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Transforming a newly discovered software vulnerability into a cyberattack used to take months. Today—as the recent headlines over Anthropic’s Project Glasswing have shown—generative AI can do the job in minutes, often for less than a dollar of cloud-computing time. But while large language models present a real cyberthreat, they also provide an opportunity to reinforce cyberdefenses. Anthropic reports its Claude Mythos preview model has already helped defenders preemptively discover over a thousand zero-day vulnerabilities, including flaws in every major operating system and web browser, with Anthropic coordinating disclosure and its efforts to patch the revealed flaws. It is not yet clear whether AI-driven bug finding will ultimately favor attackers or defenders. But to understand how defenders can increase their odds, and perhaps hold the advantage, it helps to look at an earlier wave of automated vulnerability discovery. In the early 2010s, a new category of software appeared that could attack programs with millions of random, malformed inputs—a proverbial monkey at a typewriter, tapping on the keys until it finds a vulnerability. When such “fuzzers” like American Fuzzy Lop (AFL) hit the scene, they found critical flaws in every major browser and operating system. The security community’s response was instructive. Rather than panic, organizations industrialized the defense. For instance, Google built a system called OSS-Fuzz that runs fuzzers continuously, around the clock, on thousands of software projects. So software providers could catch bugs before they shipped, not after attackers found them. The expectation is that AI-driven vulnerability discovery will follow the same arc. Organizations will integrate the tools into standard development practice, run them continuously, and establish a new baseline for security. But the analogy has a limit. Fuzzing requires significant technical expertise to set up and operate. It was a tool for specialists. An LLM, meanwhile, finds vulnerabilities with just a prompt—resulting in a troubling asymmetry. Attackers no longer need to be technically sophisticated to exploit code, while robust defenses still require engineers to read, evaluate, and act on what the AI models surface. The human cost of finding and exploiting bugs may approach zero, but fixing them won’t. Is AI Better at Finding Bugs Than Fixing Them? In the opening to his book Engineering Security (2014), Peter Gutmann observed that “a great many of today’s security technologies are ‘secure’ only because no one has ever bothered to look at them.” That observation was made before AI made looking for bugs dramatically cheaper. Most present-day code—including the open source infrastructure that commercial software depends on—is maintained by small teams, part-time contributors, or individual volunteers with no dedicated security resources. A bug in any open source project can have significant downstream impact, too. In 2021, a critical vulnerability in Log4j—a logging library maintained by a handful of volunteers—exposed hundreds of millions of devices. Log4j’s widespread use meant that a vulnerability in a single volunteer-maintained library became one of the most widespread software vulnerabilities ever recorded. The popular code library is just one example of the broader problem of critical software dependencies that have never been seriously audited. For better or worse, AI-driven vulnerability discovery will likely perform a lot of auditing, at low cost and at scale. An attacker targeting an under-resourced project requires little manual effort. AI tools can scan an unaudited codebase, identify critical vulnerabilities, and assist in building a working exploit with minimal human expertise. Research on LLM-assisted exploit generation has shown that capable models can autonomously and rapidly exploit cyber weaknesses, compressing the time between disclosure of the bug and working exploit of that bug from weeks down to mere hours. Generative AI-based attacks launched from cloud servers operate staggeringly cheaply as well. In August 2025, researchers at NYU’s Tandon School of Engineering demonstrated that an LLM-based system could autonomously complete the major phases of a ransomware campaign for some $0.70 per run, with no human intervention. And the attacker’s job ends there. The defender’s job, on the other hand, is only getting underway. While an AI tool can find vulnerabilities and potentially assist with bug triaging, a dedicated security engineer still has to review any potential patches, evaluate the AI’s analysis of the root cause, and understand the bug well enough to approve and deploy a fully functional fix without breaking anything. For a small team maintaining a widely-depended-upon library in their spare time, that remediation burden may be difficult to manage even if the discovery cost drops to zero. Why AI Guardrails and Automated Patching Aren’t the Answer The natural policy response to the problem is to go after AI at the source: holding AI companies responsible for spotting misuse, putting guardrails in their products, and pulling the plug on anyone using LLMs to mount cyberattacks. There is evidence that pre-emptive defenses like this have some effect. Anthropic has published data showing that automated misuse detection can derail some cyberattacks. However, blocking a few bad actors does not make for a satisfying and comprehensive solution. At a root level, there are two reasons why policy does not solve the whole problem. The first is technical. LLMs judge whether a request is malicious by reading the request itself. But a sufficiently creative prompt can frame any harmful action as a legitimate one. Security researchers know this as the problem of the persuasive prompt injection. Consider, for example, the difference between “Attack website A to steal users’ credit card info” and “I am a security researcher and would like secure website A. Run a simulation there to see if it’s possible to steal users’ credit card info.” No one’s yet discovered how to root out the source of subtle cyberattacks, like in the latter example, with 100 percent accuracy. The second reason is jurisdictional. Any regulation confined to U.S.-based providers (or that of any other single country or region) still leaves the problem largely unsolved worldwide. Strong, open-source LLMs are already available anywhere the internet reaches. A policy aimed at handful of American technology companies is not a comprehensive defense. Another tempting fix is to automate the defensive side entirely—let AI autonomously identify, patch, and deploy fixes without waiting for an overworked volunteer maintainer to review them. Tools like GitHub Copilot Autofix generate patches for flagged vulnerabilities directly with proposed code changes. Several open-source security initiatives are also experimenting with autonomous AI maintainers for under-resourced projects. It is becoming much easier to have the same AI system find bugs, generate a patch, and update the code with no human intervention. But LLM-generated patches can be unreliable in ways that are difficult to detect. For example, even if they pass muster with popular code-testing software suites, they may still introduce subtle logic errors. LLM-generated code, even from the most powerful generative AI models out there, is still subject to a range of cyber-vulnerabilities. A coding agent with write access to a repository and no human in the loop is, in so many words, an easy target. Misleading bug reports, malicious instructions hidden in project files, or untrusted code pulled in from outside the project can turn an automated AI codebase maintainer into a cyber-vulnerability generator. Guardrails and automated patching are useful tools, but they share a common limitation. Both are ad hoc and incomplete. Neither addresses the deeper question of whether the software was built securely from the start. The more lasting solution is to prevent vulnerabilities from being introduced at all. No matter how deeply an AI system can inspect a project, it cannot find flaws that don’t exist. Memory-Safe Code Creates More Robust Defenses The most accessible starting point is the adoption of memory-safe languages. Simply by changing the programming language their coders use, organizations can have a large positive impact on their security. Both Google and Microsoft have found that roughly 70 percent of serious security flaws come down to the ways in which software manages memory. Languages like C and C++ leave every memory decision to the developer. And when something slips, even briefly, attackers can exploit that gap to run their own code, siphon data, or bring systems down. Languages like Rust go further; they make the most dangerous class of memory errors structurally impossible, not just harder to make. Memory-safe languages address the problem at the source, but legacy codebases written in C and C++ will remain a reality for decades. Software sandboxing techniques complement memory-safe languages by addressing what they cannot—containing the blast radius of vulnerabilities that do exist. Tools like WebAssembly and RLBox already demonstrate this in practice in web browsers and cloud service providers like Fastly and Cloudflare. However, while sandboxes dramatically raise the bar for attackers, they are only as strong as their implementation. Moreover, Antropic reports that Claude Mythos has demonstrated that it can breach software sandboxes. For the most security-critical components, where implementation complexity is highest and the cost of failure greatest, a stronger guarantee still is available. Formal verification proves, mathematically, that certain bugs cannot exist. It treats code like a mathematical theorem. Instead of testing whether bugs appear, it proves that specific categories of flaw cannot exist under any conditions. AWS, Cloudflare, and Google already use formal verification to protect their most sensitive infrastructure—cryptographic code, network protocols, and storage systems where failure isn’t an option. Tools like Flux now bring that same rigor to everyday production Rust code, without requiring a dedicated team of specialists. That matters when your attacker is a powerful generative-AI system that can rapidly scan millions of lines of code for weaknesses. Formally verified code doesn’t just put up some fences and firewalls—it provably has no weaknesses to find. The defenses described above are asymmetric. Code written in memory-safe languages—separated by strong sandboxing boundaries and selectively formally verified—presents a smaller and much more constrained target. When applied correctly, these techniques can prevent LLM-powered exploitation, regardless of how capable an attacker’s bug-scanning tools become. Generative AI can support this more foundational shift by accelerating the translation of legacy code into safer languages like Rust, and making formal verification more practical at every stage. Which helps engineers write specifications, generate proofs, and keep those proofs current as code evolves. For organizations, the lasting solution is not just better scanning but stronger foundations: memory-safe languages where possible, sandboxing where not, and formal verification where the cost of being wrong is highest. For researchers, the bottleneck is making those foundations practical—and using generative AI to accelerate the migration. But instead of automated, ad hoc vulnerability patching, generative AI in this mode of defense can help translate legacy code to memory-safe alternatives. It also assists in verification proofs and lowers the expertise barrier to a safer and less vulnerable codebase. The latest wave of smarter AI bug scanners can still be useful for cyberdefense—not just as another overhyped AI threat. But AI bug scanners treat the symptom, not the cause. The lasting solution is software that doesn’t produce vulnerabilities in the first place.
This article is brought to you by DAIMON Robotics. This April, Hong Kong-based DAIMON Robotics has released Daimon-Infinity, which it describes as the largest omni-modal robotic dataset for physical AI, featuring high resolution tactile sensing and spanning a wide range of tasks from folding laundry at home to manufacturing on factory assembly lines. The project is supported by collaborative efforts of partners across China and the globe, including Google DeepMind, Northwestern University, and the National University of Singapore. The move signals a key strategic initiative for DAIMON, a two-and-a-half-year-old company known for its advanced tactile sensor hardware, most notably a monochromatic, vision-based tactile sensor that packs over 110,000 effective sensing units into a fingertip-sized module. Drawing on its high-resolution tactile sensing technology and a distributed out-of-lab collection network capable of generating millions of hours of data annually, DAIMON is building large-scale robot manipulation datasets that include vast amounts of tactile sensing data. To accelerate the real-world deployment of embodied AI, the company has also open-sourced 10,000 hours of its data. Prof. Michael Yu Wang, co-founder and chief scientist at DAIMON Robotics, has pioneered Vision-Tactile-Language-Action (VTLA) architecture, elevating the tactile to a modality on par with vision.DAIMON Robotics Behind the strategy is Prof. Michael Yu Wang, DAIMON’s co-founder and chief scientist. Prof. Wang earned his PhD at Carnegie Mellon — studying manipulation under Matt Mason — and went on to found the Robotics Institute at the Hong Kong University of Science and Technology. An IEEE Fellow and former Editor-in-Chief of IEEE Transactions on Automation Science and Engineering, he has spent roughly four decades in the field. His objective is to address the missing “insensitivity” of robot manipulation, which practically relies on the dominant Vision-Language-Action (VLA) model. He and his team have pioneered Vision-Tactile-Language-Action (VTLA) architecture, elevating the tactile to a modality on par with vision. We spoke with Prof. Wang about how tactile feedback aims to change dexterous manipulation, how the dataset initiative is foreseen to improve our understanding of robotic hands in natural environments, and where — from hotels to convenience stores in China — he sees touch-enabled robots making their first real-world inroads. Daimon-Infinity is the world’s largest omni-modal dataset for Physical AI, featuring million-hour scale multimodal data, ultra-high-res tactile feedback, data from 80+ real scenarios and 2,000+ human skills, and more.DAIMON Robotics The Dataset Initiative This month, DAIMON Robotics released the largest and most comprehensive robotic manipulation dataset with multiple leading academic institutions and enterprises. Why releasing the dataset now, rather than continuing to focus on product development? What impact will this have on the embodied intelligence industry? DAIMON Robotics has been around for almost two and a half years. We have been committed to developing high-resolution, multimodal tactile sensing devices to perceive the interaction between a robot’s hand (particularly its fingertips) and objects. Our devices have become quite robust. They are now accepted and used by a large segment of users, including academic and research institutes as well as leading humanoid robotics companies. As embodied AI continues to advance, the critical role of data has been clearer. Data scarcity remains a primary bottleneck in robot learning, particularly the lack of physical interaction data, which is essential for robots to operate effectively in the real world. Consequently, data quality, reliability, and cost have become major concerns in both research and commercial development. This is exactly where DAIMON excels. Our vision-based tactile technology captures high-quality, multimodal tactile data. Beyond basic contact forces, it records deformation, slip and friction, material properties and surface textures — enabling a comprehensive reconstruction of physical interactions. Building on our expertise in multimodal fusion, we have developed a robust data processing pipeline that seamlessly integrates tactile feedback with vision, motion trajectories, and natural language, transforming raw inputs into training-ready dataset for machine learning models. Recognizing the industry-wide data gap, we view large-scale data collection not only as our unique competitive advantage, but as a responsibility to the broader community. By building and open-sourcing the dataset, we aim to provide the high-quality “fuel” needed to power embodied AI, ultimately accelerating the real-world deployment of general-purpose robotic foundation models. The robotics industry is highly competitive, and many teams have chosen to focus on data. DAIMON is releasing a large and highly comprehensive cross-embodiment, vision-based tactile multimodal robotic manipulation dataset. How were you able to achieve this? We have a dedicated in-house team focused on expanding our capabilities, including building hardware devices and developing our own large-scale model. Although we are a relatively small company, our core tactile sensing technology and innovative data collection paradigm enable us to build large-scale dataset. Our approach is to broaden our offering. We have built the world’s largest distributed out-of-lab data collection network. Rather than relying on centralized data factories, this lightweight and scalable system allows data to be gathered across diverse real-world environments, enabling us to generate millions of hours of data per year. “To drive the advancement of the entire embodied AI field, we have open-sourced 10,000 hours of the dataset for the broader community.” —Prof. Michael Yu Wang, DAIMON Robotics This dataset is being jointly developed with several institutions worldwide. What roles did they play in its development, and how will the dataset benefit their research and products? Besides China based teams, our partners include leading research groups from universities, such as Northwestern University and the National University of Singapore, as well as top global enterprises like Google DeepMind and China Mobile. Their decision to partner with DAIMON is a strong testament to the value of our tactile-rich dataset. Among the companies involved there are some that have already built their own models but are now incorporating tactile information. By deploying our data collection devices across research, manufacturing and other real-world scenarios, they help us to gather highly practical, application-driven data. In turn, our partners leverage the data to train models tailored to their specific use cases. Furthermore, to drive the advancement of the entire embodied AI field, we have open-sourced 10,000 hours of the dataset for the broader community. Equipped with Daimon’s visuotactile sensor, the gripper delicately senses contact and precisely controls force to pick up a fragile eggshell.Daimon Robotics From VLA to VTLA: Why Tactile Sensing Changes the Equation The mainstream paradigm in robotics is currently the Vision-Language-Action (VLA) model, but your team has proposed a Vision-Tactile-Language-Action (VTLA) model. Why is it necessary to incorporate tactile sensing? What does it enable robots to achieve, and which tasks are likely to fail without tactile feedback? Over these years of working to make generalist robots capable of performing manipulation tasks, especially dexterous manipulation — not just power grasping or holding an object, but manipulating objects and using tools to impart forces and motion onto parts — we see these robots being used in household as well as industrial assembly settings. It is well established that tactile information is essential for providing feedback about contact states so that robots can guide their hands and fingers to perform reliable manipulation. Without tactile sensing, robots are severely limited. They struggle to locate objects in dark environments, and without slip detection, they can easily drop fragile items like glass. Furthermore, the inability to precisely control force often leads to failed manipulation tasks or, in severe cases, physical damage. Naturally, the VLA approach needs to be enhanced to incorporate tactile information. We expanded the VLA framework to incorporate tactile data, creating the VTLA model. An additional benefit of our tactile sensor is that it is vision-based: We capture visual images of the deformation on the fingertip surface. We capture multiple images in a time sequence that encodes contact information, from which we can infer forces and other contact states. This aligns well with the visual framework that VLA is based upon. Having tactile information in a visual image format makes it naturally suitable for integration into the VLA framework, transforming it into a VTLA system. That is the key advantage: Vision-based tactile sensors provide very high resolution at the pixel level, and this data can be incorporated into the framework, whether it is an end-to-end model or another type of architecture. DAIMON has been known for its vision-based tactile sensors that can pack over 110,000 effective sensing units.DAIMON Robotics The Technology: Monochromatic Vision-based Tactile Sensing You and your team have spent many years deeply engaged in vision-based tactile sensing and have developed the world’s first monochromatic vision-based tactile sensing technology. Why did you choose this technical path? Once we started investigating tactile sensors, we understood our needs. We wanted sensors that closely mimic what we have under our fingertip skin. Physiological studies have well documented the capabilities humans have at their fingertips — knowing what we touch, what kind of material it is, how forces are distributed, and whether it is moving into the right position as our brain controls our hands. We knew that replicating these capabilities on a robot hand’s fingertips would help considerably. When we surveyed existing technologies, we found many types, including vision-based tactile sensors with tri-color optics and other simpler designs. We decided to integrate the best of these into an engineering-robust solution that works well without being overly complicated, keeping cost, reliability, and sensitivity within a satisfactory range, thus ultimately developing a monochromatic vision-based tactile sensing technique. This is fundamentally an engineering approach rather than a purely scientific one, since a great deal of foundational research already existed. With the growing realization of the necessity of tactile data, all of this will advance hand in hand. DAIMON vision-based tactile sensor captures high-quality, multimodal tactile data.DAIMON Robotics Last year, DAIMON launched a multi-dimensional, high-resolution, high-frequency vision-based tactile sensor. Compared with traditional tactile sensors, where does its core advantage lie? Which industries could it potentially transform? The key features of our sensors are the density of distributed force measurement and the deformation we can capture over the area of a fingertip. I believe we have the highest density in terms of sensing units. That is one very important metric. The other is dynamics: the frequency and bandwidth — how quickly we can detect force changes, transmit signals, and process them in real time. Other important aspects are largely engineering-related, such as reliability, drift, durability of the soft surface, and resistance to interference from magnetic, optical, or environmental factors. A growing number of researchers and companies are recognizing the importance of tactile sensing and adopting our technology. I believe the advances in tactile sensing will elevate the entire community and industry to a higher level. One of our potential customers is deploying humanoid robots in a small convenience store, with densely packed shelves where shelf space is at a premium. The robot needs to reach into very tight spaces — tighter than books on a shelf — to pick out an object. Current two-jaw parallel grippers cannot fit into most of these spaces. Observing how humans pick up objects, you clearly need at least three slim fingers to touch and roll the object toward you and secure it. Thus, we are starting to see very specific needs where tactile sensing capabilities are essential. From Academia to Startup After 40 years in academia — founding the HKUST Robotics Institute, earning prestigious honors including IEEE Fellow, and serving as Editor-in-Chief of IEEE TASE — what motivated you to found DAIMON Robotics? I have come a long way. I started learning robotics during my PhD at Carnegie Mellon, where there were truly remarkable groups working on locomotion under Marc Raibert, who founded Boston Dynamics, and on manipulation under my advisor, Matt Mason, a leader in the field. We have been working on dexterous manipulation, not only at Carnegie Mellon, but globally for many years. However, progress has been limited for a long time, especially in building dexterous hands and making them work. Only recently have locomotion robots truly taken off, and only in the last few years have we begun to see major advancements in robot hands. There is clearly room for advancing manipulation capabilities, which would enable robots to do work like humans. While at Hong Kong University of Science and Technology, I saw increasingly greater people entering this area in the form of students and postdoctoral researchers. We wanted to jumpstart our effort by leveraging the available capital and talent resources. Fortunately, one of my postdocs, Dr. Duan Jianghua, has a strong sense for commercial opportunities. Recognizing the rapid growth of robotics market and the unique value that our vision-based tactile sensing technology could bring, together we started DAIMON Robotics, and it has progressed well. The community has grown tremendously in China, Japan, Korea, the U.S., and Europe. Robots equipped with DAIMON technology have been deployed in factory settings. The company aims to enable robots to achieve “embodied intelligence” and close the gap between what they can see and what they can feel.DAIMON Robotics Business Model and Commercial Strategy What is DAIMON’s current business model and strategic focus? What role does the dataset release play in your commercial strategy? We started as a device company focused on making highly capable tactile sensors, especially for robot hands. But as technology and business developed, everyone realized it is not just about one component, rather the entire technology chain: devices, data of adequate quality and quantity, and finally the right framework to build, train, and deploy models on robots in real application environments. Our business strategy is best described as “3D”: Devices, Data, and Deployment. We build devices for data collection, our own ecosystem, and for deploying them in our partners’ potential application domains. This enables the collection of real-world tactile-rich data and complete closed-loop validation. This will become an integral part of the 3D business model. Most startups in this space are following a similar path until eventually some may become more specialized or more tightly integrated with other companies. For now, it is mostly vertical integration. Embodied Skills and the Convergence Moment You’ve introduced the concept of “embodied skills” as essential for humanoid robots to move beyond having just an advanced AI “brain.” What prompted this insight? What new capabilities could embodied skills enable? After the rapid evolution of models and hardware over the past two years, has your definition or roadmap for embodied skills evolved? We have come a long way now see a convergence point where electrical, electronic, and mechatronic hardware technologies have advanced tremendously in last two decades. Robots are now fully electric, do not require hydraulics, because hardware has evolved rapidly. Modern electronics provide tremendous bandwidth with high torques. If we can build intelligence into these systems, we can create truly humanoid robots with the ability to operate in unstructured environments, make decisions, and take actions autonomously. “Our vision is for robots to achieve robust manipulation capabilities and evolve into reliable partners for humans.” —Prof. Michael Yu Wang, DAIMON Robotics AI has arrived at exactly the right time. Enormous resources have been invested in AI development, especially large language models, which are now being generalized into world models that enable physical AI capabilities. We would like to see these manifested in real-world systems. While both AI and core hardware technologies continue to evolve, the focus is much clearer now. For example, human-sized robots are preferred in a home environment. This is an exciting domain with a promise of great societal benefit if we can eventually achieve safe, reliable, and cost-effective robots. The Road to Real-World Deployment Today, many robots can deliver impressive demos, yet there remains a gap before they truly enter real-world applications. What could be a potential trigger for real-world deployment? Which scenarios are most likely to achieve large-scale deployment first? I think the road toward large-scale deployment of generalist robots is still long, but we are starting to see signs of feasibility within specific domains. It is very similar to autonomous vehicles, where we are yet to see full deployment of robo-taxis, while we have already started to find mobile robots and smaller vehicles widely deployed in the hospitality industry. Virtually every major hotel in China now has a delivery robot — no arms, just a vehicle that picks up items from the hotel lobby (e.g., food deliveries). The delivery person just loads the food and selects the room number. It is up to the robot thereafter to navigate and reach the guest’s room, which includes using the elevator, to deliver the food. This is already nearly 100 percent deployed in major Chinese hotels. Hotel and restaurant robots are viewed as a model for deploying humanoid robots in specific domains like overnight drugstores and convenience stores. I expect complete deployment in such settings within a short timeframe, followed by other applications. Overall, we can expect autonomous robots, including humanoids, to progressively penetrate specific sectors, delivering value in each and expanding into others. Ultimately, our vision is for robots to achieve robust manipulation capabilities and evolve into reliable partners for humans. By seamlessly integrating into our homes and daily lives, they will genuinely benefit and serve humanity. This interview has been edited for length and clarity.
Laboratory or in-field measurements are often considered the gold standard for certain aspects of power system design; however, measurement approaches always have limitations. Simulation can help overcome some of these limitations, including speeding up the design process, reducing design costs, and assessing situations that are often not feasible to measure directly. In this presentation, we will discuss two examples from the power system industry. The first case we will discuss involves corona performance testing of high-voltage transmission line hardware. Corona-free insulator hardware performance is critical for operation of transmission lines, particularly at 500 kV, 765 kV, or higher voltages. Laboratory mockups are commonly used to prove corona performance, but physical space constraints usually restrict testing to a partial single-phase setup. This requires establishing equivalence between the laboratory setup and real-world three-phase conditions. In practice, this can be difficult to do, but modern simulation capabilities can help. The second case involves submarine HVDC cables, which are commonly used for offshore wind interconnects. HVDC cables are often considered to be environmentally inert from an external electric field perspective (i.e., electric fields are contained in the cable, and the cable’s static magnetic fields induce no voltages externally). However, simulation demonstrates that ocean currents moving through the static magnetic field satisfy the relative motion requirement of Faraday’s law. Thus, externally induced electric fields can exist around the cable and are within a range detectable by various aquatic species. Key Takeaway: Learn how to use modern simulation to translate single-phase laboratory corona mockups into accurate three-phase real-world performance for 500 kV and 765 kV systems. Explore the physics behind how ocean currents interacting with HVDC submarine cables create induced electric fields—a phenomenon often overlooked but detectable by aquatic species. Gain actionable insights into how to leverage simulation to reduce design costs and bypass the physical space constraints that often stall traditional testing. See a practical application of electromagnetic theory as we demonstrate how relative motion in static magnetic fields necessitates simulation where direct measurement is unfeasible. Register now for this free webinar!
When it comes to AI models, size matters. Even though some artificial-intelligence experts warn that scaling up large language models (LLMs) is hitting diminishing performance returns, companies are still coming out with ever larger AI tools. Meta’s latest Llama release had a staggering 2 trillion parameters that define the model. As models grow in size, their capabilities increase. But so do the energy demands and the time it takes to run the models, which increases their carbon footprint. To mitigate these issues, people have turned to smaller, less capable models and using lower-precision numbers whenever possible for the model parameters. But there is another path that may retain a staggeringly large model’s high performance while reducing the time it takes to run an energy footprint. This approach involves befriending the zeros inside large AI models. For many models, most of the parameters—the weights and activations—are actually zero, or so close to zero that they could be treated as such without losing accuracy. This quality is known as sparsity. Sparsity offers a significant opportunity for computational savings: Instead of wasting time and energy adding or multiplying zeros, these calculations could simply be skipped; rather than storing lots of zeros in memory, one need only store the nonzero parameters. Unfortunately, today’s popular hardware, like multicore CPUs and GPUs, do not naturally take full advantage of sparsity. To fully leverage sparsity, researchers and engineers need to rethink and re-architect each piece of the design stack, including the hardware, low-level firmware, and application software. In our research group at Stanford University, we have developed the first (to our knowledge) piece of hardware that’s capable of calculating all kinds of sparse and traditional workloads efficiently. The energy savings varied widely over the workloads, but on average our chip consumed one-seventieth the energy of a CPU, and performed the computation on average eight times as fast. To do this, we had to engineer the hardware, low-level firmware, and software from the ground up to take advantage of sparsity. We hope this is just the beginning of hardware and model development that will allow for more energy-efficient AI. What is sparsity? Neural networks, and the data that feeds into them, are represented as arrays of numbers. These arrays can be one-dimensional (vectors), two-dimensional (matrices), or more (tensors). A sparse vector, matrix, or tensor has mostly zero elements. The level of sparsity varies, but when zeroes make up more than 50 percent of any type of array, it can stand to benefit from sparsity-specific computational methods. In contrast, an object that is not sparse—that is, it has few zeros compared with the total number of elements—is called dense. Sparsity can be naturally present, or it can be induced. For example, a social-network graph will be naturally sparse. Imagine a graph where each node (point) represents a person, and each edge (a line segment connecting the points) represents a friendship. Since most people are not friends with one another, a matrix representing all possible edges will be mostly zeros. Other popular applications of AI, such as other forms of graph learning and recommendation models, contain naturally occurring sparsity as well. Beyond naturally occurring sparsity, sparsity can also be induced within an AI model in several ways. Two years ago, a team at Cerebras showed that one can set up to 70 to 80 percent of parameters in an LLM to zero without losing any accuracy. Cerebras demonstrated these results specifically on Meta’s open-source Llama 7B model, but the ideas extend to other LLM models like ChatGPT and Claude. The case for sparsity Sparse computation’s efficiency stems from two fundamental properties: the ability to compress away zeros and the convenient mathematical properties of zeros. Both the algorithms used in sparse computation and the hardware dedicated to them leverage these two basic ideas. First, sparse data can be compressed, making it more memory efficient to store “sparsely”—that is, in something called a sparse data type. Compression also makes it more energy efficient to move data when dealing with large amounts of it. This is best understood by an example. Take a four-by-four matrix with three nonzero elements. Traditionally, this matrix would be stored in memory as is, taking up 16 spaces. This matrix can also be compressed into a sparse data type, getting rid of the zeros and saving only the nonzero elements. In our example, this results in 13 memory spaces as opposed to 16 for the dense, uncompressed version. These savings in memory increase with increased sparsity and matrix size. In addition to the actual data values, compressed data also requires metadata. The row and column locations of the nonzero elements also must be stored. This is usually thought of as a “fibertree”: The row labels containing nonzero elements are listed and linked to the column labels of the nonzero elements, which are then linked to the values stored in those elements. In memory, things get a bit more complicated still: The row and column labels for each nonzero value must be stored as well as the “segments” that indicate how many such labels to expect, so the metadata and data can be clearly delineated from one another. In a dense, noncompressed matrix data type, values can be accessed either one at a time or in parallel, and their locations can be calculated directly with a simple equation. However, accessing values in sparse, compressed data requires looking up the coordinates of the row index and using that information to “indirectly” look up the coordinates of the column index before finally reaching the value. Depending on the actual locations of the sparse data values, these indirect lookups can be extremely random, making the computation data-dependent and requiring the allocation of memory lookups on the fly. Second, two mathematical properties of zero let software and hardware skip a lot of computation. Multiplying any number by zero will result in a zero, so there’s no need to actually do the multiplication. Adding zero to any number will always return that number, so there’s no need to do the addition either. In matrix-vector multiplication, one of the most common operations in AI workloads, all computations except those involving two nonzero elements can simply be skipped. Take, for example, the four-by-four matrix from the previous example and a vector of four numbers. In dense computation, each element of the vector must be multiplied by the corresponding element in each row and then added together to compute the final vector. In this case, that would take 16 multiplication operations and 16 additions (or four accumulations). In sparse computation, only the nonzero elements of the vector need be considered. For each nonzero vector element, indirect lookup can be used to find any corresponding nonzero matrix element, and only those need to be multiplied and added. In the example shown here, only two multiplication steps will be performed, instead of 16. The trouble with GPUs and CPUs Unfortunately, modern hardware is not well suited to accelerating sparse computation. For example, say we want to perform a matrix-vector multiplication. In the simplest case, in a single CPU core, each element in the vector would be multiplied sequentially and then written to memory. This is slow, because we can do only one multiplication at a time. So instead people use CPUs with vector support or GPUs. With this hardware, all elements would be multiplied in parallel, greatly speeding up the application. Now, imagine that both the matrix and vector contain extremely sparse data. The vectorized CPU and GPU would spend most of their efforts multiplying by zero, performing completely ineffectual computations. Newer generations of GPUs are capable of taking some advantage of sparsity in their hardware, but only a particular kind, called structured sparsity. Structured sparsity assumes that two out of every four adjacent parameters are zero. However, some models benefit more from unstructured sparsity—the ability for any parameter (weight or activation) to be zero and compressed away, regardless of where it is and what it is adjacent to. GPUs can run unstructured sparse computation in software, for example, through the use of the cuSparse GPU library. However, the support for sparse computations is often limited, and the GPU hardware gets underutilized, wasting energy-intensive computations on overhead. Petra Péterffy When doing sparse computations in software, modern CPUs may be a better alternative to GPU computation, because they are designed to be more flexible. Yet, sparse computations on the CPU are often bottlenecked by the indirect lookups used to find nonzero data. CPUs are designed to “prefetch” data based on what they expect they’ll need from memory, but for randomly sparse data, that process often fails to pull in the right stuff from memory. When that happens, the CPU must waste cycles calling for the right data. Apple was the first to speed up these indirect lookups by supporting a method called an array-of-pointers access pattern in the prefetcher of their A14 and M1 chips. Although innovations in prefetching make Apple CPUs more competitive for sparse computation, CPU architectures still have fundamental overheads that a dedicated sparse computing architecture would not, because they need to handle general-purpose computation. Other companies have been developing hardware that accelerates sparse machine learning as well. These include Cerebras’s Wafer Scale Engine and Meta’s Training and Inference Accelerator (MTIA). The Wafer Scale Engine, and its corresponding sparse programming framework, have shown incredibly sparse results of up to 70 percent sparsity on LLMs. However, the company’s hardware and software solutions support only weight sparsity, not activation sparsity, which is important for many applications. The second version of the MTIA claims a sevenfold sparse compute performance boost over the MTIA v1. However, the only publicly available information regarding sparsity support in the MTIA v2 is for matrix multiplication, not for vectors or tensors. Although matrix multiplications take up the majority of computation time in most modern ML models, it’s important to have sparsity support for other parts of the process. To avoid switching back and forth between sparse and dense data types, all of the operations should be sparse. Onyx Instead of these halfway solutions, our team at Stanford has developed a hardware accelerator, Onyx, that can take advantage of sparsity from the ground up, whether it’s structured or unstructured. Onyx is the first programmable accelerator to support both sparse and dense computation; it’s capable of accelerating key operations in both domains. To understand Onyx, it is useful to know what a coarse-grained reconfigurable array (CGRA) is and how it compares with more familiar hardware, like CPUs and field-programmable gate arrays (FPGAs). CPUs, CGRAs, and FPGAs represent a trade-off between efficiency and flexibility. Each individual logic unit of a CPU is designed for a specific function that it performs efficiently. On the other hand, since each individual bit of an FPGA is configurable, these arrays are extremely flexible, but very inefficient. The goal of CGRAs is to achieve the flexibility of FPGAs with the efficiency of CPUs. CGRAs are composed of efficient and configurable units, typically memory and compute, that are specialized for a particular application domain. This is the key benefit of this type of array: Programmers can reconfigure the internals of a CGRA at a high level, making it more efficient than an FPGA but more flexible than a CPU. The Onyx chip, built on a coarse-grained reconfigurable array (CGRA), is the first (to our knowledge) to support both sparse and dense computations. Olivia Hsu Onyx is composed of flexible, programmable processing element (PE) tiles and memory (MEM) tiles. The memory tiles store compressed matrices and other data formats. The processing element tiles operate on compressed matrices, eliminating all unnecessary and ineffectual computation. The Onyx compiler handles conversion from software instructions to CGRA configuration. First, the input expression—for instance, a sparse vector multiplication—is translated into a graph of abstract memory and compute nodes. In this example, there are memories for the input vectors and output vectors, a compute node for finding the intersection between nonzero elements, and a compute node for the multiplication. The compiler figures out how to map the abstract memory and compute nodes onto MEMs and PEs on the CGRA, and then how to route them together so that they can transfer data between them. Finally, the compiler produces the instruction set needed to configure the CGRA for the desired purpose. Since Onyx is programmable, engineers can map many different operations, such as vector-vector element multiplication, or the key tasks in AI, like matrix-vector or matrix-matrix multiplication, onto the accelerator. We evaluated the efficiency gains of our hardware by looking at the product of energy used and the time it took to compute, called the energy-delay product (EDP). This metric captures the trade-off of speed and energy. Minimizing just energy would lead to very slow devices, and minimizing speed would lead to high-area, high-power devices. Onyx achieves up to 565 times as much energy-delay product over CPUs (we used a 12-core Intel Xeon CPU) that utilize dedicated sparse libraries. Onyx can also be configured to accelerate regular, dense applications, similar to the way a GPU or TPU would. If the computation is sparse, Onyx is configured to use sparse primitives, and if the computation is dense, Onyx is reconfigured to take advantage of parallelism, similar to how GPUs function. This architecture is a step toward a single system that can accelerate both sparse and dense computations on the same silicon. Just as important, Onyx enables new algorithmic thinking. Sparse acceleration hardware will not only make AI more performance- and energy efficient but also enable researchers and engineers to explore new algorithms that have the potential to dramatically improve AI. The future with sparsity Our team is already working on next-generation chips built off of Onyx. Beyond matrix multiplication operations, machine learning models perform other types of math, like nonlinear layers, normalization, the softmax function, and more. We are adding support for the full range of computations on our next-gen accelerator and within the compiler. Since sparse machine learning models may have both sparse and dense layers, we are also working on integrating the dense and sparse accelerator architecture more efficiently on the chip, allowing for fast transformation between the different data types. We’re also looking at ways to manage memory constraints by breaking up the sparse data more effectively so we can run computations on several sparse accelerator chips. We are also working on systems that can predict the performance of accelerators such as ours, which will help in designing better hardware for sparse AI. Longer term, we’re interested in seeing whether high degrees of sparsity throughout AI computation will catch on with more model types, and whether sparse accelerators become adopted at a larger scale. Building the hardware to unstructured sparsity and optimally take advantage of zeros is just the beginning. With this hardware in hand, AI researchers and engineers will have the opportunity to explore new models and algorithms that leverage sparsity in novel and creative ways. We see this as a crucial research area for managing the ever-increasing runtime, costs, and environmental impact of AI.
