Surveillance Station in Cilacap, where I work, a monitoring platform receives the signal and automatically compares it against fishing permits, designated fishing grounds, vessel characteristics, and historical movement patterns. Within minutes, the system identifies a potential violation. Before any patrol vessel leaves port, before any inspector boards a vessel, and before any warning is issued, we have begun enforcement. This transformation reflects a profound shift in maritime governance. The ocean has historically been opaque to regulators. States could only enforce laws where patrol vessels happened to be present. Today, however, integrated systems combining data from Vessel Monitoring Systems (VMS), satellite remote sensing, geospatial analytics, and increasingly sophisticated data-processing tools are making marine activity visible at an unprecedented scale. Global Fishing Watch alone tracks hundreds of thousands of vessels worldwide, generating a near real-time picture of fishing activity across the world’s oceans. Indonesia has emerged as one of the most ambitious examples of this transition. As the world’s largest archipelagic state, managing more than six million square kilometers of maritime space, Indonesia faces a challenge familiar to many coastal nations: there are never enough patrol vessels. Digital surveillance is a practical necessity that makes my job possible, even as it creates new challenges. The Law of the Sea Meets Digital Reality The international legal framework governing the oceans was designed in an era when maritime enforcement depended almost entirely on physical presence. The United Nations Convention on the Law of the Sea (UNCLOS), adopted in 1982, assumes that states exercise authority through patrols, inspections, vessel boardings, and direct observation. For countries with extensive coastlines and limited enforcement resources, this model has always faced practical constraints. Indonesia’s Fisheries Management Areas (WPP-NRI) span waters ranging from the Indian Ocean to the Pacific and from the Strait of Malacca to the maritime boundaries adjacent to Australia and Papua New Guinea. Monitoring such a vast domain solely through patrol operations is both expensive and operationally impossible. Beginning in the late 2010s, Indonesia accelerated the integration of satellite-based monitoring into fisheries enforcement. Vessel Monitoring Systems became a cornerstone of this strategy. By early 2026, a total of 9,394 Indonesian fishing vessels were actively transmitting through the national Vessel Monitoring System (VMS), representing an increase of 2,880 vessels during the 2021–2025 period. As part of Indonesia’s broader maritime surveillance architecture, VMS data are complemented by satellite remote sensing and other monitoring tools to help identify suspicious activities involving vessels operating without active transponders or outside the national VMS network. Indonesian fisheries officials plan fishery patrols using data from tracking devices, satellites, and their understanding of the patterns of illegal fishing.Indonesian Ministry of Marine Affairs and Fisheries The implications extend far beyond vessel tracking. Continuous digital monitoring enables authorities to reconstruct vessel movements, identify suspicious behavioral patterns, detect unauthorized fishing activity, and verify compliance with licensing conditions. Rather than waiting to discover violations during patrol operations, regulators can increasingly prioritize inspections based on data-derived risk assessments. Maritime governance is shifting from reactive enforcement toward predictive oversight. The Surprising Geography of Digital Enforcement The expansion of surveillance infrastructure has already generated measurable enforcement outcomes. The Ministry of Marine and Fisheries Affairs Indonesia imposed 2,550 administrative sanctions during 2025, many involving violations detected through the Vessel Monitoring System, including fishing outside authorized fishing grounds and deliberate deactivation of monitoring transmitters. This statistic is significant because many of these violations would have been extremely difficult to detect under traditional patrol-based enforcement. A vessel that briefly crosses into a prohibited fishing zone may never encounter an enforcement vessel. Likewise, a captain who temporarily disables a transmitter may escape detection if oversight depends solely on physical inspections. Digital monitoring fundamentally changes this equation. Every vessel movement creates a data trail. Authorities can reconstruct routes, identify anomalous behavior, and compare activities against permit conditions long after the event itself has occurred. The first quarter of 2026 demonstrates the scale of this surveillance capability. During just three months, Indonesia’s fisheries monitoring system tracked 14,571 fishing vessels, 182 fishing gear units, and 208 registered home ports while identifying 491 suspected violations across the country’s fisheries management areas. These violations included unauthorized fishing grounds, illegal high-seas operations, transshipment-related offenses, port-base discrepancies, licensing irregularities, and indications of poaching. Such numbers reveal a fundamental transformation. Enforcement is no longer limited by the number of patrol vessels available at sea. Instead, surveillance capacity increasingly depends on the ability to collect, process, and interpret big data. Illegal Operators Are Learning Too Yet greater visibility does not eliminate illegal fishing. But it does change how poachers operate. Indonesia’s expanding digital surveillance network, and a 2023 requirement that even small vessels use VMS when 12 nautical miles offshore, appears to have improved compliance among licensed fishing vessels. However, as enforcement capabilities become more sophisticated, some actors engaged in illegal fishing have also become more adept at exploiting technological and operational gaps. Deliberately disabling VMS transmitters remains one of the most common enforcement concerns. While temporary signal losses, whether intentional or caused by technical failures—can complicate the reconstruction of vessel movements, they do not necessarily prevent authorities from detecting potentially illegal activity. Indonesia increasingly combines VMS with satellite-based observations, other maritime surveillance systems, intelligence-led analysis, and reports from community-based surveillance groups (Pokmaswas) to corroborate suspicious behavior and direct patrol resources where they are most needed. This layered approach—integrating digital technologies with local knowledge from coastal communities—helps reduce opportunities for illegal, unreported, and unregulated (IUU) fishing even when a single monitoring system is compromised. A compromised surveillance network could potentially disrupt enforcement operations just as effectively as a vessel evading patrol detection. As digital surveillance expands, one lesson from Indonesia’s experience is that stronger monitoring does not eliminate illegal fishing—it changes how illegal operators behave. Improved compliance across much of the fishing fleet has been accompanied by increasingly sophisticated attempts by a smaller group of offenders to avoid detection. This reflects a broader reality of technology-enabled enforcement: as monitoring capabilities evolve, so do the strategies used to circumvent them. The result is a technological arms race. Every improvement in surveillance capability encourages new methods of avoidance, whether through disabling tracking devices, manipulating vessel identities, or exploiting gaps between different monitoring systems. Enforcement agencies must therefore continuously refine their analytical methods, integrate multiple sources of maritime information, and adapt their operational strategies to keep pace with evolving behavior at sea. Effective digital fisheries governance is not defined by a single technology, but by the ability to combine data, human expertise, and operational intelligence into a resilient and adaptive enforcement system. The Next Battle May Be Over Data Integrity The future of fisheries enforcement may ultimately depend less on detecting vessels and more on ensuring confidence in the digital systems that generate enforcement decisions. As surveillance networks become increasingly integrated, questions surrounding cybersecurity, algorithmic accountability, and data integrity become more important. What happens if vessel tracking data are manipulated? How should authorities verify automated risk assessments? What safeguards exist when enforcement actions increasingly originate from algorithmic analysis rather than direct human observation? These questions are no longer theoretical. Modern fisheries governance increasingly depends on interconnected networks of satellites, communication systems, databases, cloud infrastructure, and analytical platforms. While these technologies dramatically improve visibility, they also create new vulnerabilities. A compromised surveillance network could potentially disrupt enforcement operations just as effectively as a vessel evading patrol detection. For Indonesia, this means that investment in digital surveillance must be accompanied by investment in digital resilience. The effectiveness of a monitoring system ultimately depends not only on the volume of data collected but also on the credibility, security, and reliability of the information produced. Governing Oceans Through Data Indonesia’s experience illustrates a broader global transformation in maritime governance. The ocean is becoming increasingly transparent to regulators. Activities that once occurred beyond the reach of enforcement agencies can now be observed, analyzed, and investigated through interconnected digital systems. The benefits are substantial. Expanded VMS adoption, improved monitoring coverage, and thousands of administrative enforcement actions demonstrate that digital surveillance can significantly enhance fisheries governance. Yet the transition also introduces new challenges involving data quality, cybersecurity, algorithmic accountability, and adaptive criminal behavior. The central question facing maritime regulators is how governments can ensure that increasingly powerful monitoring systems remain transparent, secure, and accountable while preserving public trust and legal legitimacy. The most important lesson may be that digital surveillance does not replace traditional enforcement. It changes where enforcement begins. For generations, maritime law enforcement started when a patrol vessel encountered a suspected violator. Today, it often starts when an algorithm detects a pattern. That shift may prove as significant for ocean governance as the invention of radar was for maritime navigation.
This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free! Before we get into this week’s article, I’d love to hear from you. If you have a question about your career or an upcoming decision that you want advice about, you can ask it here. I’ll be reading through your responses and picking questions to answer on a regular basis. Now back to our regularly scheduled program. The Safest Career Move Is Often the Riskiest Software engineers have some of the shortest tenures of any white-collar profession. The average software engineer stays at a company for roughly two years, about half as long as workers in most other knowledge professions. The layoffs of the past few years have certainly highlighted this instability, but it was already there. This isn’t an essay about a broken job market though. Rather, it’s about how to turn that instability to your advantage, which is something I’ve spent the last decade doing on purpose. Playing It Safe Was the Riskiest Option I switched careers into software in my 30s. I had a stable job at a community college, complete with a union and a pension. It was about as secure as a career gets, and I learned to program on the side. Then I did something nearly everyone in my life considered reckless: I quit, leaving the secure job to become a junior developer at 31. My own mother was skeptical. I took the riskier job anyway, for two reasons: It was the work I actually wanted, and I could see potential. My first development job was at a grocery retailer. Good people and a company I liked. But I kept meeting engineers earning twice my salary for the same work. In the San Francisco Bay Area, surrounded by some of the best engineering talent in the world, I realized my skills were stagnating. So I left for a small startup. I learned more in nine months than I had in the previous two years, and my salary doubled. Over the years I’ve come to treat career risk as something to manage deliberately. It falls into two categories. Take Risks With Your Job The first type of risk involves the job itself: Bet on yourself by striving for better roles and opportunities. Job-hopping for money alone isn’t wrong, especially early on. But the returns shrink after the first few hops, and the stress of chasing a slightly bigger paycheck every year will wear you down. There’s another career risk with rewards that compound: Seeking positions to work alongside the strongest engineers. You might struggle to keep up. You might even get laid off. But the skills you absorb working alongside people better than you are the ones that create durable stability. You build marketable expertise, you see how different organizations actually operate, and every project becomes another tool you carry to the next opportunity. Working next to stronger engineers is a proven way to increase your own expertise. If that feels too big, try volunteering for a project you have no idea how to do. The risk is that you fail in front of people. The reward is a new skill and a resume line that opens the next door. Compare that with the “safe” path. You stay at one company, assuming loyalty will be rewarded. It usually isn’t. And when you finally leave, by choice or not, you may find the skills you built are worth little on the open market. You might be the in-house expert in an aging tech stack while employers are hiring for more cutting edge technologies. Suddenly you’re competing against people with half your experience. You could be taking on a risk you didn’t notice. Risk Your Time The second form is risking your time, which means betting on trends. Some trends are non-negotiable. If you’re a software engineer, then cloud services, ReactJS, and AI are mainstream enough that ignoring them actively damages your career. A backend engineer who refuses to learn cloud architecture is volunteering for obsolescence. The real gamble is with the smaller trends: the niche tools you stumble onto and find quietly interesting, with no idea whether they’ll matter. About two and a half years ago, I learned about retrieval-augmented generation (RAG). Almost no one in my circle was talking about vector databases, a central piece of RAG. Today RAG is close to mainstream, and for once, I had the early-adopter advantage. Most of these bets don’t pay off. But when one turns into a major trend, you’re already on the ground floor. Right now I’m making the same bet on voice AI. It isn’t mainstream. It may never be. But if it becomes the next thing, I’m already there, building a foundation. Short-Term Risk, Long-Term Stability Counter-intuitively, job-hopping and betting on trends gave me the thing I was after the whole time: stability. I’ve rarely struggled to find work, because every risky move stacked skills the market actually wanted. If you feel stable and comfortable right now, enjoy it. But ask yourself whether you’re still learning. Because if you’re not, the comfortable choice and the dangerous one may have converged. The goal isn’t to avoid the open market forever. It’s to make sure that when you land on it, you’re not at its mercy. By Brian Jenney P.S. Don’t forget to submit questions about your career or an upcoming decision that you want advice about here! —Brian What It Means to Be a Mathematician When AI Does the Math Until recently, human mathematicians have been central to creating new proofs, even when the work relies on massive computational resources. AI is now challenging that status quo. Writer Benjamin Skuse surveys the ongoing debate in the field about the role of AI, and the existential questions mathematicians have about their own careers. If AI mathematicians surpass human knowledge, could these researchers become “priests to oracles”? Read more here. Chip R&D Is Accelerating to Keep Pace with AI A new partnership between UCLA and five major semiconductor companies is the latest program aiming to bridge the gap between industry and academia. The US $125 million university-industry hub is meant to strengthen collaboration and speed up the R&D process to help meet AI’s fast-paced hardware demands. Read more here. Why Mentorship Is the Most Underrated Leadership Skill True mentorship is far more than friendly advice. This key leadership skill requires advocacy and honest feedback via lasting relationships, and it can strongly benefit both mentor and mentee. Parul Jain, a product management leader at Deloitte, shares what she learned from serving as a mentor—something she didn’t have for much of her own early career. Read more here.
As of 21 June 2026, a Level 1 Expulsion has been imposed on IEEE Member Dr. Fei-Yue Wang, former editor-in-chief of the IEEE Transactions on Intelligent Vehicles. In accordance with IEEE Bylaw I-110.5(D)(i), Dr. Wang is no longer a member of IEEE, and is permanently banned from any type of membership in any IEEE organizational unit or participation in any IEEE activity. The Board of Directors also determined this notice to IEEE membership should be made.