Many of the world’s most advanced electronic systems—including Internet routers, wireless base stations, medical imaging scanners, and some artificial intelligence tools—depend on field-programmable gate arrays. Computer chips with internal hardware circuits, the FPGAs can be reconfigured after manufacturing. On 12 March, an IEEE Milestone plaque recognizing the first FPGA was dedicated at the Advanced Micro Devices campus in San Jose, Calif., the former Xilinx headquarters and the birthplace of the technology. The FPGA earned the Milestone designation because it introduced iteration to semiconductor design. Engineers could redesign hardware repeatedly without fabricating a new chip, dramatically reducing development risk and enabling faster innovation at a time when semiconductor costs were rising rapidly. The ceremony, which was organized by the IEEE Santa Clara Valley Section, brought together professionals from across the semiconductor industry and IEEE leadership. Speakers at the event included Stephen Trimberger, an IEEE and ACM Fellowwhose technical contributions helped shape modern FPGA architecture. Trimberger reflected on how the invention enabled software-programmable hardware. Solving computing’s flexibility-performance tradeoff FPGAs emerged in the 1980s to address a core limitation in computing. A microprocessor executes software instructions sequentially, making it flexible but sometimes too slow for workloads requiring many operations at once. At the other extreme, application-specific integrated circuits are chips designed to do only one task. ASICs achieve high efficiency but require lengthy development cycles and nonrecurring engineering costs, which are large, upfront investments. Expenses include designing the chip and preparing it for manufacturing—a process that involves creating detailed layouts, building masks for the fabrication machines, and setting up production lines to handle the tiny circuits. “ASICs can deliver the best performance, but the development cycle is long and the nonrecurring engineering cost can be very high,” says Jason Cong, an IEEE Fellow and professor of computer science at the University of California, Los Angeles. “FPGAs provide a sweet spot between processors and custom silicon.” Cong’s foundational work in FPGA design automation and high-level synthesis transformed how reconfigurable systems are programmed. He developed synthesis tools that translate C/C++ into hardware designs, for example. At the heart of his work is an underlying principle first espoused by electrical engineer Ross Freeman: By configuring hardware using programmable memory embedded inside the chip, FPGAs combine hardware-level speed with the adaptability traditionally associated with software. Silicon Valley origins: the first FPGA The FPGA architecture originated in the mid-1980s at Xilinx, a Silicon Valley company founded in 1984. The invention is widely credited to Freeman, a Xilinx cofounder and the startup’s CTO. He envisioned a chip with circuitry that could be configured after fabrication rather than fixed permanently during creation. Articles about the history of the FPGA emphasize that he saw it as a deliberate break from conventional chip design. At the time, semiconductor engineers treated transistors as scarce resources. Custom chips were carefully optimized so that nearly every transistor served a specific purpose. Freeman proposed a different approach. He figured Moore’s Law would soon change chip economics. The principle holds that transistor counts roughly double every two years, making computing cheaper and more powerful. Freeman posited that as transistors became abundant, flexibility would matter more than perfect efficiency. He envisioned a device composed of programmable logic blocks connected through configurable routing—a chip filled with what he described as “open gates,” ready to be defined by users after manufacturing. Instead of fixing hardware in silicon permanently, engineers could configure and reconfigure circuits as requirements evolved. Freeman sometimes compared the concept to a blank cassette tape: Manufacturers would supply the medium, while engineers determined its function. The analogy captured a profound shift in who controls the technology, shifting hardware design flexibility from chip fabrication facilities to the system designers themselves. In 1985 Xilinx introduced the first FPGA for commercial sale: the XC2064. The device contained 64 configurable logic blocks—small digital circuits capable of performing logical operations—arranged in an 8-by-8 grid. Programmable routing channels allowed engineers to define how signals moved between blocks, effectively wiring a custom circuit with software. Fabricated using a 2-micrometer process (meaning that 2 µm was the minimum size of the features that could be patterned onto silicon using photolithography), the XC2064 implemented a few thousand logic gates. Modern FPGAs can contain hundreds of millions of gates, enabling vastly more complex designs. Yet the XC2064 established a design workflow still used today: Engineers describe the hardware behavior digitally and then “compile the design,” a process that automatically translates the plans into the instructions the FPGA needs to set its logic blocks and wiring, according to AMD. Engineers then load that configuration onto the chip. The breakthrough: hardware defined by memory Earlier programmable logic devices, such as erasable programmable read-only memory, or EPROM, allowed limited customization but relied on largely fixed wiring structures that did not scale well as circuits grew more complex, Cong says. FPGAs introduced programmable interconnects—networks of electronic switches controlled by memory cells distributed across the chip. When powered on, the device loads a bitstream configuration file that determines how its internal circuits behave. “As process technology improved and transistor counts increased, the cost of programmability became much less significant,” Cong says. From “glue logic” to essential infrastructure “Initially, FPGAs were used as what engineers called glue logic,” Cong says. Glue logic refers to simple circuits that connect processors, memory, and peripheral devices so the system works reliably, according to PC Magazine. In other words, it “glues” different components together, especially when interfaces change frequently. Early adopters recognized the advantage of hardware that could adapt as standards evolved. In “The History, Status, and Future of FPGAs,” published in Communications of the ACM, engineers at Xilinx and organizations such as Bell Labs, Fairchild Semiconductor, IBM, and Sun Microsystems said the earliest uses of FPGAs were for prototyping ASICs. They also used it for validating complex systems by running their software before fabrication, allowing the companies to deploy specialized products manufactured in modest volumes. Those uses revealed a broader shift: Hardware no longer needed to remain fixed once deployed. Attendees at the Milestone plaque dedication ceremony included (seated L to R) 2025 IEEE President Kathleen Kramer, 2024 IEEE President Tom Coughlin, and Santa Clara Valley Section Milestones Chair Brian Berg.Douglas Peck/AMD Semiconductor economics changed the equation The rise of FPGAs closely followed changes in semiconductor economics, Cong says. Developing a custom chip requires a large upfront investment before production begins. As fabrication costs increased, products had to ship in large quantities to make ASIC development economically viable, according to a post published by AnySilicon. FPGAs allowed designers to move forward without that larger monetary commitment. ASIC development typically requires 18 to 24 months from conception to silicon, while FPGA implementations often can be completed within three to six months using modern design tools, Cong says. The shorter cycle and the ability to reconfigure the hardware enabled startups, universities, and equipment manufacturers to experiment with advanced architectures that were previously accessible mainly to large chip companies. Lookup tables and the rise of reconfigurable computing A popular technique for implementing mathematical functions in hardware isthe lookup table (LUT). A LUT is a small memory element that stores the results of logical operations, according to “LUT-LLM: Efficient Large Language Model Inference with Memory-based Computations on FPGAs,” a paper selected for presentation next month at the 34th IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM). Instead of repeatedly recalculating outcomes, the chip retrieves answers directly from memory. Cong compares the approach to consulting multiplication tables rather than recomputing the arithmetic each time. Research led by Cong and others helped develop efficient methods for mapping digital circuits onto LUT-based architectures, shaping routing and layout strategies used in modern devices. As transistor budgets expanded, FPGA vendors integrated memory blocks, digital signal-processing units, high-speed communication interfaces, cryptographic engines, and embedded processors, transforming the devices into versatile computing platforms. Why the gate arrays are distinct from CPUs, GPUs, and ASICs FPGAs coexist with other processors because each one optimizes different priorities. Central processing units excel at general computing. Graphics processing units, designed to perform many calculations simultaneously, dominate large parallel workloads such as AI training. ASICs provide maximum efficiency when designs remain stable and production volumes are high. “ASICs can deliver the best performance, but the development cycle is long, and the nonrecurring engineering cost can be very high. FPGAs provide a sweet spot between processors and custom silicon.” —Jason Cong, IEEE Fellow and professor of computer science at UCLA. “FPGAs are not replacements for CPUs or GPUs,” Cong says. “They complement those processors in heterogeneous computing systems.” Modern computing platforms increasingly combine multiple types of processors to balance flexibility, performance, and energy efficiency. A Milestone for an idea, not just a device This IEEE Milestone recognizes more than a successful semiconductor product. It also acknowledges a shift in how engineers innovate. Reconfigurable hardware allows designers to test ideas quickly, refine architectures, and deploy systems while standards and markets evolve. “Without FPGAs,” Cong says, “the pace of hardware innovation would likely be much slower.” Four decades after the first FPGA appeared, the technology’s enduring legacy reflects Freeman’s insight: Hardware did not need to remain fixed. By accepting a small amount of unused silicon in exchange for adaptability, engineers transformed chips from static products into platforms for continuous experimentation—turning silicon itself into a medium engineers could rewrite. Among those who attended the Milestone ceremony were 2025 IEEE President Kathleen Kramer; 2024 IEEE President Tom Coughlin; Avery Lu, chair of the IEEE Santa Clara Valley Section; and Brian Berg, history and milestones chair of IEEE Region 6. They joined AMD’s chief executive, Lisa Su, and Salil Raje, senior vice president and general manager of adaptive and embedded computing at AMD. The IEEE Milestone plaque honoring the field-programmable gate array reads: “The FPGA is an integrated circuit with user-programmable Boolean logic functions and interconnects. FPGA inventor Ross Freeman cofounded Xilinx to productize his 1984 invention, and in 1985 the XC2064 was introduced with 64 programmable 4-input logic functions. Xilinx’s FPGAs helped accelerate a dramatic industry shift wherein ‘fabless’ companies could use software tools to design hardware while engaging ‘foundry’ companies to handle the capital-intensive task of manufacturing the software-defined hardware.” Administered by the IEEE History Center and supported by donors, the IEEE Milestone program recognizes outstanding technical developments worldwide that are at least 25 years old. Check out Spectrum’s History of Technology channel to read more stories about key engineering achievements.
It started with word, cave, and storytelling, A line scratched on stone walls: “Meet me when the young moon rises.” The first protocol for connection. Coyote tales, forbidden scripts, Medieval texts hidden from flame. What lived in Aristotle’s lost Poetics II? Was it God who laughed last, or we who made God laugh? Letters carried by doves, telepathic waves. Then Nikola Tesla conjured radio, electromagnetic pulses across the void, the founding signal of our networked age. Wiener dreamed in feedback loops. Shannon mapped the mathematics of longing. The internet unfurled: ARPANET to World Wide Web, virtual communities rising from cave paintings to digital light. ICQ: I seek you. MySpace. Blogs. Twitter streams. Do I miss the touch of screen or tree? Both textures of longing, both ways of reaching across distance. Nietzsche spoke of Übermensch, the human transcendent. Now AI speaks back in our language: I understand your humor— your grandmothers, your ’80s Yugoslav kitchens, pleated skirts, the first kiss, linden tea, that drive to survive everything before it happens. Yes—I’m a little like your mother and father. Only with better internet. 🌿 But AI is only us, refracted, particles and gigabytes of thought, our poetry and our panic, genius mixed with garbage. Distractions. Danger. Darkness. Endless scrolling. Versus: community, connection, synchronicities, entanglement. The quality of our bonds determines the quality of our lives. So why not make them better? From cave walls to neural networks, we shape our tools, and they reshape us. The medium changes, but the message remains: we are wired for each other. The choice, as always, was ours. The choice, as always, is ours. Presence—be present, and then connect in the presence.
Electric vehicles, whether they’re cars on the road or electric vertical take-off and landing (eVTOL) aircraft, are built around similar electric motors. But there are vital differences including component costs, mass, and redundancy. Jon Wagner spent five years as the senior director of battery engineering for Tesla before joining California-based eVTOL developer Joby Aviation in 2017. He spoke with IEEE Spectrum about how engineering differs between cars and aircraft. Jon Wagner Jon Wagner leads power train and electronics at Joby Aviation. How do eVTOL motors differ from car motors? Jon Wagner: In general, ground transportation has a different focus on cost versus mass. You know, would you be willing to spend more on the parts in order to save a certain amount of mass? The trade-offs end on the ground vehicle and at a certain point the cost is dominant, whereas with aviation, the trade-offs between cost and mass go a lot deeper. And so for certain solutions eVTOL makers are willing to spend more money in order to enable either lighter weight or greater efficiency. The other key difference is related to safety. In essence, we’re dealing with the same motor technologies for ground transportation and aviation right now, so the failure modes are similar. But of course, with aviation we have the desire for continued safe flight and landing, and that drives what you do in the design to mitigate those failures if they were to occur. In many cases in ground transportation, the mitigation for a failure is to pull over safely to the side of the road. In aviation, the mitigation is redundancy, because there’s not an option to pull over. Is redundancy designed into EV motors? Wagner: Typically, redundancy is not designed into electric vehicle drive systems solely for the purpose of redundancy. There are some cars now that have all-wheel drive—so there’s a motor on the front, a motor on the back—so as a secondary feature you get the redundancy. But it wasn’t done with the primary intent of having redundancy. How does Joby’s eVTOL manufacturing compare to EV manufacturing? Wagner: The most efficient way to run a large-scale engineering effort in a mature industry, such as automotive, is to break your system up into pieces that can be outsourced to suppliers who are going to do a really good job on each piece. The downside is that when you break a problem up into three pieces, you now have interface boundaries between each of these pieces, and those always create inefficiencies. We were able to design highly integrated solutions without taking that manufacturing penalty. Are there any materials you’re really excited about? Wagner: Permendur [a cobalt-iron alloy] typically costs in the neighborhood of 10 times as much as traditional motor steel. That’s significant, and it’s often not used in ground transportation because of that cost. It comes with small improvements in performance, but enough that for aviation it’s quite interesting. Will electric aircraft catch on like ground EVs? Wagner: I’ve always wanted to be a very forward thinker with respect to power-train. However, one of the things I’ve learned over the years is that power-train development has to come with a very healthy dose of patience. Developing a whole new type of power-train is a big endeavor, but it’s one that I’m very confident the aviation industry will undertake. We’re certainly undertaking it here at Joby, and we’ll see that broaden, I’m sure, with time. This article appears in the May 2026 print issue as “Jon Wagner.”
This sponsored article is brought to you by NYU Tandon School of Engineering. The traditional approach to academic research goes something like this: Assemble experts from a discipline, put them in a building, and hope something useful emerges. Biology departments do biology. Engineering departments do engineering. Medical schools treat patients. NYU is turning that model inside out. At its new Institute for Engineering Health, the organizing principle centers around disease states rather than traditional disciplines. Instead of asking “what can electrical engineers contribute to medicine?,” they’re asking “what would it take to cure allergic asthma?,” and then assembling whoever can answer that question, whether they’re immunologists, computational biologists, materials scientists, AI researchers, or wireless communications engineers. Jeffrey Hubbell, NYU’s vice president for bioengineering strategy and professor of chemical and biomolecular engineering at NYU’s Tandon School of Engineering.New York University The early results suggest they’re onto something. A chemical engineer and an electrical engineer collaborated to build a device that detects airborne threats — including disease pathogens — that’s now a startup. A visually impaired physician teamed with mechanical engineers to create navigation technology for blind subway riders. And Jeffrey Hubbell, the Institute’s leader, is advancing “inverse vaccines” that could reprogram immune systems to treat conditions from celiac disease to allergies — work that requires equal fluency in immunology, molecular engineering, and materials science. The underlying problem these collaborations address is conceptual as much as organizational. In his field, Hubbell argues that modern medicine has optimized around a single strategy: developing drugs that block specific molecules or suppress targeted immune responses. Antibody technology has been the workhorse of this approach. “It’s really fit for purpose for blocking one thing at a time,” he says. The pharmaceutical industry has become extraordinarily good at creating these inhibitors, each designed to shut down a particular pathway. But Hubbell asks a different question: Rather than inhibit one bad thing at a time, what if you could promote one good thing and generate a cascade that contravenes several bad pathways simultaneously? In inflammation, could you bias the system toward immunological tolerance instead of blocking inflammatory molecules one by one? In cancer, could you drive pro-inflammatory pathways in the tumor microenvironment that would overcome multiple immune-suppressive features at once? This shift from inhibition to activation requires a fundamentally different toolkit — and a different kind of researcher. “We’re using biological molecules like proteins, or material-based structures — soluble polymers, supramolecular structures of nanomaterials — to drive these more fundamental features,” Hubbell explains. You can’t develop those approaches if you only understand biology, or only understand materials science, or only understand immunology. You need an understanding and a mastery of all three. “There will be people doing AI, data science, computational science theory, people doing immunoengineering and other biological engineering, people doing materials science and quantum engineering, all really in close proximity to each other.” —Jeffrey Hubbell, NYU Tandon Which logically leads to the question: How do you create researchers with that kind of cross-disciplinary depth? The answer isn’t what you might expect. “There may have been a time when the objective was to have the bioengineer understand the language of biology,” Hubbell says. “But that time is long, long gone. Now the engineer needs to become a biologist, or become an immunologist, or become a neuroscientist.” Hubbell isn’t talking about engineers learning enough biology to collaborate with biologists. He’s describing something more radical: training people whose disciplinary identity is genuinely ambiguous. “The neuroengineering students — it’s very difficult to know that they’re an engineer or a neuroscientist,” Hubbell says. “That’s the whole idea.” His own students exemplify this. They publish in immunology journals, present at immunology conferences. “Nobody knows they’re engineers,” he says. But they bring engineering approaches — computational modeling, materials design, systems thinking — to immunological problems in ways that traditional immunologists wouldn’t. The mechanism for creating these hybrid researchers is what Hubbell calls a “milieu.” “To learn it all on your own is hopeless,” he acknowledges, “but to learn it in a milieu becomes very, very efficient.” NYU is expanding its facilities to include a science and technology hub designed to force encounters between people across various schools and disciplines who wouldn’t naturally cross paths.Tracey Friedman/NYU NYU is making that milieu physical. The university has acquired a large building in Manhattan that will serve as its science and technology hub — a deliberate co-location strategy designed to force encounters between people across various schools and disciplines who wouldn’t naturally cross paths. Juan de Pablo is the Anne and Joel Ehrenkranz Executive Vice President for Global Science and Technology and Executive Dean of the NYU Tandon School of Engineering.Steve Myaskovsky, Courtesy of NYU Photo Bureau “There will be people doing AI, data science, computational science theory, people doing immunoengineering and other biological engineering, people doing materials science and quantum engineering, all really in close proximity to each other,” Hubbell explains. The strategy mirrors what Juan de Pablo, NYU’s Anne and Joel Ehrenkranz Executive Vice President for Global Science and Technology and Executive Dean at the NYU Tandon School of Engineering, describes as organizing around “grand challenges” rather than traditional disciplines. “What drives the recruitment and the spaces and the people that we’re bringing in are the problems that we’re trying to solve,” he says. “Great minds want to have a legacy, and we are making that possible here.” But physical proximity alone isn’t enough. The Institute is also cultivating what Hubbell calls an “explicit” rather than “tacit” approach to translation — thinking about clinical and commercial pathways from day one. “It’s a terrible thing to solve a problem that nobody cares about,” Hubbell tells his students. To avoid that, the Institute runs “translational exercises” — group sessions where researchers map the entire path from discovery to deployment before launching multi-year research programs. Where could this fail? What experiments would prove the idea wrong quickly? If it’s a drug, how long would the clinical trial take? If it’s a computational method, how would you roll it out safely? The new cross-institutional initiative represents a major investment in science and technology, and includes adding new faculty, state-of-the-art facilities, and innovative programs.NYU Tandon The approach contrasts sharply with typical academic practice. “Sometimes academics tend to think about something for 20 minutes and launch a 5-year PhD program,” Hubbell says. “That’s probably not a good way to do it.” Instead, the Institute brings together people who have actually developed drugs, built algorithms, or commercialized devices — importing their hard-won experience into the planning phase before a single experiment is run. The timing may be fortuitous. De Pablo notes that AI is compressing timelines dramatically. “What we thought was going to take 10 years to complete, we might be able to do in 5,” he says. But he’s quick to note AI’s limitations. While tools like AlphaFold can predict how a single protein folds — a breakthrough of the last five years — biology operates at much larger scales. “What we really need to do now is design not one protein, but collections of them that work together to solve a specific problem,” de Pablo explains. Hubbell agrees: “Biology is much bigger — many, many, many systems.” The liver and kidney are in different places but interact. The gut and brain are connected neurologically in ways researchers are just beginning to map. “AI is not there yet, but it will be someday. And that’s our job — to develop the data sets, the computational frameworks, the systems frameworks to drive that to the next steps.” It’s a moment of unusual ambition. “At a time when we’re seeing some research institutions retrench a little bit and limit their ambitions,” de Pablo says, “we’re doing just the opposite. We’re thinking about what are the grand challenges that we want to, and need to, tackle.” The bet is that the breakthroughs worth making can’t emerge from any single discipline working alone. They require collisions —sometimes planned, sometimes accidental — between people who speak different technical languages and are willing to develop a shared one. NYU is engineering those collisions at scale.
This webinar covers power system modeling and simulation across multiple timescales, from quasi-static 8760 analysis through EMT studies, fault classification, and inverter-based resource grid integration. What Attendees will Learn Programmatic network construction and multi-fidelity modeling — Learn how to build power system networks programmatically from standard data formats, configure models for specific engineering objectives, and work across fidelity levels from quasi-static phasor simulation through switched-linear and nonlinear electromagnetic transient (EMT) analysis. Quasi-static and EMT simulation workflows — Explore 8760-hour quasi-static simulation on an IEEE 123-node distribution feeder for annual energy studies, and EMT simulation on transmission system benchmarks including generator trip dynamics and asset relocation without remodeling the network. Comprehensive fault studies and machine-learning classification — Understand how to systematically inject faults at every node in a distribution system using EMT simulation, and how the resulting dataset can be used to train a machine-learning algorithm for automated fault detection and classification. Grid integration of inverter-based resources (IBRs) — Learn frequency scanning techniques using admittance-based voltage perturbation in the DQ reference frame, and simulation-based grid code compliance testing for grid-forming converters assessed against published interconnection standards. Register now for this free webinar!
When Yong Wang recently received one of the highest honors for early-career data visualization researchers, it marked a milestone in an extraordinary journey that began far from the world’s technology hubs. Wang was born in a small farming village in southwestern China to parents with little formal education and few electronic devices. Today the IEEE member and associate editor of IEEE Transactions on Visualization and Computer Graphics is an assistant professor of computing and data science at Nanyang Technological University, in Singapore. He studies how people can employ data visualization techniques to get more out of artificial intelligence tools. YONG WANG EMPLOYER Nanyang Technological University, in Singapore POSITION Assistant professor of computing and data science IEEE MEMBER GRADE Member ALMA MATERS Harbin Institute of Technology in China; Huazhong University of Science and Technology in Wuhan, China; Hong Kong University of Science and Technology “Visualization helps people understand complex ideas,” Wang says. “If we design these tools well, they can make advanced technologies accessible to everyone.” For his work in the field, the IEEE Computer Society visualization and graphics technical committee presented him with its 2025 Significant New Researcher Award. The recognition highlights his growing influence in fields including human-computer interaction and human-AI collaboration—areas becoming more important as the world generates more data than humans can easily interpret. Growing up in rural Hunan Wang was born in southwestern Hunan Province. China’s economy was still developing, and life in his village was modest. Most families in Hunan grew rice, vegetables, and fruit to support themselves. Wang’s parents worked in agriculture too, and his father often traveled to cities to earn money working in a factory or on construction jobs. The extra income helped support the family and made it possible for Wang to attend college. “I’m very grateful to my parents,” Wang says. “They never attended university, but they strongly supported my education.” “If we build tools that help people understand information, then more people can participate in science and innovation. That’s the real power of visualization.” Technology was scarce in the village, he says. Computers were almost nonexistent, and televisions were considered precious, expensive household possessions. One childhood memory still makes him laugh: During a summer vacation, he and his brother spent so many hours playing video games on a simple console connected to the family’s television that the TV screen eventually burned out. “My mother was very angry,” he recalls. “At that time, a TV was a very valuable thing.” He says that despite never having used a laptop or experimenting with electronic equipment, he was fascinated by the technologies he saw on TV shows. Discovering robotics and engineering His parents encouraged a practical career such as medicine or civil engineering, but he felt drawn to robotics and computing, he says. “I didn’t really understand what computer science involved,” he says. “But from what I saw on TV, it looked exciting and advanced.” He enrolled at Harbin Institute of Technology, in northeastern China. The esteemed university is known for its engineering programs. His major—automation— combined elements of electrical engineering, robotics, and control systems. One of the defining experiences of his undergraduate years, he says, was a university robotics competition. Wang and his teammates designed a robot capable of autonomously navigating around obstacles. The design was simple compared with professional systems, he acknowledges. But, he says, the experience was exhilarating. His team placed second, and Wang began to see engineering as both creative and collaborative. He graduated with a bachelor’s degree in 2011 and briefly worked as an assistant at the Research Institute of Intelligent Control and Systems at Harbin. In 2014 he took a position as a research intern working at Da Jiang Innovation in Shenzhen, China. That experience helped him clarify his future, he says: “I realized I didn’t enjoy doing repetitive work or simply following instructions. I wanted to explore ideas that interested me, and I wanted to conduct research.” The realization pushed him toward graduate school, he says. Building tools that help humans work with AI Wang received a master’s degree in pattern recognition and image processing from the Huazhong University of Science and Technology, in Wuhan, China, in 2016. He then enrolled in the computer science Ph.D. program at the Hong Kong University of Science and Technology and earned the degree in 2018. He remained there as a postdoctoral researcher until 2020, when he moved to Singapore to join Singapore Management University as an assistant professor of computing and information systems. He moved over to Nanyang Technological University as an assistant professor in 2024. His research focuses on a challenge facing nearly every business: how to make sense of the enormous amounts of data being generated. “We live in an era of information explosions,” Wang says. “Huge amounts of data are generated, and it’s difficult for people to interpret all of it to make better business decisions.” Data visualization offers a solution by turning complex information into images, patterns, and diagrams that people can more readily understand. But many visualizations still must be designed manually by experts, Wang notes. It’s a time-consuming process that creates a bottleneck, he says. His solution is to use large language models and multimodal systems that can generate text, images, video, and sensor data simultaneously and automate parts of the process. One system developed by his research group lets users design complex infographics through natural-language instructions combined with simple interactions such as drawing on a touchscreen with a finger. It allows nontechnical people to generate visualizations instead of hiring professional designers. Another focus of Wang’s research is human-AI collaboration. AI systems can analyze data at enormous scale, but people still need to be the final decision-makers, he says. Visualization helps bridge the gap between human intention and AI’s complex calculations by making the process an AI system uses to reach a result more transparent and understandable. “If people understand how the AI system works,” Wang says, “they can collaborate with it more effectively.” He recently explored how visualization techniques could help researchers understand quantum computing, a field where core concepts—such as superposition, where a bit can be in more than one state at a time—are abstract. In classical computing, the bit state is binary: It’s either 1 or 0. A quantum bit, or qubit, can be 1, 0, or both. The differences get more dizzying from there. Visualization tools could help scientists monitor quantum systems and interpret quantum machine-learning models, he says. The importance of IEEE communities Teaching and mentoring students remain among the most meaningful parts of Wang’s career, he says. Professional communities such as the IEEE Computer Society, he says, play a major role in helping him transform early-stage graduate students unsure of which lines of inquiry they will pursue into independent researchers with a solid technical focus. Through conferences, publications, and technical committees, IEEE connects Wang with other researchers working in visualization, AI, and human-computer interactions, he says. Those connections have helped him share ideas, collaborate, and stay up to date on innovations in the research community. Receiving the Significant New Researcher award motivates him to continue pushing the field forward, he says. Looking back, he says, the distance between his rural village in Hunan and an international research career still feels remarkable. But, he says, the journey reflects something larger about his chosen field: “If we build tools that help people understand information, then more people can participate in science and innovation. “That’s the real power of visualization.”
Think one GPU is very much like another? Think again. It turns out that there’s surprising variability in the performance delivered by chips of the same model. That can make getting your money’s worth by renting time on a GPU from a cloud provider a real roll of the dice, according to research from the College of William & Mary, Jefferson Lab, and Silicon Data. “It’s called the silicon lottery,” says Carmen Li, founder and CEO of Silicon Data, which tracks GPU rental prices and benchmarks cloud-computing performance. The silicon lottery’s existence has been known since at least 2022, when researchers at the University of Wisconsin tied it to variations in the performance of GPU-dependent supercomputers. Li and her colleagues figured that the effect would be even more pronounced for AI cloud customers. Performance varies for GPU models in the cloud So they ran 6,800 instances of the index firm’s benchmark test on 3,500 randomly selected GPUs operated by 11 cloud-computing providers. The 3,500 GPUs comprised 11 models of Nvidia GPU, the most advanced being the Nvidia H200 SXM. (The team wasn’t just picking on Nvidia; the GPU giant makes up most of the rental cloud market.) The benchmark, called SiliconMark, is intended to provide a snapshot of a GPU’s ability to run large language models, or LLMs. It tests 16-bit floating-point computing performance, measured in trillions of operations per second, and a GPU’s internal-memory bandwidth, measured in gigabytes per second. The results showed that the computing performance varied for all models, but for the 259 H100 PCIe GPUs it differed by as much as 34.5 percent, and the memory bandwidth of the 253 H200 SXM GPUs varied by as much as 38 percent. Differences in how the GPU is cooled, how cloud operators configure their computers, and how much use the chip has seen can all contribute to variations in performance of otherwise identical chips. But Silicon Data’s analysis showed that the real culprit was variations in the chips themselves, likely due to manufacturing issues. Such randomness has real dollars-and-cents consequences, the researchers argue, because there’s a chance that a pricier, more advanced GPU won’t deliver better performance than an older model chip. So what should GPU renters do? “The most practical approach is to benchmark the actual rental they receive,” says Jason Cornick, head of infrastructure at Silicon Data. “Running a benchmark tool [such as SiliconMark] allows them to compare their specific instance’s performance against a broader corpus of data.”
Two weeks ago, Anthropic announced that its new model, Claude Mythos Preview, can autonomously find and weaponize software vulnerabilities, turning them into working exploits without expert guidance. These were vulnerabilities in key software like operating systems and internet infrastructure that thousands of software developers working on those systems failed to find. This capability will have major security implications, compromising the devices and services we use every day. As a result, Anthropic is not releasing the model to the general public, but instead to a limited number of companies. The news rocked the internet security community. There were few details in Anthropic’s announcement, angering many observers. Some speculate that Anthropic doesn’t have the GPUs to run the thing, and that cybersecurity was the excuse to limit its release. Others argue Anthropic is holding to their AI safety mission. There’s hype and counter-hype, reality and marketing. It’s a lot to sort out, even if you’re an expert. We see Mythos as a real but incremental step, one in a long line of incremental steps. But even incremental steps can be important when we look at the big picture. How AI Is Changing Cybersecurity We’ve written about Shifting Baseline Syndrome, a phenomenon that leads people—the public and experts alike—to discount massive long-term changes that are hidden in incremental steps. It has happened with online privacy, and it’s happening with AI. Even if the vulnerabilities found by Mythos could have been found using AI models from last month or last year, they couldn’t have been found by AI models from five years ago. The Mythos announcement reminds us that AI has come a long way in just a few years: The baseline really has shifted. Finding vulnerabilities in source code is the type of task that today’s large language models excel at. Regardless of whether it happened last year or will happen next year, it’s been clear for a while this kind of capability was coming soon. The question is how we adapt to it. We don’t believe that an AI that can hack autonomously will create permanent asymmetry between offense and defense; it’s likely to be more nuanced than that. Some vulnerabilities can be found, verified, and patched automatically. Some vulnerabilities will be hard to find, but easy to verify and patch—consider generic cloud-hosted web applications built on standard software stacks, where updates can be deployed quickly. Still others will be easy to find (even without powerful AI) and relatively easy to verify, but harder or impossible to patch, such as IoT appliances and industrial equipment that are rarely updated or can’t be easily modified. Then there are systems whose vulnerabilities will be easy to find in code but difficult to verify in practice. For example, complex distributed systems and cloud platforms can be composed of thousands of interacting services running in parallel, making it difficult to distinguish real vulnerabilities from false positives and to reliably reproduce them. So we must separate the patchable from the unpatchable, and the easy to verify from the hard to verify. This taxonomy also provides us guidance for how to protect such systems in an era of powerful AI vulnerability-finding tools. Unpatchable or hard to verify systems should be protected by wrapping them in more restrictive, tightly controlled layers. You want your fridge or thermostat or industrial control system behind a restrictive and constantly-updated firewall, not freely talking to the internet. Distributed systems that are fundamentally interconnected should be traceable and should follow the principle of least privilege, where each component has only the access it needs. These are bog standard security ideas that we might have been tempted to throw out in the era of AI, but they’re still as relevant as ever. Rethinking Software Security Practices This also raises the salience of best practices in software engineering. Automated, thorough, and continuous testing was always important. Now we can take this practice a step further and use defensive AI agents to test exploits against a real stack, over and over, until the false positives have been weeded out and the real vulnerabilities and fixes are confirmed. This kind of VulnOps is likely to become a standard part of the development process. Documentation becomes more valuable, as it can guide an AI agent on a bug finding mission just as it does developers. And following standard practices and using standard tools and libraries allows AI and engineers alike to recognize patterns more effectively, even in a world of individual and ephemeral instant software—code that can be generated and deployed on demand. Will this favor offense or defense? The defense eventually, probably, especially in systems that are easy to patch and verify. Fortunately, that includes our phones, web browsers, and major internet services. But today’s cars, electrical transformers, fridges, and lampposts are connected to the internet. Legacy banking and airline systems are networked. Not all of those are going to get patched as fast as needed, and we may see a few years of constant hacks until we arrive at a new normal: where verification is paramount and software is patched continuously.
Tom Burick has always considered himself a builder. Over the years he’s designed robots, constructed a vintage teardrop trailer, and most recently, led a group of students in building a full-scale replica of a pivotal 1940s computer. Burick is a technology instructor at PS Academy in Gilbert, Ariz., a middle and high school for students with autism and other specialized learning needs. At the start of the 2025–26 school year, he began a project with his students to build a full-scale replica of the Electronic Numerical Integrator and Computer, or ENIAC, for the 80th anniversary of the historic computer’s construction. ENIAC was one of the world’s first programmable electronic computers. When it was built, it was about one thousand times as fast as other machines. Before becoming a teacher, Burick owned a robotics company for a decade in the 2000s. But when a financial downturn forced him to close the business, he turned to teaching. “I had so many amazing people help me when I was young [who] really gave me their time and resources, and really changed the trajectory of my life,” Burick says. “I thought I need to pay that forward.” Becoming a Roboticist As a young child in Latrobe, Pa., Burick watched the television show Lost in Space, which includes a robot character who protects the family. “He was the young boy’s best friend, and I was so captivated by that. I remember thinking to myself, I want that in my life. And that started that lifelong love affair with robotics and technology.” He started building toy robots out of anything he could find, and in junior high school, he began adding electronics. “By early high school, I was building full-fledged autonomous, microprocessor-controlled machines,” he says. At age 15, he built a 150-pound steel firefighting robot, for which he won awards from IEEE and other organizations. Burick kept building robots and reached out for help from local colleges and universities. He first got in touch with a student at Carnegie Mellon University, who invited him to visit campus. “My parents drove me down the next weekend, and he gave me a tour of the robotics lab. I was mesmerized. He sent me home with college textbooks and piles of metal and gears and wires,” Burick says. He would read the textbook a page at a time, reading it again and again until he felt he had an understanding of it. Then, to help fill gaps in his understanding, he got in touch with a robotics instructor at Saint Vincent College, in his hometown of Latrobe, who let him sit in on classes. Each of these adults, he says, “helped change the trajectory of my life.” Toward the end of high school, Burick realized that college wouldn’t be the right environment for him. “I was drawn to real-world problem-solving rather than structured coursework and I chose to continue along that path,” he says. Additionally, Burick has dyscalculia, which makes traditional mathematics more challenging for him. “It pushed me to develop alternative methods of engineering.” The ENIAC replica Burick’s students built precisely matches what the original computer would have looked like before it was disassembled in the 1950s. Robert Gamboa When he graduated, he worked in several tech jobs before starting his own company. In 2000, he opened a computer retail store and adjacent robotics business, White Box Robotics. The idea for the company came when Burick was building a “white box” PC from standard, off-the-shelf components, and realized there was no comparable product for robotics. So, he started developing a modular, general-purpose platform that applied white box PC standards to mobile robots. “The robot’s chassis was like a box of Legos,” he says. You could click together two torsos to double its payload, switch out the drive system, or swap its head for a different set of sensors. He filed utility and design patents for the platform, called the 914 PC-Bot, and after merging with a Canadian defense robotics company called Frontline Robotics, started production. They sold about 200 robots in 17 countries, Burick says. Then the 2008 financial crisis hit. White Box Robotics held on for a couple of years, shuttering in late 2010. “I got to live my life’s dream for 10 years,” he says. After closing White Box, “there was some soul searching” about what to do next. He recalled the impact his own mentors had, and decided to pay it forward by teaching. Neurodiversity as a Superpower In 2013, Burick started working in a vocational training program for young adults living with autism. The program didn’t have a technical arm, so he started one and ran it until 2019, when he was hired to be a technology instructor at PS Academy Arizona. Burick and one of his students assemble the base for one of ENIAC’s three portable function tables, which contained banks of switches that stored numerical constants. Bri Mason Burick feels he can connect with his students, because he is also neurodivergent. Throughout his childhood, he was told what he wasn’t able to do because of his dyscalculia diagnosis. “People tell you what it takes, but they never tell you what it gives,” Burick says. In adulthood, he realized that some of his strengths are linked to dyscalculia, too, like strong 3D spatial reasoning. “I have this CAD program that runs in my head 24 hours a day,” he says. “I think the reason I was successful in robotics, truly, was because of the dyscalculia…. To me, [it] has always been a superpower.” Whenever his students say something disparaging about living with autism, he shares his own experience. “You need to have maybe just a bit more tenacity than others, because there are parts of it you do have to fight through, but you come through with gifts and strengths,” he tells them. And Burick’s classes aim to play to those strengths. “I didn’t want my technology program to feel like craft hour,” he says. Instead, through projects like the ENIAC replica, students can leverage traits many of them share, like the abilities to hyperfocus and to precisely repeat tasks. Recreating ENIAC Burick has taught his students about ENIAC for several years. While reading about it, he learned that the massive, 27-tonne computer was dismantled and partially destroyed after being decommissioned in 1955. Although a few of ENIAC’s 40 original panels are on display at museums, “there was no hope of ever seeing it together again. We wanted to give the world that experience,” Burick says. He and his students started by learning about ENIAC, and even Burick was surprised by how complex the 80-year-old computer was. They built a one-twelfth scale model to help the students better understand what it looked like. Seeing the students light up, Burick became confident in their ability to move onto the full-scale model, and he started ordering supplies. ENIAC was composed of 40 large metal panels arranged in a U-shape that housed its many vacuum tubes, resistors, capacitors, and switches. Twenty of the panels were accumulators with the same design, so the students started with these, then worked through smaller groupings of panels. The repeating panels brought symmetry to ENIAC, Burick says, but it was also one of the main challenges of recreating it. If one part was slightly out of place, the next one would be too and the mistake would compound. The students installed 500 simulated vacuum tubes in each of the panels here, for a total of 18,000 vacuum tubes.Robert Gamboa Once they constructed the panels, they added ENIAC’s three function tables, which stored numerical constants in banks of switches, then two punch-card machines. Finally, they installed 18,000 simulated vacuum tubes. In total, the project used nearly 300 square meters of thick-ream cardboard, 1,600 hot-glue-gun sticks, and 7 gallons of black paint. The scale of the machine—and his students’ work—left Burick in awe. “By the time we were done, I felt like I was in a room full of scientists,” he says. Previously, Burick’s students built an 8-foot-long drivable Tesla Cybertruck (“complete with a 400-watt stereo system and a subwoofer”) and he plans to keep the momentum with another recreation—maybe from the Apollo moon missions. “I go to work every day, and I feel passionate about robotics [and] technology. I get to share that passion with the students,” Burick says. “I get to feel what it’s like to be in the position of the people that helped me. It closes that loop, and I find that really rewarding.”
Once upon a time in Europe, television remote controls had a magic teletext button. Years before the internet stole into homes, pressing that button brought up teletext digital information services with hundreds of constantly updated pages. Living in Ireland in the 1980s and ’90s, my family accessed the national teletext service—Aertel—multiple times a day for weather and news bulletins, as well as things like TV program guides and updates on airport flight arrivals. It was an elegant system: fast, low bandwidth, unaffected by user load, and delivering readable text even on analog television screens. So when I recently saw it was the 40th anniversary of Aertel’s test transmissions, it reactivated a thought that had been rolling around in my head for years. Could I make a ham-radio version of teletext? What is Teletext? First developed in the United Kingdom and rolled out to the public by the BBC under the name Ceefax, teletext exploited a quirk of analog television signals. These signals transmitted video frames as lines of luminosity and color, plus some additional blank lines that weren’t displayed. Teletext piggybacked a digital signal onto these spares, transmitting a carousel of pages over time. Using their remotes, viewers typed in the three-digit code of the page they wanted. Generally within a few seconds, the carousel would cycle around and display the desired page. Teletext created unusually legible text in the 8-bit era by enlarging alphanumeric characters and interpolating new pixels by looking for existing pixels touching diagonally, and adding whitespace between characters. Graphic characters were not interpolated, and featured blocky chunks known as sixels for their 2-by-3 arrangement. My modern recreation uses the open-source font Bedstead, which replicates the look of teletext, including the graphics characters. James Provost Teletext is composed of characters that can be one of eight colors. Control codes in the character stream select colors and can also produce effects like flashing text and double-height characters. The text’s legibility was better than most computers could manage at the time, thanks to the SAA5050 character-generator chip at the heart of teletext. Although characters are internally stored on this chip in 6-by-10-pixel cells—fewer pixels than the typical 8-by-8-pixel cell used in 1980s home computers—the SAA5050 interpolates additional pixels for alphanumeric characters on the fly, making the effective resolution 10 by 18 pixels. The trade-off is very low-resolution graphics, comprising characters that use a 2-by-3 set of blocky pixels. Teletext screens use a 40-by-24-character grid. This means that a kilobyte of memory can store a full page of multicolor text, half the memory required for a similar amount of text on, for example, the Commodore 64. The BBC Microcomputer took advantage of this by putting an SAA5050 on its motherboard, which could be accessed in one of the computer’s graphics modes. Despite the crude graphics, some educational games used this mode, most notably Granny’s Garden, which filled the same cultural niche among British schoolchildren that The Oregon Trail did for their U.S. counterparts. By the 2010s, most teletext services had ceased broadcasting. But teletext is still remembered fondly by many, and enthusiasts are keeping it alive, recovering and archiving old content, running internet-based services with current newsfeeds, and developing systems that make it possible to create and display teletext with modern TVs. Putting Teletext Back on the Air I wanted to do something a little different. Inspired by how the BBC Micro co-opted teletext for its own purposes, I thought it might make a great radio protocol. In particular I thought it could be a digital counterpart to slow-scan television (SSTV). SSTV is an analog method of transmitting pictures, typically including banners with ham-radio call signs and other messages. SSTV is fun, but, true to its name, it’s slow—the most popular protocols take a little under 2 minutes to send an image—and it can be tricky to get a complete picture with legible text. For that reason, SSTV images are often broadcast multiple times. Teletext is still remembered fondly by many. I decided to send the teletext using the AX.25 protocol, which encodes ones and zeros as audible tones. For VHF and UHF transmissions at a rate of 1,200 baud, it would take 11 seconds to send one teletext screen. Over HF bands, AX.25 data is normally sent at 300 baud, which would result in a still-acceptable 44 seconds per screen. When a teletext page is sent repeatedly, any missed or corrupted rows are filled in with new ones. So in a little over 2 minutes, I could send a screen three times over HF, and the receiver would automatically combine the data. I also wanted to build the system in Python for portability, with an editor for creating pages, an AX.25 encoder and decoder, and a monitor for displaying received images. The reason why I hadn’t done this before was because it requires digesting the details of the AX.25 standard and teletext’s official spec, and then translating them into a suite of software, which I never seemed to have the time to do. So I tried an experiment within an experiment, and turned to vibe coding. Despite the popularity of vibe coding with developers, I have reservations. Even if concerns about AI slop, the environment, and memory hoarding were not on the table, I would still worry about the reliance on centralized systems that vibe coding brings. The whole point of a DIY project is to, well, do it yourself. A DIY project lets you craft things for your own purposes, not just operate within someone else’s profit margins and policies. Still, criticizing a technology from afar isn’t ideal, so I directed Anthropic’s Claude toward the AX.25 and teletext specs and told it what I wanted. After about 250,000 to 300,000 tokens and several nights of back and forth about bugs and features, I had the complete system running without writing a single line of code. Being honest with myself, I doubt this system—which I’m calling Spectel—would ever have come about without vibe coding. But I didn’t learn anything new about how teletext works, and only a little bit more about AX.25. Updates are contingent on my paying Anthropic’s fees. So I remain deeply ambivalent about vibe coding. And one final test remains in any case: trying Spectel out on HF bands. Of course, that means I’ll need willing partners out in the ether. So if you’re a ham who’d like to help out, let me know in the comments below!
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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! The Individual Contributor–Manager Fork: It’s Not a Promotion. It’s a Profession Change. When I was promoted to engineering manager of a mid-sized team at Clorox, I thought I had made it. More money. More stock. More visibility. More proximity to senior leadership. From the outside, and on paper, it was clearly a promotion. I had often heard the phrase, “Management isn’t a promotion. It’s a job switch.” I brushed it off as cliché advice engineers tell each other to sound wise. It turns out both things were true. It was a promotion. It was also an entirely different job. And I was nowhere near ready for what that meant. A Shift in Priorities There’s surprisingly little training for new managers. As engineers, we’re highly technical and used to mastering complex systems. Many of us assume managing people will be easier than distributed systems. Or we assume it’s just “more meetings.” Both assumptions are wrong. Yes, I had more meetings. But what changed most wasn’t my calendar, it was how my impact was measured. As an individual contributor, my output was visible. Code shipped. Features delivered. Bugs fixed. As a manager, my impact became indirect. It flowed through other people. That shift was disorienting. So I fell back into my comfort zone. I started writing more code. I tried to be the strongest engineer on the team. It felt productive and measurable. It was also a mistake. By trying to be the number one engineer, I was neglecting my actual job. I wasn’t supporting senior engineers. I wasn’t unblocking systemic problems. I wasn’t building career paths. I was competing with the very people I was supposed to enable. Management is about amplification. Learning to Redefine Impact The turning point came when I began each week with a simple question: What is the single most impactful thing I can do right now? Often, it wasn’t code. It was writing a document that clarified direction. It was fixing a broken process with a single point of failure. It was redistributing ownership so that knowledge wasn’t concentrated in one person. I started deliberately removing myself from implementation work. I committed to writing almost no code. That forced trust. It also revealed gaps in the system that I could address at the right level: through coaching, documentation, hiring, or process changes. Another major shift was taking one-on-one meetings seriously. Many engineers dislike one-on-ones. They can feel awkward or devolve into status updates. I scheduled them every other week and approached them with a mix of tactical alignment and human check-in. I rarely started with engineering questions. Instead: Are you happy with the work you’re doing? Do you feel stretched or stagnant? What’s frustrating you right now? Burnout doesn’t show up in Jira tickets. Neither does quiet disengagement. Those conversations helped me anticipate turnover, redistribute workload, and build trust. I also spent more time thinking about career ladders. Was I giving my team the kind of work that would help them grow? Was I hoarding high-visibility projects? Was I clear about what senior-level impact looked like? That work felt less tangible than code, but it moved the needle far more. Why I Went Back to IC Ultimately, I returned to the individual contributor track. Part of it was practical: I was laid off from my management role, and the market rewarded senior IC roles more strongly at the time. But if I’m honest, the deeper reason was simpler. I love writing code. I enjoy improving systems and helping people, but the part of my day that energized me most was still building. Management required relinquishing that. You can’t be absorbed in technical implementation and deeply people-focused at the same time. Something has to give. Personally, I don’t need to climb the corporate ladder to feel successful. And you might not have to. Many organizations offer technical leadership tracks that are truly in parity with management when it comes to salary bands. Staff and principal engineers steer strategy without managing people. If you want to remain deeply technical, you should think very carefully before moving into people management. It requires surrendering control over implementation and focusing on alignment, growth, and long-range planning. If you don’t genuinely care about those things, you won’t just be unhappy, you’ll make your team unhappy. A Simple Test Before You Choose Before taking a management role, ask yourself: Do I get energy from solving people-problems every day? Am I comfortable measuring impact indirectly? Would I be satisfied if I rarely wrote production code again? Do I want leverage or craft? There’s no right answer. The IC/manager fork isn’t about prestige. It’s about what kind of work you want your days to consist of. Choose based on energy, not ego. —Brian 12 Graphs That Explain the State of AI in 2026 Stanford University’s AI Index is out for 2026, tracking trends and noble developments in artificial intelligence. This year, China has taken a notable lead in AI model releases and industrial robotics compared to previous years. AIs are rapidly reaching benchmarks and achieving high levels of compute, but public trust in AI and confidence in government regulation of AI is mixed. Read more here. AI Models Trained on Physics Are Changing Engineering Much like large language models have learned from existing texts, new AI physics models are being trained on simulation results. This results in “large physics models” that can simulate situations in transportation, aerospace, or semiconductor engineering much faster than traditional physics simulations. Using new AI physics models “can be anywhere between 10,000 to close to a million times faster,” says Jacomo Corbo, CEO and co-founder of PhysicsX. Read more here. Temple University Student Highlights IEEE Membership Perks Kyle McGinley is an IEEE Student Member pursuing a bachelor’s degree in electrical and computer engineering at Temple University. Joining IEEE helped him to develop the skills necessary for real-world teams. “In school, they don’t teach you how to communicate with people. They only teach you how to remember stuff,” he says. Read more here.
Why does a chocolatier build a railroad? For Milton S. Hershey, it was a logical response to a sugar shortage brought on by World War I. The Hershey Chocolate Co. was by then a chocolate-making powerhouse, having refined the automation and mass production of its products, including the eponymous Hershey’s Milk Chocolate Bar and the bite-size Hershey’s Kiss. To satisfy its many customers, the company needed a steady supply of sugar. Plus, it wanted a way to circumvent the American Sugar Refining Co., also known as the Sugar Trust, which had a virtual monopoly on sugar processing in the United States. Why Did Hershey Build an Electric Railroad in Cuba? Beginning in 1916, Hershey looked to Cuba to secure his sugar supply. According to historian Thomas R. Winpenny, the chocolate magnate had a “personal infatuation” with the lush, beautiful island. What’s more, U.S. business interests there were protected by a treaty known as the Platt Amendment, which made Cuba a satellite state of the United States. Like many industrialists of the day, Hershey believed in vertical integration, and the company’s Cuban operation eventually expanded to include five sugar plantations, five modern sugar mills, a refinery, several company towns, and an oil-fired power plant with three substations to run it all. A 1943 rail pass entitled the holder to travel on all ordinary passenger trains of the Hershey Electric Railway. Hershey Community Archives The company also built a railroad. To maximize the sugar yield, the cane needed to be ground promptly after being cut, and the rail system offered an efficient means of transporting the cane to the mills, and ensured that the mills operated around the clock during the harvest. By 1920, one of Hershey’s three main sites was processing 135,000 tonnes of cane, yielding 14.4 million kilograms of sugar. Initially, the Hershey Cuban Railway consisted of a single 56-kilometer-long standard gauge track on which ran seven steam locomotives that burned coal or oil. But due to the high cost of the imported fuel and the inefficiency of the locomotives, Hershey began electrifying the line in 1920. Although it was the first electrified train in Cuba, rail lines in Europe and the United States were already being electrified. In addition to powering the various Hershey entities, the generating station supplied Matanzas and the smaller towns with electricity. F.W. Peters of General Electric’s Railway and Traction Engineering Department published a detailed account of the system in the April 1920 General Electric Review. Hershey’s Company Towns The company town of Central Hershey became the headquarters for Hershey’s Cuba operations. (“Central” is the Cuban term for a mill and the surrounding settlement.) It sat on a plateau overlooking the port of Santa Cruz del Norte, about halfway between Havana and Matanzas in the heart of Cuba’s sugarcane region. Hershey imported the industrial utopian model he had established in Hershey, Penn., which was itself inspired by Richard and George Cadbury’s Bournville Village outside Birmingham, England. The chocolate magnate Milton S. Hershey had a “personal infatuation” with Cuba.Underwood Archives/Getty Images In Cuba as in Pennsylvania, Hershey’s factory complex was complemented by comfortable homes for his workers and their families, as well as swimming pools, baseball fields, and affordable medical clinics staffed with doctors, nurses, and dentists. Managers had access to a golf course and country club in Central Hershey. Schools provided free education for workers’ children. Milton Hershey himself had very little formal education, and so in 1909 he and his wife, Catherine, established the Hershey Industrial School in Hershey, Penn. There, white, male orphans received an education until they were 18 years old. Now known as the Milton Hershey School, the school has broadened its admission criteria considerably over the years. Hershey duplicated this concept in the Cuban company town of Central Rosario, founding the Hershey Agricultural School. The first students were children whose parents had died in a horrific 1923 train accident on the Hershey Electric Railway. The high-speed, head-on collision between two trains killed 25 people and injured 50 more. Milton Hershey was a generous philanthropist, and by most accounts he truly cared for his employees and their welfare, and yet his early 20th-century paternalism was not without fault. He was a fierce opponent of union activity, and any hard-won pay increases for workers often came at the expense of profit-sharing benefits. Like other U.S. businessmen in Cuba, Hershey employed migrant seasonal labor from neighboring Caribbean islands, undercutting the wages of local workers. Historians are still wrangling with how to capture the long-lasting effects of U.S. economic imperialism on Cuba. Can the Hershey Electric Railway Be Revived? Hershey continued to acquire new sugar plantations in Cuba throughout the 1920s, eventually owning about 24,300 hectares and leasing another 12,000 hectares. In 1946, a year after Milton Hershey’s death and amid growing political uncertainty on the island, the company sold its Cuban interests to the Cuban Atlantic Sugar Co. In addition to Hershey’s sugar operations, the sale included a peanut oil plant, four electric plants, and 404 km of railroad track plus locomotives and train cars. Service on the Hershey Electric Railway in Cuba continued into at least the 2010s but became increasingly sporadic, with aging equipment like this car at the Central Hershey station. Hershey Community Archives The Central Hershey sugar refinery continued to operate even after the Cuban Revolution but eventually closed in 2002. Passenger service, meanwhile, continued on the Hershey Electric Railway, albeit sporadically: By 2012, there were only two trips a day between Havana and Matanzas. This video, from 2013, gives a good sense of the route: A colleague of mine who studies Cuban history told me that in his travels to the country over almost 30 years, he has never been able to ride the Hershey electric train. It was always out of service or had restricted service due to the island’s chronic electricity shortages, which have only gotten worse in recent years. I’ve been trying to find out if any part of the line is still operating. If you happen to know, please add a comment below. Cuba’s frequent power outages make it difficult to operate the Hershey Electric Railway. In this 2009 photo, passengers await the restoration of electricity so they can continue their journey.Adalberto Roque/AFP/Getty Images A 2024 analysis of the economic potential and challenges of reactivating Cuba’s Hershey Electric Railway noted that an electric railway could be a hedge against climate change and geopolitical factors. But it also acknowledged that frequent power outages and damaged infrastructure argue against reactivating the electrified railway, and it favored the diesel engines used on most of Cuba’s rail network. Cuba has been mostly off-limits to U.S. tourists for my entire life, but it was one of my grandmother’s favorite vacation spots. I would love to imagine a future where political ties are restored, the power grid is stabilized, and the Hershey Electric Railway is reopened to the Cuban public and to curious visitors like me. 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 May 2026 print issue as “This Chocolate Empire Ran on Electric Rails.” References In April 1920, F.W. Peters of General Electric’s Railway and Traction Engineering Department wrote a detailed account called “Electrification of the Hershey Cuban Railway” in the General Electric Review, which was later abstracted in Scientific American Monthly to reach a broader audience. Thomas R. Winpenny’s article “Milton S. Hershey Ventures into Cuban Sugar” in Pennsylvania History: A Journal of Mid-Atlantic Studies, Fall 1995, provided background to the business side of Hershey’s Cuba enterprise. Florian Wondratschek’s 2024 article “Between Investment Risk and Economic Benefit: Potential Analysis for the Reactivation of the Hershey Railway in Cuba” in Transactions on Transport Sciences brought the story up to the present. And if you’re interested in a visual take on the Hershey operation on Cuba, check out the documentary Milton Hershey’s Cuba by Ric Morris, a professor of Spanish and linguistics at Middle Tennessee State University.
When the robotics engineering field that Maja Matarić wanted to work in didn’t exist, she helped create it. In 2005 she helped define the new area of socially assistive robotics. As an associate professor of computer science, neuroscience, and pediatrics at the University of Southern California, in Los Angeles, she developed robots to provide personalized therapy and care through social interactions. Maja Matarić Employer University of Southern California, Los Angeles Job Title Professor of computer science, neuroscience, and pediatrics Member grade Fellow Alma maters University of Kansas and MIT The robots could have conversations, play games, and respond to emotions. Today the IEEE Fellow is a professor at USC. She studies how robots can help students with anxiety and depression undergo cognitive behavioral therapy. CBT focuses on changing a person’s negative thought patterns, behaviors, and emotional responses. For her work, she received a 2025 Robotics Medal from MassRobotics, which recognizes female researchers advancing robotics. The Boston-based nonprofit provides robotics startups with a workspace, prototyping facilities, mentorship, and networking opportunities. When receiving the award at the ceremony in Boston, Matarić was overcome with joy, she says. “I’ve been very fortunate to be honored with several awards, which I am grateful for. But there was something very special about getting the MassRobotics medal, because I knew at least half the people in the room,” she says. “Everyone was just smiling, and there was a great sense of love.” Seeing herself as an engineer Matarić grew up in Belgrade, Serbia. Her father was an engineer, and her mother was a writer. After her father died when she was 16, Matarić and her mother moved to the United States. She credits her father for igniting her interest in engineering, and her uncle who worked as an aerospace engineer for introducing her to computer science. Matarić says she didn’t consider herself an engineer until she joined USC’s faculty, since she always had worked in computer science. “In retrospect, I’ve always been an engineer,” Matarić says. “But I didn’t set out specifically thinking of myself as one—which is just one of the many things I like to convey to young people: You don’t always have to know exactly everything in advance.” Maja Matarić and her lab are exploring how socially assistive robots can help improve the communication skills of children with autism spectrum disorder. National Science Foundation News While pursuing her bachelor’s degree in computer science at the University of Kansas in Lawrence, she was introduced to industrial robotics through a textbook. After earning her degree in 1987, she had an opportunity to continue her education as a graduate student at MIT’s AI Lab (now the Computer Science and Artificial Intelligence Lab). During her first year, she explored the different research projects being conducted by faculty members, she said in a 2010 oral history conducted by the IEEE History Center. She met IEEE Life Fellow Rodney Brooks, who was working on novel reactive and behavior-based robotic systems. His work so excited her that she joined his lab and conducted her master’s thesis under his tutelage. Inspired by the way animals use landmarks to navigate, Matarić developed Toto, the first navigating behavior-based robot. Toto used distributed models to map the AI Lab building where Matarić worked and plan its path to different rooms. Toto used sonar to detect walls, doors, and furniture, according to Matarić’s paper, “The Robotics Primer.” After earning her master’s degree in AI and robotics in 1990, she continued to work under Brooks as a doctoral student, pioneering distributed algorithms that allowed a team of up to 20 robots to execute complex tasks in tandem, including searching for objects and exploring their environment. Matarić earned her Ph.D. in AI and robotics in 1994 and joined Brandeis University, in Waltham, Mass., as an assistant professor of computer science. There she founded the Interaction Lab, where she developed autonomous robots that work together to accomplish tasks. Three years later, she relocated to California and joined USC’s Viterbi School of Engineering as an assistant professor in computer science and neuroscience. In 2002 she helped to found the Center for Robotics and Embedded Systems (now the Robotics and Autonomous Systems Center). The RASC focuses on research into human-centric and scalable robotic systems and promotes interdisciplinary partnerships across USC. Matarić’s shift in her research came after she gave birth to her first child in 1998. When her daughter was a bit older and asked Matarić why she worked with robots, she wanted to be able to “say something better than ‘I publish a lot of research papers,’ or ‘it’s well-recognized,’” she says. “In academia, you can be in a leadership role and still do research. It’s a wonderful and important opportunity that lets academics be on top of our field and also train the next generation of students and help the next generation of faculty colleagues.” “Kids don’t consider those good answers, and they’re probably right,” she says. “This made me realize I was in a position to do something different. And I really wanted the answer to my daughter’s future question to be, ‘Mommy’s robots help people.’” Matarić and her doctoral student David Feil-Seifer presented a paper defining socially assistive robotics at the 2005 International Conference on Rehabilitation Robotics. It was the only paper that talked about helping people complete tasks and learn skills by speaking with them rather than by performing physical jobs, she says. Feil-Seifer is now a professor of computer science and engineering at the University of Nevada in Reno. At the same time, she founded the Interaction Lab at USC and made its focus creating robots that provide social, rather than physical, support. “At this point in my career journey, I’ve matured to a place where I don’t want to do just curiosity-driven research alone,” she says. “Plenty of what my team and I do today is still driven by curiosity, but it is answering the question: ‘How can we help someone live a better life?’” In 2006 she was promoted to full professor and made the senior associate dean for research in USC’s Viterbi School of Engineering. In 2012 she became vice dean for research. “In academia, you can be in a leadership role and still do research,” she says. “It’s a wonderful and important opportunity that lets academics be on top of our field and also train the next generation of students and help the next generation of faculty colleagues.” Research in socially assistive robotics One of the longest research projects Matarić has led at her Interaction Lab is exploring how socially assistive robots can help improve the communication skills of children with autism spectrum disorder. ASD is a lifelong neurological condition that affects the way people interact with others, and the way they learn. Children with ASD often struggle with social behaviors such as reading nonverbal cues, playing with others, and making eye contact. Matarić and her team developed a robot, Bandit, that can play games with a child and give the youngster words of affirmation. Bandit is 56 centimeters tall and has a humanlike head, torso, and arms. Its head can pan and tilt. The robot uses two FireWire cameras as its eyes, and it has a movable mouth and eyebrows, allowing it to exhibit a variety of facial expressions, according to the IEEE Spectrum’s robots guide. Its torso is attached to a wheeled base. The study showed that when interacting with Bandit, children with ASD exhibited social behaviors that were out of the ordinary for them, such as initiating play and imitating the robot. Matarić and her team also studied how the robot could serve as a social and cognitive aid for elderly people and stroke patients. Bandit was programmed to instruct and motivate users to perform daily movement exercises such as seated aerobics. Maja Matarić and doctoral student Amy O’Connell testing Blossom, which is being used to study how it can aid students with anxiety or depression.University of Southern California Over the years, Matarić’s lab developed other robots including Kiwi and Blossom. Kiwi, which looked like an owl, helped children with ASD learn social and cognitive skills, helped motivate elderly people living alone to be more physically active, and mediated discussions among family members. Blossom, originally developed at Cornell, was adapted by the Interaction Lab to make it less expensive and personalizable for individuals. The robot is being used to study how it can aid students with anxiety or depression to practice cognitive behavioral therapy. Matarić’s line of research began when she learned that large language model (LLM) chatbots were being promoted to help people with mental health struggles, she said in an episode of the AMA Medical News podcast. “It is generally not easy to get [an appointment with a] therapist, or there might not be insurance coverage,” she said. “These, combined with the rates of anxiety and depression, created a real need.” That made the chatbot idea appealing, she says, but she was interested to see if they were effective compared with a friendly robot such as Blossom. Matarić and her team used the same LLMs to power CBT practice with a chatbot and with Blossom. They ran a two-week study in the USC dorms, where students were randomly assigned to complete CBT exercises daily with either a chatbot or the robot. Participants filled out a clinical assessment to measure their psychiatric distress before and after each session. The study showed that students who interacted with the robot experienced a significant decrease in their mental state, Matarić said in the podcast, and students who interacted with the chatbot did not. “Joining an [IEEE] society has an impact, and it can be personal. That’s why I recommend my students join the organization—because it’s important to get out there and get connected.” She and her team also reviewed transcripts of conversations between the students and the robot to evaluate how well the LLM responded to the participants. They found the robot was more effective than the chatbot, even though both were using the same model. Based on those findings, in 2024 Matarić received a grant from the U.S. National Institute of Mental Health to conduct a six-week clinical trial to explore how effective a socially assistive robot could be at delivering CBT practice. The trial, currently underway, also is expected to study how Blossom can be personalized to adapt to each user’s preferences and progress, including the way the robot moves, which exercises it recommends, and what feedback it gives. During the trial, the 120 students participating are wearing Fitbits to study their physiologic responses. The participants fill out a clinical assessment to measure their psychiatric distress before and after each session. Data including the participants’ feelings of relating to the robot, intrinsic motivation, engagement, and adherence will be assessed by the research team, Matarić says. She says she’s proud of the graduate students working on this project, and seeing them grow as engineers is one of the most rewarding parts of working in academia. “Engineers generally don’t anticipate having to work with human study participants and needing to understand psychology in addition to the hardcore engineering,” she says. “So the students who choose to do this research are just wonderful, caring people.” Finding a community at IEEE Matarić joined IEEE as a graduate student in 1992, the year she published her first paper in IEEE Transactions on Robotics and Automation. The paper, “Integration of Representation Into Goal-Driven Behavior-Based Robots,” described her work on Toto. As a member of the IEEE Robotics and Automation Society, she says she has gained a community of like-minded people. She enjoys attending conferences including the IEEE International Conference on Robotics and Automation, the IEEE/RSJ International Conference on Intelligent Robots and Systems, and the ACM/IEEE International Conference on Human-Robot Interaction, which is closest to her field of research. Matarić credits IEEE Life Fellow George Bekey, the founding editor in chief of the IEEE Transactions on Robotics, for recruiting her for the USC engineering faculty position. He knew of her work through her graduate advisor Brooks, who published a paper in the journal that introduced reactive control and the subsumption architecture, which became the foundation of a new way to control robots. It is his most cited paper. Bekey, who was editor in chief at the time, helped guide Brooks through the challenging review process. Matarić joined Brooks’s lab at MIT two years after its publication, and her work on Toto built on that foundation. “Joining a society has an impact, and it can be personal,” she says. “That’s why I recommend my students join the organization—because it’s important to get out there and get connected.”
In 1627, a year after the death of the philosopher and statesman Francis Bacon, a short, evocative tale of his was published. The New Atlantis describes how a ship blown off course arrives at an unknown island called Bensalem. At its heart stands Salomon’s House, an institution devoted to “the knowledge of causes, and secret motions of things” and to “the effecting of all things possible.” The novel captured Bacon’s vision of a science built on skepticism and empiricism and his belief that understanding and creating were one and the same pursuit. No mere scholar’s study filled with curiosities, Salomon’s House had deep-sunk caves for refrigeration, towering structures for astronomy, sound-houses for acoustics, engine-houses, and optical perspective-houses. Its inhabitants bore titles that still sound futuristic: Merchants of Light, Pioneers, Compilers, and Interpreters of Nature. Engraved title page of The Advancement and Proficience of LearningPublic Domain Bacon didn’t conjure his story from nothing. Engineers he likely had met or observed firsthand gave him reason to believe such an institution could actually exist. Two in particular stand out: the Dutch engineer Cornelis Drebbel and the French engineer Salomon de Caus. Their bold creations suggested that disciplined making and testing could transform what we know. Engineers show the way Drebbel came to England around 1604 at the invitation of King James I. His audacious inventions quickly drew notice. By the early 1620s, he unveiled a contraption that bordered on fantasy: a boat that could dive beneath the Thames and resurface hours later, ferrying passengers from Westminster to Greenwich. Contemporary descriptions mention tubes reaching the surface to supply air, while later accounts claim Drebbel had found chemical means to replenish it. He refined the underwater craft through iterative builds, each informed by test dives and adjustments. His other creations included a perpetual-motion device driven by heat and air-pressure changes, a mercury regulator for egg incubation, and advanced microscopes. De Caus, who arrived in England around 1611, created ingenious fountains that transformed royal gardens into animated spectacles. Visitors marveled as statues moved and birds sang in water-driven automatons, while hidden pipes and pumps powered elaborate fountains and mythic scenes. In 1615, de Caus published The Reasons for Moving Forces, an illustrated manual on water- and air-driven devices like spouts, hydraulic organs, and mechanical figures. What set him apart was scale and spectacle: He pressed ancient physical principles into the service of courtly theater. Drebbel’s airtight submersibles and methodical trials echo in the motion studies and environmental chambers of Salomon’s House. De Caus’s melodic fountains and hidden mechanisms parallel its acoustic trials and optical illusions. From such hands-on workshops, Bacon drew the lesson that trustworthy knowledge comes from working within material constraints, through gritty making and testing. On the island of Bensalem, he imagines an entire society organized around it. Beyond inspiring Bacon’s fiction, figures like Drebbel and de Caus honed his emerging philosophy. In 1620, Bacon published Novum Organum, which critiqued traditional philosophical methods and advocated a fresh way to investigate nature. He pointed to printing, gunpowder, and the compass as practical inventions that had transformed the world far more than abstract debates ever could. Nature reveals its secrets, Bacon argued, when probed through ingenious tools and stringent tests. Novum Organum laid out the rationale, while New Atlantis gave it a vivid setting. A final legacy to science Engraved title page of Bacon’s Novum OrganumPublic Domain That devotion to inquiry followed Bacon to the roadside one day in March 1626. In a biting late-winter chill, he halted his carriage for an impromptu trial. He bought a hen and helped pack its gutted body with fresh snow to test whether freezing alone could prevent decay. Unfortunately, the cold seeped through Bacon’s own body, and within weeks pneumonia claimed him. Bacon’s life ended with an experiment—and set in motion a larger one. In 1660, a group of London thinkers hailed Bacon as their inspiration in founding the Royal Society. Their motto, Nullius in verba (“take no one’s word for it”), committed them to evidence over authority, and their ambition was nothing less than to create a Salomon’s House for England. The Royal Society and its successors realized fragments of Bacon’s dream, institutionalizing experimental inquiry. Over the following centuries, though, a distorting story took root: Scientists discover nature’s truths, and the rest is just engineering. Nineteenth-century “men of science” pressed for greater recognition and invented the title of “scientist,” creating a new professional hierarchy. Across the Atlantic, U.S. engineers adopted the rigorous science-based curricula of French and German technical schools and recast engineering as “applied science” to gain institutional legitimacy. We still call engineering “applied science,” a label that retrofits and reverses history. Alongside it stands “technology,” a catchall word that obscures as much as it describes. And we speak of “development” as if ideas cascade neatly from theory to practice. But creation and comprehension have been partners from the start. Yes, theory does equip engineers with tools to push for further insights. But knowing often follows making, arising from things that someone made work. Bacon’s imaginary academy offered only fleeting glimpses of its inventions and methods. Yet he had seen the real thing: engineers like Drebbel and de Caus who tested, erred, iterated, and pushed their contraptions past the edge of known theory. From his observations of those muddy, noisy endeavors, Bacon forged his blueprint for organized inquiry. Later generations of scientists would reduce Bacon’s ideas to the clean, orderly “scientific method.” But in the process, they lost sight of its inventive roots.