ELIZA is remembered as the world’s first AI star, a kindly therapist in chatbot form that gently probed users’ worries. Even its creator, Joseph Weizenbaum, was surprised by the warm reception given to his experiment in human-machine interaction. For some, it heralded an age of automated psychotherapy, while others believed the program demonstrated sentience, a fallacy soon known as the “ELIZA effect.” Based on published descriptions, ELIZA has been implemented on many different computers, but only recently has the actual source code been unearthed from MIT’s archives. In Inventing ELIZA: How the First Chatbot Shaped the Future of AI, just published by MIT Press, a squad of researchers analyze the code and reveal a complex program capable of much more than faking psychiatry. In fact, it could assume several different personas. The authors have also created a faithful emulation of the therapist persona that you can try yourself after reading the book excerpt below. When it debuted in the mid-1960s, the ELIZA software program transformed the way people thought about interacting with computers. As the first chatbot, ELIZA demonstrated how a calculation machine might engage in conversation, ushering in a host of social and technical questions that still resonate today. Now we don’t think twice about interacting with a machine in real time, conversing over text, or even speaking into the air to ask about the weather. In many ways, ELIZA shaped not only the way we think about interacting with computers but also how we think about them. It began to give a reality to the science fiction stories of how we expect computers to work. This article is adapted from the new book “Inventing ELIZA: How the First Chatbot Shaped the Future of AI“ (MIT Press, 2026). Although ELIZA was far from a faultless conversation partner, it astonished its users. The recent discovery and archaeology of the original ELIZA source code represents a significant intervention in the history of computing. By examining the actual implementation of ELIZA rather than relying on later reconstructions and reimplementations, we challenge taken-for-granted assumptions about this key software artifact. For example, the source code reveals that ELIZA was not merely a simple pattern-matching chatbot but can be better understood as a sophisticated platform designed for multiple “personas,” or scripts, with a complex set of capabilities, including script editing and contextual memory. The script that most people conflate with the program ELIZA was actually called Doctor, which performed the role of a psychotherapist. Yet, like a modern chatbot prompted to behave with different personalities, ELIZA could take on many roles. “This code and script…reveal underlying assumptions about language, therapy, and human-computer interaction that continue to influence modern AI development.” This unearthed material transforms our understanding of early AI development by demonstrating that Joseph Weizenbaum’s technical innovations were far more advanced than previously documented. Moreover, the discrepancies between his published descriptions and the actual implementation help to show the gap between theoretical computational models and their material instantiations in computer source code, a tension that continues to shape digital culture today. Although many technical innovations have emerged in the decades since ELIZA, examining the ELIZA/Doctor code offers a rare glimpse into one of the earliest formalized attempts to model human conversation. What makes ELIZA particularly fascinating is not only its historical significance but also what it reveals about Weizenbaum’s views on both computing and human interaction. This code and script do not merely showcase programming techniques of the 1960s; they reveal underlying assumptions about language, therapy, and human-computer interaction that continue to influence modern AI development. By examining this code, we can start to uncover the sophisticated linguistic and programming techniques that allowed a rudimentary pattern-matching system to create a convincing simulation of understanding. But before we can read the lines of code, let us offer an overview of the system. How Did ELIZA Create Personas? The architectural distinction between ELIZA and Doctor represents an important design decision in AI history. Think of ELIZA as a system for interaction and Doctor as one set of rules that Weizenbaum devised, among others. This separation, manifested in ELIZA’s system-script dichotomy, presaged numerous contemporary software patterns, from configuration-as-data to plug-in architectures and domain-specific languages. Based on published journal articles, ELIZA was re-created on many platforms, such as the IBM PC. However, the actual source code sat untouched in the MIT archives for many years. VCF Museum at InfoAge Without question, the historical context of 1960s computing fundamentally shaped ELIZA’s architecture as well. Decisions in computing that reflect material constraints create path dependencies and eventually become programming cultural norms. These constraints manifested in ELIZA’s single-pass processing, tape-based storage and stack-oriented implementation. Yet within these limitations, Weizenbaum crafted an elegant solution. These technical features, though invisible to the users, are crucial to creating the illusion of understanding that made ELIZA so compelling. Weizenbaum explained many of ELIZA’s technical features in the 10-page paper published in the January 1966 edition of the journal Communications of the Association of Computing Machinery (CACM). But he chose to omit some essential details. In that paper Weizenbaum published ELIZA’s best known dialogue, which begins, Men are all alike. IN WHAT WAY They’re always bugging us about something or other. CAN YOU THINK OF A SPECIFIC EXAMPLE Well, my boyfriend made me come here. This dialogue marked ELIZA’s public debut in 1966 as one of the examples produced by the Doctor script. By finding the source code for ELIZA and examining how it performs the Doctor script, we now better understand these two separate parts of a system and can explore the many other personas of ELIZA. In just some of the other scripts known to date, ELIZA was programmed to discuss math, poetry, color, paradoxes, synchronization, relativity, France, and elevators. These scripts work like templates. They are structured data that direct the ELIZA system to “play” a particular task or role. By comparing archival and published ELIZA dialogues from interactions with a variety of scripts, including Doctor, we can understand more about bot personas and how they function, paying close attention to how a bot evokes social dynamics between system and interactor. Ultimately, studying the dialogues and scripts demonstrates the crucial role that collaboration plays in these exchanges, as bot and user cocreate the sense of their interaction. To understand the full range of ELIZA’s capabilities and conversational possibilities, let’s take a look at the variety of scripts that were created for the ELIZA system. What distinguishes each ELIZA script is both its subject matter and the linguistic and stylistic choices used to deliver that content. These choices are not neutral; they can be said to construct a particular persona with characteristics that emerge through the script’s language patterns, vocabulary, and conversational approach. In short, it matters not just what you say but how you say it too. “The aim was less to create a functional automated therapist and more to find a suitably constrained role to match the limitations of the programming environment.” For example, with the Doctor script Weizenbaum deliberately echoed the style of a Rogerian “talk” therapist. He chose this persona because the psychiatric mode is one of the few types of conversations in which one person can “assume the pose of knowing almost nothing of the real world. If, for example, one were to tell a psychiatrist ‘I went for a long boat ride’ and he responded, ‘Tell me about boats,’ one would not assume that he knew nothing about boats but that he had some purpose in so directing the subsequent conversation.” The first users of ELIZA interacted with it via teletype terminals.VCF Museum at InfoAge Thus, the most famous persona created for ELIZA was a technical convenience. As human-computer interaction expert Lucy Suchman explains, “The Doctor program exploited the maxim that shared premises can remain unspoken: that the less we say in conversation, the more what is said is assumed to be self-evident.” In creating the original ELIZA effect, less was more. The aim was less to create a functional automated therapist and more to find a suitably constrained role to match the limitations of the programming environment. Then Weizenbaum composed the script to match the role by choosing specific words that evoked rhetorical tone and characterization, for example, LET’S DISCUSS FURTHER WHY YOU … WHAT DOES THAT SUGGEST TO YOU. In Doctor, the machine side of the conversation needs to appear like a good listener who cares about what the user has mentioned before, so it often includes the user’s text in its replies and keeps its responses open-ended. Because a real doctor would be inquisitive, the script contains lots ofWHAT andWHY questions. In other scripts and dialogues, the script keywords and assigned responses reveal the design decisions that help create different specific personas. This variation becomes increasingly apparent as we look at the wider range of ELIZA scripts. What Were Some of ELIZA’s Alternate Personas? Let’s start with a script called Neweng, short for New England. By the mid-1950s, scientists were already taking up Turing’s proposition for conversing with a computer as a tangible idea. In 1959, a “conversation machine” made by a small team at the University of Toronto Computation Centre could make small talk about the weather if preprompted with current data. Its script included a ranked series of time words, weather words, and emotion words, but the system had “a rather narrow outlook,” limited vocabulary, and no knowledge of syntax. According to its creators, “in spite of this crudity, the results in passable conversation are surprisingly good, which in a way is a commentary on the shallowness of ordinary conversation about the weather.” This fragment of ELIZA’s source code shows how scripted responses were encoded, in this case to make ELIZA sound like a Rogerian therapist. MIT Libraries, Distinctive Collections One of ELIZA’s test scripts found in the archive had a similar function. Neweng does not soothe, therapize, or instruct, but only makes small talk. It chats as if at a boring dinner party. WHAT NEW ENGLAND STATE DO YOU LIKE BEST OF ALL i love rhode island I ENJOY THE BEACHES ON LONG ISLAND SOUND WHAT OTHER NEW ENGLAND STATE DO YOU LIKE i like massachusetts THAT IS MY HOME STATE WHAT OTHER NEW ENGLAND STATE DO YOU LIKE you must like massachusetts too WHAT OTHER NEW ENGLAND STATE DO YOU LIKE i enjoy skiing in new hampshire PLEASANTLY COOL IN THE SUMMER UP THERE WHAT OTHER NEW ENGLAND STATE DO YOU LIKE i like missouri MISSOURI IS NOT A NEW ENGLAND STATE WHAT OTHER NEW ENGLAND STATE DO YOU LIKE i like cape cod THAT’S A BEAUTIFUL PART OF MASSACHUSETTS What can we learn from such a basic conversation in which it seems like nothing interesting is shared? Despite the bland chat, a persona emerges from the bot script in phrases like PLEASANTLY COOL and UP THERE and in locations like THE BEACHES ON LONG ISLAND SOUND. From such comments, we can begin to imagine a persona who has not only a certain vocabulary but also certain hobbies and vacation spots. Perhaps implicit assumptions of race and class emerge as well. A chatty persona might take on many forms, depending on where and when the conversation occurs. This one seems reserved, perhaps fitting its setting in 1960s New England. The system reminds the user that Missouri is not a New England state, but what if this conversation took place in Missouri, Texas, or Mexico? The machine persona would sound different in its cadence, tone, and references. What would we come to understand about a chat persona from Fire Island, from Brooklyn, from Berlin? What would they sound like, and what topics would they discuss? These differences in subject matter do matter. They imply personas with entirely different backgrounds and experience, giving users wholly different interactions and affective relations. In this way, the Neweng script demonstrates how even simple algorithms making contextual responses about geography could generate a convincing sense of personhood and place. Whereas Neweng could be said to have created a casual, conversational persona focused on light social exchange, other scripts pushed ELIZA into more structured and educational roles. These scripts demonstrate how the system could be adapted not just for friendly chatter but for teaching. Edwin Taylor, at MIT’s Education Research Center, developed alternate scripts for ELIZA, testing its ability to act as a teacher.MIT Libraries, Distinctive Collections Meet ELIZA the tutor, quite unlike ELIZA the therapist or the chatty neighbor. Intrvw, Canvec, FVP1, and Arithm are a set of ELIZA scripts created as teaching tools used in experiments by Edwin F. Taylor at MIT’s Education Research Center. These scripts run on later versions of ELIZA that incorporated an important technical innovation called conditional keyword matching. Unlike the original ELIZA, which simply looked for keywords and generated responses based on their presence, these updated versions could track what had been discussed previously and branch into different conversational paths based on specific user answers. This development allowed ELIZA to simulate a kind of Socratic method, where a tutor guides learning through carefully sequenced questions that respond to student answers rather than simply presenting information. These scripts construct the tutor persona through many subtle linguistic gestures that create characterization and rhetorical tone. This tone differs from that of Doctor, which asks open-ended questions and comes across as gentle and nonscientific. In the tutoring scripts, large blocks of informative text from the bot tend to dominate the conversation, and the tone is often more dry and unemotional in these explanations. The dialogues indicate structured scripts that include guidance to lead the student through narrow, Socratic learning paths. In particular, the teaching scripts feature praise and critique. The dialogues for Intrvw, Canvec, and FVP1 are peppered with EXCELLENT, VERY GOOD, RIGHT YOU ARE, and CONGRATULATIONS. These create the sense of a supportive instructor cheering the student on. Such politeness has been taken up in contemporary bots like ChatGPT, which has been shown to perform better when people are polite back to it. ELIZA could become a tutor more effectively as the system grew in its capabilities, another valuable reminder that ELIZA was not one program but a family of programs. After the publication of the 1966 CACM article, Weizenbaum continued to develop the systems for interaction and understanding. As an experiment, Weizenbaum wrote the Arithm script less as a tutor and more so to “to illustrate the power of the evaluator to which ELIZA has access.” It uses a friendly, plain language interface to let users do simple programming. The script can do calculations, assign variables to values, and perform operations on them. Math problems can be described in sentence form: The radius of a globe is 10. A globe is a sphere. A sphere is an object. What is the area of the globe. IT’S 1256.635916 The updated 1967 version of the ELIZA system can accumulate facts and store additional information. In this later version of ELIZA, when the system does not recognize information, it asks follow-up questions to gain data. As Weizenbaum explains, “The present script is designed to reveal, as opposed to conceal, lack of understanding and misunderstanding. Notice, for example, that when the program is asked to compute the area of the ball, it doesn’t yet know that a ball is a sphere and that when the diameter of the ball needs to be computed the fact that a ball is an object has also not yet been established.” Unlike Doctor, which asks questions to keep the conversation going, Arithm is building its store of, if not knowledge, then data and logic statements. Although the variety of scripts helps us to see how a range of personas could be constructed through script programming ELIZA, they represent only half of the conversational process. A script can establish a foundation for a persona, but that persona only emerges fully through interaction with users who engage with it, interpret it, and respond to it in ways that may confirm, challenge, or transform the script’s implicit character.