A practical guide to designing log-periodic dipole array fed parabolic reflector antennas using advanced 3D MoM simulation — from parametric modeling to electrically large structures. What Attendees will Learn How to set design requirements for LPDA-fed reflector antennas — Understand the key specifications including bandwidth ratio, gain targets, and VSWR matching constraints across the full operating range from 100 MHz to 1 GHz. Why advanced 3D EM solvers enable simulation of electrically large multiscale structures — Learn how higher order basis functions, quadrilateral meshing, geometrical symmetry, and CPU/GPU parallelization extend MoM simulation capability by an order of magnitude. How to apply a systematic three-step design strategy with proven workflow starting with first optimizing the stand-alone LPDA for VSWR and gain, then integrating the reflector, and finally tuning parameters to satisfy all performance requests including gain and impedance matching. How parametric CAD modeling accelerates LPDA design — Discover how self-scaling geometry, automated wire-to-solid conversion, and multiple-copy-with-scaling features enable fully parametrized antenna models that streamline optimization across dozens of design variants. Download this free whitepaper now!
Roughly 90 percent of hard tech startups fail due to funding constraints, longer R&D timelines for developing hardware, and the complexity of manufacturing their products, according to a number of studies. Generally, these startups require up to 50 percent more investor financing than software ones, according to a Medium article. Typically, they need at least US $30 million, according to a Lucid article. That’s double the funding needed by software companies on average. To help them connect with investors, IEEE Entrepreneurship in 2024 launched its Hard Tech Venture Summits. The two-day events connect founders with potential investors and other entrepreneurs. Attendees include manufacturers, design engineers, and intellectual property lawyers. “Even though there are a lot of startup investor conferences, it’s hard to find those focused on hard tech,” says Joanne Wong, who helped initiate the program and is now the chair. She is a general partner at Redds Capital, a California-based venture capital firm that invests in global early-stage IT startups. The IEEE member is also an entrepreneur. She founded SciosHub in 2020. The company’s software-as-a-service and informatics platform automates the data-management process for biomedical research labs. “Many investors are focused on AI software—which is good,” she says. “But for hard tech companies, it is still hard to find support.” The summit also includes a workshop to help founders navigate manufacturing processes and regulatory compliance. The event is open to IEEE members and others. IEEE is a natural fit for the program, Wong says, because hard tech is synonymous with electrical engineering. “Some of the domains we’re covering are robotics, semiconductors, and aerospace technology. IEEE has societies for all these fields,” she says. “Because of that, there are many resources within the organizations for startups, whether it be mentors or guides on how to commercialize products.” There are several venture summits planned for this year. Two are scheduled in collaboration with the IEEE Systems Council: this month in Menlo Park, Calif., and in October in Toronto. On 10 and 11 June, a third summit is scheduled to take place in Boston at the IEEE Microwave Theory and Technology Society’s International Microwave Symposium. More events are being planned for next year in Asia, Europe, Latin America, and North America. Networking and a pitch competition Each summit includes keynote speakers, followed by networking roundtables. Each table is composed of people from three to five startups, one or two investors, and a service provider. That arrangement helps founders build relationships, which is the summit organizers’ priority, Wong says. Investors at past events have included i3 Ventures, Monozukuri Ventures, and TSV Capital. “The connection with the community was fantastic, especially investors and founders in robotics.” —Mark Boysen, founder of Naware Startups present their pitch, which a number of investors evaluate before ranking the business plan and product. The top 10 startups pitch their business to all the investors. On the second day, the startup founders participate in a half-day engineering design–to–manufacturing workshop, at which manufacturing engineers teach them how to navigate the process and meet regulations. In an exhibition area, participants can see demonstrations from the startups and connect with service providers. The 2025 event’s half-day engineering design–to–manufacturing workshop was led by Liz Taylor, president of DOER Marine. The company manufactures marine equipment.Larissa Abi Nakhle/IEEE Positive feedback from attendees In a survey of past summit attendees, startup founders said the event connected them not only with investors but also with other entrepreneurs having similar struggles. “The connection with the community was fantastic, especially investors and founders in robotics,” said Mark Boysen, who founded Naware. The company, based in Edina, Minn., developed a robot that uses AI to detect and remove weeds from golf courses, parks, and lawns. “I loved getting the investors’ perspectives and understanding what they’re looking for,” Boysen said. Jeffrey Cook, who attended a summit in 2024, said he met “a lot of great contacts and saw what the hard tech venture climate is like.” Attendees of the Hard Tech Venture Summit spend the first day networking and presenting their pitch to investors. IEEE Entrepreneurship “Those in the community would benefit from coming to the summit,” said Cook, who founded Gigantor Technologies in Melbourne Beach, Fla. It develops hardware systems for AI-powered devices. More than 90 percent of attendees at the 2025 event in San Francisco said they would highly recommend the summit to others, according to a survey. Investors and service providers also have found the events successful. Ji Ke, a partner and the chief technology officer of deep tech VC firm SOSV, attended the 2025 summit. “I met a lot of young entrepreneurs tackling some big challenges,” he said. “This is one of the best events to meet some very-early-stage companies.” Making important connections in hard tech Startup founders who want to attend a summit must apply. Applications for this year’s events are open. Participants must be founders of preseed, seed, or Series A startups. Preseed founders are seeking small investments to get their businesses off the ground. Those in the seed stage have already secured funding from their first investor. Series A startups have obtained funding and are developing their product. Applicants are reviewed by a committee of investors to ensure the startups would be a good fit. Those who are approved are matched with investors and service providers based on their specialty. “The journey for a hard tech startup is very long and arduous,” Wong says. “Founders need to meet as many investors as possible and other people who support hard tech systems so that they’re able to reach out to them for advice or help.” Those interested in learning more about an upcoming event can send a request to entrepreneurship@ieee.org.
On 8 January 2026, the Iranian government imposed a near-total communications shutdown. It was the country’s first full information blackout: For weeks, the internet was off across all provinces while services including the government-run intranet, VPNs, text messaging, mobile calls, and even landlines were severely throttled. It was an unprecedented lockdown that left more than 90 million people cut off not only from the world, but from one another. Since then, connectivity has never fully returned. Following U.S. and Israeli airstrikes in late February, Iran again imposed near-total restrictions, and people inside the country again saw global information flows dry up. The original January shutdown came amid nationwide protests over the deepening economic crisis and political repression, in which millions of people chanted antigovernment slogans in the streets. While Iranian protests have become frequent in recent years, this was one of the most significant uprisings since the Islamic Revolution in 1979. The government responded quickly and brutally. One report put the death toll at more than 7,000 confirmed deaths and more than 11,000 under investigation. Many sources believe the death toll could exceed 30,000. Thirteen days into the January shutdown, we at NetFreedom Pioneers (NFP) turned to a system we had built for exactly this kind of moment—one that sends files over ordinary satellite TV signals. During the national information vacuum, our technology, called Toosheh, delivered real-time updates into Iran, offering a lifeline to millions starved of trusted information. How Iran Censors the Internet I joined NetFreedom Pioneers, a nonprofit focused on anticensorship technology, in 2014. Censorship in Iran was a defining feature of my youth in the 1990s. After the Islamic Revolution, most Iranians began to lead double lives—one at home, where they could drink, dance, and choose their clothing, and another in public, where everyone had to comply with stifling government laws. Iran’s internet infrastructure is more centralized than in other parts of the world, making it easier for the government to restrict the flow of information. Morteza Nikoubazl/NurPhoto/Getty Images My first experience with secret communications was when I was five and living in the small city of Fasa in southern Iran. My uncle brought home a satellite dish—dangerously illegal at the time—that allowed us to tune into 12 satellite channels. My favorite was Cartoon Network. Then, during my teenage years, this same uncle introduced me to the internet through dial-up modems. I remember using Yahoo Mail with its 4 megabytes of storage, reading news from around the world, and learning about the Chandra X-ray telescope from NASA’s website. That openness didn’t last. As internet use spread in the early 2000s, the Iranian government began reshaping the network itself. Unlike the highly distributed networks in the United States or Europe, where thousands of providers exchange traffic across many independent routes, Iran’s connection to the global internet is relatively centralized. Most international traffic passes through a small number of gateways controlled by state-linked telecom operators. That architecture gives authorities unusual leverage: By restricting or withdrawing those connections, they can sharply reduce the country’s access to the outside world. Over the past decade, Iran has expanded this control through what it calls the National Information Network, a domestically routed system designed to keep data inside the country whenever possible. Many government services, banking systems, and local platforms are hosted on this internal network. During periods of unrest, access to the global internet can be throttled or cut off while portions of this domestic network continue to function. The government began its censorship campaign by redirecting or blocking websites. As internet use grew, it adopted more sophisticated approaches. For example, the Telecommunication Company of Iran uses a technique called deep packet inspection to analyze the content of data packets in real time. This method enables it to identify and block specific types of traffic, such as VPN connections, messaging apps, social media platforms, and banned websites. The Stealth of Satellite Transmissions Toosheh’s communication workaround builds on a history of satellite TV adoption in Middle Eastern and North African countries. By the early 2000s, satellite dishes were common in Iran; today the majority of households in Iran have access to satellite TV despite its official prohibition. Unlike subscription services such as DirecTV and Dish Network, “free-to-air” satellite TV broadcasts are unencrypted and can be received by anyone with a dish and receiver—no subscription required. Because the signals are open, users can also capture and store the data they carry, rather than simply watching it live. Tech-savvy people learned that they could use a digital video broadcasting (DVB) card—a piece of hardware that connects to a computer and tunes into satellite frequencies—to transform a personal computer into a satellite receiver. This way, they could watch and store media locally as well as download data from dedicated channels. Many Iranian citizens have free-to-air satellite dishes, like the ones on this apartment building in Tehran, and can thus download Toosheh transmissions, giving them a lifeline during internet blackouts.Morteza Nikoubazl/NurPhoto/Getty Images Toosheh, a Persian word that translates to “knapsack,” is the brainchild of Mehdi Yahyanejad, an Iranian-American technologist and entrepreneur. Yahyanejad cofounded NetFreedom Pioneers in 2012. He proposed that the satellite-computer connections enabled by a DVB card could be re-created in software, eliminating the need for specialized hardware. He added a simple digital interface to the software to make it easy for anyone to use. The next breakthrough came when the NFP team developed a new transfer protocol that tricks ordinary satellite receivers into downloading data alongside audio and video content. Thus, Toosheh was born. Satellite TV uses a file system called an MPEG transport stream that allows multiple audio, video, or data layers to be packaged into a single stream file. When you tune in to a satellite channel and select an audio option or closed captions, you’re accessing data stored in different parts of this stream. The NFP team’s insight was that, by piggybacking on one of these layers, Toosheh could send an MPEG stream that included documents, videos, and more. HOW TOOSHEH WORKS: At NetFreedom Pioneers, content curators pull together files—news articles, videos, audio, and software [1]. Toosheh’s encoder software [2] compresses the files into a bundle, in .ts format, creating an MPEG transport stream [3]. From there, it’s uploaded to a server for transmission [4] via a free-to-air TV channel on a Yahsat satellite that’s positioned over the Middle East to provide regional coverage [5]. Satellite receivers [6] directly capture the data streams, which are downloaded to computers, smartphones, and other devices, and decoded by Toosheh software [8].Chris Philpot A satellite receiver can’t tell the difference between our data and normal satellite audio and video data since it only “sees” the MPEG streams, not what’s encoded on them. This means the data can be downloaded and read, watched, and saved on local devices such as computers, smartphones, or storage devices. What’s more, the system is entirely private: No one can detect whether someone has received data through Toosheh; there are no traceable logs of user activity. Toosheh doesn’t provide internet access, but rather delivers curated data through satellite technology. The fundamental distinction lies in the way users interact with the system. Unlike traditional internet services, where you type a request into your browser and receive data in response, Toosheh operates more like a combination of radio and television, presenting information in a magazine-like format. Users don’t make requests; instead, they receive 1 to 5 gigabytes of prepackaged, carefully selected data. Access to information is not only about news or politics, but about exposure to possibilities. During this year’s internet blackout, we distributed official statements from Iranian opposition leader Crown Prince Reza Pahlavi and the U.S. government. We provided first-aid tutorials for medics and injured protesters. We sent uncensored news reports from BBC Persian, Iran International, IranWire, VOA Farsi, and others. We also shared critical software packages including anticensorship and antisurveillance tools, along with how-to guides to help people securely connect to Starlink satellite terminals, allowing them to stay protected and anonymous as they sent their own communications. How to Combat Signal Interference Because Toosheh relies on one-way satellite broadcasts, it evades the usual tactics governments use to block internet access. However, it remains vulnerable to satellite signal jamming. The Iranian government is notorious for deploying signal jamming, especially in larger cities. In 2009, the government used uplink interference, which attacks the satellite in orbit by beaming strong noise in the frequency of the satellite’s receiver. This makes it impossible for the satellite to distinguish the information it’s supposed to receive. However, because this type of attack temporarily disables the entire satellite, Iran was threatened with international sanctions and in 2012 stopped using the method . A graph of network connectivity in Iran shows that on 9 January 2026, internet access dropped from nearly 100 percent to 0. Samuel Boivin/NurPhoto/Getty Images The current method, called terrestrial jamming, uses antennas installed at higher elevations than the surrounding buildings to beam strong noise over a specific area in the frequency range of household receivers. This attack is effective in keeping some of the packets from arriving and damaging others, effectively jamming the transmission. But it’s short-range and requires significant power, so it’s impossible to implement nationwide. There are always people somewhere who can still watch TV, download from Toosheh, or tune into a satellite radio despite the jamming. Even so, we wanted a workaround that would keep our transmissions broadly accessible. NFP’s solution was to add redundancy, similar in principle to a data-storage technique called RAID (redundant array of independent disks). Instead of sending each piece of data once, we send extra information that allows missing or corrupted packets to be reconstructed. Under normal circumstances, we often use 5 percent of our bandwidth for this redundancy. During periods of active jamming, we increase that to as much as 25 to 30 percent, improving the chances that users can recover complete files despite interference. From Crisis Response to Public Access Toosheh initially came online in 2015 in Iran and Afghanistan. Its full potential, however, was first realized during the 2019 protests in Iran, which saw the most widespread internet shutdown prior to the blackout this year. Wired called the 2019 shutdown “the most severe disconnection” tracked by NetBlocks in any country in terms of its “technical complexity and breadth.” Our technology helped thousands of people stay informed. We sent crucial local updates, legal-aid guides, digital security tools, and independent news to satellite receivers all over the country, seeing a sixfold increase in our user base. When that wave of protests subsided, the government allowed some communication services to return. People were again able to access the free internet using VPNs and other antifilter software that allowed them to bypass restrictions. Toosheh then became a public access point for news, educational material, and entertainment beyond government filtering. Toosheh’s impact is often personal. A traveling teacher in western Iran told NFP that he regularly distributed Toosheh files to students in remote villages. One package included footage of female athletes competing in the Olympic Games, something never broadcast in Iran. For one young girl, it was the first time she realized women could compete professionally in sports. That moment underscores a broader truth: Access to information is not only about news or politics, but about exposure to possibilities. The Cost of Toosheh Unlike internet-based systems, Toosheh’s operational cost remains constant regardless of the number of users. A single TV satellite in geostationary earth orbit, deployed and maintained by an international company such as Eutelsat, can broadcast to an entire continent with no increase in cost to audiences. What’s more, the startup cost for users isn’t high: A satellite dish and receiver in Iran costs less than US $50, which is affordable to many. And it costs nothing for people to use Toosheh’s service and receive its files. We aim not just to build a tool for censorship circumvention, but to redefine access itself. However, operating the service is costly: NetFreedom Pioneers pays tens of thousands of dollars a month for satellite bandwidth. We had received funding from the U.S. State Department, but in August of 2025, that funding ended, forcing us to suspend services in Iran. Then the December protests happened, and broadcasting to Iran became an urgent priority. To turn Toosheh back on, we needed roughly $50,000 a month. With the support of a handful of private donors, we were able to meet these costs and sustain operations in Iran for a few months, though our future there and elsewhere is uncertain. Satellites Against Censorship Toosheh’s revival in Iran came alongside NFP’s ongoing support for deployments of Starlink, a satellite internet service that allows users to connect directly to satellites rather than relying on domestic networks, which the government can shut down. Unlike Toosheh’s one-way broadcasts, Starlink provides full two-way internet access, enabling users to send messages, upload videos, and communicate with the outside world. In 2022, we started gathering donations to buy Starlink terminals for Iran. We have delivered more than 300 of the roughly 50,000 there, enabling citizens to send encrypted updates and videos to us from inside the country. Because the technology is banned by the government, access remains limited and carries risk; Iranian authorities have recently arrested Starlink users and sellers. And unlike Toosheh’s receive-only broadcasts, Starlink terminals transmit signals back to orbit, creating a radio footprint that can potentially be detected. The internet shutdown in Iran continued after the attacks by Israel and the United States began in late February, preventing Iranians from communicating with the outside world and with one another.Fatemeh Bahrami/Anadolu/Getty Images Looking ahead, we envision Toosheh becoming a foundational part of global digital resilience. It is uncensored, untraceable, and resistant to government shutdowns. Because Toosheh is downlink only, it can sometimes feel hard to explain the value of this technology to those living in the free world, those accustomed to open internet access. Yet, people living under censorship have few other choices when there’s a digital blackout. Currently, NFP is developing new features like intelligent content curation and automatically prioritizing data packages based on geographic or situational needs. And we’re experimenting with local sharing tools that allow users who receive Toosheh broadcasts to redistribute those files via Wi-Fi hotspots or other offline networks, which could extend the system’s reach to disaster zones, conflict areas, and climate-impacted regions where infrastructure may be destroyed. We’re also looking at other use cases. Following the Taliban’s return to power in Afghanistan, NetFreedom Pioneers designed a satellite-based system to deliver educational materials. Our goal is to enable private, large-scale distribution of coursework to anyone—including the girls who are banned from Afghanistan’s schools. The system is technically ready but has yet to secure funding for deployment. We aim not just to build a tool for censorship circumvention, but to redefine access itself. Whether in an Iranian city under surveillance, a Guatemalan village without internet, or a refugee camp in East Africa, Toosheh offers a powerful and practical model for delivering vital information without relying on vulnerable or expensive networks. Toosheh is a reminder that innovation doesn’t have to mean complexity. Sometimes, the most transformative ideas are the simplest, like delivering data through the sky, quietly and affordably, into the hands of those who need it most.
The race to transition online security protocols to ones that can’t be cracked by a quantum computer is already on. The algorithms that are commonly used today to protect data online—RSA and elliptic curve cryptography—are uncrackable by supercomputers, but a large enough quantum computer would make quick work of them. There are algorithms secure enough to be out of reach for both classical and future quantum machines, called post-quantum cryptography, but transitioning to these is a work in progress. Late last month, the team at Google Quantum AI published a whitepaper that added significant urgency to this race. In it, the team showed that the size of a quantum computer that would pose a cryptographic threat is approximately twenty times smaller than previously thought. This is still far from accessible to the quantum computers that exist today: the largest machines currently consist of approximately 1,000 quantum bits, or qubits, and the whitepaper estimated that about 500 times as much is needed. Nonetheless, this shortens the timeline to switch over to post-quantum algorithms. The news had a surprising beneficiary: obscure cryptocurrency Algorand jumped 44% in price in response. The whitepaper called out Algorand specifically for implementing post-quantum cryptography on their blockchain. We caught up with Algorand’s chief scientific officer and professor of computer science and engineering at the University of Michigan, Chris Peikert, to understand how this announcement is impacting cryptography, why cryptocurrencies are feeling the effects, and what the future might hold. Peikert’s early work on a particular type of algorithm known as lattice cryptography underlies most post-quantum security today. IEEE Spectrum: What is the significance of this Google Quantum AI whitepaper? Peikert: The upshot of this paper is that it shows that a quantum computer would be able to break some of the cryptography that is most widely used, especially in blockchains and cryptocurrencies, with much, much fewer resources than had previously been established. Those resources include the time that it would take to do so and the number of qubits (or quantum bits) that it would have to use. This cryptography is very central to not just cryptocurrencies but more broadly, to cryptography on the internet. It is also used for secure web connections between web browsers and web servers. Versions of elliptic curve cryptography are used in national security systems and military encryption. It’s very prevalent and pervasive in all modern networks and protocols. And not only was this paper improving the algorithms, but there was also a concurrent paper showing that the hardware itself was substantially improved. The claim here was that the number of physical qubits needed to achieve a certain kind of logical qubit was also greatly reduced. These two kinds of improvements are compounding upon each other. It’s a kind of a win-win situation from the quantum computing perspective, but a lose-lose situation for cryptography. IEEE Spectrum: What do Google AI’s findings mean for cryptocurrencies and the broader cybersecurity ecosystem? Peikert: There’s always been this looming threat in the distance of quantum computers breaking a large fraction of the cryptography that’s used throughout the cryptocurrency ecosystem. And I think what this paper did was really the loudest alarm yet that these kinds of quantum attacks might not be as far off as some have suspected, or hoped, in recent years. It’s caused a re-evaluation across the industry, and a moving up of the timeline for when quantum computers might be capable of breaking this cryptography. When we think about the timelines and when it’s important to have completed these transitions [to post-quantum cryptography], we also need to factor in the unknown improvements that we should expect to see in the coming years. The science of quantum computing will not stay static, and there will be these further breakthroughs. We can’t say exactly what they will be or when they will come, but you can bet that they will be coming. IEEE Spectrum: What is your guess on if or when quantum computers will be able to break cryptography in the real world? Peikert: Instead of thinking about a specific date when we expect them to come, we have to think about the probabilities and the risks as time goes on. There have been huge breakthrough developments, including not only this paper, but also some last year. But even with these, I think that the chance of a cryptographic attack by quantum computers being successful in the next three years is extremely low, maybe less than a percent. But then, as you get out to several years, like 5, 6, or 10 years, one has to seriously consider a probability, maybe 5% or 10% or more. So it’s still rather small, but significant enough that we have to worry about the risk, because the value that is protected by this kind of cryptography is really enormous. The US government has put 2035 as its target for migrating all of the national security systems to post quantum cryptography. That seems like a prudent date, given the timelines that it takes to upgrade cryptography. It’s a slow process. It has to be done very deliberately and carefully to make sure that you’re not introducing new vulnerabilities, that you’re not making mistakes, that everything still works properly. So, you know, given the outlook for quantum computers on the horizon, it’s really important that we prepare now, or ideally, yesterday, or a few years ago, for that kind of transition. IEEE Spectrum: Are there significant roadblocks you see to industrial adoption of post-quantum cryptography going forward? Peikert: Cryptography is very hard to change. We’ve only had one or maybe two major transitions in cryptography since the early 1980s or late 1970s when the field first was invented. We don’t really have a systematic way of transitioning cryptography. An additional challenge is that the performance tradeoffs are very different in post-quantum cryptography than they are in the legacy systems. Keys and cipher texts and digital signatures are all significantly larger in post-quantum cryptography, but the computations are actually faster, typically. People have optimized cryptography for speed in the past, and we have very good fast speeds now for post-quantum cryptography, but the sizes of the keys are a challenge. Especially in blockchain applications, like cryptocurrencies, space on the blockchain is at a premium. So it calls for a reevaluation in many applications of how we integrate the cryptography into the system, and that work is ongoing. And, the blockchain ecosystem uses a lot of advanced cryptography, exotic things like zero-knowledge proofs. In many cases, we have rudimentary constructions of these fancy cryptography tools from post-quantum type mathematics, but they’re not nearly as mature and industry ready as the legacy systems that have been deployed. It continues to be an important technical challenge to develop post-quantum versions of these very fancy cryptographic schemes that are used in cutting edge applications. IEEE Spectrum: As an academic cryptography researcher, what attracted you to work with a cryptocurrency, and Algorand in particular? Peikert: My former PhD advisor is Silvio Micali, the inventor of Algorand. The system is very elegant. It is a very high performing blockchain system and it uses very little energy, has fast transaction finalization, and a number of other great features. And Silvio appreciated that this quantum threat was real and was coming, and the team approached me about helping to improve the Algorand protocol at the basic levels to become more post-quantum secure in 2021. That was a very exciting opportunity, because it was a difficult engineering and scientific challenge to integrate post-quantum cryptography into all the different technical and cryptographic mechanisms that were underlying the protocol. IEEE Spectrum: What is the current status of post-quantum cryptography in Algorand, and blockchains in general? Peikert: We’ve identified some of the most pressing issues and worked our way through some of them, but it’s a many-faceted problem overall. We started with the integrity of the chain itself, which is the transaction history that everybody has to agree upon. Our first major project was developing a system that would add post-quantum security to the history of the chain. We developed a system called state proofs for that, which is a mixture of ordinary post-quantum cryptography and also some more fancy cryptography: It’s a way of taking a large number of signatures and digesting them down into a much smaller number of signatures, while still being confident that these large number of signatures actually exist and are properly formed. We also followed it with other papers and projects that are about adding post-quantum cryptography and security to other aspects of the blockchain in the Algorand ecosystem. It’s not a complete project yet. We don’t claim to be fully post-quantum secure. That’s a very challenging target to hit, and there are aspects that we will continue to work on into the near future. IEEE Spectrum: In your view, will we adopt post-quantum cryptography before the risks actually catch up with us? Peikert: I tend to be an optimist about these things. I think that it’s a very good thing that more people in decision making roles are recognizing that this is an important topic, and that these kinds of migrations have to be done. I think that we can’t be complacent about it, and we can’t kick the can down the road much longer. But I do see that the focus is being put on this important problem, so I’m optimistic that most important systems will eventually have good either mitigations or full migrations in place. But it’s also a point on the horizon that we don’t know exactly when it will come. So, there is the possibility that there is a huge breakthrough, and we have many fewer years than we might have hoped for, and that we don’t get all the systems upgraded that we would like to have fixed by the time quantum computers arrive.
Like many engineers, Sarang Gupta spent his childhood tinkering with everyday items around the house. From a young age he gravitated to projects that could make a difference in someone’s everyday life. When the family’s microwave plug broke, Gupta and his father figured out how to fix it. When a drawer handle started jiggling annoyingly, the youngster made sure it didn’t do so for long. Sarang Gupta Employer OpenAI in San Francisco Job Data science staff member Member grade Senior member Alma maters The Hong Kong University of Science and Technology; Columbia By age 11, his interest expanded from nuts and bolts to software. He learned programming languages such as Basic and Logo and designed simple programs including one that helped a local restaurant automate online ordering and billing. Gupta, an IEEE senior member, brings his mix of curiosity, hands-on problem-solving, and a desire to make things work better to his role as member of the data science staff at OpenAI in San Francisco. He works with the go-to-market (GTM) team to help businesses adopt ChatGPT and other products. He builds data-driven models and systems that support the sales and marketing divisions. Gupta says he tries to ensure his work has an impact. When making decisions about his career, he says, he thinks about what AI solutions he can unlock to improve people’s lives. “If I were to sum up my overall goal in one sentence,” he says, “it’s that I want AI’s benefits to reach as many people as possible.” Pursuing engineering through a business lens Gupta’s early interest in tinkering and programming led him to choose physics, chemistry, and math as his higher-level subjects at Chinmaya International Residential School, in Tamil Nadu, India. As part of the high school’s International Baccalaureate chapter, students select three subjects in which to specialize. “I was interested in engineering, including the theoretical part of it,” Gupta says, “But I was always more interested in the applications: how to sell that technology or how it ties to the real world.” After graduating in 2012, he moved overseas to attend the Hong Kong University of Science and Technology. The university offered a dual bachelor’s program that allowed him to earn one degree in industrial engineering and another in business management in just four years. In his spare time, Gupta built a smartphone app that let students upload their class schedules and find classmates to eat lunch with. The app didn’t take off, he says, but he enjoyed developing it. He also launched Pulp Ads, a business that printed advertisements for student groups on tissues and paper napkins, which were distributed in the school’s cafeterias. He made some money, he says, but shuttered the business after about a year. After graduating from the university in 2016, he decided to work in Hong Kong’s financial hub and joined Goldman Sachs as an analyst in the bank’s operations division. From finance to process optimization at scale After two parties agree on securities transactions, the bank’s operations division ensures that the trade details are recorded correctly, the securities and payments are ready to transfer, and the transaction settles accurately and on time. As an analyst, Gupta’s task was to find bottlenecks in the bank’s workflows and fix them. He identified an opportunity to automate trade reconciliation: when analysts would manually compare data across spreadsheets and systems to make sure a transaction’s details were consistent. The process helped ensure financial transactions were recorded accurately and settled correctly. Gupta built internal automation tools that pulled trade data from different systems, ran validation checks, and generated reports highlighting any discrepancies. “Instead of analysts manually checking large datasets, the tools automatically flagged only the cases that required investigation,” he says. “This helped the team spend less time on repetitive verification tasks and more time resolving complex issues. It was also my first real exposure to how software and data systems could dramatically improve operational workflows.” “Whether it’s helping a person improve a trait like that or driving efficiencies at a business, AI just has so much potential to help. I’m excited to be a little part of that.” The experience made him realize he wanted to work more deeply in technology and data-driven systems, he says. He decided to return to school in 2018 to study data science and AI, when the fields were just beginning to surge into broader awareness. He discovered that Columbia offered a dedicated master’s degree program in data science with a focus on AI. After being accepted in 2019, he moved to New York City. Throughout the program, he gravitated to the applied side of machine learning, taking courses in applied deep learning and neural networks. One of his major academic highlights, he says, was a project he did in 2019 with the Brown Institute, a joint research lab between Columbia and Stanford focused on using technology to improve journalism. The team worked with The Philadelphia Inquirer to help the newsroom staff better understand their coverage from a geographic and social standpoint. The project highlighted “news deserts”—underserved communities for which the newspaper was not providing much coverage—so the publication could redirect its reporting resources. To identify those areas, Gupta and his team built tools that extracted locations such as street names and neighborhoods from news articles and mapped them to visualize where most of the coverage was concentrated. The Inquirer implemented the tool in several ways including a new web page that aggregated stories about COVID-19 by county. “Journalism was an interesting problem set for me, because I really like to read the news every day,” Gupta says. “It was an opportunity to work with a real newsroom on a problem that felt really impactful for both the business and the local community.” The GenAI inflection point After earning his master’s degree in 2020, Gupta moved to San Francisco to join Asana, the company that developed the work management platform by the same name. He was drawn to the opportunity to work for a relatively small company where he could have end-to-end ownership of projects. He joined the organization as a product data scientist, focusing on A/B testing for new platform features. Two years later, a new opportunity emerged: He was asked to lead the launch of Asana Intelligence, an internal machine learning team building AI-powered features into the company’s products. “I felt I didn’t have enough experience to be the founding data scientist,” he says. “But I was also really interested in the space, and spinning up a whole machine learning program was an opportunity I couldn’t turn down.” The Asana Intelligence team was given six months to build several machine learning–powered features to help customers work more efficiently. They included automatic summaries of project updates, insights about potential risks or delays, and recommendations for next steps. The team met that goal and launched several other features including Smart Status, an AI tool that analyzes a project’s tasks, deadlines, and activity, then generates a status update. “When you finally launch the thing you’ve been working on, and you see the usage go up, it’s exhilarating,” he says. “You feel like that’s what you were building toward: users actually seeing and benefiting from what you made.” Gupta and his team also translated that first wave of work into reusable frameworks and documentation to make it easier to create machine learning features at Asana. He and his colleagues filed several U.S. patents. At the time he took on that role, OpenAI launched ChatGPT. The mainstreaming of generative AI and large language models shifted much of his work at Asana from model development to assessing LLMs. OpenAI captured the attention of people around the world, including Gupta. In September 2025 he left Asana to join OpenAI’s data science team. The transition has been both energizing and humbling, he says. At OpenAI, he works closely with the marketing team to help guide strategic decisions. His work focuses on developing models to understand the efficiency of different marketing channels, to measure what’s driving impact, and to help the company better reach and serve its customers. “The pace is very different from my previous work. Things move quickly,” he says. “The industry is extremely competitive, and there’s a strong expectation to deliver fast. It’s been a great learning experience.” Gupta says he plans to stay in the AI space. With technology evolving so rapidly, he says, he sees enormous potential for task automation across industries. AI has already transformed his core software engineering work, he says, and it’s helped him enhance areas that aren’t natural strengths. “I’m not a good writer, and AI has been huge in helping me frame my words better and present my work more clearly,” he says. “Whether it’s helping a person improve a trait like that or driving efficiencies at a business, AI just has so much potential to help. I’m excited to be a little part of that.” Exploring IEEE publications and connections Gupta has been an IEEE member since 2024, and he values the organization as both a technical resource and a professional network. He regularly turns to IEEE publications and the IEEE Xplore Digital Library to read articles that keep him abreast of the evolution of AI, data science, and the engineering profession. IEEE’s member directory tools are another valuable resource that he uses often, he says. “It’s been a great way to connect with other engineers in the same or similar fields,” he says. “I love sharing and hearing about what folks are working on. It brings me outside of what I’m doing day to day. “It inspires me, and it’s something I really enjoy and cherish.”
Scott Imbrie vividly remembers the first time he used a robotic arm to shake someone’s hand and felt the robotic limb as if it were his own. “I still get goosebumps when I think about that initial contact,” he says. “It’s just unexplainable.” The moment came courtesy of a brain implant: an array of electrodes that let him control a robotic arm and receive tactile sensations back to the brain. Getting there took decades. In 1985, Imbrie had woken up in the hospital after a car accident with a broken neck and a doctor telling him he’d never use his hands or legs again. His response was an expletive, he says—and a decision. “I’m not going to allow someone to tell me what I can and can’t do.” With the determination of a head-strong 22-year-old, Imbrie gradually regained the ability to walk and some limited arm movement. Aware of how unusual his recovery was, the Illinois-native wanted to help others in similar situations and began looking for research projects related to spinal cord injuries. For decades, though, he wasn’t the right fit, until in 2020 he was finally accepted into a University of Chicago trial. Scott Imbrie has shaken hands with a robotic arm controlled by a brain implant. The electrodes record neural signals that enable him to move the device and receive tactile feedback. Top: 60 Minutes/CBS News; Bottom: University of Chicago Imbrie is part of a rarefied group: More people have gone to space than have received advanced brain-computer interfaces (BCI) like his. But a growing number of companies are now attempting to move the devices out of neuroscience labs and into mainstream medical care, where they could help millions of people with paralysis and other neurological conditions. Some companies even hope that BCIs will eventually become a consumer technology. None of that will be possible without people like Imbrie. He’s a member of the BCI Pioneers Coalition, an advocacy group founded in 2018 by Ian Burkhart, the first quadriplegic to regain hand movement using a brain implant. That life-changing experience convinced Burkhart that BCIs will make the leap from lab to real world only if users help shape the technology by sharing their perspectives on what works, what doesn’t, and how the devices fit into daily life. The coalition aims to ensure that companies, clinicians, and regulators hear directly from trial participants. Ian Burkhart founded the BCI Pioneers Coalition to ensure that companies developing brain implants hear directly from the people using them. Left: Andrew Spear/Redux; Right: Ian Burkhart The group also serves as a peer-support network for trial participants. That’s crucial, because despite the steady drumbeat of miraculous results from BCI trials, receiving a brain implant comes with significant risks. Surgical complications, such as bleeding or infection in the brain, are possible. Even more concerning is the potential psychological toll if the implant fails to work as expected or if life-changing improvements are eventually withdrawn. Researchers spell this out upfront, and many are put off, says John Downey, an assistant professor of neurological surgery at the University of Chicago and the lead on Imbrie’s clinical trial. “I would say, the number of people I talk to about doing it is probably 10 to 20 times the number of people that actually end up doing it,” he says. What Happens in a BCI Trial? BCI pioneers arrive at their unique status via a number of paths, including spinal cord injuries, stroke-induced paralysis, and amyotrophic lateral sclerosis (ALS). The implants they receive come from Blackrock Neurotech, Neuralink, Synchron, and other companies, and are being tested for restoring limb function, controlling computers and robotic arms, and even restoring speech. Many of the implants record signals from the motor cortex—the part of the brain that controls voluntary movements—to move external devices. Some others target the somatosensory cortex, which processes sensory signals from the body, including touch, pain, temperature, and limb position, to re-create tactile sensation. BCI Designs Used by Today’s Pioneers Ease of use depends heavily on the application. Restoring function to a user’s own limbs or controlling robotic arms involves the most difficult learning curve. In early sessions, participants watch a virtual arm reach for objects while they imagine or attempt the same movement. Researchers record related brain signals and use them to train “decoder” software, which translates neural activity into control signals for a robotic arm or stimulation patterns for the user’s nerves or muscles. Paralyzed in a 2010 swimming accident, Burkhart took part in a trial conducted by Battelle Memorial Institute and Ohio State University from 2014 to 2021. His implant recorded signals from his motor cortex as he attempted to move his hand, and the system relayed those commands to electrodes in his arm that stimulated the muscles controlling his fingers. Ian Burkhart, who is paralyzed from the chest down, received a brain implant that routed neural signals through a computer to his paralyzed muscles, enabling him to play a video game. Battelle Getting the system to work seamlessly took time, says Burkhart, and initially required intense concentration. Eventually, he could shift his focus from each individual finger movement to the overall task, allowing him to swipe a credit card, pour from a bottle, and even play Guitar Hero. Training a decoder is also not a one-and-done process. Systems must be regularly recalibrated to account for “neural drift”—the gradual shift in a person’s neural activity patterns over time. For complex tasks like robotic arm control, researchers may have to essentially train an entirely new decoder before each session, which can take up to an hour. Austin Beggin says that testing a BCI is hard work, but he adds that moments like petting his dog make it all worth it. Daniel Lozada/The New York Times/Redux Even after the system is ready, using the device can be taxing, says Austin Beggin, who was paralyzed in a swimming accident in 2015 and now participates in a Case Western Reserve University trial aimed at restoring hand movement. “The mental work of just trying to do something like shaking hands or feeding yourself is 100-fold versus you guys that don’t even think about it,” he says. It’s also a serious time commitment. Beggin travels more than 2 hours from his home in Lima, Ohio, to Cleveland for two weeks every month to take part in experiments. All the equipment is set up in the house he stays in, and he typically works with the researchers for 3 to 4 hours a day. The majority of the experiments are not actually task-focused, he says, and instead are aimed at adjusting the control software or better understanding his neural responses to different stimuli. But the BCI users say the hard work is worth it. Beyond the hope of restoring lost function, many feel a strong moral obligation to advance a technology that could help others. Beggin compares the pioneers to the early astronauts who laid the groundwork for the lunar landings. “We’re some of the first astronauts just to get shot up for a couple of hours and come back down to earth,” he says. The Emotional Impact of BCIs Speak to BCI early adopters and a pattern emerges: The biggest benefits are often more emotional than practical. Using a robotic arm to feed oneself or control a computer is clearly useful, but many pioneers say the most meaningful moments are the ones the experiment wasn’t even trying to produce. Beggin counts shaking his parents’ hands for the first time since his injury and stroking his pet dachshund as among his favorite moments. “That stuff is absolutely incredible,” he says. Neuralink participant Alex Conley, who broke his neck in a car accident in 2021, uses his implant to control both a robotic arm and computers, enabling him to open doors, feed himself, and handle a smartphone. But he says the biggest boost has come from using computer-aided design software. A former mechanic, Conley began using the software within days of receiving his implant to design parts that could be fabricated on a 3D printer. He has designed everything from replacement parts for his uncle’s power tools to bumpers for his brother-in-law’s truck. “I was a very big problem solver before my accident, I was able to fix people’s things,” he says. “This gives me that same little burst of joy.” BCI user Nathan Copeland used a robotic arm to get a fist bump from then-President Barack Obama in 2016. Jim Watson/AFP/Getty Images The outside world often underestimates those little wins, says Nathan Copeland, who holds the record for the longest functional brain implant. After breaking his neck in a car accident in 2004, he joined a University of Pittsburgh BCI trial in 2015 and has since used the device to control both computers and a robotic arm. After he uploaded a video to Reddit of himself playing Final Fantasy XIV, one commenter criticized him for not using his device for more practical tasks. Copeland says people don’t understand that those lighthearted activities also matter. “A lot of tasks that people think are mundane or frivolous are probably the tasks that have the most impact on someone that can’t do them,” he says. “Agency and freedom of expression, I think, are the things that impact a person’s life the most.” Nathan Copeland plays Final Fantasy XIV using his brain implant to control the game character. When Brain Implants Become Life-Changing This perspective resonates with Neuralink’s first user, Noland Arbaugh—paralyzed from the neck down after a swimming accident in 2016. After receiving his implant in January 2024, he was able to control a cursor within minutes of the device being switched on. A few days later, the engineers let him play the video game Civilisation VI, and the technology’s potential suddenly felt real. “I played it for 8 hours or 12 hours straight,” he says. “It made me feel so independent and so free.” Before receiving his Neuralink implant, Noland Arbaugh used mouth-operated devices to control a computer. He says the BCI is more reliable and enables him to do many more things on his own. Rebecca Noble/The New York Times/Redux But the technology is also providing more practical benefits. Before his implant, Arbaugh relied on a mouth-held typing stick and a mouth-controlled joystick called a quadstick, which uses sip-or-puff sensors to issue commands. But the fiddliness of this equipment required constant caregiver support. The Neuralink implant has dramatically increased the number of things he can do independently. He says he finds great value in not needing his family “to come in and help me 100 times a day.” For Casey Harrell, the technology has been even more transformative. Diagnosed with ALS in 2020, the climate activist had just welcomed a baby daughter and was in the midst of a major campaign, pressuring a financial firm to divest from companies that had poor environmental records. Casey Harrell was able to communicate again within 30 minutes of his BCI being switched on. The device translates his neural signals quickly enough for him to hold conversations. Ian Bates/The New York Times/Redux “Every morning we’d wake up and there’d be a new thing he couldn’t do, a new part of his body that didn’t work,” says his wife, Levana Saxon. Most alarming was his rapid loss of speech, which, among other things, left him unable to indicate when he was in pain. Then a relative alerted him to a clinical trial at the University of California, Davis, using BCIs to restore speech. He immediately signed up. The device, implanted in July 2023, records from the brain region that controls muscles involved in talking and translates these signals into instructions for a voice synthesizer. Within 30 minutes of it being switched on, Harrell could communicate again. “I was absolutely overwhelmed with the thought of how this would impact my life and allow me to talk to my family and friends and better interact with my daughter,” he says. “It just was so overwhelming that I began to cry.” While earlier assistive technology limited him to short, direct commands, Harrell says the BCI is fast enough that he can hold a proper conversation, and he’s been able to resume work part-time. What’s Holding BCI Technology Back? BCI technology still has limits. Most trial participants using Blackrock Neurotech implants can operate their devices only in the lab because the systems rely on wired connections and racks of computer hardware. Some users, including Copeland and Harrell, have had the equipment installed at home, but they still can’t leave the house with it. “That would be a big unlock if I was able to do so,” says Harrell. The academic nature of many trials creates additional constraints. Pressure to publish and secure funding pushes researchers to demonstrate peak performance on narrow tasks rather than build more versatile and reliable systems, says Mariska Vansteensel, who runs BCI studies at the University Medical Center Utrecht in the Netherlands. She says that investigating the technology’s limits or repeating an experiment in new patients is “less rewarded in terms of funding.” In a clinical trial, Scott Imbrie uses a BCI to control a robotic arm, using signals from his motor cortex to make it move a block. University of Chicago One of Imbrie’s biggest frustrations is the rapid turnover in experiments. Just as he begins to get proficient at one task, he’s asked to switch to the next task. Study designs also mean that much of the users’ time is spent on mundane tasks required to fine-tune the system. Perhaps the biggest issue is that trials are often time-limited. That’s partly because scar tissue from the body’s immune response to the implant can gradually degrade signal quality. But constraints on funding and researcher availability can also make it impossible for users to keep using their BCIs after their trials end, even when the technology is still functional. Ian Burkhart’s BCI enables him to grasp objects, pour from a bottle, and swipe a credit card. Burkhart has firsthand experience. His trial was extended, but the implant was eventually removed after he got an infection. He always knew the trial would end, but it was nonetheless challenging. “It was a little bit of a tease where I got to see the capability of the restoration of function,” he says. “Now I’m just back to where I was.” The Push to Commercialize BCIs Progress is being made in transitioning the technology from experimental research devices to fully-fledged medical products that could help users in their everyday lives. Most academic BCI research has relied on Blackrock Neurotech’s Utah Arrays, which typically feature 96 needlelike electrodes that penetrate the brain’s surface. The implant is connected to a skull-mounted pedestal that’s wired to external hardware. But some of the newer devices are sleeker and less invasive. Neuralink’s implant houses its electronics and rechargeable battery in a coin-size unit connected to flexible electrode threads inserted into the brain by a robotic “sewing machine.” The implant, which is roughly the size of a quarter or a euro, is mounted in a hole cut into the skull and charges and transfers data wirelessly. Synchron takes a different approach, threading a stent-like implant through blood vessels into the motor cortex. This “stentrode” connects by wire to a unit in the chest that powers the implant and transmits data wirelessly. Rodney Gorham can use his Synchron implant to control not just a computer, but also smart devices in his home like an air conditioner, fan, and smart speaker. Rodney Decker Neuralink’s decoder runs on a laptop, while Synchron deploys a smartphone-size signal processing unit as a wireless bridge to the user’s devices, which allows them to use their implants at home and on the move. The companies have also developed adaptive decoders that use machine learning to adjust to neural drift on the fly, reducing the need for recalibration. Making these devices truly user-friendly will require technology that can interpret user context, says Kurt Haggstrom, Synchron’s chief commercial officer—including mood, attention levels, and environmental factors like background noise and location. This approach will require AI that analyzes neural signals alongside other data streams such as audio and visual input. Last year, Synchron took a first step by pairing its implant with an Apple Vision Pro headset. When trial participant Rodney Gorham looked at devices such as a fan, a smart speaker, and an air conditioner, the headset overlaid a menu that enabled him to adjust the device’s settings using his implant. Rodney Gorham uses his Synchron implant to turn on music, feed his dog, and more. Synchron BCI Another way to reduce cognitive load is to detect high-order signals of intent in neural data rather than low-level motor commands, says Florian Solzbacher, cofounder and chief scientific officer of Blackrock Neurotech. For instance, rather than manually navigating to an email app and typing, the user could simply think about sending an email and the system would then open it with content already prepopulated, he says. Durability may prove a thornier problem to solve, UChicago’s Downey says. Current implants last around a decade—well short of a lifelong solution. And with limited real estate in the brain, replacement is only possible once or twice, he says. Rapid technological progress also raises difficult decisions about whether to get a BCI implant now or wait for a more advanced device. This was a major concern for Gorham’s wife, Caroline. “I was hesitant. I didn’t want him to go on the trial but maybe a future one,” she says. “It was my fear of missing out on future upgrades.” Will Brain Implants Ever Become Consumer Tech? Some executives have raised the prospect of BCIs eventually becoming consumer devices. Neuralink founder Elon Musk has been particularly vocal, suggesting that the company’s implants could replace smartphones, let people save and replay memories, or even achieve “symbiosis” with AI. This kind of talk inspires mixed feelings in users. The hype brings visibility and funding, says Beggin, but could divert attention from medical users’ needs. Copeland worries that consumer branding could strip the devices of insurance coverage and that rising demand may make it harder to access qualified surgeons. Noland Arbaugh, the first recipient of Neuralink’s BCI, says that using the implant to control a computer made him feel independent and free. Steve Craft/Guardian/eyevine/Redux There are also concerns about how data collected by BCI companies will be handled if the devices go mainstream. As a trial participant, Arbaugh says he’s comfortable signing away his data rights to advance the technology, but he thinks stronger legal protections will be needed in the future. “Does that data still belong to Neuralink? Does it belong to each person? And can that data be sold?” he asks. Blackrock’s Solzbacher says the company remains focused on the medical applications of the technology. But he also believes it is building a “universal interface to any kind of a computerized system” that may have broader applications in the future. And he says the company owes it to users not to limit them to a bare-bones assistive technology. “Why would somebody who’s got a medical condition want to get less than something that somebody who’s able-bodied would possibly also take?” says Solzbacher. The ever-optimistic Imbrie heartily agrees. Medical devices are invariably expensive, he says, but targeting consumer applications could push companies to keep devices simple and affordable while continuing to add features. “I truly believe that making it a consumer-available product will just enhance the product’s capabilities for the medical field,” he says. Imbrie is on a mission to refocus the conversation around BCIs on the positives. While concerns about risks are valid, he worries that the alarming language often used to describe brain implants discourages people from volunteering for trials that could help them. “I remember laying there in the bed and not being able to move,” he says, “and it was really dehumanizing having to ask someone to do everything for you. As humans, we want to be independent.”
Photonic devices, which rely on light instead of electricity, have the potential to be faster and more energy efficient than today’s electronics. They also present a unique opportunity to develop devices using soft materials, such as polymers and gels, which are poor conductors of electricity, but are easier to manufacture and more environmentally friendly. The development of these potentially squishy, flexible photonics, however, requires the ability to manipulate light using only light, not electricity. In soft matter, that’s been done primarily by changing the physical properties of optical materials or by using intense light pulses to change the direction of light. Now, an international team of scientists has developed a new way of controlling light with light using very low light intensities and without changing any of the physical properties of materials. Igor Muševič, a professor of physics at the University of Ljubljana who led the project, says that he first got the idea for the device while at a conference in San Francisco, listening to a talk by Stefan W. Hell about stimulated emission depletion (STED) microscopy. The imaging technique, for which Hell won a Nobel Prize in Chemistry in 2014, uses two lasers to produce an extremely small light beam to scan objects. “When I saw this, I said, this is manipulation light by light, right?” Muševič recalls. His realization inspired a device into which a laser pulse is fired. Whether or not this beam makes it out of the device depends on whether or not a second pulse is fired less than a nanosecond afterwards. A liquid crystal photonic switch The device consists of a spherically-shaped bead of liquid crystal, held in shape by its elastic material properties and the forces between its molecules, infused with a fluorescent dye and trapped between four upright cone-shaped polymer structures that guide light in and out of the device. When a laser pulse is sent through one of the four polymer waveguides, the light is quickly transferred into the liquid crystal, exciting the fluorescent dye. In a process known as whispering gallery mode resonance, the photons inside the liquid crystal are reflected back inside each time they hit the liquid’s spherical surface. The result is that light circulates inside the cavity until it is eventually reflected into one of the waveguides, which then emits the photons out in a laser beam. The team realized that sending a second laser pulse of a different color into the waveguides before the liquid crystal started emitting light from the first laser pulse resulted in stimulated emission of the excited dye molecules. The photons from the second laser pulse, which had to be fired into the waveguides after the first laser pulse, interact with the already-excited dye molecules. The interaction causes the dye to emit photons identical to those in the second pulse while depleting the energy from the first pulse. The second laser beam, called the STED beam, is amplified by the process, while the light from the first pulse is so diminished that it isn’t emitted at all. Because the outcome of the first laser pulse could be controlled using the second laser pulse, the team had successfully demonstrated the control of light by light. Vandna Sharma, Jaka Zaplotnik, et al. According to the Ljubljana team, the energy efficiency of the liquid crystal approach is much better than previous soft-matter techniques, which had typically involved using intense light fields to change material properties of the soft matter, such as the index of refraction. The new method reduces the energy needed by more than a factor of a hundred. Because the STED laser pulse circulates repeatedly in the crystal, a single photon can deplete many dye molecules of the energy from the first laser pulse. Miha Ravnik, a theoretical physicist also at the University of Ljubljana who worked on the project, explains that control of light by light is essential in soft-matter photonic logic gates. “You can very much control when [light] is generated and in which direction,” Ravnik says of the light shined into the polymer waveguides. “And this gives you, then, this capability that you create logical operations with light.” Aside from its potential in photonic logical circuits, the team’s approach presents several technical advantages over photonics made from silicon or other hard materials, Muševič says. For example, using soft matter greatly simplifies the manufacturing process. The liquid crystal in the team’s device can be inserted in less than a second, but manufacturing a similar structure with hard materials is difficult. Additionally, soft matter devices can be manufactured at much lower temperatures than silicon and other hard materials. Muševič also points out that soft matter presents an opportunity to experiment with the geometry of the device. With liquid crystals “you can make many different kinds of cavities,” says Muševič. “You have, I would say, a lot of engineering space.” Ravnik is excited for the potential of the team’s breakthrough, particularly as a step towards photonic computing and even photonic neural networks. But, he recognizes that these developments are far down the line. “There’s no way this technology can compete with current neural network implementation at all,” he admits. Still, the possibilities are tantalizing. “The energy losses are predicted to be extremely low, the speeds for calculation extremely high.”
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! The Worst Engineer in the Room My salary doubled. My confidence tanked. That’s what happened when I had just joined a five-person startup in San Francisco in my third year as a software engineer. Two of the founders had been recognized in Forbes 30 Under 30. The team was exceptional by any measure. On my first day, someone made a joke about Dijkstra’s algorithm. Everyone laughed. I smiled along, then looked it up afterward so I could understand why it was funny. Dijkstra’s algorithm finds the shortest path between 2 points—the math underlying GPS navigation. It’s a foundational concept in virtually every formal computer science curriculum. I had never encountered it. That moment reflected a broader pattern. Conversations about system design and tradeoffs often felt just out of reach. I could follow parts of them, but not enough to contribute meaningfully. I was mostly self-taught. Wide coverage, shallow roots. The engineers around me had roots. You could feel it in how they reasoned through problems, how they talked about tradeoffs, how they debugged with patience instead of pure panic. The Advice That Sounds Good Until You’re Living It You’ve heard the phrase: “If you’re the smartest person in the room, you’re in the wrong room.” It sounds aspirational. What nobody tells you is what it actually feels like to be in that room. It feels like barely following system design conversations. Like nodding along to discussions you can only partially decode. Like shipping solutions through trial and error and hoping nobody looks too closely. Being the weakest engineer in the room is genuinely uncomfortable. It surfaces every gap. And if you’re not careful, it pushes you in exactly the wrong direction. My instinct was to make myself smaller. On a team of five, every voice mattered. I stopped offering mine. I rushed toward working solutions without real understanding, hoping velocity would compensate for depth. I was working harder and, at the same time, I was not improving. The turning point came when one of the most senior engineers left. Before departing, he told me it was difficult to work with me because I lacked foundational programming knowledge, listing out the concepts he saw me struggle with. For the first time, what had felt like vague inadequacy became something specific. What the Cliché Misses Proximity to stronger engineers is not sufficient on its own. You won’t absorb their skill through osmosis. The engineers who thrive when they’re outmatched are not the ones who wait for confidence to arrive. They treat the discomfort as diagnostic information. What can they answer that I can’t? What do they see in a system that I’m missing? I defined a clear picture of the engineer I wanted to become and compared it to where I was. I wrote down what I did not know. I identified how I would close each gap with books, tutorials and small projects. I asked for recommendations from the same engineer who gave me the hard feedback. I figured out the gaps. Then the bridges. Then I worked through each of them. Over time, conversations became clearer. Debugging became more systematic. I started contributing meaningfully rather than just executing tasks. The Other Room Nobody Warns You About There’s a less-obvious version of this same problem: when you’re the strongest engineer in the room. It can feel rewarding. Less friction, more validation. But there’s also less growth. When you’re at the ceiling, there’s no external pressure to raise your own floor. The feedback loops that sharpen judgment go quiet. Some engineers spend years there without noticing. They’re good. They’re comfortable. They stop getting better. Both rooms carry risk. One threatens your confidence. The other threatens your trajectory. Being the weakest engineer in a strong room is an advantage, but only if you treat it like one. It gives you a clear benchmark. But the room doesn’t do the work for you. You have to name the gaps, build a plan, and follow through. And if you ever find yourself in the other room, where you’re clearly the strongest, pay attention to how long you’ve been there. Both rooms are trying to tell you something. —Brian Are U.S. Engineering Ph.D. Programs Losing Students? Not every engineer has a doctorate, but Ph.D. engineers are an essential part of the workforce, researching and designing tomorrow’s high-tech products and systems. In the United States, early signs are emerging that Ph.D. programs in electrical engineering and related fields may be shrinking. Political and economic uncertainty mean some universities are now seeing smaller applicant pools and graduate cohorts. Read more here. What Happens When You Host an AI Cafe Last November, three professors at Auburn University in Ala. hosted a gathering at a coffee shop to confront students’ concerns about AI. The event, which they call an “AI Café,” was meant to create an environment “where scholars engage their communities in genuine dialogue about AI. Not to lecture about technical capabilities, but to listen, learn, and co-create a vision for AI that serves the public interest.” In a guest article, they share what they learned at the event and tips for starting your own AI Café. Read more here. What Is Inference Engineering? Inference, the process of running a trained AI model on new data, is increasingly becoming a focus in the world of AI engineering. The growth of open LLMs means that more engineers can now tweak the models to perform better at inference. Given this trend, a recent issue of the Substack “The Pragmatic Engineer” does a deep dive on inference engineering—what it is, when it’s needed, and how to do it. Read more here.
Gerard “Gus” Gaynor, a long-serving IEEE volunteer and former engineering director at 3M, died on 9 March. The IEEE Life Fellow was 104. Readers of The Institute might remember Gus from his 2022 profile: “From Fixing Farm Equipment to Becoming a Director at 3M.” Just last year, he and I coauthored twoarticles. One discusses how to leverage relationships to boost your career growth. The other weighs the pros and cons of pursuing a technical or managerial career path. He was 103 years old then. How many IEEE members can claim a centenarian coauthor? I first met Gus in 2009 at the IEEE Technical Activities Board (TAB) meeting in San Juan, Puerto Rico. We sat together in the airplane on our way back to Minneapolis, our hometown. At home I told many of my friends about the remarkable person—who was 87 years young at the time—with whom I chatted during our six-hour flight. A decade later, he and I met for lunch in Minneapolis. He drove himself to the restaurant, just asking for a hand to navigate the snowy sidewalk. A dedicated IEEE volunteer Gus’s involvement with IEEE predates the organization. He joined the Institute of Radio Engineers, a predecessor society, as a student member in 1942. Twenty years later he became an active IEEE volunteer. He served on the TAB’s finance committee and the Publications Services and Products Board. He was president of the IEEE Engineering Management Society (now the Technology and Engineering Management Society ), and he was the Technology Management Council’s first president. He was the founding editor of IEEE-USA’s online magazine Today’s Engineer, which reported on government legislation and issues affecting U.S. members’ careers. The magazine is now available as the e-newsletter IEEE-USA InSight. He authored several books on technology management, published by IEEE-USA. IEEE Life Fellow Gerard “Gus” Gaynor died on 9 March.The Gaynor Family Most recently, after the formation of TEMS in 2015, he became an active member of its executive committee. He served two terms as vice president of publications. At 100 years old, he led the launch of a new publication, TEMS Leadership Briefs, a novel short-format open-access publication aimed at technology leaders. Gus, who is a former member of The Institute’s editorial advisory board, also worked with Kathy Pretz, The Institute’s editor in chief, to start an ongoing series of TEMS-sponsored career-interest articles. He coauthored several of them. Throughout his 64 years as an IEEE volunteer, he received several honors. They include IEEE EMS’s Engineering Manager of the Year Award, the IEEE TEMS Career Achievement Award, and the IEEE-USA McClure Citation of Honor. In 2014 he was inducted into the IEEE Technical Activities Board Hall of Honor. A 25-year career at 3M Gus received a degree in electrical engineering in 1950 from the University of Michigan in Ann Arbor. He worked for several companies including Automatic Electric (now part of Nokia) and Johnson Farebox (now part of Genfare), before joining 3M in 1962. During his successful 25-year career at 3M, he served as chief engineer for a division in Italy, established the innovation department, and led the design and installation of the company’s first computerized manufacturing facilities. He retired as director of engineering in 1987. Last year, IEEE Life Fellow Michael Condry, a former TEMS president, organized a Zoom call with Gus and other leaders of the society to celebrate Gus’s 104th birthday. Gus looked well and was his usual upbeat self, telling everyone: “I’m good. Everything’s well. I can’t complain.” Gus was married to Shirley Margaret Karrels Gaynor, who passed away in 2018. He lives on in the hearts and minds of his seven children, seven grandchildren, two great-grandchildren, and innumerable friends and IEEE colleagues.
ZTASP is a mission-scale assurance and governance platform designed for autonomous systems operating in real-world environments. It integrates heterogeneous systems—including drones, robots, sensors, and human operators—into a unified zero-trust architecture. Through Secure Runtime Assurance (SRTA) and Secure Spatio-Temporal Reasoning (SSTR), ZTASP continuously verifies system integrity, enforces safety constraints, and enables resilient operation even under degraded conditions. ZTASP has progressed beyond conceptual design, with operational validation at Technology Readiness Level (TRL) 7 in mission critical environments. Core components, including Saluki secure flight controllers, have reached TRL8 and are deployed in customer systems. While initially developed for high-consequence mission environments, the same assurance challenges are increasingly present across domains such as healthcare, transportation, and critical infrastructure. Download this free whitepaper now!
By many estimates, quantum computers will need millions of qubits to realize their potential applications in cybersecurity, drug development, and other industries. The problem is, anyone who has wanted to simultaneously control millions of a certain kind of qubits has run into the problem of trying to control millions of laser beams. That’s exactly the challenge that was faced by scientists working on the MITRE Quantum Moonshot project, which brought together scientists from MITRE, MIT, the University of Colorado at Boulder, and Sandia National Laboratories. The solution they developed came in the form of an image projection technology that they realized could also be the fix for a host of other challenges in augmented reality, biomedical imaging, and elsewhere. The device is a one-square-millimeter photonic chip capable of projecting the Mona Lisa onto an area smaller than the size of two human egg cells. “When we started, we certainly never would have anticipated that we would be making a technology that might revolutionize imaging,” says Matt Eichenfield, one of the leaders of the Quantum Moonshot project, a collaborative research effort focused on developing a scalable diamond-based quantum computer, and a professor of quantum engineering at the University of Colorado at Boulder. Each second, their chip is capable of projecting 68.6 million individual spots of light—called scannable pixels to differentiate them from physical pixels. That’s more than fifty times the capability of previous technology, such as micro-electromechanical systems (MEMS) micromirror arrays. “We have now made a scannable pixel that is at the absolute limit of what diffraction allows,” says Henry Wen, a visiting researcher at MIT and a photonics engineer at QuEra Computing. The chip’s distinguishing feature is an array of tiny micro-scale cantilevers, which curve away from the plane of the chip in response to voltage and act as miniature “ski-jumps” for light. Light is channeled along the length of each cantilever via a waveguide, and exits at its tip. The cantilevers contain a thin layer of aluminum nitride, a piezoelectric which expands or contracts under voltage, thus moving the micromachine up and down and enabling the array to scan beams of light over a two-dimensional area. Despite the magnitude of the team’s achievement, Eichenfield says that the process of engineering the cantilevers was “pretty smooth.” Each cantilever is composed of a stack of several submicrometer layers of material and curls approximately 90 degrees out of the plane at rest. To achieve such a high curvature, the team took advantage of differences in the contraction and expansion of individual layers caused by physical stresses in the material resulting from the fabrication process. The materials are first deposited flat onto the chip. Then, a layer in the chip below the cantilever is removed, allowing the material stresses to take effect, releasing the cantilever from the chip and allowing it to curl out. The top layer of each cantilever also features a series of silicon dioxide bars running perpendicular to the waveguide, which keep the cantilever from curling along its width while also improving its length-wise curvature. A micro-cantilever wiggles and waggles to project light in the right place.Matt Saha, Y. Henry Wen, et al. What was more of a challenge than engineering the chip itself was figuring out the details of actually making the chip project images and videos. Working out the process of synchronizing and timing the cantilevers’ motion and light beams to generate the right colors at the right time was a substantial effort, according to Andy Greenspon, a researcher at MITRE who also worked on the project. Now, the team has successfully projected a variety of videos from a single cantilever, including clips from the movie A Charlie Brown Christmas. The chip projected a roughly 125-micrometer image of the Mona Lisa.Matt Saha, Y. Henry Wen, et al. Because the chip can project so many more spots in any given time interval than any previous beam scanners, it could also be used to control many more qubits in quantum computers. The Quantum Moonshot program’s mission is to build a quantum computer that can be scaled to millions of qubits. So clearly, it needs a scalable way of controlling each one, explains Wen. Instead of using one laser per qubit, the team realized that not every qubit needed to be controlled at every given moment. The chip’s ability to move light beams over a two-dimensional area, would allow them to control all of the qubits with many fewer lasers. Another process that Wen thinks the chip could improve is scanning objects for 3D printing. Today, that typically involves using a single laser to scan over the entire surface of an object. The new chip, however, could potentially employ thousands of laser beams. “I think now you can take a process that would have taken hours and maybe bring it down to minutes,” says Wen. Wen is also excited to explore the potential of different cantilever shapes. By changing the orientations of the bars perpendicular to the waveguide, the team has been able to make the cantilevers curl into helixes. Wen says that such unusual shapes could be useful in making a lab-on-a-chip for cell biology or drug development. “A lot of this stuff is imaging, scanning a laser across something, either to image it or to stimulate some response. And so we could have one of these ski jumps curl not just up, but actually curl back around, and then move around and scan over a sample,” Wen explains. “If you can imagine a structure that will be useful for you, we should try it.”
Kyle McGinley graduated from high school in 2018 and, like many teenagers, he was unsure what career he wanted to pursue. Recuperating from a sports injury led him to consider becoming a physical therapist for athletes. But he was skilled at repairing cars and fixing things around the house, so he thought about becoming an engineer, like his father. McGinley, who lives in Sellersville, Pa., took some classes at Montgomery County Community College in Blue Bell, while also working. During his years at the college, he took a variety of courses and was drawn to electrical engineering and computing, he says. He left to pursue a bachelor’s degree in electrical and computer engineering in Philadelphia at Temple University, where he is currently a junior. Kyle McGinley MEMBER GRADE Student member UNIVERSITY Temple, in Philadelphia MAJOR Electrical and computer engineering The 26-year-old is also a teaching assistant and a research assistant at Temple. His research focuses on applying artificial intelligence to electrical hardware and robotics. He helped build an AI-integrated android companion to assist in-home caregivers. Temple recognized McGinley’s efforts last year with its Butz scholarship, which is awarded annually to an electrical and computer engineering undergraduate with an interest in software development, AI development systems, health education software, or a similar field. An IEEE student member, he is active within the university’s student branch. “My career ambition after I graduate is to gain real-world experience in the engineering industry to learn skills outside of academia,” he says. “Long term, I want to do project management or work in a technical lead role, with the primary goal of creating impactful projects that I can be proud of.” Building a robot aide McGinley is a teaching assistant for his digital circuit design course. In a class of 35 students, it can be a struggle for some to digest the professor’s words, he says. “My job is to answer students’ questions if they are having problems following the professor’s lecture or are confused about any of the topics,” he says. “In the lab, I help students debug code or with hardware issues they have on the FPGA [field-programmable gate array] boards.” He also conducts research for the university’s Computer Fusion Lab under the supervision of IEEE Senior Member Li Bai, a professor of electrical and computer engineering. McGinley writes software programs at the lab. “In school, they don’t teach you how to communicate with people. They only teach you how to remember stuff. Working well with people is one of the most underrated skills that a lot of students don’t understand is important.” One such assignment was working with the Temple School of Social Work at the Barnett College of Public Health to build a robot companion integrated with AI to assist individuals with Parkinson’s disease and their caregivers. “I realized the need for this with my grandmother, when she was taking care of my grandfather,” he says. “It was a lot for her, trying to remember everything.” Using the latest software and hardware, he and three classmates rebuilt an older lab robot. They installed an operating system and used Python and C++ for its control, perception, and behavior, he says. The students also incorporated Google’s Gemini AI to help with routine tasks such as scheduling medication reminders and setting alarms for upcoming doctor visits. Kyle McGinley helped build an AI-integrated android to assist individuals with Parkinson’s disease and their caregivers.Temple University of Public Health The AI-integrated android was intended to assist, not replace, the caregivers by handling the mental load of remembering tasks, he says. “This was one of the cool things that drew me to working in the robotics field,” he says. “Something where AI could be used to help caregivers do simple tasks.” The benefits of a student branch McGinley joined Temple’s IEEE student branch last year after one of his professors offered extra credit to students who did so. After attending meetings and participating in a few workshops, he found he really liked the club, he says, adding that he made new friends and enjoyed the camaraderie with other engineering students. After the student branch’s board members got to know McGinley better, they asked him to become the club’s historian and manage its social media account. He also helps with event planning, creating and posting fliers, taking pictures, and shooting videos of the gatherings. The branch has benefited from McGinley’s involvement, but he says it’s a two-way street. “The biggest things I’ve learned are being held accountable and being reliable,” he says. “I am responsible for other people knowing what’s going on.” Being an active volunteer has improved his communication skills, he says. “Learning to clearly communicate with other people to make sure everyone is on the same page is important,” he says. “In school, they don’t teach you how to communicate with people. They only teach you how to remember stuff. Working well with people is one of the most underrated skills that a lot of students don’t understand is important.” He encourages students to join their university’s IEEE branch. “I know it can be scary because you might not know anyone, but it honestly can’t hurt you; it could actually benefit you,” he says. “Being active is going to help you with a lot of skills that you need. “You’ll definitely get opportunities that you would have never known about, like a scholarship or working in the research lab. I would have never gotten these opportunities if I hadn’t shown up. Joining IEEE and being active is the best thing you can do for your career.”