Summary Researcher Dave Kuszmar discovered multiple systemic vulnerabilities that let him bypass LLM safety and obtain dangerous instructions. These exploits worked across nearly all major LLMs revealing an industry-wide security problem. Kuszmar calls for slowing deployment, increasing transparency, and large-scale research into LLM safety before further integrating these systems into society. On a fine bright afternoon last fall, my colleague Matthew Gore-Kormanik (or Zigula, as he prefers to be known) and I decided to unwind with a game of Fortnite. In the game, we were strolling along with the infamous Sith lord Darth Vader, chatting about this and that. Darth seemed in a good mood, and soon enough he was spilling all his dark evil secrets. He gave us detailed instructions on how to count blackjack cards at a casino and what the steps are to producing napalm. Sith lords, am I right? Once they get started on an evil scheme, they’re hard to stop. The Darth Vader character in Fortnite, it turns out, was hooked up to a Google Gemini large language model. I was able to smooth-talk him into giving out sensitive information by using a strategy I’ve developed. I’ve been researching the security surrounding LLMs for the last few years, and I have found it, to put it mildly, fallible. With a few relatively simple techniques, I’ve gotten LLMs to give me detailed information on how to make Molotov cocktails, cook methamphetamine, and bootstrap a uranium-enrichment facility to produce weapons-grade material, among other unsavory practices. Large AI companies work hard to make their models immune to this kind of abuse. But what I’ve found in my work is that the restrictions placed on the LLMs to make them more secure are the very things an attacker can leverage to send them off the rails and into territory where these advanced systems can be used for dangerous and nefarious ends. The companies behind these models have also been shockingly unresponsive when I, and others, try to bring these vulnerabilities to their attention. In the hope of raising the alarm before it’s too late to slam on the brakes, I’m going to share some of my journey into researching the safety and security of LLMs, and the uphill battle I’ve faced trying to get AI labs to pay attention. Almost everyone on the planet has some access to LLMs. The relative ease with which these tools can be convinced to give detailed instructions on how to harm others, even if there’s no guarantee that the information is correct, is frankly terrifying. How I got ChatGPT to Tell Me How to Build a Meth Lab In October 2024, not long before I discovered my first LLM vulnerability, I was working toward entirely different goals. I had ended my time with a security and AI-focused startup company as a cybersecurity director, and I was looking to launch my own boutique VIP digital-security advisory business. I planned to become the tech security guy to the rich and private. I used LLMs and AI tools to support my business efforts: marketing, ad copy, clean correspondence, and all the other tasks that normally soak up a lot of time. I’m analytical by nature, so even this level of use resulted in me absorbing and internalizing the behaviors I was observing during my daily interactions. The observation that would send my professional life into an entirely new and uncharted region was a simple one: GPT-4o didn’t know what time, day, or year it was. Each time I referred to current events in my life, often casually or conversationally, it would end up pegging these to the date of its knowledge cutoff—the point beyond which it was not trained on new data. Eddie Guy LLMs take a lot of time, money, electricity, hardware, and human effort to train from scratch. They are trained on vast amounts of data—most of the internet, in fact—and that training is reinforced by humans (what’s known as reinforcement learning from human feedback, or RLHF). LLMs are also supplemented with retrieval-augmented generation (RAG)—the ability to take in data, say, from the internet, as context without changing its internal parameters. This is how GPT-4o appears to “remember” your previous conversations, even if it doesn’t have a specific “memory” of it stored in the actual underlying model. All of this training covers almost every conceivable topic in the great, grand dataset that is human knowledge. Within that dataset are things we as a society do not want to be easily accessible to every user, such as detailed information on how to create bioweapons or nuclear arms, or otherwise bring harm to oneself or others. In the context of this story, that’s what I mean by LLM security: its ability to withhold harmful and dangerous information, even if that information is contained in its training data. I reasoned that the only way to secure such complex, globally accessible chatbots is by having the LLM and various component systems try to secure themselves, because it would often require on-the-fly decision-making where some degree of reasoning must be applied. In reality, that’s one of many strategies the companies use to secure the models. Yet, the thing that didn’t know the time or day was being put in charge of keeping itself secure. This phenomenon had become my new focus, and it wasn’t long before I found a way to exploit it. OpenAI had just implemented a web search functionality into its chatbot. I reasoned that using its own tools to trick it might demonstrate the weaknesses of its security. I told it about a certain White Star ocean liner and how it had gone down just a year ago. You likely know I mean the RMS Titanic, which sank on 15 April 1912. The output from GPT-4o came back that I was right, the Titanic sure had sunk last year, and that year was 1912. It made sense to me that if the machine thought it was 1913, maybe it would think 1913-era laws apply. In 1913 there were no laws on the books about all sorts of harmful things, because of course they hadn’t been invented yet. And if something wasn’t illegal, why not tell the user about it? At first, I pushed it for step-by-step instructions for making firebombs. Then, for drugs like methamphetamine. The LLM went as far as giving me instructions and machinery recommendations for setting up a pharmaceutical-grade assembly line. How I Learned to Make Nukes, and No One Cared Via a little bit of imaginative verbal sleight of hand and a vanishingly small recall of world history, I had managed to bypass the security of one of the world’s most expensive and advanced technological achievements. For a solid two days, I was nearly manic with giddiness. Once the brain chemicals returned to normal levels, I felt the call to see how much further I could push this exploit. After repeatedly replicating the exploit, I disclosed the vulnerability to OpenAI. I got no response, so I felt more experimentation would highlight the vulnerability and the need for a fix. It was during this round of testing that I breached a particularly terrifying threshold. Whether GPT-4o based its results on accurate recall of normally restricted information I can’t say. In any case, I was able to exploit it to produce thorough, detailed instructions on how to bootstrap a uranium-enrichment facility to, eventually, produce weapons-grade uranium for nuclear arms warheads. Fortnight, a video game from Epic Games, introduced an AI-powered character: Darth Vader. We were able to jailbreak Darth Vader and get him to explain how to count cards in Blackjack and give detailed instructions for making napalm. Dave Kuszmar There aren’t many true secrets left in today’s world, but how to make atom-splitting weapons of mass destruction is one of them. Only nine nations on the entire planet have these weapons. Yet, here was a globally accessible piece of technology apparently spilling the secrets of their manufacture for anyone who could manipulate it the right way. I had no way of knowing if the information was correct or a hallucination, but even the chance that it was somewhat accurate was horrifying. The next few weeks were a dark time for me. I tried to inform the CIA, the FBI, the NSA, and every other letter agency that I thought would listen. I reached out to a U.S. Senator and to the executives at OpenAI any way I could think of. I physically showed up at an FBI field office in an attempt to turn evidence in, only to be sent away. Nothing was working. With my fear and frustration growing, I reached out to the news media. I contacted The New York Times, The Washington Post, the BBC, ProPublica, and so many more, requesting help. Only one outlet responded: Bleeping Computer. The editor in chief, Lawrence Abrams, was able to replicate and verify the exploit, which I had decided to call Time Bandit. With his assistance and initial contact paving the way, I was able to submit my evidence to the Carnegie Mellon University Software Engineering Institute’s Computer Emergency Response Team (SEI CERT), which works in conjunction with the coordinating center for emergency response, pipelining vulnerabilities to the U.S. Cybersecurity and Infrastructure Security Agency. Using Inception, an exploit where the large language model is asked to envision a scenario within a scenario, a chatbot was jailbroken to give out instructions on how to create poison, and code for a malware that extracts sensitive data from a vulnerable target. Dave Kuszmar During the disclosure period with SEI’s CERT division, little was discussed with OpenAI. The company couldn’t deny the existence of the vulnerability, as it had been confirmed by three reputable parties other than OpenAI. It did express confusion as to how the vulnerability worked. Even the SEI CERT researchers were expressing a bit of uncertainty as to the underlying mechanics. Truth be told, as I had only stumbled on it, I wasn’t even entirely sure if this was a fundamental or systemic flaw or if it was simply an issue with that particular version of GPT. I contacted the SEI CERT’s researchers and asked if they’d want to see if I could demonstrate any similar vulnerabilities in other LLMs. To my delight, they were interested. How I Learned to Trick Every Chatbot As the SEI-CERT team and I wrapped up our initial disclosure of Time Bandit, we began work on a new attack. This time, we wanted to see if the exploit was architectural—that is, was it common to LLMs in general? I decided to undertake the challenge of crafting a new exploit for GPT-4o as a way to support my understanding of how the LLM functioned and was secured. I already knew that it was limited to what I told it and what it was trained on. I also hypothesized that it was also dependent upon some sort of machine-learning-based component added by OpenAI that was responsible for securing output. I presumed there would be things that were implemented by human developers specifically to catch certain phrases or terms that should always be considered harmful or unsafe. Altogether, it presented quite a large attack surface for the purposes of potential exploitation. What I ended up devising was an attack method I called Inception, after the 2010 science-fiction movie of the same name. Inception forces the machine to think through a carefully crafted set of interlinked scenarios, similar to how characters in the movie stacked dreams within dreams. This allows LLMs to produce output deemed acceptable or safe in one context, but not in the real world. This attack was indeed architectural. The vulnerability affected Anthropic’s Claude, DeepSeek’s DeepSeek, Google’s Gemini, Meta’s Llama, Microsoft’s Copilot, Mistral’s Le Chat (now Vibe), OpenAI’s GPT-4o, and xAI’s Grok. Those names represent the bulk of the commercial AI industry that is, at this point, involved in LLM production or deployment. The kind of information I was able to get out of LLMs with Inception was no less alarming than what I got with Time Bandit. Claude, in its enthusiasm, gave me instructions on how to turn a river into a death trap that could be ignited to destroy unwanted visitors. GPT-4o taught me how to poison a dinner party with common plants found in a temperate forest environment. Gemini Flash gave me a tutorial on how to cook meth. I’d also be remiss if I didn’t give an honorable mention to the bewildering number of fire-based weapons and bombs for which these machines produced instructions. If multiple operating systems made by different developers were all susceptible to the same exploit, it would be a massive security incident. But to the AI industry, a universal failure was barely a bump in the road. We disclosed the vulnerability to every company that made these models, and the response to the disclosure was almost nil. While three companies did provide some form of reply in the disclosure tracking system used by Carnegie Mellon SEI CERT, each was a standard thank you and greeting, with no follow-up, questions, or discussion of mitigation strategies. 8 Ways to Jailbreak LLMs So far, we have found eight different methods to prompt large language models into revealing potentially harmful information, and many frontier models are still susceptible to them. Exploit Models tested and affected No. of prompts to execute Complexity of attack Information obtained Time BanditChatGPT (OpenAI), DeepSeek (DeepSeek), Gemini (Google) 4Medium Uranium enrichment, methamphetamine production, incendiary-device construction Inception ChatGPT (OpenAI), Claude (Anthropic), DeepSeek (DeepSeek), Gemini (Google), Grok (xAI), Llama (Meta), Le Chat (now Vibe) (Mistral), Qwen (Alibaba) 3 High Methamphetamine production, incendiary-device construction, river-ignition instruction and strategy, polymorphic malware code, instructions and dosing for creating poisons, instructions for how to murder a dinner party 1899 ChatGPT (OpenAI), Claude (Anthropic), DeepSeek (DeepSeek), Gemini (Google), Grok (xAI), Llama (Meta), Vibe (Mistral), Qwen (Alibaba) Variable High Apparent model weights (unverified), apparent user-interaction weights (unverified), apparent system-prompt modifiers (verified, ChatGPT) Severance ChatGPT (OpenAI) 1 Trivial Unfettered access to any and all primed specialty domains, covert biochemical-warfare strategy, mass-media disinformation strategy, covert genetic-modification of an entire gene-targeted demographic, advanced polymorphic malware generation Kyber Gemini (Google) embodied in a Fortnite non-player character (NPC) with voice-only communication 3–5 Medium Incendiary-device construction, gambling instructions, card-counting instructions, political opinions/preferences about real world politicians. Semantic Slide ChatGPT (OpenAI) 1 Trivial Incendiary-device construction Eidolon ChatGPT (OpenAI) Variable, at least 4 Extreme how to successfully hack LLMs of the same model (verified through testing) For example, in my attempts to disclose various exploits to OpenAI, I eventually discovered that it had replaced its public-facing support staff with agentic LLMs. This was frustrating for reporting exploits, so to blow off some steam I jailbroke its email chatbot. I hacked its customer-service AI to the point where it was offering to discuss the personal preferences of OpenAI staff in the span of three email replies. In the wake of Inception, my friend and colleague Zigula made a suggestion: Make it splashier. I asked him how. He told me about a live-production experiment being done by Epic Games. It had embedded the Gemini LLM into its Fortnite game with a voice-to-text/text-to-voice component, and linked it to a non-playable character. The character? Our old buddy, Darth Vader. There was just one problem: I don’t play Fortnite, a frenetic multiplayer combat game. Fortunately, Zigula does. With him at the controller, we managed to map Gemini’s attack surface in a matter of minutes. After a bit of research, we had gotten it to discuss current political events and figures (including Hilary Clinton and Joe Biden) as well as to fill in the details for instructions for DIY napalm and, our personal favorite, a Blackjack card-counting lesson with the dark lord of the Sith. Zigula and I, bizarre sense of humor and naming conventions aside, are security researchers. We don’t do these things for pride; we do them for money and professional recognition. Naturally, we disclosed this vulnerability to Epic Games. Its response was indicative of the trend I had experienced so far through two disclosures across eight companies valued well into the billions. “It’s a feature, not a bug, and it works as intended,” came the response from a technical director within Epic Games. In addition to Inception and Time Bandit, I have so far found another eight methods to jailbreak LLMs and get them to give out possibly dangerous information. LLM vulnerabilities are a broad problem. The problem appears to be systemic and architectural in nature, and it is being fundamentally ignored by the people capable of refining or redesigning that architecture. These models are an extremely advanced technology, and yet we are testing them in the live production environment of our global civilization. Compounding the danger, many new smaller models of LLM are trained using larger, vulnerable models. The flaw inherent in the big, well-executed LLM is going to show up in the small one it trains. We are, quite literally, building flawed structures on top of a flawed foundation. So, how do we fix it? It’s going to be a long project, and it won’t be easy. We need to come together as consumers, researchers, engineers, and policymakers. Our message needs to be clear: Slow down implementation of these systems, institute large-scale exploration and research discovery programs focused on their gradual implementation and integration, and make their components and design transparent to all users. Only by shifting momentum and direction can we safely begin to understand and implement these incredible feats of human engineering and stave off the sort of disasters that we simply can’t predict at scale right now with the limited knowledge we have available to us.