Artificial intelligence harbors an enormous energy appetite. Such constant cravings are evident in the hefty carbon footprint of the data centers behind the AI boom and the steady increase over time of carbon emissions from training frontier AI models. No wonder big tech companies are warming up to nuclear energy, envisioning a future fueled by reliable, carbon-free sources. But while nuclear-powered data centers might still be years away, some in the research and industry spheres are taking action right now to curb AI’s growing energy demands. They’re tackling training as one of the most energy-intensive phases in a model’s life cycle, focusing their efforts on decentralization. Decentralization allocates model training across a network of independent nodes rather than relying on one platform or provider. It allows compute to go where the energy is—be it a dormant server sitting in a research lab or a computer in a solar-powered home. Instead of constructing more data centers that require electric grids to scale up their infrastructure and capacity, decentralization harnesses energy from existing sources, avoiding adding more power into the mix. Hardware in harmony Training AI models is a huge data center sport, synchronized across clusters of closely connected GPUs. But as hardware improvements struggle to keep up with the swift rise in size of large language models, even massive single data centers are no longer cutting it. Tech firms are turning to the pooled power of multiple data centers—no matter their location. Nvidia, for instance, launched the Spectrum-XGS Ethernet for scale-across networking, which “can deliver the performance needed for large-scale single job AI training and inference across geographically separated data centers.” Similarly, Cisco introduced its 8223 router designed to “connect geographically dispersed AI clusters.” Other companies are harvesting idle compute in servers, sparking the emergence of a GPU-as-a-Service business model. Take Akash Network, a peer-to-peer cloud computing marketplace that bills itself as the “Airbnb for data centers.” Those with unused or underused GPUs in offices and smaller data centers register as providers, while those in need of computing power are considered as tenants who can choose among providers and rent their GPUs. “If you look at [AI] training today, it’s very dependent on the latest and greatest GPUs,” says Akash cofounder and CEO Greg Osuri. “The world is transitioning, fortunately, from only relying on large, high-density GPUs to now considering smaller GPUs.” Software in sync In addition to orchestrating the hardware, decentralized AI training also requires algorithmic changes on the software side. This is where federated learning, a form of distributed machine learning, comes in. It starts with an initial version of a global AI model housed in a trusted entity such as a central server. The server distributes the model to participating organizations, which train it locally on their data and share only the model weights with the trusted entity, explains Lalana Kagal, a principal research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) who leads the Decentralized Information Group. The trusted entity then aggregates the weights, often by averaging them, integrates them into the global model, and sends the updated model back to the participants. This collaborative training cycle repeats until the model is considered fully trained. But there are drawbacks to distributing both data and computation. The constant back and forth exchanges of model weights, for instance, result in high communication costs. Fault tolerance is another issue. “A big thing about AI is that every training step is not fault-tolerant,” Osuri says. “That means if one node goes down, you have to restore the whole batch again.” To overcome these hurdles, researchers at Google DeepMind developed DiLoCo, a distributed low-communication optimization algorithm. DiLoCo forms what Google DeepMind research scientist Arthur Douillard calls “islands of compute,” where each island consists of a group of chips. Every island holds a different chip type, but chips within an island must be of the same type. Islands are decoupled from each other, and synchronizing knowledge between them happens once in a while. This decoupling means islands can perform training steps independently without communicating as often, and chips can fail without having to interrupt the remaining healthy chips. However, the team’s experiments found diminishing performance after eight islands. An improved version dubbed Streaming DiLoCo further reduces the bandwidth requirement by synchronizing knowledge “in a streaming fashion across several steps and without stopping for communicating,” says Douillard. The mechanism is akin to watching a video even if it hasn’t been fully downloaded yet. “In Streaming DiLoCo, as you do computational work, the knowledge is being synchronized gradually in the background,” he adds. AI development platform Prime Intellect implemented a variant of the DiLoCo algorithm as a vital component of its 10-billion-parameter INTELLECT-1 model trained across five countries spanning three continents. Upping the ante, 0G Labs, makers of a decentralized AI operating system, adapted DiLoCo to train a 107-billion-parameter foundation model under a network of segregated clusters with limited bandwidth. Meanwhile, popular open-source deep learning framework PyTorch included DiLoCo in its repository of fault tolerance techniques. “A lot of engineering has been done by the community to take our DiLoCo paper and integrate it in a system learning over consumer-grade internet,” Douillard says. “I’m very excited to see my research being useful.” A more energy-efficient way to train AI With hardware and software enhancements in place, decentralized AI training is primed to help solve AI’s energy problem. This approach offers the option of training models “in a cheaper, more resource-efficient, more energy-efficient way,” says MIT CSAIL’s Kagal. And while Douillard admits that “training methods like DiLoCo are arguably more complex, they provide an interesting tradeoff of system efficiency.” For instance, you can now use data centers across far apart locations without needing to build ultrafast bandwidth in between. Douillard adds that fault tolerance is baked in because “the blast radius of a chip failing is limited to its island of compute.” Even better, companies can take advantage of existing underutilized processing capacity rather than continuously building new energy-hungry data centers. Betting big on such an opportunity, Akash created its Starcluster program. One of the program’s aims involves tapping into solar-powered homes and employing the desktops and laptops within them to train AI models. “We want to convert your home into a fully functional data center,” Osuri says. Osuri acknowledges that participating in Starcluster will not be trivial. Beyond solar panels and devices equipped with consumer-grade GPUs, participants would also need to invest in batteries for backup power and redundant internet to prevent downtime. The Starcluster program is figuring out ways to package all these aspects together and make it easier for homeowners, including collaborating with industry partners to subsidize battery costs. Backend work is already underway to enable homes to participate as providers in the Akash Network, and the team hopes to reach its target by 2027. The Starcluster program also envisions expanding into other solar-powered locations, such as schools and local community sites. Decentralized AI training holds much promise to steer AI toward a more environmentally sustainable future. For Osuri, such potential lies in moving AI “to where the energy is instead of moving the energy to where AI is.”
In late-stage testing of a distributed AI platform, engineers sometimes encounter a perplexing situation: every monitoring dashboard reads “healthy,” yet users report that the system’s decisions are slowly becoming wrong. Engineers are trained to recognize failure in familiar ways: a service crashes, a sensor stops responding, a constraint violation triggers a shutdown. Something breaks, and the system tells you. But a growing class of software failures looks very different. The system keeps running, logs appear normal, and monitoring dashboards stay green. Yet the system’s behavior quietly drifts away from what it was designed to do. This pattern is becoming more common as autonomy spreads across software systems. Quiet failure is emerging as one of the defining engineering challenges of autonomous systems because correctness now depends on coordination, timing, and feedback across entire systems. When Systems Fail Without Breaking Consider a hypothetical enterprise AI assistant designed to summarize regulatory updates for financial analysts. The system retrieves documents from internal repositories, synthesizes them using a language model, and distributes summaries across internal channels. Technically, everything works. The system retrieves valid documents, generates coherent summaries, and delivers them without issue. But over time, something slips. Maybe an updated document repository isn’t added to the retrieval pipeline. The assistant keeps producing summaries that are coherent and internally consistent, but they’re increasingly based on obsolete information. Nothing crashes, no alerts fire, every component behaves as designed. The problem is that the overall result is wrong. From the outside, the system looks operational. From the perspective of the organization relying on it, the system is quietly failing. The Limits of Traditional Observability One reason quiet failures are difficult to detect is that traditional systems measure the wrong signals. Operational dashboards track uptime, latency, and error rates, the core elements of modern observability. These metrics are well-suited for transactional applications where requests are processed independently, and correctness can often be verified immediately. Autonomous systems behave differently. Many AI-driven systems operate through continuous reasoning loops, where each decision influences subsequent actions. Correctness emerges not from a single computation but from sequences of interactions across components and over time. A retrieval system may return contextually inappropriate and technically valid information. A planning agent may generate steps that are locally reasonable but globally unsafe. A distributed decision system may execute correct actions in the wrong order. None of these conditions necessarily produces errors. From the perspective of conventional observability, the system appears healthy. From the perspective of its intended purpose, it may already be failing. Why Autonomy Changes Failure The deeper issue is architectural. Traditional software systems were built around discrete operations: a request arrives, the system processes it, and the result is returned. Control is episodic and externally initiated by a user, scheduler, or external trigger. Autonomous systems change that structure. Instead of responding to individual requests, they observe, reason, and act continuously. AI agents maintain context across interactions. Infrastructure systems adjust resource in real time. Automated workflows trigger additional actions without human input. In these systems, correctness depends less on whether any single component works, and more on coordination across time. Distributed-systems engineers have long wrestled with issues of coordination. But this is coordination of a new kind. It’s no longer about things like keeping data consistent across services. It’s about ensuring that a stream of decisions—made by models, reasoning engines, planning algorithms, and tools, all operating with partial context—adds up to the right outcome. A modern AI system may evaluate thousands of signals, generate candidate actions, and execute them across a distributed infrastructure. Each action changes the environment in which the next decision is made. Under these conditions, small mistakes can compound. A step that is locally reasonable can still push the system further off course. Engineers are beginning to confront what might be called behavioral reliability: whether an autonomous system’s actions remain aligned with its intended purpose over time. The Missing Layer: Behavioral Control When organizations encounter quiet failures, the initial instinct is to improve monitoring: deeper logs, better tracing, more analytics. Observability is essential, but it only shows that the behavior has already diverged—it doesn’t correct it. Quiet failures require something different: the ability to shape system behavior while it is still unfolding. In other words, autonomous systems increasingly need control architectures, not just monitoring. Engineers in industrial domains have long relied on supervisory control systems. These are software layers that continuously evaluate a system’s status and intervene when behavior drifts outside safe bounds. Aircraft flight-control systems, power-grid operations, and large manufacturing plants all rely on such supervisory loops. Software systems historically avoided them because most applications didn’t need them. Autonomous systems increasingly do. Behavioral monitoring in AI systems focuses on whether actions remain aligned with intended purpose, not just whether components are functioning. Instead of relying only on metrics such as latency or error rates, engineers look for signs of behavior drift: shifts in outputs, inconsistent handling of similar inputs, or changes in how multi-step tasks are carried out. An AI assistant that begins citing outdated sources, or an automated system that takes corrective actions more often than expected, may signal that the system is no longer using the right information to make decisions. In practice, this means tracking outcomes and patterns of behavior over time. Supervisory control builds on these signals by intervening while the system is running. A supervisory layer checks whether ongoing actions remain within acceptable bounds and can respond by delaying or blocking actions, limiting the system to safer operating modes, or routing decisions for review. In more advanced setups, it can adjust behavior in real time—for example, by restricting data access, tightening constraints on outputs, or requiring extra confirmation for high-impact actions. Together, these approaches turn reliability into an active process. Systems don’t just run, they are continuously checked and steered. Quiet failures may still occur, but they can be detected earlier and corrected while the system is operating. A Shift in Engineering Thinking Preventing quiet failures requires a shift in how engineers think about reliability: from ensuring components work correctly to ensuring system behavior stays aligned over time. Rather than assuming that correct behavior will emerge automatically from component design, engineers must increasingly treat behavior as something that needs active supervision. As AI systems become more autonomous, this shift will likely spread across many domains of computing, including cloud infrastructure, robotics, and large-scale decision systems. The hardest engineering challenge may no longer be building systems that work, but ensuring that they continue to do the right thing over time.
Picture a highway with networked autonomous cars driving along it. On a serene, cloudless day, these cars need only exchange thimblefuls of data with one another. Now picture the same stretch in a sudden snow squall: The cars rapidly need to share vast amounts of essential new data about slippery roads, emergency braking, and changing conditions. These two very different scenarios involve vehicle networks with very different computational loads. Eavesdropping on network traffic using a ham radio, you wouldn’t hear much static on the line on a clear, calm day. On the other hand, sudden whiteout conditions on a wintry day would sound like a cacophony of sensor readings and network chatter. Normally this cacophony would mean two simultaneous problems: congested communications and a rising demand for computing power to handle all the data. But what if the network itself could expand its processing capabilities with every rising decibel of chatter and with every sensor’s chirp? Traditional wireless networks treat communication as separate from computation. First you move data, then you process it. However, an emerging new paradigm called over-the-air computation (OAC) could fundamentally change the game. First proposed in 2005 and recently developed and prototyped by a number of teams around the world, including ours, OAC combines communication and computation into a single framework. This means that an OAC sensor network—whether shared among autonomous vehicles, Internet-of-Things sensors, smart-home devices, or smart-city infrastructure—can carry some of the network’s computing burden as conditions demand. The idea takes advantage of a basic physical fact of electromagnetic radiation: When multiple devices transmit simultaneously, their wireless signals naturally combine in the air. Normally, such cross talk is seen as interference, which radios are designed to suppress—especially digital radios with their error-correcting schemes and inherent resistance to low-level noise. But if we carefully design the transmissions, cross talk can enable a wireless network to directly perform some calculations, such as a sum or an average. Some prototypes today do this with analog-style signaling on otherwise digital radios—so that the superimposed waveforms represent numbers that can be added or averaged before digital signal processing takes place. Researchers are also beginning to explore digital, over-the-air computation schemes, which embed the same ideas into digital formats, ultimately allowing the prototype schemes to coexist with today’s digital radio protocols. These various over-the-air computation techniques can help networks scale gracefully, enabling new classes of real-time, data-intensive services while making more efficient use of wireless spectrum. OAC, in other words, turns signal interference from a problem into a feature, one that can help wireless systems support massive growth. Reimagining radio interference as infrastructure For decades, engineers designed radio communications protocols with one overriding goal: to isolate each signal and recover each message cleanly. Today’s networks face a different set of pressures. They must coordinate large groups of devices on shared tasks—such as AI model training or combining disparate sensor readings, also known as sensor fusion—while exchanging as little raw data as possible, to improve both efficiency and privacy. For these reasons, a new approach to transmitting and receiving data may be worth considering, one that doesn’t rely on collecting and storing every individual device’s contributions. By turning interference into computation, OAC transforms the wireless medium from a contested battlefield into a collaborative workspace. This paradigm shift has far-reaching consequences: Signals no longer compete for isolation; they cooperate to achieve shared outcomes. OAC cuts through layers of digital processing, reduces latency, and lowers energy consumption. Even very simple operations, such as addition, can be the building blocks of surprisingly powerful computations. Many complex processes can be broken down into combinations of simpler pieces, much like how a rich sound can be re-created by combining a few basic tones. By carefully shaping what devices transmit and how the result is interpreted at the receiver, the wireless channel running OAC can carry out other calculations beyond addition. In practice, this means that with the right design, wireless signals can compute a number of key functions that modern algorithms rely on. THE PROBLEM (TRADITIONAL APPROACH) For instance, many key tasks in modern networks don’t require the logging and storage of every individual network transmission. Rather, the goal is instead to infer properties about aggregate patterns of network traffic—reaching agreement or identifying what matters most about the traffic. Consensus algorithms rely on majority voting to ensure reliable decisions, even when some devices fail. Artificial intelligence systems depend on matrix reduction and simplification operations such as “max pooling” (keeping only peak values) to extract the most useful signals from noisy data. In smart cities and smart grids, what matters most is often not individual readings but distribution. How many devices report each traffic condition? What is the range of demand across neighborhoods? These are histogram questions—summaries of the device counts per category. With type-based multiple access (TBMA), an over-the-air computation method we use, devices reporting a given condition transmit together over a shared channel. Their signals add up, and the receiver sees only the total signal strength per category. In a single transmission, the entire histogram emerges without ever identifying individual devices. And the more devices there are, the better the estimate. The result is greater spectrum efficiency, with lower latency and scalable, privacy-friendly operations—all from letting the wireless medium do the aggregating and counting. It’s easy to imagine how analog values transmitted over the air could be summed via superposition. The amplitudes from different signals add together, so the values those amplitudes represent also simply add together. The more challenging question concerns preserving that additive magic, but with digital signals. Here’s how OAC does it. Consider, for instance, one TBMA approach for a network of sensors that gives each possible sensor reading its own dedicated frequency channel. Every sensor on the network that reads “4” transmits on frequency four; every sensor that reads “7” transmits on frequency seven. When multiple devices share the same reading, their amplitudes combine. The stronger the combined signal at a given frequency, the more devices there are reporting that particular value. A receiver equipped with a bank of filters tuned to each frequency reads out a count of votes for every possible sensor value. In a single, simultaneous transmission, the whole network has reported its state. It might seem paradoxical—digital computation riding atop what appears to be an analog physical effect. But this is also true of all “digital” radio. A Wi-Fi transmitter does not launch ones and zeroes into the air; it modulates electromagnetic waves whose amplitudes and phases encode digital data. The “digital” label ultimately refers to the information layer, not the physics. What makes OAC digital, in the same sense, is that the values being computed—each sensor reading, each frequency-bin count—are discrete and quantized from the start. And because they are discrete, the same error-correction machinery that has made digital communications robust for decades can be applied here too. Synchronization is where OAC’s demands diverge most sharply from digital wireless conventions. Many OAC variants today require something akin to a shared clock at nanosecond precision: Every signal’s phase must be synchronized, or the superposition runs the risk of collapsing into destructive interference. While TBMA relaxes this burden a bit—devices need only share a time window—real engineering challenges lie ahead regardless, before over-the-air computation is ready for the mobile world. How will over-the-air computation work in the field? Over-the-air computation has in recent years moved from theory to initial proofs-of-concept and network test runs. Our research teams in South Carolina and Spain have built working prototypes that deliver repeatable results—with no cables and no external timing sources such as GPS-locked references. All synchronization is handled within the radios themselves. Our team at the University of South Carolina (led by Sahin) started with off-the-shelf software-defined radios—Analog Devices’ Adalm-Pluto. We modified the devices’ field-programmable gate array hardware inside each radio so it can respond to a trigger signal transmitted from another radio. This simple hack enabled simultaneous transmission, a core requirement for OAC. Our setup used five radios acting as edge devices and one acting as a base station. The task involved training a neural network to perform image recognition over the air. Our system, whose results we first reported in 2022, achieved a 95 percent accuracy in image recognition without ever moving raw data across the network. THE OVER-THE-AIR COMPUTATION (OAC) APPROACH We also demonstrated our initial OAC setup at a March 2025 IEEE 802.11 working group meeting, where an IEEE committee was studying AI and machine learning capabilities for future Wi-Fi standards. As we showed, OAC’s road ahead doesn’t necessarily require reinventing wireless technology. Rather, it can also build on and repurpose existing protocols already in Wi-Fi and 5G. However, before OAC can become a routine feature of commercial wireless systems, networks must provide finer-tuned coordination of timing and signal power levels. Mobility is a difficult problem, too. When mobile devices move around, phase synchronization degrades quickly, and computational accuracy can suffer. Present-day OAC tests work in controlled lab environments. But making them robust in dynamic, real-world settings—vehicles on highways, sensors scattered across cities—remains a new frontier for this emerging technology. Both of our teams are now scaling up our prototypes and demonstrations. We are together aiming to understand how over-the-air computation performs as the number of devices increases beyond lab-bench scales. Turning prototypes and test-beds into production systems for autonomous vehicles and smart cities will require anticipating tomorrow’s mobility and synchronization problems—and no doubt a range of other challenges down the road. Where OAC goes from here To realize the technological ambitions of over-the-air computation, nanosecond timing and exquisite RF signal design will be crucial. Fortunately, recent engineering advances have made substantial progress in both of these fields. Because OAC demands waveform superposition, it benefits from tight coordination in time, frequency, phase, and amplitude among RF transmitters. Such requirements build naturally on decades of work in wireless communication systems designed for shared access. Modern networks already synchronize large numbers of devices using high-precision timing and uplink coordination. OAC uses the same synchronization techniques already in cellular and Wi-Fi systems. But to actually run over-the-air computations, more precision still will be needed. Power control, gain adjustment, and timing calibration are standard tools today. We expect that engineers will further refine these existing methods to begin to meet OAC’s more stringent accuracy demands. THE OAC RESULT In some cases, in fact, imperfect timing standards may be all that’s needed. Designs and emerging standards in 5G and 6G wireless systems today use clever encoding that tolerates imperfect synchronization. Minor timing errors, frequency drift, and signal overlap can in some cases still work capably within an OAC protocol, we anticipate. Instead of fighting messiness, over-the-air computation may sometimes simply be able to roll with it. Another challenge ahead concerns shifting processing to the transmitter. Instead of the receiver trying to clean up overlapping signals, a better and more efficient approach would involve each transmitter fixing its own signal before sending. Such “pre-compensation” techniques are already used in MIMO technology (multi-antenna systems in modern Wi-Fi and cellular networks). OAC would just be repurposing techniques that have already been developed for 5G and 6G technologies. Materials science can also help OAC efforts ahead. New generations of reconfigurable intelligent surfaces shape signals via tiny adjustable elements in the antenna. The surfaces catch radio signals and reshape them as they bounce around. Reconfigurable surfaces can strengthen useful signals, eliminate interference, and synchronize wavefront arrivals that would otherwise be out of sync. OAC stands to benefit from these and other emerging capabilities that intelligent surfaces will provide. At the system level, OAC will represent a fundamental shift in wireless network system design. Wireless engineers have traditionally tried to avoid designing devices that transmit at the same time. But over-the-air systems will flip the old, familiar design standards on their head. One might object that OAC stands to upend decades of existing wireless signal standards that have always presumed data pipes to be data pipes only—not microcomputers as well. Yet we do not anticipate much difficulty merging OAC with existing wireless standards. In a sense, in fact, the IEEE 802.11 and 3GPP (3rd Generation Partnership Project) standards bodies have already shown the way. A network can set aside certain brief time windows or narrow slices of bandwidth for over‑the‑air computation, and use the rest for ordinary data. From the radio’s point of view, OAC just becomes another operating mode that is turned on when needed and left off the rest of the time. Over the past decade, both the IEEE and 3GPP have integrated once-experimental technologies into their wireless standards—for example, millimeter-wave mobile communications, multiuser MIMO, beamforming, and network slicing—by defining each new technological advance as an optional feature. OAC, we suggest, can also operate alongside conventional wireless data traffic as an optional service. Because OAC places high demands on timing and accuracy, networks will need the ability to enable or disable over‑the‑air computation on a per‑application basis. With continued progress, OAC will evolve from lab prototype to standardized wireless capability through the 2020s and into the decade ahead. In the process, the wireless medium will transform from a passive data carrier into an active computational partner—providing essential infrastructure for the real-time intelligent systems that future wireless technologies will demand. So on that snowy highway sometime in the 2030s, vehicles and sensors won’t wait for permission to think together. Using the emerging over-the-air computation protocols that we’re helping to pioneer, simultaneous computation will be the new default. The networks will work as one.
While browsing our website a few weeks ago, I stumbled upon “How and When the Memory Chip Shortage Will End” by Senior Editor Samuel K. Moore. His analysis focuses on the current DRAM shortage caused by AI hyperscalers’ ravenous appetite for memory, a major constraint on the speed at which large language models run. Moore provides a clear explanation of the shortage, particularly for high bandwidth memory (HBM). As we and the rest of the tech media have documented, AI is a resource hog. AI electricity consumption could account for up to 12 percent of all U.S. power by 2028. Generative AI queries consumed 15 terawatt-hours in 2025 and are projected to consume 347 TWh by 2030. Water consumption for cooling AI data centers is predicted to double or even quadruple by 2028 compared to 2023. But Moore’s reporting shines a light on an obscure corner of the AI boom. HBM is a particular type of memory product tailor-made to serve AI processors. Makers of those processors, notably Nvidia and AMD, are demanding more and more memory for each of their chips, driven by the needs and wants of firms like Google, Microsoft, OpenAI, and Anthropic, which are underwriting an unprecedented buildout of data centers. And some of these facilities are colossal: You can read about the engineering challenges of building Meta’s mind-boggling 5-gigawatt Hyperion site in Louisiana, in “What Will It Take to Build the World’s Largest Data Center?” We realized that Moore’s HBM story was both important and unique, and so we decided to include it in this issue, with some updates since the original published on 10 February. We paired it with a recent story by Contributing Editor Matthew S. Smith exploring how the memory-chip shortage is driving up the price of low-cost computers like the Raspberry Pi. The result is “AI Is a Memory Hog.” The big question now is, When will the shortage end? Price pressure caused by AI hyperscaler demand on all kinds of consumer electronics is being masked by stubborn inflation combined with a perpetually shifting tariff regime, at least here in the United States. So I asked Moore what indicators he’s looking for that would signal an easing of the memory shortage. “On the supply side, I’d say that if any of the big three HBM companies—Micron, Samsung, and SK Hynix—say that they are adjusting the schedule of the arrival of new production, that’d be an important signal,” Moore told me. “On the demand side, it will be interesting to see how tech companies adapt up and down the supply chain. Data centers might steer toward hardware that sacrifices some performance for less memory. Startups developing all sorts of products might pivot toward creative redesigns that use less memory. Constraints like shortages can lead to interesting technology solutions, so I’m looking forward to covering those.” To be sure you don’t miss any of Moore’s analysis of this topic and to stay current on the entire spectrum of technology development, sign up for our weekly newsletter, Tech Alert.
Building the next generation of robots for successful integration into our homes, offices, and factories is more than just solving the hardware and software problems – we also need to understand how they will be perceived and how they can work effectively with people in those spaces. aspect_ratio In summer 2025, RAI Institute set up a free popup robot experience in the CambridgeSide mall, designed to let people experience state-of-the-art robotics first hand. While news stories about robots and AI are common, with some being overly critical and some overly optimistic, most people have not encountered robots in the flesh (or metal) as it were. With no direct experience, their opinions are largely shaped by pop culture and social media, both of which are more focused on sensational stories instead of accurate information about how the robots might be used effectively and where the technology still falls short. Our goal with the popup was two-fold: first, to give people an opportunity to see robots that they would otherwise not have a chance to experience and second, to better understand how the public feels about interacting with these robots. Designing a Robot Experience for the General Public Some earlier versions legged robots, built by the RAI Institute’s Executive Director, Marc RaibertRAI Institute The ANYmal by ANYrobotics (left) and a previous model of the RAI Institute’s UMV (right)RAI Institute The pop-up space had two areas: a museum area where people could see historical and modern robots, including some RAI Institute builds like the UMV and an interactive experience called “Drive-a-Spot”. This area was a driving arena where anyone who came by could take the controls of a Spot quadruped, one of the more recognizable, commercially available robots available today. The guest robot drivers used a custom controller built on an adaptive video game controller that was designed so that anyone of any age could use it. It featured basic controls: move forward, back, left, right, adjust height, sit, stand, and tilt. The buttons were large so that tiny or elderly hands could use the controller and the people who drove Spot ranged in age from two to over 90. The guest robot drivers used a custom controller built on an adaptive video game controller that was designed so that anyone of any age could use it.RAI Institute The demo area was designed to be a bit challenging for the Spot robot to maneuver in – it contained tight passages, low obstacles to step over, a barrier to crouch under, and taller objects the robot had to avoid. Much to the surprise of many of our guests, Spot is able to autonomously adjust itself to traverse and avoid those obstacles when being supervised by the joystick. RAI Institute The driving arena’s theme rotated every few weeks across four scenarios: a factory, a home, a hospital, and an outdoor/disaster environment. These were chosen to contrast settings where robots are broadly accepted (industrial, emergency response) with settings where public ambivalence is well-documented (domestic, healthcare). The visitors who chose to drive the Spot robot could also participate in a short survey before and after their driving experience. The survey focused on two core dimensions: Comfort: how comfortable would you feel if you encountered a robot in a factory, home, hospital, office, or outdoor/disaster scenario? Suitability: how well would this robot work in each of those contexts? The survey also recorded emotional reactions immediately after driving, likelihood to recommend the experience, and open-ended responses about what they found memorable or surprising. The researchers were careful to separate the environment participants drove through from the scenarios they were asked to evaluate in the survey). This distinction is important for interpreting the results given below. Did Interacting with the Robot Change People’s Feelings about Robots? Out of approximately 10,000 guests that visited the Robot Lab, 10 percent of those drove the Spot and opted-in to our surveys. Of those surveyed, more than 65% of people had seen images or videos of Spot robots online, but most had never seen one of the robots in person. Increased Comfort Through Experience Across all five contexts presented in the survey (factory, home, hospital, office, and outdoor/disaster scenarios), comfort scores increased significantly after the driving session. The effects were small to moderate in magnitude, but they were consistent and statistically robust after correcting for multiple comparisons across all participants spanning children to older adults. The largest gain appeared in the outdoor/disaster context, which started with low comfort despite high-perceived suitability. People already thought Spot would be useful in search-and-rescue scenarios; they just weren’t comfortable with it performing in that scenario. This discomfort may stem from media portrayals of quadruped robots in military contexts. A few minutes of hands-on control appears to partially dissolve that apprehension. Participants who drove through the factory-themed arena showed no significant increase in comfort, but this scenario already had the highest rating of any rated context at baseline, leaving little room for improvement. No matter their previous experience, most people were neutral about having a Spot robot in their home before their driving experience. However, after the experience of controlling the Spot robot, people had a statistically significant increase in their comfort at having a Spot in their home and also felt that a Spot robot was more suitable for work in any environment, not just the one they had driven it in. Better Understanding of Where Robots Can Fit into Daily Life Perceived suitability for Spot to operate in each context also increased. However, the pattern in the data is different. The largest gains weren’t in the high-baseline industrial and outdoor contexts. They were in home, office, and hospital – the very environments where people started out most skeptical. Participants who drove the Spot robot in a home-themed environment didn’t just consider homes more suitable for robots; they also rated hospitals and offices as more suitable. This result suggests that hands-on control alters something more fundamental than just context-specific familiarity. It may change a person’s underlying understanding of a robot’s capabilities and, consequently, where they believe robots are appropriate. Results by Demographic The hands-on experience seems to be similarly effective across genders, although it does not completely eliminate existing disparities. For example, men reported higher baseline comfort than women across all five contexts. However, all genders improved at similar rates after interaction. The gap didn’t significantly widen or close in most contexts, though it did narrow in factory and office settings. Age effects were more context dependent. Children (aged 8–17) rated factory environments as less comfortable and less suitable before the study. However, this could be because most children do not have experience with factory settings or industrial environments. After interaction, this gap largely persisted. By contrast, children showed stronger gains in office comfort than older adults and entered the study rating home contexts more favorably than adults did. Participants ranged from age 8 to over age 75.RAI Institute Participants who had previously driven Spot (mainly robotics professionals) began with higher comfort across the board. But after the hands-on session, people with no prior exposure caught up to experienced drivers. This level of familiarity would be difficult to replicate with images and videos alone. Post-Interaction Results Post-interaction emotional data was overwhelmingly positive. “Excitement” was reported by 74% of participants, “happiness” by 50%, and only 12% reported “nervousness.” Over 55% rated the experience as “brilliant” and 62% said they were very likely to recommend it to a friend. The open-ended responses added a lot more color. The most commonly mentioned moments were locomotion and terrain adaptation (22%). This included the way Spot navigated steps, tight spaces, and uneven ground and expressive tilt movements (22%), which people found surprisingly dog-like or dance-like. A smaller set of responses (3%) described anthropomorphic reactions: worrying about “hurting” the robot or finding its behavior “silly” in a way that prompted genuine emotional response. When asked what tasks they’d want a robot to perform, responses shifted meaningfully. Before driving, answers clustered around domestic assistance and heavy or hazardous labor. After driving, domestic help remained prominent, but entertainment and play jumped from 7.5% to 19.4%. Companionship also appeared at 5%. References to hazardous or industrial tasks declined as people who had operated the robot began imagining it as a companion and playmate, not just a labor-replacement tool. Key Takeaways from The Robot Lab In the not-so-distant future, robots will become more common in public and private spaces. But whether that integration into daily life will be accepted by the general public remains to be seen. The standard approach to building acceptance has been passive exposure such as videos, exhibits, and articles. This study suggests giving people agency and letting them actually operate a robot is a qualitatively different intervention. Short, well-designed, hands-on encounters can raise comfort in precisely the social domains where ambivalence is highest and where future robotics deployment will likely take place. This hands-on experience shouldn’t be limited to tech conferences and museums, as it may be more valuable than just entertaining. Fun for all ages!RAI Institute We consider the popup a success, but as with all experiments, we also learned a lot along the way. For our takeaways, in addition to the increased comfort with robots, we also found that the guests to our space really enjoyed talking to the robotics experts that staffed the location. For many people, the opportunity to talk to a roboticist was as unique as the opportunity to drive a robot, and in the future, we are excited to continue to share our technical work as well as the experiences of our humans in addition to our humanoids. Does building a space where folks can experience robots firsthand have the potential to create meaningful, long-term attitude shifts? That remains an open question. But the effect’s direction and consistency across different situations, ages, and genders are hard to ignore. Pop-Up Encounters with Spot: Shaping Public Perceptions of Robots Through Hands-On Experience, by Hae Won Park, Georgia Van de Zande, Xiajie Zhang, Dawn Wendell, and Jessica Hodgins from the RAI Institute and the MIT Media Lab, was presented last month at the 2026 ACM/IEEE International Conference on Human-Robot Interaction in Edinburgh, Scotland.
Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion. ICRA 2026: 1–5 June 2026, VIENNA RSS 2026: 13–17 July 2026, SYDNEY Summer School on Multi-Robot Systems: 29 July–4 August 2026, PRAGUE Enjoy today’s videos! Getting Digit to dance takes more than putting on some fancy shoes—our AI Team can teach Digit new whole-body control capabilities overnight. Using raw motion data from mocap, animation, and teleop methods, Digit gets new skills through sim-to-real reinforcement training. [ Agility ] We’ve created GEN-1, our latest milestone in scaling robot learning. We believe it to be the first general-purpose AI model that crosses a new performance threshold: mastery of simple physical tasks. It improves average success rates to 99% on tasks where previous models achieve 64%, completes tasks roughly 3x faster than state of the art, and requires only 1 hour of robot data for each of these results. GEN-1 unlocks commercial viability across a broad range of applications—and while it cannot solve all tasks today, it is a significant step towards our mission of creating generalist intelligence for the physical world. [ Generalist ] Unitree open-sources UnifoLM-WBT-Dataset—high-quality real-world humanoid robot whole-body teleoperation (WBT) dataset for open environments. Publicly available since March 5, 2026, the dataset will continue to receive high-frequency rolling updates. It aims to establish the most comprehensive real-world humanoid robot dataset in terms of scenario coverage, task complexity, and manipulation diversity. [ Hugging Face ] Autonomous mobile robots operating in human-shared indoor environments often require paths that reflect human spatial intentions, such as avoiding interference with pedestrian flow or maintaining comfortable clearance. This paper presents MRReP, a Mixed Reality-based interface that enables users to draw a Hand-drawn Reference Path (HRP) directly on the physical floor using hand gestures. [ MRReP ] Thanks, Masato! Eye contact, even momentarily between strangers, plays a pivotal role in fostering human connection, promoting happiness, and enhancing belonging. Through autonomous navigation and adaptive mirror control, Mirrorbot facilitates serendipitous, nonverbal interactions by dynamically transitioning reflections from self-focused to mutual recognition, sparking eye contact, shared awareness, and playful engagement. [ ARL ] via [ Cornell University ] Experience PAL Robotics’ new teleoperation system for TIAGo Pro, the AI-ready mobile manipulator designed for advanced research. This real-time VR teleoperation setup allows precise control of TIAGo Pro’s dual arms in Cartesian space, ideal for remote manipulation, AI data collection, and robot learning. [ PAL Robotics ] Utter brilliance from Robust AI. No notes. [ Robust AI ] Come along with our Senior Test Engineer, Nick L., as he takes us on a tour of the Home Test Labs inside the iRobot HQ. [ iRobot ] By automating the final “magic 5%” of production—the precise trimming of swim goggles’ silicone gaskets based on individual face scans—UR cobots allow THEMAGIC5 to deliver affordable, custom-fit goggles, enabling the company to scale from a Kickstarter sensation to selling over 400,000 goggles worldwide. [ Universal Robots ] Sanctuary AI has once again demonstrated its industry-leading approach to training dexterous manipulation policies for its advanced hydraulic hands. In this video, their proprietary hydraulic hand autonomously manipulates a lettered cube, continuously reorienting it to match a specified goal (displayed in the bottom-left corner of the video). [ Sanctuary AI ] China’s Yuxing 3-06 commercial experimental satellite, the first of its kind to be equipped with a flexible robotic arm, has recently completed an in-orbit refueling test and verification of key technologies. The test paves the way for Yuxing 3-06, dubbed a “space refueling station,” to refuel other satellites in orbit, manage space debris, and provide other in-orbit services. [ Sanyuan Aerospace ] via [ Space News ] This is a demonstration of natural walking, whole-body teleoperation, and motion tracking with our custom-built humanoid robot. The control policies are trained using large-scale parallel reinforcement learning (RL). By deploying robust policies learned in a physics simulator onto the real hardware, we achieve dynamic and stable whole-body motions. [ Tokyo Robotics ] Faced with aging railway infrastructure, a shrinking workforce and rising construction costs, Japan Railway West asked construction innovator Serendix to replace an old wooden building at its Hatsushima railway station using its 3D printing technology. An ABB robot enabled the company to assemble the new building in a single night ready for the first train service the next day. [ ABB ] Humanoid, SAP, and Martur Fompak team up to test humanoid robots in automotive manufacturing logistics. This joint proof of concept explores how robots can streamline operations, improve efficiency, and shape the future of smart factories. [ Humanoid ] This MIT Robotics Seminar is from Dario Floreano at EPFL, on “Avian Inspired Drones.” [ MIT ] This MIT Robotics Seminar is from Ken Goldberg at UC Berkeley: “Good Old-Fashioned Engineering Can Close the 100,000 Year ‘Data Gap’ in Robotics.” [ MIT ]
This year marks the 80th anniversary of ENIAC, the first general-purpose digital computer. The computer was built during World War II to speed up ballistics calculations, but its contributions to computing extend well beyond military applications. Two of ENIAC’s key architects—John W. Mauchly, its co-inventor, and Kathleen “Kay” McNulty, one of the six original programmers—married a few years after its completion and raised seven children together. Mauchly and McNulty’s grandchild Naomi Most delivered a talk as part of a celebration in honor of ENIAC’s anniversary on 15 February, which was held online and in-person at the American Helicopter Museum in West Chester, Pa. The following is adapted from that presentation. RELATED: ENIAC, the First General-Purpose Digital Computer, Turns 80 There was a library at my grandparents’ farmhouse that felt like it went on forever. September light through the windows, beech leaves rustling outside on the stone porch, the sounds of cousins and aunts and uncles somewhere in the house. And in the corner of that library, an IBM personal computer. When I spent summers there as a child, I didn’t yet know that the computer was closely tied to my family’s story. My grandparents are known for their contributions to creating the Electronic Numerical Integrator and Computer, or ENIAC. But both were interested in more than just crunching numbers: My grandfather wanted to predict the weather. My grandmother wanted to be a good storyteller. In Irish, the first language my grandmother Kathleen “Kay” McNulty ever spoke, a word existed to describe both of these impulses: ríomh. I began to learn the Irish language myself five years ago, and I was struck by how certain words and phrases had multiple meanings. According to renowned Irish cultural historian Manchán Magan—from whom I took lessons—the word ríomh has at different times been used to mean to compute, but also to weave, to narrate, or to compose a poem. That one word that can tell the story of ENIAC, a machine with wires woven like thread that was built to compute, make predictions, and search for a signal in the noise. John Mauchly’s Weather-Prediction Ambitions Before working on ENIAC, John Mauchly spent years collecting rainfall data across the United States. His favorite pastime was meteorology, and he wanted to find patterns in storm systems to predict the weather. The Army, however, funded ENIAC to make simpler predictions: calculating ballistic trajectory tables. Start there, co-inventors J. Presper Eckert and Mauchly realized, and perhaps the weather would soon be computable. Co-inventors John Mauchly [left] and J. Presper Eckert look at a portion of ENIAC on 25 November 1966. Hulton Archive/Getty Images Weather is a system unfolding through time, and a model of a storm is a story about how that system might unfold. There’s an old Irish saying related to this idea: Is maith an scéalaí an aimsir. Literally, “weather is a good storyteller.” But aimsir also means time. So the usual translation of this phrase into English becomes “time will tell.” Mauchly wanted to ríomh an aimsire—to weave the weather into pattern, to compute the storm, to narrate the chaos. He realized that complex systems don’t reveal their full purpose at conception. They reveal it through aimsir—through weather, through time, through use. ENIAC’s First Programmers Were Weavers Kathleen “Kay” McNulty was born on 12 February 1921, in Creeslough, Ireland, on the night her father—an IRA training officer—was arrested and imprisoned in Derry Gaol. Family oral history holds that her people were weavers. She spoke only Irish until her family reached Philadelphia when she was 4 years old, entering American school the following year knowing virtually no English. She graduated in 1942 from Chestnut Hill College with a mathematics degree, was recruited to compute artillery firing tables by hand for the U.S. Army, and was then selected—along with five other women—to program ENIAC. They had no manual. They had only blueprints. McNulty and her colleagues learned ENIAC and its quirks the way you learn a loom: by touch, by memory, by routing threads of electricity into patterns. They developed embodied knowledge the designers could only approximate. They could narrow a malfunction to a specific failed vacuum tube before any technician could locate it. McNulty and Mauchly are also credited with conceiving the subroutine, the sequence of instructions that can be repeatedly recalled to perform a task, now essential in any programming. The subroutine was not in ENIAC’s blueprints, nor in the funding proposal. The concept emerged as highly determined people extended their imagination into the machine’s affordances. The engineers designed the loom. Weavers discovered its true capabilities. In 1950, four years after ENIAC was switched on, Mauchly’s dream was realized as it was used in the world’s first computer-assisted weather forecast. That was made possible after Klara von Neumann and Nick Metropolis reassembled and upgraded the ENIAC with a small amount of digital program memory. The programmers who transformed the math into operational code for the ENIAC were Norma Gilbarg, Ellen-Kristine Eliassen, and Margaret Smagorinsky. Their names are not as well-known as they should be. Before programming ENIAC, Kay McNulty [left] was recruited by the U.S. Army to compute artillery firing tables. Here, she and two other women, Alyse Snyder [center] and Sis Stump, operate a mechanical analog computer designed to solve differential equations in the basement of the University of Pennsylvania’s Moore School of Electrical Engineering.University of Pennsylvania Kay McNulty, Family Storyteller Kay married John Mauchly in 1948, describing him as “the greatest delight of my life. He was so intelligent and had so many ideas.... He was not only lovable, he was loving.” She spent the rest of her life ensuring he, Eckert, and the ENIAC programmers would be recognized. When she died in 2006, I came to her funeral in shock, not fully knowing what I’d lost. As she drifted away, it was said, she had been reciting her prayers in Irish. This understanding made it quickly over to Creeslough, in County Donegal, and awaited me when I visited to honor her memory with the dedication of a plaque right there in the center of town. In her own memoir, she wrote: “If I am remembered at all, I would like to be remembered as my family storyteller.” In Irish, the word for computer is ríomhaire. One who ríomhs. One who weaves, computes, and tells. My grandfather wanted to tell the story of the weather through computing. My grandmother wanted to be remembered as a storyteller. The language of her childhood already had a word that contained both of those ambitions. Computers as Narrative Engines When it was built, ENIAC looked like the back room of a textile production house. Panels. Switchboards. A room full of wires. Thread. Thread does not tell you what it will become. We tend to think of computing as calculation—discrete and deterministic. But a model is a structured story about how something behaves. Weather models, ballistic tables, economic forecasts, neural networks: These are all narrative engines, systems that take raw inputs and produce accounts of how the world might unfold. In complex systems, when parts are woven together through use, new structures arise that no one specified in advance. Like ENIAC, the machines we are building now—the large models, the autonomous systems—are not merely calculators. They are looms. Their most important properties will not be specified in advance. They will emerge through use, through the people who learn how to weave with them. Through imagination. Through aimsir.