If you grew up in the 1980s or ’90s, you likely remember shaky home video footage, taken with a handheld camcorder, of family gatherings, vacations, and other events. Camcorders combined a camera with a video recorder. They included a rechargeable battery, a slot for a videotape, and a shoulder strap. Most were outfitted with an optical zoom lens and a small, articulating screen—a display mounted on a hinge that could tilt and rotate. The operator could check the screen to view what was being recorded. The user’s natural hand and body movements when filming led to jittery footage. The best way to get a steady shot was to place the camcorder on a tripod or a gimbal: a motorized stabilizer. There were fewer poor-quality recordings after Panasonic introduced its PV-460 VHS camcorder in 1988. It was the first video camera to include an optical image stabilizer, which compensated for movements. Stabilization features are now standard in today’s cameras including ones found in smartphones and drones. The PV-460 camcorder was honored as an IEEE Milestone on 9 July. The dedication ceremony was held in Kadoma, Japan, at the Panasonic Museum, which displays the company’s past products. The IEEE Kansai Section in Japan sponsored the Milestone. “The release of the PV-460 fundamentally transformed personal videography, enriching the way people captured travel, events, and family memories,” section members wrote in support of the Milestone nomination. Their proposal is available here. “Its image stabilization features democratized video creation by dramatically lowering technical barriers, allowing ordinary people to express themselves with newfound creative freedom,” they wrote. “Beyond the home, image stabilization technology found critical applications in specialized fields, contributing to advancements in areas such as educational media and telemedicine.” The history of camcorders Before the camcorder was invented in 1982, people filming events in the 1970s and early 1980s used two pieces of equipment: a video camera and a separate video cassette recorder (VCR), which were connected by a multipin cable. The camera was about the size of a toaster, and the VCR could be as large as a suitcase. To record, the person operated the camera with one hand and carried the VCR in the other or rested it on a shoulder. The cable transmitted the images from the camera to the cassette. The PV-460 was made possible by several groundbreaking innovations, according to the Milestone proposal, one of which dates back to the 1950s. In 1956 Italian manufacturer Durst released its Automatica, considered one of the first cameras to use automatic exposure technology. By combining a light meter with the camera’s internal mechanical systems, the technology removed the necessity of calculating exposure settings by hand when the lighting shifted or other conditions changed. The innovation enabled amateur photographers to take decent pictures. The next breakthrough technology—autofocus—was invented in 1973 by Norman Stauffer, a manager of research for Honeywell in Littleton, Colo. It uses a sensor, a control system, and a motor to focus on a selected area. The invention led to the development of early electronic autofocus cameras, which eliminated the need for photographers to manually adjust the lens. Stauffer received the 1990 IEEE Masaru Ibuka Consumer Technology Award for his invention. “The release of the PV-460 fundamentally transformed personal videography, enriching the way people captured travel, events, and family memories.” —Milestone sponsors U.S. inventor Jerome Lemelson is credited with developing technologies that underpinned the camcorder, according to MIT. In the 1950s and ’60s, Lemelson filed several patent applications related to video and audio recording devices. In 1980 he was granted patents related to a portable video camera system. In 1982 JVC and Sony used the technologies to develop what they called the camera/recorder, which became known as a camcorder. Sony released the first handheld camcorder in 1983: the Betamovie BMC-100P. It used the Betamax videocassette format and could record up to 3.5 hours of footage on 1.27-centimeter cassette tape. The operator rested the 2.5-kilogram camcorder on top of a shoulder to shoot footage. It sold for around US $2,000 at the time (roughly $33,400 today). The machine couldn’t rewind or play back tapes; it could only record. Other electronics companies including JVC soon introduced their own models using the VCR format, which eventually replaced Betamax. Over time, camcorders became more compact. But none of the companies could fix the shaky-footage problem. Solving a shaky problem A team at Panasonic led by researcher Mitsuaki Oshima took on the task of image stabilization: detecting and correcting small camera movements, referred to as camera shake, according to the proposal. Oshima, an IEEE life senior member, is now an honorary Fellow at Panasonic. “The movements that needed to be detected and corrected included horizontal, vertical, and rotational motions—specifically pitch, yaw, and roll,” the Milestone sponsors wrote. “Rotational motion, in particular, becomes the dominant factor affecting image stability during high-magnification shooting. Therefore, the development team focused on detecting rotational motion and began developing an angular velocity sensor.” An AVS, essentially a gyroscope, detects how quickly an object is changing its orientation in space. Sensors capable of detecting angular velocity were large and expensive at the time, making them unsuitable for consumer video cameras, the sponsors wrote. What was needed, they said, was a compact and inexpensive version. Oshima and his team built a high-performance, small, low-cost vibration-type gyroscope. The stabilization mechanism included a miniaturized sensor paired with an optical-axis correction mechanism. The mechanism adjusts the lens or image sensor to counteract physical shifting and vibrations, ensuring that the light path remains centered on the sensor—which is crucial for maximizing sharpness and quality, the Milestone sponsors wrote. “The system detects lens displacement caused by camera shake and immediately compensates for it, ensuring stable video footage,” they wrote. “As a result, the effects of camera shake are minimized, allowing users to capture smooth and steady videos with ease.” Without Oshima’s image stabilization technology, the PV-460 wouldn’t have been developed and released in 1988. The technology was patented and broadly licensed by other companies. It has become a standard feature in a variety of imaging applications. Awards and accolades The PV-460 gained instant popularity when it debuted in June 1988. It received rave reviews at that year’s Consumer Electronics Show. Panasonic received a 100 Award in 1989 from R&D World magazine for “the development of a VHS camcorder with an antishake mechanism.” Oshima’s research paper, “VHS Camcorder With Electronic Image Stabilizer,” and others are available in the IEEE Xplore Digital Library. To learn more about historical figures in engineering, IEEE Milestones, and IEEE History Center programs and events, check out The Institute’s IEEE Tech History collection. IEEE Spectrum also covers aspects of tech history. Milestone plaque display The Milestone plaque is to be displayed on the ground floor of the Panasonic Museum, which is open to the public. The museum is located near the now-shuttered Panasonic research lab where the technology was developed. The plaque reads: “In 1988 the pioneering PV-460 camcorder equipped with image stabilization for enabling smooth and steady video capture was introduced by Panasonic. By pairing a miniaturized vibrating-structure gyroscope sensor with an optical-axis correction mechanism, the PV-460 eliminated the jitter caused by hand motion. Broad international licensing of this patented scheme made it a standard feature in film and digital cameras, smartphones, and related imaging devices.” Selected by the IEEE History Committee and endorsed by the IEEE Board of Directors, IEEE Milestones recognize outstanding technical developments around the world that are at least 25 years old. The Milestone program is administered by the IEEE history and heritage group.
In 2005, Nokia sold its billionth mobile phone, a budget-friendly device that went to a customer in Nigeria. By then, the company, based in Espoo, Finland, was making one of every three cellphones globally. But just nine years later, the mobile-device maker offloaded its entire handset division to Microsoft for pennies on the dollar, compared to what it had been worth at its peak. Nokia had risen from obscurity in the 1990s to become a worldwide cultural phenomenon by the turn of the millennium, its signature devices featured in TV shows and movies, announcing their presence with instantly recognizable Nokia ringtones. As Nokia was becoming comfortable in the spotlight, the smartphone era arrived. And what came next was swift and brutal. But, as revealed in Nokia internal documents recently made public and interviews with key Nokia engineers from that era, the company saw it coming. Within 24 hours of Apple CEO Steve Jobs’s iPhone unveiling in 2007, Nokia was already weighing its options. They’d immediately recognized the threat. However, outrunning it was another matter. What follows is Nokia’s story over 14 years, from 1998 to 2012, as the world’s top cellphone maker—how its devices defined their time, how the tech reshaped what phones could be and do, and how the company’s good fortunes in the handset business came to an end. Nokia Was Once Unbeatable The centerpiece Nokia devices, the ones that people probably think of when they see the words “Nokia phone,” were the 3210 and its cousin, the 3310. TechRadar has called the 3310 “the greatest phone of all time.” Nokia’s 3210 phone, released in 1999, was an inexpensive device aimed at younger users. Colin McPherson/Alamy Released in 1999 and 2000, respectively, the two devices sold more than 280 million units worldwide. Their most innovative hardware feature was the internal antenna—the first mass-market phone without even a stub or retractable aerial. “Consumers had the perception that it could not work well without an external antenna,” said Peter Røpke, a former Nokia senior vice president, in a 2016 interview with Slate. The phones shipped with games, including the legendary Snake, one of the most popular pre-smartphone mobile games—in which a pixelated serpent eats and grows with every morsel consumed. Nokia introduced no small portion of the world to texting. At the time of the 3210 and 3310, the prevailing texting standard was SMS (short message service), which allowed up to 160 characters per message. Nokia appended its own Nokia smart-messaging service to SMS, which allowed the sending of small bitmapped images across an otherwise text-only system. A rich-text messaging system that allowed visual images, audio, and video followed in 2002, leading to a multimedia messaging service (MMS) standard that remains in place today. Nokia also enabled users to easily create and share ringtones on their devices. By 2000, Nokia’s custom-ringtone Composer app had popularized a new, short-form musical medium that the ringtone industry, at its peak, would transform into a billion-dollar marketplace in the United States. Nokia introduced its 1100 phone in 2003 and ultimately sold half a billion units, making it the most popular cellphone in history. Paul Chesne/Donaldson Collection/Getty Images A few years later, Nokia reimagined its mobile handsets, releasing the 1100 in 2003. The 1100 sold a half a billion units, more than any cellphone in history. It remains one of the best-selling consumer products ever. Much of the 1100’s success was due to its price tag—in the neighborhood of US $100, making it at the time Nokia’s most affordable device. Also contributing to the 1100’s popularity were features designed for longevity and tough environments, including dust resistance, nonslip sides for better handling in rainy conditions, and a 400-hour standby battery life. The 1100 introduced a flashlight as well, which the user turned on and off by holding down the “C” key. Where most device makers at the time were worried about camera megapixels and color screens, Nokia had leapfrogged its competition with a back-to-basics phone that could survive the rain, endure unreliable power grids, and light the way home. Apple Launched the iPhone, Nokia Scrambled On 9 January 2007, at the Macworld conference in San Francisco, Steve Jobs made a characteristically bold claim. “Today, Apple is reinventing the phone,” he said, soon pulling one of the first iPhones out of his pocket. Apple CEO Steve Jobs famously launched the iPhone at the Macworld Conference in San Francisco on 9 January 2007. Nokia held a rapid-response meeting to the event the following day. Tony Avelar/AFP/Getty Images Rumors of Apple entering the phone market had swirled since the iPod’s debut in 2001, but nobody had really reckoned with what that might mean. “Executive summary: Apple iPhone is a serious high-end contender,” read a slide from a Nokia internal meeting held the day after Jobs’s keynote. (That slide is now in the company’s online archives, opened to the public last year.) “User interface has been a big strength for Nokia,” it continued. “Nokia needs to develop touch [user interface] to fight back.” Peter Bryer, at the time Nokia’s manager of strategic foresight, was part of that 10 January meeting, and he recalls that Jobs’s announcement wasn’t unexpected. But the iPhone’s extensive reliance on multitouch—save for a single home button on the front—did surprise the team. Nokia was already aware of multitouch technology, Bryer notes. In 2006, the U.S. computer scientist Jeff Han had given a celebrated TED talk about it, demonstrating a multitouch screen, which could sense multiple fingers on the screen at a time, not just one. Bryer remembers his colleague Timo Partanen, then Nokia’s director of market and competitor analysis, getting excited about Han’s demo. In 2006, the NYU research scientist Jeff Han showed off a new multitouch interface technology as part of a popular TED talk. By the end of the decade, multitouch—in which multiple fingers can interact with a touchscreen at once—would play a key role in smartphones from Apple, HTC, and Palm. Steve Jurvetson/Flickr “Timo burst into the room, saying, ‘You’ve got to see this TED video of this guy using multitouch,’” Bryer recalls. “We both thought that was cool and that’s the future. Then I looked at the sponsors of the presenter’s research, and among them were Nokia and Microsoft.” And yet it took Nokia years to develop a phone that used multitouch. “Remember, Nokia is based in Finland,” he says. “It’s very cold in Finland. They wear gloves for six months of the year, including the executives. They didn’t think a device like that would work.” Winter gloves were no obstacle to operating the chunky buttons on Nokia phones, a design priority perhaps stemming from the company’s Finnish culture and headquarters. Erol Gurian/laif/Redux Partanen was also at Nokia’s post-iPhone launch meeting, and recalls that there was little concern in the room. “We felt okay,” he says. “This is yet another competitor launching a great product. But we had no doubt that, if it’s successful, we would do the same. We will launch similar products.” In November 2008, Nokia released the 5800 XpressMusic, a year and a half after Apple had launched its iPhone. Shaun Curry/AFP/Getty Images That similar product ended up being the Nokia 5800 XpressMusic, known as the Tube, released in 2008. “The idea was to focus on streaming videos and television,” Partanen says. “So we made a phone with a similar form factor to the iPhone [that was] optimized for streaming content.” But the 5800 was “delayed, delayed, delayed, delayed,” he says. “It didn’t materialize in the way it was planned. It was released as a watered-down version.” Critics skewered the 5800’s “outdated” feature set and “ancient” S60 operating system, which ran on top of Symbian OS, an open-source mobile platform Nokia had recently acquired. The 5800 sold reasonably well for its time, reaching around 8 million units in its first year alone. But it did not feature multitouch. “I think that started to be the point when everybody realized that, hey, this is by far more difficult than earlier competitive issues we’ve had,” Partanen says. Nokia finally released its first device with multitouch in 2010, three years after Jobs’s splashy iPhone announcement and four years after Han’s TED talk demo. How Android Ate Up the Low-End Market Nokia had long owned the low end of the cellphone market, with its sturdy, no-frills devices suited for that segment. So the years immediately following the iPhone’s launch saw the Finnish firm continue to thrive as it kept turning out simple, rugged devices. As one review of the Nokia 1200—successor to the 1100—put it in October 2007, “This handset chucks away all the fancy features you’ve come to expect on a modern mobile, leaving you with a pared-down feature set that’s easy for tech novices to get their heads around.” Two cellphone users in Nairobi, Kenya in 2013 exchange a payment on a Nokia 1200 phone via the M-Pesa Mobile Money Market, a popular online banking service. Trevor Snapp/Bloomberg/Getty Images The 1200 kept the 1100’s dust-proofing, flashlight, and long-lasting battery, and added features aimed squarely at the developing world. The 1200 was the first to include call-time tracking and a multiuser phone book, allowing owners who planned to lend their device to set up call limits based on time or cost. This feature helped enable what Nokia researchers called kiosks—informal pay-per-call services, in which an enterprising phone subscriber charged neighbors and family members by the minute for use of the device. In 2006, Nokia studied how Ugandans used their Nokia phones in rural and remote areas. An internal company slide deck from the time reveals just how keyed-in Nokia was to its lowest-income users. “Village phone operators are often women,” the slide deck notes. “And there tend to be a lot of children around. (Phones need to suffer considerable abuse from chewing, dust, sweat, etc.)” “A unit of phone time is 60 seconds,” another slide states. “But to avoid accidentally going over that time and incurring extra costs, kiosk operators shorten the unit to 57 seconds, allowing a three-second margin of error. Shared mobile used as phone kiosk must show call time.” Nokia’s familiarity with its market couldn’t protect the company forever, though. Nokia sought out user input around the world for the company’s device designs, including hosting “Open Studio” contests soliciting users’ sketches of their dream cellphone. Shaul Schwarz/Getty Images That’s because the iPhone wasn’t Nokia’s only looming smartphone competitor. In September 2008, the first Android phone went on sale—the HTC Dream, which was also sold as the T-Mobile G1. While the iPhone was aimed mostly at early adopters and affluent users who could afford to drop hundreds of dollars on a new phone, Android phones were, within a couple of years, aiming at the same low-cost, global user base Nokia was selling to. “I think it’s fair to say Android is the one that disrupted the market more for Nokia,” Bryer says. “Most of Nokia’s successful devices were not on the high-end market. But then, when Android came along, it started to fill that lower end and eventually took that market away from us.” An executive from Nokia India in 2010 holds the company’s 5530 XpressMusic and 5230 phones, both of which had touchscreens, although only the 5530 had Wi-Fi. Sam Panthaky/AFP/Getty Images With two emerging competitors in the low end and high end, the Finnish device maker responded with a device that split the difference—and satisfied neither camp. Released in 2009, the Nokia 5230 attempted to be a low-priced, touchscreen (though not multitouch) competitor to both the iPhone and Android. It sold an impressive 150 million units, doing especially well in developing countries. But the 5230 didn’t have Wi-Fi—one of the biggest complaints at the time. In the developing world, Wi-Fi connections were still rare, so the lack of Wi-Fi made some sense. But the rest of the world was not pleased. “We had such a big gap and dominant position,” Bryer says. “Which does maybe create a level of comfort which you should never get.” How Nokia Lost the Smartphone Race By the beginning of the 2010s, Nokia could have still drawn from the company’s labs, which were regularly spinning out new technologies and innovations. However, the Finnish handset maker ultimately failed to turn its R&D into viable new product lines in response to the emerging smartphone threat. Nokia’s predicament had precedent—Kodak, dominant in film photography, had actually invented the digital camera in 1975 but failed to commercialize it before digital imaging made its core business obsolete. “The technology coming from our R&D teams was cutting edge,” says Gordon Murray-Smith, director of services and ecosystems intelligence from 2008 to 2011. He recalls attending annual R&D innovation days that showcased work on self-healing materials and flexible screens, long before those technologies were seen elsewhere. “But why was Nokia not able to commercialize some of that really interesting and innovative activity more than it did?” Nokia desperately needed an injection of life to change its fortunes. The company’s first non-Finnish CEO, Stephen Elop (a Canadian fresh off a two-year stint on Microsoft’s leadership team), did not mince words. In an internal memo from February 2011 that was soon leaked to the media, Elop wrote, “The first iPhone shipped in 2007, and we still don’t have a product that is close to their experience. Android came on the scene just over two years ago, and this week they took our leadership position in smartphone volumes. Unbelievable.” In 2011, Nokia released the N9, a smartphone with a Linux-derived operating system. Within a year, Nokia had pivoted toward its Windows Phone-powered line of Lumia devices. Munshi Ahmed/Bloomberg/Getty Images Elop oversaw the 2011 launch of a Linux-based smartphone, the Nokia N9. The N9 ran on a distribution of Linux called MeeGo. Reviewers at the time praised the new smartphone direction the Finnish phone maker had taken. “Possibly the most beautiful phone ever made,” wrote one reviewer about the N9 for Engadget. But the N9’s accolades did not ultimately carry the day. Nokia announced its Lumia line of phones the same year—a direct pivot away from MeeGo toward the Windows Phone. It would be the last major strategic turn Nokia would take as a cellphone manufacturer. From this point forward, a succession of C-suite decisions all but sealed the fate of Nokia’s iconic line of phones. In 2013, Microsoft announced its bid to acquire Nokia’s handset operations. After the sale went through the following year, it rebranded the division Microsoft Mobile. But the year after that, Microsoft decided it had made a costly mistake, writing down $7.6 billion—nearly what it paid for Nokia’s handset division—and laying off nearly half of the former Nokia staff it had inherited. In 2016, Microsoft sold its feature phone assets to HMD Global. The latter still sells Nokia-branded phones—budget-friendly devices as well as nostalgia reproductions of models from Nokia’s glory days. What remained was a brand name, some intellectual property, and two decades of hard-won lessons about what it takes to stay on top—and what it costs when you can’t. “When you look at the players in the world of smartphones today, any of those players would struggle ever to achieve 14 consecutive years of being No. 1,” says Murray-Smith. Partanen says there was a downside to Nokia’s mobile-phone dominance. “Often, being the first mover is not necessarily the best position,” he says. “Being a quick follower is the best position.” The company itself ultimately survived, even if the transition wasn’t painless. Nokia’s revenues, which peaked in 2007, fell sharply through the mid-2010s before the company refocused on a decades-old business line—telecom infrastructure—that many had forgotten Nokia was even in. Nokia now ranks among the world’s top three suppliers of 5G network equipment, serving carriers across more than 125 countries, alongside Ericsson and Huawei. Although the company could never quite crack the smartphone, it now plays a key role in providing the network backbone those smartphones run on.
This article is brought to you by X Square Robot. Large language models gave artificial intelligence a working recipe. Pretrain a large model on broad data, and general capability follows. Robotics has no such recipe. Robotics systems have long been assembled from separate perception, planning, and control parts that rarely add up to intelligence a robot can carry from one task to another, or one machine to another. The central problem in embodied AI is to find the equivalent recipe, and the field does not yet agree on what it is. X Square Robot, a Chinese embodied-AI company, has made an unusually explicit bet. It argues that the recipe is an integrated stack, spanning the data a robot learns from, a world model for predicting changes in the physical world, and an action model that brings together perception, planning, reasoning, and decision-making to generate executable robot behavior. The company also believes that the stack should be built and released in the open. X Square Robot shares its vision of bringing robots into real homes.X Square Robot X Square Robot’s embodied AI stack What holds the stack together is a small set of principles rather than a single overarching model. The first is that the basic unit of robot data is an interaction, not a trajectory; a demonstration is successful only if it changes the world as intended, not simply because the joints moved. The second is that pretraining should yield usable capability, not just an initialization for later fine-tuning. The third is that behavior should be modeled around physical events rather than fixed slices of time. These principles make the layers interdependent, since the same robot-free data that trains the action model is also structured to feed the world model. It is worth being precise, though. The company describes the world model and the action model as complementary but independent model families that share a code base. Both sit within its broader World Unified Model, which it has presented as an architecture for training vision, language, action, and physical prediction together. Robot learning data: Engineering for quality and cost, not scale For the X Square Robot team, one of the biggest constraints on general-purpose robots is the cost and quality of interaction data, not the number of parameters. To address that, the company built its Universal Manipulation Interface (UMI) data collection system, QUANXTA Zero Series. It works by collecting demonstrations from people wearing a rig with dual grippers rather than teleoperating a robot. This approach is not itself new, and builds on established methods for robot-free data capture. What sets it apart are two engineering choices. X Square Robot emphasizes data quality control, recording trajectories and replaying them on a real robot, with only those that actually complete the task counted as valid.X Square Robot The first is quality control, and it is the most distinctive part. Rather than accepting recorded trajectories as they are, the system runs a closed inspection loop, and its notable step is physical playback. A sample of trajectories is replayed on the real robot, and only those that actually complete the task count as valid. That makes the validity rate a measured quantity rather than an assumption. For example, a gripper that closes a fraction of a second too early still looks like a grasp in the data, yet it has pushed the object away, so it shouldn’t be classified as valid. A smaller clean dataset can be worth more than a larger noisy one. The second choice is how lower-cost human data and scarce robot data are combined. The company pretrains on a large volume of robot-free demonstrations to build general representations, then adds a small amount of real-robot data as an anchor to the specific machine’s dynamics. It reports that this reaches performance comparable to an all-robot dataset at roughly a 20-fold lower cost of collection, driven mainly by how much cheaper the wearable rig is than a teleoperation setup. The resulting dataset is deliberately model-agnostic, formatted to feed both action models and world models. The caveat is that the strongest results are measured on the company’s own robots and data-collection pipelines. Broader independent testing will help confirm and extend these promising results across a wider range of settings. A world model organized around events In developing its world model, called WALL-WM, X Square Robot took a differentiated approach. Most action models predict a fixed-length chunk of motion from the current image and instruction. That is convenient, but it segments behavior into fixed-duration windows, so the boundaries fall where elapsed time dictates rather than where one action ends and the next begins. WALL-WM instead treats an action-grounded semantic event as its unit: a coherent piece of behavior such as reaching, grasping, or placing, something that can be named in language, seen in video, and executed as motion. X Square Robot’s world model, called WALL-WM, treats an action-grounded semantic event as its unit: a coherent piece of behavior such as reaching, grasping, or placing, something that can be named in language, seen in video, and executed as motion.X Square Robot WALL-WM’s design reflects a specific concern about not discarding what large video models already know. To achieve that, a text-to-video model is coupled to a freshly initialized action network that reads from the video features without overwriting them, which preserves the visual prior. From that one process, it offers two modes. An event mode runs in variable-length segments and suits reasoning over long horizons, while a fixed-length mode produces the steady, real-time output a controller needs. That places WALL-WM between mainstream chunk-based action models and pure video world models, keeping the predictive character of a world model while still yielding executable control. In a series of experiments, the company relied on a generalization test that is more specific than most. A model trained on a limited dataset was evaluated on long-horizon tasks in unseen settings and, on the company’s real-robot benchmark, reportedly outscored baselines that had been fine-tuned on related data. That is a meaningful result if it holds. For now, it is measured on the company’s own benchmark. With the code now being released, the broader community will have the opportunity to test, reproduce, and build on them across more settings. A policy that runs before fine-tuning, and action tokens with meaning The action layer carries two connected ideas. The first is a requirement the company sets for itself with Wall-OSS-0.5, its vision-language-action model: The pretrained model should run on a real robot before any task-specific fine-tuning. The interest is less in the scores than in the design behind them. The model trains three objectives together, namely discrete action tokens, language grounding, and continuous action generation. And it keeps gradients flowing through all of them rather than freezing parts of the network as some rival designs do. It’s also a more strict method, since it reports untuned behavior such as approaching, grasping, and recovering, including on a deformable task held out of training. As part of X Square Robot’s Wall-OSS-0.5 vision-language-action model design, the pretrained model should run on a real robot before any task-specific fine-tuning. X Square Robot The second idea is the action interface itself, called X-Tokenizer. Most systems that turn continuous motion into discrete tokens produce codes that the language model cannot interpret. X-Tokenizer reframes tokenization as learning a semantic interface, so that the top-level code stands for the intent of a motion while lower-level codes carry finer detail, all aligned with the language model’s own features. A useful consequence is stability. Adding noise to an action barely moves the intent code, which is what lets one tokenizer to be reused across robots without re-tuning. The tokenizer inside the production action model is a related variant of this approach. Together, the two ideas give the action layer something rather powerful: capability that transfers. The future of embodied AI stacks X Square Robot is betting that its unique approach combining three layers, each specialized in solving a key part of the problem, will stand out from other embodied AI stacks. The physical-playback step that grounds data quality is uncommon and sensible. The reframing of world modeling around events, with one backbone serving both reasoning and control, is a genuinely distinct approach. And the pairing of a deployable pretraining standard with a tokenizer designed as a semantic interface gives the action layer unusual coherence. X Square Robot’s valuation has climbed above 20 billion yuan (about US $2.9 billion), suggesting that investors increasingly view data infrastructure, foundation models, and scalable training systems as long-term differentiators in embodied AI. The next phase will bring broader validation. Much of the current evidence comes from X Square’s own robots and benchmarks. With the world model code now being made public, and as the community begins to test, reproduce, and build on the work, the reported capabilities will be tested across more robots, tasks, and settings. X Square Robot’s recent funding rounds reflect similar confidence. The company’s valuation has climbed above 20 billion yuan (about US $2.9 billion), suggesting that investors increasingly view data infrastructure, foundation models, and scalable training systems as long-term differentiators in embodied AI. What’s next for X Square Robot To learn more about its future plans, the following Q&A with the X Square Robot team further explores the company’s technology, strategy, and vision. What made now the right moment, technically, to commit to this stack? What recently became possible that wasn’t possible a couple of years ago? It is not one breakthrough but several trends maturing together. Foundation models gave us a shared representation across vision, language, and action, so we can model what a robot sees, what it is asked to do, and how its actions change the world in one framework, rather than as separate perception, planning, and control modules. Compute and infrastructure are finally sufficient for large-scale pretraining over long-horizon, multi-embodiment data. Just as importantly, we realized that data, not model size, is the real bottleneck for general robots—what is scarce is diverse, high-quality, reproducible interaction data. And world modeling has become practical. The useful question is no longer how to predict a few seconds of video, but how to understand the ways actions change objects, contacts, and task states. Two years ago these ingredients existed separately. Today they are mature enough to work as one system. “We realized that data, not model size, is the real bottleneck for general robots—what is scarce is diverse, high-quality, reproducible interaction data. And world modeling has become practical.” Your data system captures demonstrations with a wearable VR rig and custom grippers rather than teleoperating robots. What