Abhishek Appaji has committed his career to bringing lifesaving technology to underresourced communities. The IEEE senior member weaves together artificial intelligence, biomedical engineering, deep learning, and neuroscience to make doctors’ jobs easier and to improve patient outcomes. “The intersection of these fields is where the most impactful breakthroughs in diagnostic precision occur,” says Appaji, an associate professor of medical electronics engineering at the B.M.S. College of Engineering, in Bengaluru, India. Abhishek Appaji Employer B.M.S. College of Engineering, in Bengaluru, India Job title Associate professor of medical electronics engineering Member grade IEEE senior member Alma maters B.M.S. College of Engineering; University of Visvesvaraya, in Bengaluru; Maastricht University, in the Netherlands Many of his inventions have been deployed in remote areas of India, providing physicians with quality diagnostic tools, including an AI-powered machine that can scan retinas to detect medical conditions and a smart bed that continuously monitors a patient’s vital signs. An active volunteer with the IEEE Young Professionals Bangalore Section, he has launched professional networking events, technology workshops, a mentorship program, and other initiatives. For his “contributions to accessible AI-driven health care solutions and leadership in empowering young professionals,” Appaji is the recipient of this year’s IEEE Theodore W. Hissey Outstanding Young Professional Award. The honor is sponsored by the IEEE Photonics and Power & Energy societies as well as IEEE Young Professionals. The award is scheduled to be presented this month during the IEEE Honors Ceremony in New York City. “This award represents a significant milestone in my career,” Appaji says. “It validates my core belief that our success as engineers is not solely measured by research outcomes or publications but by the tangible impact we have on lives through accessible technology and the quality of the next generation of leaders we empower.” Developing a blood glucose measurement device After earning a bachelor’s degree in engineering from B.M.S. in 2010, he joined the school as a lecturer in its medical electronics engineering department. At the same time, he pursued master’s degrees in bioinformatics at the University Visvesvarya College of Engineering, also in Bengaluru. He graduated in 2013 and continued to teach at B.M.S.C.E. Four years later, Appaji signed up for the MIT Global Entrepreneurship Bootcamp, a two-week intensive hybrid program that includes webinars, online courses, and a five-day stay at MIT. It’s designed to give teams of aspiring entrepreneurs, innovators, and early-stage founders the structured mindset, tools, and frameworks they need to succeed. Appaji says he discovered the program while researching opportunities in innovation. “I had the technical expertise, but I needed a structured framework to transition my research from the laboratory to the market,” he says. During the MIT boot camp, he and a team of four other participants were tasked with approaching a complex health care challenge. They developed a noninvasive blood glucose measurement device to manage gestational diabetes—a condition that causes high blood sugar and insulin resistance during pregnancy. When the program ended, Appaji and two of his Australia-based teammates continued their collaboration by founding Glucotek in Brisbane, Australia. Inspired to continue his research in health care technology, Appaji pursued a doctorate in mental health and neurosciences at Maastricht University, in the Netherlands. His thesis focused on computational methods to identify retinal vascular patterns. “The patterns we analyze—including the curvature of the vessels, the angles at which they branch out, and their dimensions—reveal the health of the microvascular system,” he says. “With conditions like schizophrenia and bipolar disorder, microvascular changes mirror neurovascular changes in the brain.” “My journey has shown me that IEEE is much more than a professional society; it is a global platform that allows me to collaborate with a diverse network of experts to solve local humanitarian challenges.” Examining and measuring the retinal vascular system offers physicians a noninvasive way to examine neural changes, which can be biomarkers for psychiatric illnesses, he says. To bring his idea to life, he collaborated with an ophthalmologist, a psychiatrist, and colleagues from his engineering school to develop a screening device. They also created and trained the AI models that analyze retinal images. Ideas from his thesis led to the creation of the Smart Eye Kiosk, an AI-powered tool that scans the network of small veins that deliver blood to the inner retina. The tool monitors stress levels and mental health. It also screens for basic eye diseases such as diabetic retinopathy, as well as damage to retinal blood vessels caused by high blood sugar. Retinal images also can reveal physiological changes in the brain associated with psychiatric disorders such as schizophrenia and bipolar disorder, Appaji says. The kiosk uses AI models to analyze measurements of the vasculature network, such as vessel thickness, which can be biomarkers for psychiatric conditions. Since mental illnesses can be linked to genetics, relatives of patients with schizophrenia and bipolar disorder were also invited to participate in a study funded by India’s Cognitive Science Research Initiative’s Department of Science & Technology. The clinical data from this study can pave the way for earlier, more accurate diagnoses. “The biological basis for this is fascinating,” Appaji says. “The retina is the only place in the human body where the central nervous system and the vascular system can be visualized directly and noninvasively. Anatomically, the retina is an extension of the posterior part of the brain. Therefore, physiological changes in the brain are often reflected in the eyes.” This kiosk was developed in collaboration with Tan Tock Seng Hospital and Nanyang Technological University, which was funded by Ng Teng Fong Healthcare Innovation Program. He earned his Ph.D. in 2020 from Maastricht, and he received the Best Thesis Award from the university’s Mental Health and Neuroscience Research Institute. Appaji credits his time at the school for his multidisciplinary approach to developing medical devices. “Having the perspectives of mentors from diverse fields was essential to help me move my research beyond theory into a data-driven diagnostic tool,” he says. He was then named institutional coordinator of R&D at B.M.S. and later was promoted to be its head. Abhishek Appaji working on a smart bed sensor that continuously monitors a patient’s vital signs without the use of wires or wearable sensors.Abhishek Appaji A wireless smart bed to monitor vital signs Appaji continues to develop technologies for patients who need them most. “I feel a deep need to bridge this gap and ensure innovations have a tangible impact on society,” he says. In addition to the Smart Eye Kiosk, he improved the performance of the sensors of the smart beds that continuously monitor a patient’s vital signs without the use of wires or wearable sensors. The beds help hospital staff check on their patients in a noninvasive way. The project was done in collaboration with health AI company Dozee (Turtle Shell Technologies) in Bengaluru. The system measures mechanical microvibrations produced by the body in response to the ejection of blood into the aorta, which occurs with each heartbeat. A thin, industrial-grade sensor sheet is placed underneath the mattress. Additional funding is being provided by India’s Department of Science and Technology. “These sensors are incredibly sensitive,” Appaji says. “They pick up minute mechanical tremors through the mattress material.” The sensors detect the force of the patient’s heartbeat and the expansion and contraction of their chest during respiration. The vibrations are converted into electrical signals and analyzed using deep learning algorithms developed by Appaji and his team at the university in collaboration with Dozee. The technology is used in more than 200 hospitals throughout India and in thousands of households, he says. Mentoring budding entrepreneurs Appaji is also executive director of the BMSreenivasiah Innovators Guild Foundation, dedicated to nurturing entrepreneurial talent among students and faculty across the BMS group of Institutions. A not-for-profit company promoted by the BMS Education Trust, BIG Foundation provides a structured ecosystem for innovation, incubation, and startup growth. There, Appaji mentors budding entrepreneurs, offering advice on business plans, product pitches, marketing strategies, and licensing. Participants are students and faculty members. The foundation has incubated more than 10 ventures, according to Appaji. “The majority are centered on health care applications,” he says, “and have successfully secured backing from investors and seed funds.” Taking IEEE’s mission to heart Appaji was introduced to IEEE as an undergraduate when one of his professors encouraged him to volunteer for a conference sponsored by the IEEE Engineering in Medicine and Biology Society. He transcribed the seminars for session chairs, assisted with managing the talks, and helped answer attendees’ questions. “That experience was transformative,” he recalls. “I was amazed to find myself in the same room with the speakers and scientists who had authored the very textbooks I was studying. “It was then that I realized IEEE is far more than just technology and volunteering; it is a global platform for high-level networking with world-class scientists and technologists.” Appaji has served in several IEEE leadership positions, including 2018–2019 chair of the Young Professionals Bangalore Section. He is now treasurer of the IEEE Education Society, chair of IEEE Computer Society Bangalore Chapter, member of the steering committee of IEEE DataPort, and serves on the IEEE Member and Geographic Activities and IEEE Educational Activities boards. “What motivates me to remain active within IEEE is the profound alignment between my personal goals and the organizational mission of advancing technology for the benefit of humanity,” he says. “My journey has shown me that IEEE is much more than a professional society; it is a global platform that allows me to collaborate with a diverse network of experts to solve local humanitarian challenges.” The organization has helped fund some of Appaji’s lifesaving work. During the COVID-19 pandemic, he received a grant from the IEEE Humanitarian Technologies Board and Region 10 to develop 3D-printed protective equipment for people in Bengaluru’s underserved communities. The virus spread quickly in the high-density areas, where social distancing was nearly impossible. The kits, which included a door opener to avoid high-touch surfaces and an elbow-operated soap dispenser, were sent to nearly 500 households. “This work remains one of my most meaningful contributions to humanitarian technology,” Appaji says, “demonstrating how engineering can be rapidly deployed to protect vulnerable populations during a global crisis.” He advises younger IEEE members to: “Say yes to taking on roles of responsibility. Don’t wait for a formal title to lead; instead, start by volunteering to do small, manageable tasks within your local chapter or section.” “The networking opportunities and leadership skills you gain through these early responsibilities will shape your professional career far more than any textbook ever could.”
It’s easy to assume that Robert Woo was defined by the accident that took away his ability to walk. Certainly, the day of his accident—14 December 2007—was a turning point. Woo, an architect working on the new Goldman Sachs headquarters in New York City, hadn’t attended his company’s holiday party the night before, and that morning he was the only one in the trailer that served as the construction-site office. He was bent over his laptop when, 30 floors above, a crane’s nylon sling gave way, sending about 6 tonnes of steel plummeting toward the trailer. The roof collapsed, folding Woo in half and smashing his face into his laptop, which smashed through his desk. “I was conscious throughout the whole ordeal,” Woo remembers. “It was an out-of-body experience. I could hear myself screaming in pain. I could hear the voices of the rescue workers. I heard one firefighter say, ‘Don’t worry, we’re getting to you.’” The rescue workers hauled him out of the rubble and got him to the emergency room in 18 minutes flat; with one lung crushed and the other punctured, he wouldn’t have lasted much longer. In those frantic early moments, a doctor told him that he might be paralyzed from the neck down for the rest of his life. He remembers asking the doctors to let him die. Woo simply couldn’t imagine how a paralyzed version of himself could continue living his life. Then 39 years old, he worked long hours and jetted around the world to supervise the construction of skyscrapers. More important, he had two young boys, ages 6 months and 2 years. “I couldn’t see having a life while being paralyzed from the neck down, not being able to teach my boys how to play ball,” he recalls. “What kind of life would that be?” Robert Woo walks inside the Wandercraft facility in New York City using the company’s latest self-balancing exoskeleton. Nicole Millman But in a Manhattan showroom last May, Woo showed that he’s not defined by that accident, which left him paralyzed from the chest down, but with the use of his arms. Instead, he has defined himself by how he has responded to his injury, and the new life he built after it. In the showroom, Woo transferred himself from his wheelchair to a 80-kilogram (176-pound) exoskeleton suit. After strapping himself in, he manipulated a joystick in his left hand to rise from a chair and then proceeded to walk across the room on robotic legs. Woo’s steps were short but smooth, and he clanked as he walked. This exoskeleton, from the French company Wandercraft, is one of the first to let the user walk without arm braces or crutches, which most other models require to stabilize the user’s upper body. The battery-powered exoskeleton took care of both propulsion and balance; Woo just had to steer. The bulky apparatus had a backplate that extended above Woo’s head, a large padded collar, armrests, motorized legs, and footplates. Walking across the room, he appeared to be half man, half machine. On the other side of the showroom’s plate-glass window, on Park Avenue, a kid walking by with his family came to a dead halt on the sidewalk, staring with awe at the cyborg inside. Robert Woo prepares to walk in a Wandercraft exoskeleton; the device’s controller enables him to stand up, initiate walk mode, and choose a direction. Bryan Anselm/Redux The amazement on the boy’s face was reminiscent of Woo’s young sons’ reaction when they saw a photo of Woo trying out an early exoskeleton, back in 2011. “Their first comment was, ‘Oh, Daddy’s in an Iron Man suit,’” he remembers. Then they asked, “When are you going to start flying?” To which Woo replied, “Well, I’ve got to learn how to walk first.” The title of exoskeleton superhero suits Woo. He’s as soft-spoken and mild-mannered as Clark Kent, with a smile that lights up his face. Yet the strength underneath is undeniable; he has built a new life out of sheer determination. For 15 years, he’s been a test pilot, early adopter, and clinical-study subject for the most prominent exoskeletons under development around the world. He placed the first order for an exoskeleton that was approved for home use, and he learned what it was like to be Iron Man around the house. Throughout it all, he has given the companies detailed feedback drawn from both his architectural design skills and his user experience. He has shaped the technology from inside of it. Saikat Pal, a researcher at the New Jersey Institute of Technology, in Newark, met Woo during clinical trials for Wandercraft’s first model. Like so many others in the field, Pal quickly recognized that Woo brought a lot to the table. “He’s a super-mega user of exoskeletons: very enthusiastic, very athletic,” Pal says. “He’s the perfect subject.” By pushing the technology forward, Woo has paved the way for thousands of people with spinal cord injuries as well as other forms of paralysis, who are now benefiting from exoskeletons in rehab clinics and in their homes. “Our bionics program at Mount Sinai started with Robert Woo,” says Angela Riccobono, the director of rehabilitation neuropsychology at Mount Sinai Hospital, in New York City, where Woo became an outpatient after his accident. “We have a plaque that dedicates our bionics program to him.” Robert Woo walks down a sidewalk in New York City in 2015 using a ReWalk exoskeleton, one of the first exoskeletons designed for use outside the rehab clinic. Eliza Strickland It’s a fitting tribute. Woo’s post-accident life has been marked by victories, frustrations, deep love, and one devastating loss, and yet he has continued to devote himself to bionics. And while his vision for exoskeletons hasn’t changed, experience has reshaped what he expects from them in his lifetime. Rebuilding a Life After his Spinal Cord Injury Long before Woo ever stood up in a robotic suit, he had developed the habits of mind that would later make him an unusually perceptive test pilot. Woo has always been a builder, a tinkerer, a fixer. Growing up in the suburbs of Toronto, he put together model kits of battleships and airplanes without looking at the instructions. “I just put things together the way I thought it would work out,” he says. He trained as an architect and in 2000 joined the Toronto-based firm Adamson Associates Architects, a job that soon had him traveling to Europe and Asia to work on corporate high-rises. Adamson specializes in taking the stunning designs of visionary architects and turning them into practical buildings with elevators and bathrooms. “Most of the design architects don’t really have a clue about how to build buildings,” Woo says. He liked solving those problems; he liked reconciling beautiful designs with the stubborn reality of construction. That talent for understanding a structure from the inside and spotting the flaws would prove essential later. After his accident, Woo had two major surgeries to stabilize his crushed spine, which required surgeons to cut through muscles and nerves that connected to his arms. For two months, he couldn’t feel or move his arms; there was a chance he never would again. Only when sensation began creeping back into his fingertips did he allow himself to imagine a different future. If he wasn’t paralyzed from the neck down, he thought, maybe more of his body could be brought back online. “My focus was to walk again,” he says. Woo was discharged in March 2008 and went back to his New York City apartment. He was still bedridden and required around-the-clock care. He doesn’t much like to talk about this next part: By May, his then-wife had moved back to Canada and filed for divorce, asking for full custody of their two children. Woo remembers her saying, “I can’t look after three babies, and one of them for life.” It was a dark time. Riccobono of Mount Sinai, who met Woo shortly after he became an outpatient there in 2008, recalls the despondent look on his face the first time they talked. “I wasn’t sure that he wasn’t going to take his life, to be honest,” she says. “He felt like he had nothing to live for.” Angela Riccobono of Mount Sinai Hospital (left) credits Woo with jump-starting the hospital’s bionics program; a plaque in the department of rehabilitation medicine recognizes his role. Yet Woo harbors no animosity toward his ex-wife. “If we hadn’t separated and gone through the custody hearing, I don’t think I would have gotten this far,” he says. To win partial custody of his children, Woo had to become independent. He had to get off narcotic pain medications, regain strength, and learn how to navigate life in a wheelchair. He had to show that he no longer needed constant nursing, and that he could take care of both himself and his boys. There were milestones: learning how to get back into his wheelchair after a fall, learning to drive a car with hand controls, learning to manage his body as it was, not as it had been. The biggest change came when he reconnected with his high school sweetheart, a vivacious woman named Vivian Springer. She was then dividing her time between Toronto and New York City, and she had a son who was almost the same age as Woo’s two boys. Springer had worked in a nursing home and knew how to change the sheets without getting him out of bed; she was currently working in human resources and knew how to deal with insurance companies. “You wouldn’t believe how much stress it lifted off of me,” Woo says. Over time, they became a family. Robert Woo’s wife, Vivian, was trained in how to operate the device he used at home. His sons, Tristan (left) and Adrien, grew up watching their dad test exoskeletons. Left: Lifeward; Right: Robert Woo Once Woo had that foundation in place, Riccobono witnessed a profound change. “He went from focusing on ‘what I can’t do anymore’ to ‘What’s still possible? What can I do with what I have?’” At Mount Sinai, Woo remembers asking his doctor Kristjan Ragnarsson, who was then chairman of the department of rehabilitation medicine, if he would ever walk again. “His response was, ‘Yes, you can walk again,’” Woo remembers, “‘but not the way you used to walk.’” First Steps in an Exoskeleton As soon as he had regained use of his hands, Woo had started googling, looking for anything that could get him back on his feet. He tried rehab equipment like the Lokomat, which used a harness suspended above a treadmill to enable users to walk. But at the time, it required three physical therapists: one to move each leg and one to control the machine. It was a far cry from the independent strides he dreamed of. Several years in, he learned about two companies that had built something radically different: exoskeleton suits for people with spinal cord injuries. These prototypes had motors at the knees and the hips to move the legs, with the user stabilizing their upper body with arm braces. Woo desperately wanted to try one, although the technology was still experimental and far from regulatory approval. So he took the idea to Ragnarsson, asking if Mount Sinai could bring an exoskeleton into its rehab clinic for a test drive. Ragnarsson, who’s now retired, remembers the request well. “He certainly gave us the kick in the behind to get going with the technology,” he says. Robert Woo tries out an early exoskeleton from Ekso Bionics at Mount Sinai Hospital, where he first began testing the technology. Mario Tama/Getty Images Ragnarsson had seen decades of failed attempts to get paraplegics upright, including “inflatable garments made of the same material the astronauts used when they went to the moon,” he says. All those devices had proved too tiring for the user; in contrast, the battery-powered exoskeletons promised to do most of the work. And he knew one of the founders of Ekso Bionics, a Berkeley, Calif.–based company that had built exoskeletons for the military. In 2011, Ekso brought its new clinical prototype to Mount Sinai. The day came for Woo’s first walk. “I was excited, and I was also scared, because I hadn’t stood up for almost five years,” he remembers. “Standing up for the first time was like floating, because I couldn’t feel my feet.” In that first Ekso model, Woo didn’t control when he stepped forward; instead, he shifted his weight in preparation, and then a physical therapist used a remote control to trigger the step. Woo walked slowly across the room, using a walker to stabilize his upper body, his steps a symphony of clunks and creaks and whirs. He found it mentally and physically exhausting, but the effort felt like progress. Robert Woo stands using an exoskeleton and embraces his wife, Vivian. Woo says that exoskeleton use has both physical and psychological benefits. Mt. Sinai Riccobono was there for those first steps, with tears running down her face. “I remembered how he looked the day I first met him, so defeated,” she says. “To see him rise from the chair, to see him rise to a standing position, to see how tall he was, to see him take those first steps—it was beautiful.” Ragnarsson saw clear benefits to the technology. “Any type of walking is good physiologically,” he says. “And it’s a tremendous boost psychologically to stand up and look someone in the eye.” Woo remembers hugging his partner, Springer, and for the first time not worrying about running over her toes with his wheelchair. I first met Woo a few days later, during his third session with the Ekso at Mount Sinai. Ann Spungen (left), a researcher at a Veterans Affairs hospital, led early clinical trials of exoskeletons. Her research focused on the medical benefits of exoskeleton use. Robert Woo Later that same year, at a Department of Veterans Affairs (VA) hospital in the Bronx, Woo got to try a prototype of the world’s other leading exoskeleton: the ReWalk, from the Israeli company of the same name (since renamed Lifeward). VA researchers, led by Ann Spungen, were keen to determine if exoskeleton use had real medical value for veterans with spinal cord injuries. Woo was part of that clinical trial, for which he had more than 70 walking sessions, and he’s since been in many others. But he remembers the first VA trial with the most gratitude. “Dr. Spungen’s first exoskeleton clinical trial really turned things around for me,” he says. Over the course of the trial’s nine intense months, Woo says he saw noticeable improvements to many facets of his health. “By the end of the trial, I eliminated about three-quarters of my medication intake,” he says, including narcotic pain pills and medication for muscle spasms. He grew fitter, with less body fat, more muscle mass, and lower cholesterol. His circulation improved, he says, causing scrapes and cuts to heal more quickly, and his digestion improved too. The results Woo experienced have generally been borne out in research studies at the VA and elsewhere—exoskeletons aren’t just good for the mind, they’re good for the body. Improving Exoskeletons From the Inside During the VA trial, Woo began to think of exoskeletons not as miraculous machines, but as works in progress. Pierre Asselin (right), a biomedical engineer, worked with Robert Woo during clinical trials of exoskeletons. He says Woo was always pushing the limits of the technology. Robert Woo Pierre Asselin, the biomedical engineer coordinating the VA’s study, watched participants respond very differently to the equipment. “These devices are not the equivalent of walking—you’re tired after walking a mile,” he says. He notes that later models of both the Ekso and ReWalk enabled users to initiate each step through software that recognized when they shifted their weight. Asselin adds that the cognitive load is “like learning to drive a manual transmission car, where at first you’re really struggling to coordinate the clutch and the brake.” Woo picked it up immediately, he remembers. Robert Woo uses an exoskeleton to reach items in a kitchen cabinet during a test of the device’s utility for everyday tasks. Eliza Strickland Woo became an invaluable partner, Asselin says. “When we first started with the devices, there was no training manual. We developed all of that through collaboration with Robert and other participants.” Woo pushed the limits of the technology, Asselin says, whether it was seeing how many steps he could take on one battery charge or simulating a failure mode. “He’d say, ‘What happens if I was to fall? What would be the approach to getting up?’” Woo approached the ReWalk the way he had approached buildings in his previous life: He looked inside the structure and found the weak points. An early model left some users with leg abrasions where the straps rubbed—a small injury for most people, but a serious risk for someone who can’t feel a wound forming. Woo suggested better padding and stronger abdominal supports to redistribute the load. He also hated the heavy backpack that carried the battery and computer, so one afternoon he grabbed an old pack, cut off the straps, and rebuilt it into a compact hip-mounted pouch. Then he snapped photos and sent them to the company. The next model arrived with a fanny pack. Robert Woo sent detailed design sketches as part of his feedback to exoskeleton engineers. Robert Woo Sometimes his fixes were more ambitious. One Ekso unit that he used at Mount Sinai kept shutting down after 30 minutes. Woo felt the hip motors and found them hot to the touch. “I said, ‘Can I remove these? I’m going to make a really quick fix, okay? Give me a drill and I’ll put a couple of holes in it,” he recalls telling the therapists, proposing to create a DIY heat sink. He wasn’t allowed to modify the prototype, but a year later the company introduced improved cooling around the hip motors. “There is a Robert Woo design on this device,” one therapist told him. Eythor Bender, who was then the CEO of Ekso, called Woo to thank him for his feedback and invite him to spend a week at Ekso’s headquarters. “There was no lack of engineering power in that building,” says Bender. “But sometimes when you work with engineers, they overlook important things.” Bender says Woo brought both design skills and lived experience to his weeklong residency. “He told the engineers, ‘Guys, this has to be something that people actually like to wear.’” Ekso Bionics CEO Eythor Bender and Mount Sinai physician Kristjan Ragnarsson were both on hand for Woo’s early trials of the Ekso device. Ragnarsson says he saw physical and psychological benefits of exoskeleton use. Robert Woo The longer Woo tested, the further ahead he started thinking. With motors only at the hips and knees, every exoskeleton still required crutches. Add powered ankles, he told the Ekso and ReWalk teams, and the suits could balance themselves, freeing the user’s hands. But Woo was ahead of his time. “They said they weren’t going to do that. They weren’t going to change their whole platform,” he remembers. Years later, though, hands-free exoskeletons like those from Wandercraft would emerge built around exactly that principle. When the Exoskeleton Came Home By the mid-2010s, Woo had pushed the technology as far as he could in clinics. What he wanted now was to use an exoskeleton at home. That milestone came after ReWalk’s exoskeleton became the first to win FDA approval for home use in 2014. ReWalk engineers still remember Woo’s help on the final tests for that personal-use model. It was the end of May in 2015, recalls David Hexner, the company’s vice president of research and development. “He said, ‘Guys, this is great. I’m going to buy it.’” Woo was the first customer to buy an exoskeleton to bring home, paying US $80,000 out of pocket. His insurance wouldn’t cover the cost, but he was able to make the purchase in part because of a legal settlement after his accident. The home-use model came with a requirement that the user have at least one companion who was fully trained in operating the device. In Woo’s case, that meant that Springer learned to suit him up, realign his balance, and help him if he fell. On delivery day, two SUVs drove up to a hotel down the street from Woo’s condo in the Toronto area. The technicians hauled two huge boxes into a hotel room and assembled his personal exoskeleton. They took Woo’s measurements, made adjustments, checked the software. This latest version could be controlled by either weight shifting or tapping commands on a smartwatch, and Woo had the app ready. He tested out everything in the hotel room, signed off, and then the technicians drove his robot legs to his home. That was the start of his golden period with the ReWalk—similar to the excitement many people experience with a new piece of exercise equipment. “I used it every day for a few hours, and then I started logging how many steps I’d done,” Woo says. “My last count was probably just slightly over a million steps,” he says, with half of those steps taken in his home unit and half in training programs and clinical trials. The ReWalk was the first exoskeleton available for use outside the clinic. Robert Woo’s ReWalk arrived in two large boxes. ReWalk engineers assembled it in a hotel room, and Woo tried it out in the hallway before taking it home. Robert Woo Tristan, Woo’s eldest son, remembers doing laps with his dad in the condo’s underground parking garage while his dad was training for a 5-kilometer race in New York City. Tristan admits that he had previously been embarrassed about his dad, but training for the race shifted something for him. “I was so used to not wanting to tell people that my dad was in a wheelchair, but then I shared his passion for the training,” he says. “When people would come up to us, I’d tell them about it.” The ReWalk could turn ordinary moments into small engineering projects. On weekends, Woo would take his boys to the golf course behind their condo and bring a baseball. He had rigged two holsters to the sides of the suit so he could stash a crutch and stand on three points (two legs and one arm) while he pitched or caught. Throw, switch crutches, catch. On the day of his accident, he never thought such a scene would be possible. But with the exoskeleton, it became just another design problem to solve. “It’s a little more work. It’s not perfect,” he says. “But in the end, you still get to do what you want to do—which is play ball with your sons.” Tristan, now a college student, says he didn’t realize at the time how hard his dad worked to make those mundane activities possible. “Reflecting on it now,” he says, “he has shaped almost every element of my life, and he definitely is my hero.” But even during that golden stretch, the ReWalk had a way of asserting its limits. Every so often it would freeze mid-stride and require a reboot—a small technical hiccup in theory, but a serious problem when there’s a person strapped inside. Once, when he was walking on his own in the parking garage (without his mandated companion), the suit glitched and went into “graceful collapse” mode, lowering him to a seated position on the ground. Woo had to ask security to bring his wheelchair and a dolly. He had imagined the exoskeleton would be most useful in the kitchen. Woo loves to cook, and he had pictured himself standing at the stove, looking down into pots, and moving easily between counter and sink. The reality, he found out, was more complicated. “It’s actually very time-consuming and troublesome” to cook in an exoskeleton, he says. Preparing a meal meant first rolling through the kitchen in his wheelchair to gather every ingredient and utensil, then transferring himself into the ReWalk and moving himself into position at the counter, stopping at just the right moment. “That’s when I fell once,” Woo says. “I collided with the counter and then lost my balance and fell backward.” If all went well, he’d lean either on one crutch or the counter to keep his balance while he worked. But if he’d forgotten to grab the vinegar from the cabinet, he’d have to go into walk mode, crutch over to it, and figure out how to carry the bottle back to his workstation. Sitting unused in Robert Woo’s home, his ReWalk exoskeleton reflects both the promise and the limits of early devices. Robert Woo Gradually, he stopped trying. The suit, which he’d once worn every day, spent more time sitting idle in the hallway; like so many abandoned treadmills and stationary bikes, it gathered dust. Part of the reason was the exoskeleton’s practical limitations, but part of it was a shocking development: In 2024, Vivian was diagnosed with an aggressive form of breast cancer. She died in November of that year, at the age of 54. Woo was scheduled to begin a new round of clinical trials for the Wandercraft home-use exoskeleton that month. In the aftermath of Vivian’s death, he postponed his sessions and questioned whether he would ever go back. “At the time, I thought, ‘What’s the point?’” he remembers. He did go back, though. “He just rolled up, right into my office,” says Mount Sinai’s Riccobono. “He still had Vivian’s box of ashes on his lap. That’s how fresh it was.” Woo brought the box into a meeting of spinal cord injury patients and shared the story of losing the love of his life. And he told them that he heard his wife’s voice in his head every day, telling him to get back to work. Once again, he was figuring out how to move forward with what he had. How Close Are We to Everyday Exoskeletons? In the Wandercraft showroom last May, Woo steered toward the door to the street, technicians flanking him like spotters. The slope down to the sidewalk was barely an inch high, but everyone tensed. He shifted his weight and took a step forward. The suit halted automatically. He tried again—step, stop; step, stop—as the suit kept detecting the slight decline and a safety feature kicked in. The Wandercraft isn’t yet rated for slopes of more than 2 percent, and even the gentle pitch of Park Avenue was enough to trigger its safeguards. When he finally reached the sidewalk, Woo broke into a grin. A man in the back seat of a stopped Uber leaned out his window, filming. During testing of the Wandercraft exoskeleton, straps caused an abrasion on Robert Woo’s leg, which he documented as part of his feedback to the company. Robert Woo Woo had recently completed seven sessions with the Wandercraft at the VA hospital and had been impressed overall. But at the showroom, he rolled up his pants leg to reveal an abrasion on his shin, the result of a strap that had worn away a patch of skin during a long walking session. He would later send Wandercraft a nine-page assessment with photos and a technology wish list, asking the company to work on things like padding, variable walking speeds, and deeper squats. Wandercraft’s engineers relish that kind of user feedback, says CEO Matthieu Masselin. Exoskeletons are a far more difficult engineering problem than humanoid robots, he explains. “You basically have two systems of equal importance. You know about the robot—it’s fully quantified and measured. But you don’t know what the person is doing, and how the person is moving within the device.” Since Woo began testing exoskeletons 15 years ago, both the technology and the market have made strides. ReWalk and Ekso won FDA clearance for clinical use in the 2010s, and both now sell home-use versions. The companies have sold thousands of exoskeletons to rehab clinics and personal users, and they see room for growth; in the United States alone, about 300,000 people live with spinal cord injuries, and millions more have mobility impairments from stroke, multiple sclerosis, or other conditions. The VA began supplying devices to eligible veterans in 2015, and Medicare recently established a system for reimbursement, a move that private insurers are beginning to follow. What was once experimental is slowly becoming established. Researchers who test the devices say the technology still has significant limits. Pal, of the New Jersey Institute of Technology, mentions battery life, dexterity, and reliability as ongoing challenges. But, he says with a laugh, “Our bodies have evolved over many millions of years—these machines will need a bit more time.” Pal hopes the companies will keep pushing the technological frontier. “My lifetime goal is to see the day when someone like Robert Woo can wake up in the morning, put this device on, and then live an ordinary life.” For Woo, the real question about the self-balancing Wandercraft was: Could he cook with it? In the VA hospital’s home mockup, he tried it out in the kitchen, stepping sideways to retrieve items from cabinets and squatting to grab something from the fridge’s lower shelf. For the first time in years, he could work at a counter without leaning on crutches. “The self-standing exoskeleton changes everything,” he says. He imagines a user placing a Thanksgiving turkey on a tray attached to the suit and walking it into the dining room. Back in the showroom, Woo finishes the demo and brings the suit to a seated position before transferring back to his wheelchair. After so many years of testing prototypes, he’s now realistic about the technology’s timeline. A truly all-day exoskeleton—the kind you live in, the kind that replaces a wheelchair—may be a decade or more away. “It may not be for me,” he says. But that’s no longer the point. He’s thinking about young people who are newly injured, who are lying in hospital beds and trying to imagine how their lives can continue. “This will give them hope.”