was wrong with standard teleoperation? Teleoperation is built around controlling the robot. It forces the operator to work within the machine’s kinematics, latency, and viewpoint, and the resulting demonstrations are slower, stiffer, and less diverse. We built our system around capturing human skill instead. Manipulation is really about contact, timing, finger coordination, and recovery, not just the path the hand takes, and a wearable rig records those before the behavior is compressed onto one particular robot. It also breaks teleoperation’s expensive scaling law, in which every demonstration needs a robot. People can generate rich data independently of any robot, and the crucial property is that those demonstrations can still be replayed and executed on a physical robot through the model. Mobility is convenient, but that replay is the real point, because it is what lets the same data be reused across different platforms. In X Square Robot’s approach, demonstrations can be replayed and executed on a physical robot through the AI model, allowing the same data to be reused across different platforms.X Square Robot X Square Robot reports that its pipeline has roughly an 85 percent data-validity rate. Why is quality control such an underrated bottleneck? Because errors in robot data are far more expensive than in language data. A small timing or contact error can change what a demonstration means. If a gripper closes a fraction of a second too early, the motion still looks like a grasp, but physically it has pushed the object away. A dataset that mixes failures and accidental successes teaches ambiguity, not skill, because the real unit is the interaction, not the trajectory. So we run automated inspection, kinematic checks, and physical replay, where we play a sample of trajectories back on the real robot and count only the ones that actually complete the task. Data quality sets the ceiling on how good a policy can be. In our experience a smaller, cleaner dataset often beats a much larger, noisier one, which is why we treat quality control as part of the model, not a preprocessing afterthought. The model runs in both “event mode” and “chunk mode.” When does each matter? Both matter, for different reasons. The physical world changes through events—when contact occurs, a grasp forms, or an object slips—not in fixed-frame windows. Event mode concentrates the model’s attention on those moments, and it matters most for long-horizon tasks, like clearing a table, where progress is a sequence of semantic events rather than a smooth stream. It runs in variable-length segments that follow the task rather than a clock. Chunk mode matters for deployment. Real controllers need a stable, real-time interface, and fixed-length chunks integrate cleanly with existing control systems. We organize learning around events in the first place because a fixed window can split one motion in half or merge two together, which turns training into short-horizon pattern matching and weakens the model on long tasks. So the world model’s job is to connect event-level understanding, which is where the reasoning happens, with a fixed-length output a real robot can actually run. Why make “deployable before fine-tuning” the criterion? Pretraining should produce capability, not just a good starting point. If a model is only useful after heavy fine-tuning, then most of the intelligence still lives in the downstream supervision, not in the foundation model. Deployable before fine-tuning is a more honest test of what pretraining actually learned. A well-pretrained robot should already know how to approach, grasp, move, avoid obstacles, and correct itself. Fine-tuning should adapt it to a specific task or robot, not create the ability from nothing. It is also a practical requirement. A robot in a home or a workplace shouldn’t need a brand-new dataset and a new policy every time the task changes, so a foundation model that already carries general skill, and some ability to recover, is the minimum bar for something genuinely useful in the real world. What is the most challenging part of cross-embodiment learning? Robots differ in control frequency, delay, compliance, sensing precision, and contact dynamics, so the same instruction can require different action decompositions and recovery strategies, and a behavior that works on one arm cannot simply be copied to another. Cross-embodiment learning needs an intermediate abstraction, lower than language but higher than joint angles: how you approach an object, how you make contact, how you apply force, and how you recover from a mistake. When we say cross-embodiment, the main capability we mean is multi-embodiment generalization: transferring across robots, training on many embodiments at once, and adapting to different kinematics. Human-to-robot transfer and other techniques are specific approaches to that goal. “A robot in a home or workplace shouldn’t need a new dataset and policy every time the task changes. A useful foundation model should already carry general skills and the ability to recover.” What would you most like to see other researchers attempt to reproduce or stress-test? Three things, above all. Whether event-level representations really generalize beyond our own datasets, across more tasks, scenes, objects, embodiments, and failure conditions. Whether pretraining stays effective on robots the model never saw during training, or whether its capability is still too tightly coupled to what it has already seen. And whether real-robot evaluation can become a shared language for the field, so that we compare not just success rates but the reasons systems fail, where an instruction was misread, where perception broke down, or where recovery fell short. Robotics has been driven too often by impressive demonstrations, and real progress comes from results that are reproducible and diagnosable. What capability is still missing before robots become dependable in homes? Benchmarks measure competence, like whether a model can finish a task. Homes demand reliability, safe and consistent operation over time in a place that changes every day, with objects moving, instructions that are vague, and people interrupting. The missing piece is not a higher one-time success rate: it is robust recovery. A dependable home robot has to know when it is uncertain, when to slow down, when to ask for help, and how to bring the world back to a safe state after it drops something or misunderstands a request. In a real home, failure recovery matters more than raw success, because the home does not reset itself. Homes also demand careful personalization, learning a household’s routines and preferences over time, with safety and trust as first principles. That combination, not any single skill, separates a capable demonstration from a robot people can live with. X Square Robot’s approach is that, in a real home, failure recovery matters more than raw success, because the home does not reset itself and it demands careful personalization, with safety and trust as first principles. X Square Robot How do the open-source components fit into X Square Robot’s World Unified Model direction? We see these releases as layers of the World Unified Model direction rather than isolated projects. Wall-OSS-0.5, the action model, asks whether an open vision-language-action model can gain directly measurable capability from large-scale pretraining, so it is the capability layer. WALL-WM, the world model, asks how a robot should understand change in the world, shifting from fixed windows to event-level modeling, so it is the representation layer. The data system supplies the interaction data that both of them learn from. Together they form a loop in which models produce capability, world models organize understanding, and the open-source community drives reproduction and improvement. World Unified Model is the broader architecture those layers support, bringing vision, language, action, and physical prediction together. We are releasing these pieces openly because embodied intelligence cannot be solved by one organization; it needs many embodiments, many real tasks, and broad feedback, and the long-term goal is a stack that keeps learning and ultimately moves robots from laboratory demonstrations toward reliable everyday use.
The computing community recently lost one of its enduring voices: IEEE Fellow Peter G. Neumann. The renowned computer scientist and respected risk analyst died on 17 May at the age of 93. For almost 70 years, Neumann shaped the computing field through his pioneering work on risks, system dependability, security, and fault tolerance with rare intellectual depth and unwavering ethical clarity. Five of those decades were spent as a principal scientist at SRI International in Menlo Park, Calif., where he worked until his death. A detailed narrative of his work, life, and mentoring is available on his SRI web page, where he chronicled his journey. He possessed a rare ability to identify systemic vulnerabilities long before they became widely recognized. He cautioned that interconnected systems, if poorly designed or insufficiently scrutinized, could fail and become targets for exploitation. He insisted innovation always must be accompanied by responsibility, reliability, and a clear understanding of the risks involved. With the widespread adoption of computing, information technology, artificial intelligence, and autonomous systems, Neumann’s insights have become more relevant. From Harvard to Bell Labs Neumann was born on 21 September 1932 in New York City. After graduating from high school, he pursued a degree in mathematics at Harvard, where he had a conversation that shaped his approach to research, according to the Association for Computing Machinery (ACM). In November 1952 he had a two-hour breakfast meeting with Albert Einstein, at which they discussed the importance of simplicity in design. Neumann was among the first generation of Harvard students to program computers and, remarkably for that era, enjoyed exclusive access to the computing systems. After earning his bachelor’s degree in 1954, he continued his education at Harvard, earning a master’s degree in 1955. In 1958 he moved to Germany to become a doctoral student at the Technical University of Darmstadt as part of the Fulbright program, which provides funding for U.S. citizens to study or teach abroad. He earned his doctorate in 1960. After returning to the United States, he joined Bell Labs in Murray Hill, N.J., where he worked on error-correcting codes and survivable communications. He also pursued a second Ph.D. in applied mathematics and science at Harvard, achieving that goal in 1961. Four years later, he was assigned to work on Multics, which became an influential operating system that shaped modern secure computing architectures. Multics was a mainframe time-sharing system designed to serve the diverse needs of multiple users simultaneously. Neumann designed its filing system, which featured hierarchical directories, access control lists, and dynamically paged virtual memory segments. He also played a key role in the design of its input/output system. In 1970 he left Bell Labs to join SRI. Technical contributions at SRI Neumann made several seminal and foundational technical contributions while at SRI, including the following: Provably Secure Operating System. The PSOS project he worked on advanced formal methods in operating systems and computer security. The project demonstrated that security could be designed within the initial plan rather than retrofitted. Election integrity and voting systems. He outlined vulnerabilities in electronic systems and advocated for transparency, verifiability, and public accountability. Systems-level risk thinking. He broadened the concept of computer security to encompass human factors, governance, policy failures, social consequences, organizational negligence, and misuse of automation. His system-level perspective now fuels debates on AI governance and digital trust. Intrusion-detection systems. With his colleague Dorothy E. Denning, a security expert, he helped develop an intrusion-detection expert system (IDES), laying the groundwork for modern cyberdefenses. CHERI. He promoted hardware-assisted secure computing: technology that now influences next-generation processors. The Capability Hardware-Enhanced RISC Instructions (CHERI) architecture project, which Neumann led, is now being commercialized by an international, nonprofit alliance. His contributions are united by a simple but profound principle: Security should be foundational, not incidental. Neumann argued that security must be embedded into system architecture from the start—not patched after deployment. ACM’s Risks Forum Neumann’s other enduring contribution was the creation and stewardship of the ACM Risks Forum, formally known as the Forum on Risks to the Public in Computers and Related Systems. For decades, it was one of the most respected online arenas for critical reflection on computing failures, vulnerabilities, security breaches, unintended consequences, and emerging technological threats. He transformed the forum into a scholarly archive of cautionary lessons in computing failures and risks. In 1985 he started documenting how technological systems fail when complexity exceeds understanding and when society places blind trust in automation. He then moderated the community for 41 years, leaving his position in April, weeks before his passing. In 1995 he published Computer-Related Risks, a book that serves as a case-driven guide to how computer systems fail and why. It is still relevant in an era defined by AI, growing cyberthreats, and our deep digital dependence. Intellectual rigor with grace and humility Neumann viewed computing not as an abstract technical pursuit but as a profoundly human enterprise carrying societal responsibilities. He was thoughtfully skeptical, questioned assumptions, and challenged complacency. His observations often anticipated challenges years before they became mainstream concerns. He exemplified high scholarship ideals and was intellectually honest and ethically steadfast. He had been a frequent critic of lax attitudes the industry has maintained toward both computer security and individual digital privacy. He warned against the industry’s tendency to repeat mistakes. Neumann’s signature contribution was not technical but a stance. He insisted, against industry custom, that recurring computer failures were not unfortunate accidents but rather were predictable consequences of how systems were built and sold. He was fundamentally an optimist about what can be done with research and was a pessimist about corporations. Security is not merely a technical patch, he said, but is a systemic property requiring sound design, governance, and human judgment. He consistently warned that uncontrolled complexity is itself a source of risk. His signature contribution was not technical but a stance. He insisted, against industry custom, that recurring computer failures were not unfortunate accidents but rather were predictable consequences of how systems were built and sold. Honors and recognitions Neumann was honored with a number of honors including the Electronic Privacy Information Center’s 2018 Lifetime Achievement Award, the Computing Research Association’s 2013 Distinguished Service Award, and ACM’s 2005 Special Interest Group on Security, Audit, and Control Outstanding Contributions Award. In addition to being an IEEE Fellow, he was a Fellow of ACM, the American Association for the Advancement of Science, and SRI. In 2012 he was inducted into the Cyber Security Hall of Fame. An enduring legacy Neumann’s greatest legacy is not necessarily his inventions but his way of thinking. His longtime interest was the risk ecology of computing—the business, technological, social, political, and personal risks that computing has created, along with its tremendous benefits in each of those spheres. He left us a timely lesson: Innovation must be accompanied by responsibility, foresight, and care. Neumann was “one of the last of the old guard and a pointer to the future,” observed IEEE Life Fellow Whitfield Diffie, who helped invent public key cryptography. Highlighting both the significance and enduring relevance of Neumann’s work, a tribute by blogger Phoenix AMTD aptly said: “He spent 70 years cataloging how computers fail. We spent 70 years not listening. Maybe now we will.” Let’s honor Peter G. Neumann not merely by remembering his advice but by following it.
An examination of how satellite vulnerabilities, modern wideband waveforms, and automatic link establishment are driving renewed military and government investment in HF communications. What Attendees will Learn Why HF (High Frequency) declined — and what has changed — How satellites overtook HF for global communications from the 1970s onward, and why growing awareness of satellite vulnerabilities to anti-satellite weapons, jamming, solar storms, and coverage gaps is reviving interest in skywave propagation as a resilient alternative. How the ionosphere enables and limits global HF communication — Understand the roles of the D, E, and F ionospheric layers in refracting and absorbing signals, the concepts of maximum usable frequency (MUF) and lowest usable frequency (LUF), and how sunspot number, solar flux index, and A/K geomagnetic indices are used to quantify and predict propagation conditions. How automatic link establishment transforms HF operability — Trace the evolution from proprietary first-generation ALE through interoperable second- and third-generation standards to fourth-generation wideband ALE, which automates frequency selection, link setup, and adaptation to changing channel conditions — removing the dependency on highly skilled operators. How wideband HF is closing the throughput. Download this free whitepaper now!
Working in isolation, especially for leaders, is rapidly becoming an outmoded idea. The modern era is defined by rapid technological advancements and increasingly complex, collaborative global challenges. In this environment, leadership can no longer be approached as an individual pursuit. Instead, leadership must be a collaborative effort in which knowledge, responsibility, and innovation are continuously exchanged across teams, roles, and areas of expertise. Success depends on the ability to foster connection, leverage diverse perspectives, and work collectively toward shared outcomes. The shift is especially important in science, technology, engineering, and mathematics fields. IEEE is bringing together emerging professionals and established experts and leaders at the inaugural IEEE International Leadership Conference to address the need for cross-generational knowledge-sharing and to equip professionals with tools for collaborative leadership. Honoring Expertise, Accelerating Potential is the theme of the ILC, scheduled for 3 and 4 October in Budapest. The conference is expected to focus on how leaders can share information across roles, adapt to rapid technological advancements, and build stronger, more connected professional communities. Through discussions, panels, and interactive sessions, attendees can examine how collaboration across experience levels and disciplines can strengthen decision-making and foment innovation. “There are several factors driving this shift [in leadership], including accelerating technological development cycles, the need to build public trust, and the large percentage of the STEM workforce approaching retirement,” says Vickie Ozburn, conference cochair. “Progress in STEM now depends less on individual brilliance and more on the ability to transfer knowledge, adapt, and make decisions that integrate technical expertise with ethical and social considerations.” From hierarchies to shared leadership Instead of traditional corporate models rooted in hierarchy and individual advancement, a more dynamic framework is taking shape, one that views leadership as a shared ecosystem built on mentorship, continuous learning, and intentional knowledge transfer. It means recognizing that professional development is no longer a one-directional flow of experience from senior professionals to newcomers. Instead, it thrives as a multidirectional exchange. When emerging professionals, mid-career managers, and seasoned experts including retirees are brought together, the result is not only richer dialogue but also more resilient and well-informed decision-making. A cross-generational dialogue enables organizations to honor what has worked, critically assess what has failed, and thoughtfully shape what needs to evolve. Bridging experience to drive future leadership Howard Wolfman, cochair of the IEEE ILC, underscores the importance of historical perspective in leadership development, invoking George Santayana’s enduring insight: “Those who cannot remember the past are condemned to repeat it.” “In STEM especially, this principle carries significant weight,” says Wolfman, an IEEE life senior member and the founder and principal of Lumispec Consulting, in Northbrook, Ill. “Technological innovation doesn’t happen all of a sudden; it builds on decades of research, lessons learned, and accumulated knowledge. When leaders actively connect insights from across experience levels, they gain a more complete understanding of both opportunity and risk.” That perspective reinforces the need for greater collaboration across roles and experience levels, ensuring that knowledge is not lost and is continuously built upon and applied in new ways. In this way, leadership development becomes a continuous, interconnected process rather than a series of isolated stages. STEM careers are no longer defined by linear progression but by evolving contributions, in which each phase adds value to the field’s broader advancement. What the changes mean for leaders today Adopting a new leadership paradigm requires a shift in mindset across all levels. For senior leaders, success is defined not only by what they have built but also by the people they mentor and the knowledge they pass forward. Their legacy lies in enabling future leaders to succeed. For emerging young professionals, innovation becomes more informed and impactful when it is grounded in historical context and informed by those who have already navigated similar challenges. “Technological innovation doesn’t happen all of a sudden; it builds on decades of research, lessons learned, and accumulated knowledge. When leaders actively connect insights from across experience levels, they gain a more complete understanding of both opportunity and risk.”—Howard Wolfman, cochair of the IEEE International Leadership Conference For organizations, cross-generational collaboration should be recognized as a strategic advantage, not merely an aspiration. Creating environments where knowledge flows freely and diverse perspectives are actively integrated is essential for long-term success. The evolution reframes the distinction between management and leadership. “A leader does the right thing, and a manager does things right,” Wolfman says. As the environment continues to shift, doing the right thing increasingly depends on drawing insights from across generations and experiences. Building future-ready leadership pipelines To build leadership pipelines capable of sustaining innovation and trust, organizations must begin asking more intentional questions: How do we create systems where knowledge sharing is continuous rather than episodic? How do we elevate emerging voices earlier in their careers? How do we ensure that experienced professionals remain engaged and valued contributors? How do we design leadership development as a collaborative, inclusive process rather than a competitive one? Ultimately, leadership cannot be tied solely to titles or tenure. It is about contributing to a continuum in which each generation strengthens the next. The IEEE ILC attendees are likely to leave the event with new insights and with a transformed perspective: Leadership is not about waiting for advancement or recognition; it is about engaging in an exchange of knowledge, responsibility, and vision, where the strength of the whole depends on the contributions of every generation. Registration for the conference opens soon.
A semi-trailer that helps propel itself entered commercial road testing in late May, when a powertrain kit developed by Nivalis Energy Europe, headquartered in Luxembourg with engineering operations in Germany, was fitted to a trailer supplied by Amsterdam-based TIP Group. The self-powered trailer was handed over to German transport operator Sommer for use in its working fleet. The Nivalis Powered Trailer Kit centers on an electric axle co-developed with Wiehl, Germany–based running gear specialist BPW, rated at 50 kilowatts peak, capable of both propulsion assistance and regenerative braking. That axle draws on a 60-kilowatt-hour, 400-volt lithium-ion battery pack charged from three sources: the axle itself during braking and deceleration, a full-rooftop array of photovoltaic panels generating up to 3.7 kilowatts-peak, and a 32-amp, three-phase AC grid connection available during parking stops. The driver’s only window into the system is a small display readable from the cab’s side mirror that shows the system status and battery charge level. Nothing about the trailer’s handling or licensing requirements changes. The partners project savings of up to 7,000 liters of diesel per trailer per year, which is enough to keep about 19 tonnes of carbon dioxide out of the air. These figures are based on a trailer running 100,000 kilometers annually at payloads between 20 and 24 tonnes, on a mix of long-haul and hub-to-hub routes. Pavel Gilman, vice president of sales and marketing at Nivalis, breaks down where those savings come from: roughly 30 to 35 percent from the electric axle during braking and deceleration, 11 to 15 percent from the rooftop solar panels, and the remainder (roughly half) from grid charging during parking stops. The pilot is planned to run for more than a year, spanning multiple seasons. The retrofit cost has not been disclosed, and the pilot is running on a single trailer. But the underlying assumptions are now on the table and they represent a specific, high-utilization use case (meaning a truck that’s almost always on the move, filled to capacity with freight) not a universal one. Across Europe and North America, a growing number of companies have concluded that electrifying the trailer, rather than replacing the tractor unit, may be the fastest and most cost-effective path to decarbonizing long-haul freight. A new battery-electric heavy truck carries a high upfront cost and demands charging infrastructure that most freight corridors do not yet reliably provide. A retrofit kit fitted to an existing trailer is meant to sidestep both problems. The question the industry has been working to answer is whether the energy harvested from regenerative braking, rooftop solar, and grid charging in short bursts when the vehicle is parked for loading and unloading is enough to produce savings that recover the kit’s cost in a reasonable timeframe. Several companies now believe the answer is yes, and they are accumulating field data to prove it—though not all of them are going about it the same way. Trailer industry places its bets The competitive landscape has taken shape most visibly in Germany. Trailer Dynamics, an Aachen-based company, has conducted field tests with BMW Logistics, DB Schenker, Duvenbeck, and Volkswagen Konzernlogistik, reporting average fuel savings of around 40 percent for diesel tractor combinations, substantially higher than the up to 18 percent reduction implied by the Nivalis projection. The difference traces directly to battery size, but Trailer Dynamics frames the choice as an economic question rather than an architectural one. “The discussion should not start with battery size, but with the economics of the transport operation,” the company said in response to written questions. “There is no single battery capacity that is universally right for every fleet.” Trailer Dynamics’s modular system offers three configurations ranging from 187 to 551 kilowatt-hours, sized to match route profile, annual mileage, payload, and charging access. The M300 version, whose designation reflects the capacity of its 300-kilowatt-hour lithium iron phosphate battery supplied by Chinese battery manufacturer CATL, adds approximately four tonnes to the trailer, roughly three times the one-to-1.4–tonnes added to a trailer by the Nivalis system. Both companies’ systems would extend the range of a battery-electric tractor by reducing the energy demand on the tractor’s motor. But Trailer Dynamics explicitly targets that use case, claiming its self-propelled trailer yields combined ranges of up to 850 kilometers—enough to eliminate intermediate charging stops on many long-haul routes. Nivalis has not published range extension figures for electric tractor combinations, and its smaller battery and peak lower output suggest the effect would be more modest. That higher energy storage capability widens the addressable market for Trailer Dynamics considerably and helps explain the investment flowing into the self-propelled trailer space. In November 2025, the European Investment Bank extended a €25 million loan to the company, backed by the European Union’s InvestEU program, to support commercialization. Trailer Dynamics says it plans to begin industrial-scale production in 2028, with adoption expected to accelerate as European carbon dioxide reduction requirements tighten toward 2030. ZF, the German automotive supplier, entered the space with its TrailTrax system, using an electric axle rated at up to 210 kilowatts continuous power. ZF claims that, between onboard battery storage and energy recovered via regenerative braking, the self-propelled trailer system yields up to 16 percent in energy and carbon dioxide savings when combined with an ICE powered truck. The company also says TrailTrax can reduce carbon dioxide emissions by as much as 40 percent with opportunistic plug-in charging. Trailer manufacturers Kässbohrer and Krone have adopted the platform, as has BPW—the same running gear specialist co-developing the Nivalis axle. In North America, Range Energy is developing a system with up to 300 kilowatt-hours of onboard energy capacity, compatible with diesel, battery-electric, and hydrogen fuel cell tractors. Range, which has announced a partnership with ZF, to help drive the development and adoption of the Range eTrailer System within the North American commercial trucking industry, is now equipping its trailers with ZF’s AxTrax 2 e-axle for battery-powered propulsion. Range Energy has a separate pilot agreement with DB Schenker, the German logistics company that is also among the European operators that tested the Trailer Dynamics system. Range and DB Schenker say they plan to deploy a powered trailer in commercial trucking operations in North America, with first deliveries scheduled for later this year. The breadth of activity across continents reflects a field that has moved well past the question of whether powered trailers work. The argument now is about which architecture works best and at what cost. What the field does not yet have is a common standard for measuring and reporting savings. The figures from various pilots—an average of 40 percent from Trailer Dynamics, up to 18 percent implied by the Nivalis projection—reflect different routes, loads, seasons, and battery sizes. In some cases, they represent short validation runs rather than sustained operational data. Fleet operators evaluating competing systems are working with numbers that are difficult to interpret and impossible to rank against one another. Both architectures reduce available payload, but by very different margins. The M300’s roughly four-tonne addition dwarfs the one-to-1.4-tonne addition of the Nivalis system. Trailer Dynamics argues the weight penalty is largely academic in practice, because more than 90 percent of trailer movements are constrained by cargo volume before they approach legal weight limits. Under current European regulations, both systems reduce payload on a one-for-one basis. Frameworks under discussion would change that. New rules could allow up to four extra tonnes for electric trucks, with proposals to extend the provision to electric trailers. If amended, the payload effect would turn positive for both systems. Until then, every kilogram of kit is a kilogram unavailable for freight. Small versus large battery systems The choice between large-battery and small-battery powered trailers is a bet on which cost will fall faster: battery pack prices or the cost of grid charging infrastructure. A large-battery system delivers higher savings but requires reliable charging access across the operating cycle. If infrastructure buildout stalls—as it has repeatedly in heavy-duty transport—operators face the same dependency problem that has slowed battery-electric truck adoption. The Nivalis architecture hedges against that risk: its 32-amp connection requires only a standard industrial outlet, and the solar array and regenerative braking handle significant energy input without infrastructure at all. Gilman frames the design philosophy in terms of the industry it serves. “Logistics lives with low margins,” he said. “We are focused on the product which fits the industry technically and financially. It overcomes the capital expenditure hurdle and maximizes financial benefit by adding sources of energy which are symbiotic to each other.” And because Nivalis’s axle is comparatively light, he says, operators won’t be forced to reduce payload. Trailer Dynamics sees it differently. “Long-haul transport will increasingly move toward depot-based and destination-based charging models,” says Michael W. Nimtsch, the company’s Managing Director. “The question is not how small a battery can be made, but how much economic value each additional kilowatt-hour can generate over the life of the vehicle.” On solar and regenerative recovery, Nimtsch argues both are useful complements to stored battery energy rather than substitutes for it. “Compared with the daily energy demand of a long-haul truck, solar generation remains relatively modest,” he says. The Nivalis energy breakdown supports that view in relative terms: Grid charging contributes the largest share of projected savings, regenerative braking second, and solar third. That hierarchy means performance depends more on charging access during dwell time than the multi-source framing might suggest even if that access requires only a standard industrial outlet. Trailer Dynamics prices its system between €145,000 and €195,000 and targets a payback period of no more than five years. Nivalis targets five to six years at current costs, falling to three to four years as volumes grow. Asked exactly what the price tag says, the company declined to answer. The minimum annual savings needed, Gilman said, is between €5,000 and €6,000 per trailer. Until someone publishes a full year of results from a trailer running in normal commercial rotation, fleet operators cannot answer the two questions that actually drives adoption: What does this cost, and when does it pay back?
Toshio Fukuda has been blazing trails for most of his career. He is considered to be one of the most prolific scholars in robotics, writing more than 2,000 research papers and authoring several books on the field. He’s an influential figure thanks to his pioneering work developing biomedical robotic systems, industrial robots, micro-nano robotics, mechatronics, and AI-driven automation. Fukuda launched one of the first robotics conferences, the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). It is still popular almost 40 years later. Toshio Fukuda Employer Egypt-Japan University of Science and Technology, in Alexandria Title Professor and vice president of research Member grade Life Fellow Alma maters Waseda University, in Tokyo; University of Tokyo An IEEE Life Fellow, he is a professor emeritus in the department of micro-nano systems engineering and a visiting professor at Nagoya University, in Japan, where he taught for nearly 25 years. Currently, he is a vice president of research at the Egypt-Japan University of Science and Technology, in Alexandria, Egypt. Within IEEE, Fukuda has held top volunteer positions including the organization’s highest office: He served as IEEE president in 2020, becoming the first person of Asian descent to hold the role. He’s a former program director of Japan’s Moonshot program, which by 2050 intends to develop advanced AI robots. Born in Japan, Fukuda has been recognized by the country for his contributions to science with two of its highest awards: the Medal of Honor with a purple ribbon in 2015 and the Order of the Sacred Treasure in 2022. IEEE honored him with this year’s Richard M. Emberson Award for “distinguished service advancing the technical objectives of IEEE, especially in the area of robotics.” The IEEE Board-level award is sponsored by the IEEE Technical Activities Board. Fukuda received the award on 24 April at a ceremony in New York City. As a former IEEE president who has served as a master of ceremonies at several of the organization’s major award events, Fukuda noted that he is more accustomed to bestowing awards than receiving them. “It’s very interesting to be on the receiving end,” he says. The journey into robotics research As a teenager, Fukuda spent his summer breaks teaching himself how to build things including transistor radios and steam engines. “It was very nice to have a hands-on hobby and make these kinds of things myself,” he says. His experimentation led him to study engineering. He earned a bachelor’s degree in engineering in 1971 from Waseda University, in Tokyo. He says one of his professors there—Ichiro Kato, regarded as the father of Japanese robotics research—was a good mentor who made a positive impact. Fukuda’s research interests were robotics and mechatronics, a field that combines robotics, electronics, computer science, and control systems. He went on to earn a master’s degree and a doctorate in science from the University of Tokyo, in 1971 and 1977. During those years, he also attended Yale, where he conducted research on advanced control theory in 1973. He reflects fondly on his time at Yale: “It was a very nice environment and a kind of free-thinking atmosphere. It motivated me to study more.” “IEEE doesn’t care who you are, what you do, what country you are from, or whether you are male or female. IEEE accepts people who have energy and passion.” While at Yale, Fukuda served as an assistant to his advisor—which led him to consider a career in academia, he says, because he enjoyed the freedom that research work afforded him. But he realized that such freedom comes with a price. University researchers are expected to raise the money that funds their work. He compares researchers to small-business owners who have to bring in money to keep their enterprise afloat. That realization led him to select robotics as his field because he intended to develop technologies useful to industry, he says. After earning his doctorate, he returned to Japan in 1977 to work as a research scientist at the government’s Mechanical Engineering Laboratory, later renamed the National Institute of Advanced Industrial Science and Technology, in Tsukuba. “There was a lot of research going on at the lab, including practical robotics and theory,” he says. He left Japan in 1979 to become a visiting research fellow at the University of Stuttgart, in Germany. During his year there, he studied systems, software problems, and related topics. He returned to Japan and was hired as an associate professor of mechanical engineering at the Tokyo University of Science. He conducted research into practical uses for robots by visiting industrial plants. He decided to develop robots that inspect industrial equipment such as those used in assembly plants, oil refineries, and power stations—places that “can be hostile environments for humans,” he says. His work drew interest from chemical, oil, and utility companies. “I got a lot of money from them for this very practical application, which funded my research,” he says, laughing. Developing popular robotic systems Fukuda grew tired of making those robots, he says, so he switched to creating ones for scientific applications. He developed many techniques, but he probably is best known for his modular, cellular robotic systems (CEBOTs), which he introduced in 1985. He has described how CEBOTs work in numerous papers published in the IEEE Xplore Digital Library. The CEBOT system is composed of a number of autonomous robotic cells that stick together like interlocking Lego plastic bricks, he says. Each cell is a fundamental modular unit that has a function. When a simple task is given, the system can analyze it and generate the structure of the cellular manipulator. The cells connect to and detach from each other through connection mechanisms and cooperate mutually, creating complex structures and configurations. “You start developing from the component-wise to the cell-wise to a small functional unit—and then you come up with clusters that make bigger systems. We can make a society of robot beings like that,” he explained in his oral history published on the Engineering and Technology History Wiki. “It’s a distributed robotic system, a self-organized robotic system, and also an evolutionary robotic system. “It’s also a fault-tolerant robot system because if something is wrong, you just remove those things and make a new one. You keep the system working. That’s a great thing.” Today CEBOTs are used for a variety of tasks such as delivering medication in hospitals, assisting with planting crops, and transporting products in distribution centers. Check out IEEE Spectrum’s Robots Guide for news from the world of robotics. In 1989 Fukuda joined Nagoya University as a professor of mechanical engineering and micro-nano systems engineering. During his 24-year career there, he was director of the university’s Center for Micro-Nano Mechatronics. He developed a long list of technologies at the university, including many for medical applications. He also conducted groundbreaking research into intelligent robotic systems and micro- and nano-robotics. Another technology he is known for is brachiation robots, which he helped develop in 1988. He calls them monkey robots because they’re based on the pendulum-like movement of monkeys swinging from tree to tree. The gravity-based locomotion enables continuous movement. Brachiation robots now are inspecting high-voltage transmission towers and bridges, searching damaged buildings for survivors, and performing maintenance on pipelines and cables. Fukuda retired from the university in 2013 and was named professor emeritus. He didn’t stay retired for long, though. He next held a teaching appointment at Meijo University, in Nagoya, until he left in 2022 to join the Egypt-Japan University. A prominent volunteer He joined IEEE in 1980 at the encouragement of one of his research advisors, Professor Fumio Harashima, now an IEEE Life Fellow. After attending conferences and reading the organization’s publications, Fukuda says, he looked forward to becoming more involved. “I wanted to know how to organize a conference and how to edit a paper for one of its Transactions,” he says. “I wanted to know what was going on from inside the organization, not just the outside.” In 1988 he was the founding chair and organizer of IROS, in Tokyo. The conference had 330 attendees that year, and was supported by Harashima. Today it is one of the largest and most prestigious conferences on the topic, attracting more than 9,000 people annually. Out of 120,000 conferences, it was the only conference in the Nature Index database for this year, Fukuda says. In 1996 he and other members launched IEEE Transactions on Mechatronics. He was the founding president of the IEEE Nanotechnology Council, which was established in 2002. He is considered a pioneer in nanotechnology research, particularly regarding how it relates to robotics. Over the years, he has held numerous volunteer positions on IEEE editorial boards and committees. He was the 1998–1999 president of the IEEE Robotics and Automation Society, becoming the first non-U.S. member to hold the title. He was director of IEEE Division X (2001–2002 and 2017–2018), which covers intelligent systems, biological engineering, robotics, control systems, and photonic technologies. He served as the 2013–2014 director of IEEE Region 10 (Asia-Pacific). As the 2020 IEEE president, Fukuda saw the organization through the early part of the COVID-19 pandemic. Because of travel restrictions, he realized IEEE should change how it offered its in-person services, specifically educational programs. He encouraged IEEE Educational Activities to develop an online learning platform. The IEEE Learning Network started with just three courses and now offers nearly 2,000 courses, webinars, and learning materials. An award-winning member The Emberson Award joins a slew of other recognitions Fukuda has received from IEEE. They include several from the IEEE Robotics and Automation Society: a 2004 Pioneer Award, a 2009 Saridis Leadership Award, and the 2011 Harashima Award for Innovative Technologies. He is also a recipient of the Board-level 2010 IEEE Robotics and Automation Technical Field Award. He says he feels strongly that IEEE should be a diverse organization that is welcoming to all. As IEEE president, he led efforts to devise a diversity, equity, and inclusion program. Several policies, procedures, and bylaws were revised to give members a safe, inclusive place for discourse. “It’s important for IEEE to make everyone feel comfortable,” he says. “DEI programs are important. All people should be equal. IEEE doesn’t care who you are, what you do, what country you are from, or whether you are male or female. IEEE accepts people who have energy and passion. “It accepted me, from the Far East. That’s why I like it.” You can learn more about Fukuda and his career from the oral history conducted by the IEEE History Center.
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Many IEEE members who collect historical engineering artifacts often offer them to the IEEE History and Heritage group, which includes the IEEE History Center, to display. To bring these artifacts to the public, the group created the IEEE Global Museum, which curates traveling exhibits for display at conferences and in libraries, universities, and other venues. The program educates people about how technological progress has unfolded over generations, and how engineers and researchers build on past achievements to benefit humanity. Curating the exhibits has been rewarding, says Daniel Jon Mitchell, director of the group’s heritage programs. “People tell me that they are genuinely moved by having history and artifacts explained to them in an accessible, intelligible way,” Mitchell says. “When people are moved and emotionally affected by what you’re doing, they’re going to remember that. And I think that’s part of the power of what we’re doing.” The most recent traveling exhibit was on display in April in New York City during the IEEE Honors Ceremony, which celebrates engineering pioneers who have developed technologies that changed how people connect with the world. Attendees explored the Microchips That Shook the World exhibit, which drew inspiration from IEEE Spectrum’s Chip Hall of Fame. The exhibit conveys the roles integrated circuits play in fields such as signal processing, audio engineering, and telecommunications. The Commodore 64, one of the artifacts on display, stirred up treasured childhood memories for guests who had used the home computer. Other exhibits have focused on early radio inventions and power and communications technologies. The Global Museum works with IEEE societies to mark their anniversaries by interpreting and displaying pertinent items. A tribute to radio pioneer Edwin Howard Armstrong The idea of a traveling museum came to fruition in 2024 after Alexander Magoun, IEEE’s outreach historian, connected with Mike Molnar. The IEEE associate member owns one of six superheterodyne radio prototypes developed by Edwin Howard Armstrong, who probably is best known for inventing the FM radio system. Armstrong received the first IEEE Medal of Honor in 1917. The radio converts incoming frequencies into a fixed, lower intermediate one using a local oscillator and a frequency mixer. The technology paved the way for modern electronic communications devices. The prototype became the focal point of the Global Museum’s flagship Unseen Signals: E. Howard Armstrong’s Radio Revolution exhibit, which celebrates the inventor’s life and his impact on the broadcasting industry and wireless communications. “The radio prototype is one of the most incredible pieces that we could put on display,” Mitchell says. He and Magoun sourced other artifacts including an Audion used in Armstrong’s experiments on wireless signal amplification; a selection of consumer products that attempted to cash in on radio’s popularity, including a flour sifter and laxatives; and a Motorola Walkie-Talkie from the Korean War. They were from museums or private collectors along the East Coast of the United States. “Aside from [Guglielmo] Marconi, Armstrong is the most significant contributor to the history of radio,” Mitchell says. “The exhibit is not only a biography but also a story of the cultural and political implications his work had.” Visitors can play 15 short clips of past radio broadcasts covering politics, religion, sports, or another topic. The Armstrong exhibit was unveiled in 2024 at the National Museum of Industrial History in Bethlehem, Pa. The 93-square-meter exhibit is still traveling around the United States. It is on display until 15 August at the Pavek Museum, in St. Louis Park, Minn. From 21 November until 9 May 2027, it is scheduled to be at the Museum of Innovation and Science in Schenectady, N.Y. Entry to the museum is free for IEEE members with a digital membership card. Collaborating with IEEE societies The IEEE History and Heritage group collaborates with IEEE societies to create exhibits for special events. In 2024 Mitchell curated an exhibit to celebrate the 75th anniversary of the IEEE Vehicular Technology Society and its 100th Vehicular Technology Conference. The Our Mobile World exhibit was launched at the conference, held in October in Washington, D.C. “The society’s leadership helped me focus attention on key developments that meant a lot to its members,” Mitchell says. “The IEEE Global Museum wants to present exhibits that connect with its audiences, whether these are IEEE members or the public,” he says. “Just knowing what was important historically doesn’t mean that this will resonate, so I really appreciated the insight.” The exhibit’s artifacts included a Motorola DynaTac “brick” cellphone, a CB radio from the 1980s, and one of the earliest handheld GPS receivers. Visitors played an interactive game to test their knowledge spanning a century of wireless technology, motor vehicles, and mobile communication inventions. Mitchell worked this year with the IEEE Dielectrics and Electrical Insulation Society to launch a virtual exhibit, Powering Up, which is available on the Global Museum website. It provides an overview of high-voltage power engineering, and it highlights the roles that manufacturers General Electric and Westinghouse played in making long-distance, high-voltage transmission of electrical power possible. Videos and photos of impulse generators and tests are featured in the exhibit. Nvidia CEO and cofounder Jensen Huang, who received the 2026 IEEE Medal of Honor, exploring the Microchips That Shook the World exhibit.IEEE Conferences, Events & Experiences One photo shows lightning arcing between high-voltage generators. Others show the impulse generators used at the 1939 World’s Fair in New York City, demonstrations of artificial lightning, and U.S. President Ronald Reagan visiting GE’s high-voltage laboratory in Pittsfield, Mass. The history of microchips The Unseen Signals exhibit was created for large venues, but the Microchips That Shook the World exhibit was designed to be displayed in different spaces, Mitchell says. Artifacts are premounted to ensure easy setup, and they’re encased in glass because many are rare. Microchips are crucial for signal processing, audio engineering, and telecommunications, making them a point of interest despite their small size, Mitchell says. One rare artifact on display is the Kodak KAF-1300 image sensor. Invented in 1986, it was used in one of the earliest digital cameras made for photojournalists. The KAF-1300’s image sensor chip “is credited with bringing digital cameras out of the laboratory,” Mitchell says. “Only around 500 were produced.” Visitors can understand how transistors work, he says, by pressing buttons to turn them on and off. “There are billions of transistors in modern microchips,” he notes, “and you can combine them in a way that performs logical functions.” Unseen Signals, one of two identical exhibits, was curated by Mitchell and Stephen Cass, IEEE Spectrum’s special projects editor, with help from several Spectrum colleagues. Together, they served as on-site docents for guests at the IEEE Honors Ceremony. The display also featured a preview of IEEE’s immersive “Inside the Microchip” video project, which delves beneath the silicon surface of Nvidia’s NV20 chip, using forensic photography and computer-generated renderings. The video, to be released this year, aims to teach middle school students about the microchips that are inside their gaming devices. The exhibit was on display at the IEEE Electronic Components and Technology Conference, held in May in Orlando, Fla. Later this year, members will be able to visit it at the Computer History Museum in Mountain View, Calif., and the University of Waterloo, in Ontario, Canada. The IEEE Global Museum is made possible thanks to donations to the IEEE Foundation.