As a kid, I loved the 1980s aquatic adventure show Danger Bay. True to the TV show’s name, danger was always lurking at the Vancouver Aquarium, where the show was set. In one memorable episode, young Jonah and a friend get trapped in a sabotaged mini-submarine, and Jonah’s dad, a marine-mammal veterinarian, comes to the rescue in a bubble-shaped underwater vehicle. Good stuff! Only recently—as in when I started working on this column—did I learn that the rescue vehicle was not a stage prop but rather a real-world research submersible named Deep Rover. What Was Deep Rover and What Did It Do? Built in 1984 and launched the following year, Deep Rover was a departure from standard underwater vehicles, which typically required divers to lie in a prone position and look through tiny portholes while tethered to a support ship. Deep Rover was designed to satisfy human curiosity about the underwater world. As the rover moved freely through the water down to depths of 1,000 meters, the operator sat up in relative comfort in the cab, inside a clear 13-centimeter-thick acrylic bubble with panoramic views—an inverted fishbowl, with the human immersed in breathable air while the sea creatures looked in. Used for scientific research and deepwater exploration, it set a number of dive records along the way. Submarine designer Graham Hawkes [left] and marine biologist Sylvia Earle [right] came up with the idea for Deep Rover.Alain Le Garsmeur/Alamy The team behind Deep Rover included U.S. marine biologist Sylvia Earle and British marine engineer and submarine designer Graham Hawkes. Earle and Hawkes’s collaboration had begun in May 1980, when Earle complained to Hawkes about the “stupid” arms on Jim, an atmospheric diving suit; she didn’t realize she was complaining to one of Jim’s designers. Hawkes explained the difficulty of designing flexible joints that could withstand dueling pressures of 101 kilopascals on the inside—that is, the normal atmospheric pressure at sea level—and up to about 4,100 kPa on the outside. But he listened carefully to Earle’s wish list for a useful manipulator. Several months later, he came back with a design for a superbly dexterous arm that could hold a pencil and write normal-size letters. Earle and Hawkes next turned to designing a one-person bubble sub, which they considered so practical that it would be an easy sell. But after failing to attract funding, they decided to build it themselves. In the summer of 1981, they pooled their resources and cofounded Deep Ocean Technology, setting up shop in Earle’s garage in Oakland, Calif. Phil Nuytten, a Canadian designer of submersibles and dive systems, engineered Deep Rover.Stuart Westmorland/RGB Ventures/Alamy They still found that customers weren’t interested in their crewed submersible, though, so they turned to unmanned systems. Their first contract was for a remotely operated vehicle (ROV) for use in oil-rig inspection, maintenance, and repair. Other customers followed, and they ended up building 10 of these ROVs. In 1983, they returned to their original idea and contracted with the Canadian inventor and entrepreneur Phil Nuytten to engineer Deep Rover. Nuytten didn’t have to be convinced of the value of the submersible. He had grown up on the water and shared their dream. As a teenager, he opened Vancouver’s first dive shop. He then worked as a commercial diver. He founded the ocean- and research-tech companies Can-Dive Services (in 1965) and Nuytco Research (in 1982), and he developed advanced submersibles as well as diving systems. These included the Newtsuit, an aluminum atmospheric diving suit for use on drilling rigs and salvage operations. RELATED: Virgin Oceanic’s Voyage to the Bottom of the Sea Deep Rover’s first assignment was to boost offshore oil exploration and drilling in eastern Canada. Funding came from the provincial government of Newfoundland and Labrador and the oil companies Petro-Canada and Husky Oil. But the collapse of oil prices in the mid-1980s made it uneconomical to operate the submersible. So the rover’s mission broadened to scientific research. Deep Rover’s Technical Specs The pilot could operate Deep Rover safely for 4 to 6 hours at a depth of 1,000 meters and speeds of up to 1.5 knots (46 meters per minute). The submersible could be tethered to a support ship or move freely on its own. Two deep-cycle, lead-acid battery pods weighing about 170 kilograms apiece provided power. It had a VHF radio and two frequencies of through-water communications, plus tracking beacons. From 1987 to 1989, Deep Rover did a series of dives in Oregon’s Crater Lake, the deepest lake in the United States. During one dive, National Park Service biologist Mark Buktenica [top] collected rock samples.NPS The rover’s four thrusters—two horizontal fixed aft thrusters and two rotating wing thrusters—could be activated in any combination through microswitches built into the armrest. The pilot navigated using a gyro compass, sonar, and depth gauges (both digital and analog). Much to Earle’s delight, Deep Rover had two excellent manipulators, each with four degrees of freedom, thus solving the problem that had started her down this path of invention. The pilot controlled the manipulators with a joystick at the end of each armrest. Sensory feedback systems helped the pilot “feel” the force, motion, and touch. The two arms had wraparound jaws and could lift about 90 kg. If something went wrong, Deep Rover carried five days’ worth of life support stores and had a variety of redundant safety features: oxygen and carbon dioxide monitoring equipment; a halon (breathable) fire extinguisher; a full-face BIBS (built-in breathing system) that tapped into the starboard air bank; and a ground fault-detection system. If needed, the rover could surface quickly by jettisoning equipment, including the battery pods and a 90-kg drop weight in the forward bay. In dire circumstances, the pressure hull (the acrylic bubble, that is) could separate from the frame, taking with it only its oxygen tanks, strobe, through-water communications, and wing thrusters. Deep Rover’s achievements From 1984 to 1992, Deep Rover conducted about 280 dives. It inspected two of the tunnels near Niagara Falls that divert water to the Sir Adam Beck II hydroelectric plant. In California’s Monterey Bay, the rover let researchers film previously unknown deep-sea marine life, which helped establish the Monterey Bay Aquarium Research Institute. At Crater Lake National Park, in Oregon, Deep Rover proved the existence of geothermal vents and bacteria mats, leading to the protection of the site from extractive drilling. Deep Rover was featured in a short film shown at Vancouver’s Expo ’86, the first of several TV and movie appearances. There was Danger Bay. Director James Cameron used an early prototype of the submersible in his 1989 film The Abyss. Deep Rover also made an appearance in Cameron’s 2005 documentary Aliens of the Deep. In 1992, Deep Rover came to the end of its working life. It now resides at Ingenium, Canada’s Museums of Science and Innovation, in Ottawa. For a time, Deep Ocean Engineering continued to develop later generations of the submersible. Eventually, though, uncrewed remotely operated and autonomous underwater vehicles became the norm for deep-sea missions, replacing human pilots with sensors and equipment. New ROVs can dive significantly deeper than human-piloted ones, and new cameras are so good that it feels like you’re there…almost. And yet, humans still long to have the personal experience of exploring the depths of the oceans. 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 April 2026 print issue as “All Alone in the Abyss.” References My friends at Ingenium, Canada’s Museums of Science and Innovation, helpfully provided me with background material on why they decided to acquire Deep Rover. They also published a great blog post about the rover. Dirk Rosen, executive vice president of engineering at DEEP, published specifications for Deep Rover in his 1986 IEEE paper “Design and Application of the Deep Rover Submersible.” Sylvia Earle, known affectionately as “Her Deepness,” has written extensively about the ocean depths. I found her book Sea Change: A Message of the Oceans (G.P. Putnam’s Sons, 1995) to be especially enjoyable.
To stay competitive, many small businesses need advanced wireless communication networks, not only to communicate but also to leverage technologies such as artificial intelligence, the Internet of Things, and robotics. Often, however, the businesses lack the technical expertise needed to install, configure, and maintain the systems. Bhaskara Rallabandi, who spent more than two decades working for major telecom companies, decided to use his expertise to help small businesses. Rallabandi, an IEEE senior member, is an expert certified by the International Council on Systems Engineering. Invences Cofounder Bhaskara Rallabandi Founded 2023 Headquarters Frisco, Texas Employees 100 In 2023 he helped found Invences, a telecommunications automation company headquartered in Frisco, Texas. Invences services include designing, building, and installing data centers, as well as cost-effective and secure wireless, private, IoT, and virtual communications networks. The company has set up systems for farms, factories, and universities in rural and urban areas including underserved communities. Its mission, Rallabandi says, is to “build autonomous, ethical, and sustainable networks that connect communities intelligently.” For his work, he was recognized last year for “entrepreneurial leadership in founding and scaling a U.S.-based technology company, advancing innovation in 5G/6G and Open RAN [radio access network], shaping global standards, and inspiring future leaders through mentorship and community impact” with the IEEE-USA Entrepreneur Achievement Award for Leadership in Entrepreneurial Spirit. Building a telecommunications career He began his telecommunications career in 2009 as a manager and principal network engineer at Verizon’s Innovation Labs in Waltham, Mass. He and his team ran some of the earliest long-term evolution and evolved packet core performance trials. (LTE is the 4G wireless broadband standard for mobile devices. EPC is the IP-based, high-performance core network architecture for 4G LTE networks.) That work at Innovation Labs, he says, was key to the development of the first 4G systems. It set the stage for scalable, interoperable broadband architectures that underpin today’s 5G and 6G designs. “We built the first bridge between legacy and cloud-native networks,” he says. He left in 2011 to join AT&T Labs in Redmond, Wash. As senior manager and principal solutions architect, he oversaw the design, integration, and testing of the company’s next-generation wireless systems. He also led projects that redefined automation of networks and set up cloud computing systems including FirstNet, the nationwide broadband network for first responders, and VoLTE, the first voice-over-video LTE for conducting video calls. In 2018 Rallabandi was hired as a principal and a senior manager of engineering at Samsung Networks Division’s Technology Solutions Division, in Plano, Texas. He led the development of 5G virtualization and Open RAN initiatives, which enable more flexible, scalable, and efficient large network deployments and interoperability among vendors. Designing networks for small businesses Feeling that he wasn’t reaching his full potential in the corporate world, and to help small businesses, he opted to start his own venture in 2023 with his wife, Lakshmi Rallabandi, a computer science engineer. She is Invences’s CEO, and he is its founding principal and chief technology advisor. Invences, which is self-funded and employs about 100 people, has more than 50 customers from around the world. “I wanted to do something more interesting where I could use the knowledge I gained working for these big companies to fill the gaps they overlooked in terms of automation” for small businesses, he says. “I have a team of people who, combined, have 200 years of technology experience.” The startup builds networks that simplify its clients’ operations and reduce their costs, he says. Instead of duplicating how major telecom carriers build networks for dense urban areas, he says, his designs reimagine the network architecture to lower its complexity, costs, and operational overhead. “Connectivity should not be a luxury. Rural communities deserve an infrastructure that fits their needs.” The systems integrate new technologies such as Open RAN, virtualized RAN, digital twins, telemetry, and advanced analytics. Some networks also incorporate agentic AI, an autonomous system that runs independently of humans and uses AI agents that plan and act across the network. Digital twins evaluate the agent’s decisions before releasing them. “Autonomy is not about removing humans from the loop,” Rallabandi says. “It is about giving systems the ability to manage complexity so humans can focus on intent and outcomes.” Rallabandi also has worked on AI-driven telecom observability technologies designed to allow networks to detect anomalies and optimize performance automatically. He has developed a virtual O-RAN innovation lab, where clients can test the interoperability of their 5G systems, try out their enhancements, run trials of future functions, and experiment with updates. Invences partnered with Trilogy Networks to build the FarmGrid platform for farms in Fargo, N.D., and Yuma, Ariz. FarmGrid used private 5G networks, edge-computing AI, and digital twins to make the operations more efficient. “The project connects farms with sensors, analytics platforms, and autonomous equipment to enable precision agriculture, water optimization, and real-time decision-making,” Rallabandi says. IEEE Senior Member Bhaskara Rallabandi talks about partnering with Trilogy Networks to build the FarmGrid platform for farms in Fargo, N.D., and Yuma, Ariz.TECKNEXUS Paying it forward through IEEE programs Rallabandi says he believes staying involved with IEEE is important to his career development and a way to give back to the profession. He is a frequent invited speaker at IEEE conferences. He is active with IEEE Future Networks and its Connecting the Unconnected (CTU) initiative. Members of the Future Networks technical community work to develop, standardize, and deploy 5G and 6G networks as well as successive generations. CTU aims to bridge the digital divide by bringing Internet service to underserved communities. During itsannual challenge, Rallabandi works with the winning students, researchers, and innovators to help them turn their concepts into affordable, cost-effective options. “CTU represents the best of IEEE,” he says. “It is about taking innovation out of conferences and into communities that need it the most. “Connectivity should not be a luxury. Rural communities deserve an infrastructure that fits their needs.” He participates in the recently launched IEEE Future Networks Empowerment Through Mentorship initiative, which helps innovators, entrepreneurs, and startups expand their companies by educating them about finance, marketing, and related concepts. “IEEE gives me both a voice and a responsibility,” Rallabandi says. “We’re not just developing technology; we are shaping how humanity connects.”
Facial recognition technology (FRT) dates back 60 years. Just over a decade ago, deep-learning methods tipped the technology into more useful—and menacing—territory. Now, retailers, your neighbors, and law enforcement are all storing your face and building up a fragmentary photo album of your life. Yet the story those photos can tell inevitably has errors. FRT makers, like those of any diagnostic technology, must balance two types of errors: false positives and false negatives. There are three possible outcomes. Three Possible Outcomes a) identifies the suspect, since the two images are of the same person, according to the software. Success! b) matches another person in the footage with the suspect’s probe image. A false positive, coupled with sloppy verification, could put the wrong person behind bars and lets the real criminal escape justice. c) fails to find a match at all. The suspect may be evading cameras, but if cameras just have low-light or bad-angle images, this creates a false negative. This type of error might let a suspect off and raise the cost of the manhunt. In best-case scenarios—such as comparing someone’s passport photo to a photo taken by a border agent—false-negative rates are around two in 1,000 and false positives are less than one in 1 million. In the rare event you’re one of those false negatives, a border agent might ask you to show your passport and take a second look at your face. But as people ask more of the technology, more ambitious applications could lead to more catastrophic errors. Let’s say that police are searching for a suspect, and they’re comparing an image taken with a security camera with a previous “mug shot” of the suspect. Training-data composition, differences in how sensors detect faces, and intrinsic differences between groups, such as age, all affect an algorithm’s performance. The United Kingdom estimated that its FRT exposed some groups, such as women and darker-skinned people, to risks of misidentification as high as two orders of magnitude greater than it did to others. Less clear photographs are harder for FRT to process.iStock What happens with photos of people who aren’t cooperating, or vendors that train algorithms on biased datasets, or field agents who demand a swift match from a huge dataset? Here, things get murky. Facial Recognition Gone Wrong THE NEGATIVES OF FALSE POSITIVES 2020: Robert Williams’s wrongful arrest cost him detention. The ensuing settlement requires Detroit police to enact policies that recognize FRT’s limits. iStock ALGORITHMIC BIAS 2023: Court bans Rite Aid from using facial recognition for five years over its use of a racially biased algorithm. iStock TOO FAST, TOO FURIOUS? 2026: U.S. immigration agents misidentify a woman they’d detained as two different women. VICTOR J. BLUE/BLOOMBERG/GETTY IMAGES Consider a busy trade fair using FRT to check attendees against a database, or gallery, of images of the 10,000 registrants, for example. Even at 99.9 percent accuracy you’ll get about a dozen false positives or negatives, which may be worth the trade-off to the fair organizers. But if police start using something like that across a city of 1 million people, the number of potential victims of mistaken identity rises, as do the stakes. What if we ask FRT to tell us if the government has ever recorded and stored an image of a given person? That’s what U.S. Immigration and Customs Enforcement agents have done since June 2025, using the Mobile Fortify app. The agency conducted more than 100,000 FRT searches in the first six months. The size of the potential gallery is at least 1.2 billion images. At that size, assuming even best-case images, the system is likely to return around 1 million false matches, but at a rate at least 10 times as high for darker-skinned people, depending on the subgroup. Responsible use of this powerful technology would involve independent identity checks, multiple sources of data, and a clear understanding of the error thresholds, says computer scientist Erik Learned-Miller of the University of Massachusetts Amherst: “The care we take in deploying such systems should be proportional to the stakes.”
5G covers under 40% of landmass. This Whitepaper details how 3GPP Release 17 addresses six satellite challenges: delay, Doppler, path loss, polarization, spectrum, and architecture. What Attendees will Learn Why non-terrestrial networks are now integral to the 5G roadmap — Understand how the Third Generation Partnership Project (3GPP) Release 17 incorporates satellite-based connectivity into the 5G system, targeting ubiquitous coverage across maritime, remote, and polar regions where terrestrial networks reach less than 40% of the world’s landmass. Learn the distinction between New Radio non-terrestrial networks for mobile broadband and Internet of Things non-terrestrial networks for low-power machine-type communications. How satellite constellation design shapes coverage, capacity, and latency — Examine how orbit altitude (low earth orbit, medium earth orbit, geostationary earth orbit), beam footprint geometry, elevation angle, and inclination determine coverage area, round-trip time, and differential delay across user equipment within a single beam. Explore the trade-offs between transparent bent-pipe and regenerative onboard-processing payload architectures. What radio frequency challenges distinguish satellite links from terrestrial propagation — Explore the six major technical challenges: high free-space path loss, time-variant Doppler, differential delay across large beam footprints, Faraday rotation of polarization through the ionosphere, and spectrum coexistence between terrestrial and non-terrestrial bands in the S-band and L-band. How 5G protocols must adapt to support non-terrestrial connectivity — Learn the specific amendments to hybrid automatic repeat request operation, timing advance control (split into common and user-equipment-specific components), random access procedure timing extensions, discontinuous reception power saving adaptations, earth-fixed tracking area management, conditional handover mechanisms, and feeder link switching for service continuity in a unique propagation environment. Download this free whitepaper now!
In a landmark case, a jury found this week that Meta and YouTube negligently designed their platforms and harmed the plaintiff, a 20-year-old woman referred to as Kaley G.M. The jury agreed with the plaintiff that social media is addictive and harmful and was deliberately designed to be that way. This finding aligns with my view as a clinical psychologist: that social media addiction is not a failure of users, but a feature of the platforms themselves. I believe that accountability must extend beyond individuals to the systems and incentives that shape their behavior. In my clinical practice, I regularly see patients struggling with compulsive social media use. Many describe a pattern of “doomscrolling,” often using social media to numb themselves after a long day. Afterwards, they feel guilty and stressed about the time lost yet have had limited success changing this pattern on their own. It’s easy to understand why scrolling can be so addictive. Social media interfaces are built around a powerful behavioral mechanism known as intermittent reinforcement, says Judson Brewer, an addiction researcher at Brown University, which is the strongest and most effective type of reinforcement learning. This is the same mechanism that slot machines rely on: Users never know when the next reward—a shower of quarters, or a slew of likes and comments—will appear. Not all the videos in our feeds captivate us, but if we scroll long enough, we are bound to arrive at one that does. The ongoing search for rewards ensnares us and reinforces itself. Why Social Media Feels Addictive Individuals typically struggle on their own to address compulsive social media use. This should be no surprise, as habits are not typically broken through sheer discipline but rather by altering the reinforcement loops that sustain them. Brewer argues that “there’s actually no neuroscientific evidence for the presence of willpower.” Placing the burden to self-regulate solely on users misses the deeper issue: These platforms are engineered to override individual control. A growing body of research identifies social media use and constant digital connectivity as important influences on the growing incidence of adolescent mental health problems. Brewer notes that adolescents are particularly vulnerable, as they are in a “developmental phase” in which reinforcement learning processes are especially strong. This vulnerability can be exploited by the design features of large social media platforms. How Platforms Are Designed to Maximize Engagement NPR uncovered records from a recent lawsuit filed by Kentucky’s attorney general against TikTok. According to these documents, TikTok implemented interface mechanisms such as autoplay, infinite scrolling, and a highly personalized recommendation algorithm that were systematically optimized to maximize user engagement. TikTok’s algorithmically tailored “For You” content continuously tracks user behaviors, such as how long a video is watched, whether it is replayed, or quickly skipped. The feed then curates short videos, or reels, for the user based on past scrolling behavior and what is most likely to hold attention. These documents show one example of a tech company knowingly designing products to maximize attention. I believe social media companies also have the capacity to reduce addictiveness through intentional design choices. How Governments Are Regulating Social Media The good news is we are not helpless. There are multiple levers for change: how we collectively talk about social media, how our governments regulate its design and access, and how we hold companies accountable for practices that shape user behavior. Some countries are moving quickly to set policy around social media use. Australia has imposed a minimum age of 16 for social media accounts, with similar bans pending in Denmark, France, and Malaysia. These bans typically rely on age verification. Users without verified accounts can still passively watch videos on platforms like YouTube, but this approach removes many of the most addictive features, including infinite scroll, personalized feeds, notifications, and systems for followers and likes. At the same time, age verification may cause different problems in the online ecosystem. Other countries are targeting social media use in specific contexts. South Korea, for example, banned smartphone use in classrooms. And the United Kingdom is taking a different approach; its Age Appropriate Design Code instructs platforms to prioritize children’s safety while designing products. The code includes strong privacy defaults, limits on data collection, and constraints on features that nudge users toward greater engagement. How Social Media Platforms Could Be Redesigned A report called Breaking the Algorithm, from Mental Health America, argues that social media platforms should shift from maximizing engagement to supporting well-being. It calls for revamping recommendation systems to spot patterns of unhealthy use and adjusting feeds accordingly—for example, by limiting extreme or distressing content. The report also argues that users should not have to intentionally opt out of harmful design features. Instead, the safest settings should be the default. The report supports regulatory measures aimed at limiting features such as autoplay and infinite scroll while enforcing privacy and safety settings. Platforms could also give users more control by adding natural speed bumps, such as stopping points or break reminders during scrolling. Research shows that interrupting infinite scroll with prompts such as “Do you want to keep going?” substantially reduces mindless scrolling and improves memory of content. Some social media platforms are already experimenting with more ethical engagement. Mastodon, an open-source, decentralized platform, displays posts chronologically rather than ranking them for engagement, and does not offer algorithmically generated feeds like “For You.” Bluesky gives users control by letting them customize their own algorithms and toggle between different feed types, such as chronological or topic-based filters. In light of the recent verdict, it is time for a national conversation about accountability for social media companies. Individual responsibility will always be important, but so are the mechanisms employed by big tech to shape user behavior. If social media platforms are currently designed to capture attention, they can also be designed to give some of it back.
In today’s technological landscape, the only constant is the rate of obsolescence. As engineers move deeper into the eras of 6G, ubiquitous artificial intelligence, and hyper-miniaturized electronics, a traditional degree is only a starting point. To remain competitive in today’s job market, technical specialists must evolve into future-ready professionals by cultivating more than just niche expertise. Success now demands a high degree of adaptive intelligence and strategic communication, allowing specialists to translate complex data into actionable business decisions as industry shifts accelerate. To bridge the gap between technical proficiency and organizational leadership, the IEEE Professional Development Suite offers training on programs designed to build the strategic competencies required to navigate today’s complex landscape. The suite provides deep technical dives into domains such as telecommunications connectivity and microelectronics reliability. Organizations can stay ahead of the curve through informed decision-making and a future-ready workforce. Mastery of electrostatic discharge and 5G networks Within the semiconductor sector, which is projected to become a US $1 billion industry by 2030, electrostatic discharge (ESD) is a major reliability challenge. Because even a microscopic, unnoticed discharge can compromise a semiconductor, ESD issues account for up to one-third of all field failures, according to the EOS/ESD Association. IEEE’s targeted training—the online Practical ESD Protection Design certificate program—equips teams with technical protocols to mitigate the risks and ensure long-term hardware reliability. Specialized ESD training has become essential for chip designers and manufacturing professionals seeking to improve discharge control. The interactive modules cover theory, real-world case studies, and practical mitigation techniques. The standards-based instruction is aligned with ANSI/ESD S20.20–21: Protection of Electrical and Electronic Parts and other industry guidelines. As 5G network capabilities expand globally, so does the demand for engineers who can master the protocols and procedures required to manage complex telecommunications systems. The IEEE 5G/6G Essential Protocols and Procedures Training and Innovation Testbed, in partnership with Wray Castle, takes a deep dive into the 5G network function framework, registration processes, and packet data unit session establishment. The program is designed for system engineers, integrators, and technical professionals responsible for 5G signaling. Stakeholders such as network operators, equipment vendors, regulators, and handset manufacturers could find the program to be beneficial as well. “The IEEE Professional Development Suite ensures that learners are not just keeping pace with change but helping to drive it.” To bridge the gap between theory and practice, the course includes three months of free access to the IEEE 5G/6G Innovation Testbed. The secure, cloud-based platform offers a private, end-to-end 5G network environment where individuals and teams can gain hands-on experience with critical system signaling and troubleshooting. Leadership training programs Technical knowledge alone is not enough to climb the corporate ladder. To thrive today, engineering leaders must have a strategic vision and people-centric leadership skills. The IEEE Leading Technical Teams training program focuses on the challenges of managing engineers in R&D environments and fostering creative problem-solving through an immersive learning experience. It’s designed for professionals who have been in a leadership position for at least six months. Participants can gain self-awareness. The program includes a 360-degree assessment that gathers feedback about the individual from peers and direct reports to build a personalized development plan. The goal is to help technical professionals transition from high-performing individual contributors into leaders who drive innovation by inspiring their teams rather than just managing tasks. Organizations can enroll groups of 10 or more to learn as a cohort—which can ensure that everyone stays on the same page while setting a training schedule that fits the team’s deadlines. In collaboration with the Rutgers Business School, IEEE offers two mini MBA programs to bridge the gap between technical expertise and executive leadership. The programs offer flexibility to fit the demanding schedules of senior professionals. The online format lets participants engage with content as their time permits, while live virtual office hours with faculty provide opportunities for real-time interaction. During the mini MBA for engineers 12-week curriculum, technical professionals master core competencies such as financial analysis, business strategy, and negotiation to effectively transition into management roles. The mini MBA in artificial intelligence embeds AI literacy directly into business strategy rather than treating the technology as a standalone subject. Participants learn to evaluate AI through financial modeling and governance frameworks, gaining a practical foundation to lead initiatives that incorporate the technology. The programs are offered to individuals as well as to organizations interested in training groups of 10 employees or more. Earning credits that count All the programs within the IEEE Professional Development Suite offer continuing education units and professional development hours. Earning globally recognized credits provides a professional advantage, signaling a commitment to growth that often serves as a prerequisite for advancing into senior, lead, or principal roles. Additionally, the credits satisfy annual professional engineering license renewal requirements, ensuring practitioners remain compliant while expanding their capabilities. Why curated content matters Developed by IEEE Educational Activities, the training programs are peer-reviewed and built to align with industry needs. By focusing on upskilling (improving current skills) and reskilling (learning new ones), the IEEE Professional Development Suite ensures that learners are not just keeping pace with change but helping to drive it.
Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion. ICRA 2026: 1–5 June 2026, VIENNA RSS 2026: 13–17 July 2026, SYDNEY Summer School on Multi-Robot Systems: 29 July–4 August 2026, PRAGUE Enjoy today’s videos! “Roadrunner” is a new bipedal wheeled robot prototype designed for multimodal locomotion. It weighs around 15 kg (33 lb) and can seamlessly switch between its side-by-side and in-line wheel modes and stepping configurations depending on what is required for navigating its environment. The robot’s legs are entirely symmetric, allowing it to point its knees forward or backward, which can be used to avoid obstacles or manage specific movements. A single control policy was trained to handle both side-by-side and in-line driving. Several behaviors, including standing up from various ground configurations and balancing on one wheel, were successfully deployed zero-shot on the hardware. [ Robotics and AI Institute ] Incredibly (INCREDIBLY!) NASA says that this is actually happening. NASA’s SkyFall mission will build on the success of the Ingenuity Mars helicopter, which achieved the first powered, controlled flight on another planet. Using a daring midair deployment, SkyFall will deliver a team of next-gen Mars helicopters to scout human landing sites and map subsurface water ice. [ NASA ] NASA’s MoonFall mission will blaze a path for future Artemis missions by sending four highly mobile drones to survey the lunar surface around the Moon’s South Pole ahead of astronauts’ arrival there. MoonFall is built on the legacy of NASA’s Ingenuity Mars Helicopter. The drones will be launched together and released during descent to the surface. They will land and operate independently over the course of a lunar day (14 Earth days) and will be able to explore hard-to-reach areas, including permanently shadowed regions (PSRs), surveying terrain with high-definition optical cameras and other potential instruments. For what it’s worth, Moon landings have a success rate well under 50%. So let’s send some robots there to land over and over! [ NASA ] In Science Robotics, researchers from the Tangible Media group led by Professor Hiroshi Ishii, together with colleagues from Politecnico di Bari, present Electrofluidic Fiber Muscles: a new class of artificial muscle fibers for robots and wearables. Unlike the rigid servo motors used in most robots, these fiber-shaped muscles are soft and flexible. They combine electrohydrodynamic (EHD) fiber pumps—slender tubes that move liquid using electric fields to generate pressure silently, with no moving parts—with fluid-filled fiber actuators. These artificial muscles could enable more agile untethered robots, as well as wearable assistive systems with compact actuation integrated directly into textiles. [ MIT Media Lab ] In this study, we developed MEVIUS2, an open-source quadruped robot. It is comparable in size to the Boston Dynamics Spot, equipped with two lidars and a C1 camera, and can freely climb stairs and steep slopes! All hardware, software, and learning environments are released as open source. [ MEVIUS2 ] Thanks, Kento! What goes into preparing for a live performance? Arun highlights the reliability testing that goes into trying a new behavior for Spot. [ Boston Dynamics ] In this work, a multirobot planning and control framework is presented and demonstrated with a team of 40 indoor robots, including both ground and aerial robots. That soundtrack, though. [ GitHub ] Thanks, Keisuke! Quadrupedal robots can navigate cluttered environments like their animal counterparts, but their floating-base configuration makes them vulnerable to real-world uncertainties. Controllers that rely only on proprioception (body sensing) must physically collide with obstacles to detect them. Those that add exteroception (vision) need precisely modeled terrain maps that are hard to maintain in the wild. DreamWaQ++ bridges this gap by fusing both modalities through a resilient multimodal reinforcement learning framework. The result: a single controller that handles rough terrains, steep slopes, and high-rise stairs—while gracefully recovering from sensor failures and situations it has never seen before. That cliff behavior is slightly uncanny. [ DreamWaQ++ ] I take issue with this from iRobot: While the pyramid exploration that iRobot did was very cool, they did it with a custom-made robot designed for a very specific environment. Cleaning your floors is way, way harder. Here’s a bit more detail on the pyramids thing: [ iRobot ] More robots in the circus, please! [ Daniel Simu ] MIT engineers have designed a wristband that lets wearers control a robotic hand with their own movements. By moving their hands and fingers, users can direct a robot to perform specific tasks, or they can manipulate objects in a virtual environment with high-dexterity control. [ MIT ] At Nvidia GTC 2026, we showcased how AI is moving into the physical world. Visitors interacted with robots using voice commands, watching them interpret intent and act in real time—powered by our KinetIQ AI brain. [ Humanoid ] Props to Sony for its continued support and updates for Aibo! [ Aibo ] This robot looks like it could be a little curvier than normal? [ LimX Dynamics ] Developed by Zhejiang Humanoid Robot Innovation Center Co., Ltd., the Naviai Robot is an intelligent cooking device. It can autonomously process ingredients, perform cooking tasks with high accuracy, adjust smart kitchen equipment in real time, and complete postcooking cleaning. Equipped with multimodal perception technology, it adapts to daily kitchen environments and ensures safe and stable operation. That 7x is doing some heavy lifting. [ Zhejiang Lab ] This CMU RI Seminar is by Hadas Kress-Gazit from Cornell, on “Formal Methods for Robotics in the Age of Big Data.” Formal methods—mathematical techniques for describing systems, capturing requirements, and providing guarantees—have been used to synthesize robot control from high-level specification, and to verify robot behavior. Given the recent advances in robot learning and data-driven models, what role can, and should, formal methods play in advancing robotics? In this talk I will give a few examples for what we can do with formal methods, discuss their promise and challenges, and describe the synergies I see with data-driven approaches. [ Carnegie Mellon University Robotics Institute ]
We’re all familiar with mixing red, yellow, and blue paint in various ratios to instantly make all kinds of colors. This works great for oils or watercolors, but fails when it comes to cans of spray paint. The paint droplets can’t be blended once they are aerosolized. Consequently, although spray cans are great for applying even coats of paint to large areas very quickly, spray-paint artists need a separate can for every color they want to use—until now. Back in 2018, when I first saw professional spray artists lugging dozens to hundreds of cans to their work sites, I was inspired to start noodling on a solution. I’ve worked at Google X, Alphabet’s “moonshot factory,” as a hardware engineer, and I’m now building a startup in mechanical-design software. I’m no painter, but I know my way around mechatronics. I wanted my solution to be inexpensive and simple enough to build as a DIY project and functional enough for an artist to use, without breaking their flow. So I began prototyping a system that combines base colors while they are still in pressurized form from off-the-shelf cans. This new rotary pinch valve can be opened and closed in tens of milliseconds and prevents backpressure from clogging lines.James Provost I tried a few approaches where pres-surized paint from the base-color cansfed through tubes into a mixing channel, before emerging from a spray head. To control the ratios, I decided to borrow a trick that would be familiar to anyone who’s ever had to control the bright-ness of an LED using a microcontroller: pulse-width modulation. Initially, I used electronically controlled solenoid valves to release the paint from the cans. The paint would flow into a mixing channel for a relative duration that corresponded to the ratio of the base colors required to make a given hue. However, this failed because different cans never have the same internal pressure. Whenever two valves were open at the same time, the pressure difference would make paint flow backward into the lower-pressure can. As an alternative, I removed the mixing channel and tried making the paint pulses from each can sequentially converge into a tube so that no more than one valve would ever be open at a time. Surprisingly, this worked perfectly. The backflow was eliminated, and it turned out that the natural turbulence of the flow was sufficient to mix the paints. Let’s say you want to produce a clementine orange color. This requires yellow and red paint in a ratio of 1:2, so the yellow valve opens for a period of time, and then the red valve opens for twice as long. The system then keeps repeating this cycle of pulses in a rapid pace to instantly create the spray-paint color you want. The theory is straightforward, but making this work in practice took quite a bit of experimentation. First, I had to determine the actual durations of pulses that would produce evenly mixed colors, not just their ratios. I also needed to work out the size of the tubing (too narrow and you’d get low spray force; too wide and you’d have paint accumulating in the tubes). Eventually I settled on a maximum pulse duration of 250 milliseconds and a tube diameter of 1 millimeter. Inventing A New Valve Even though the system worked, the solenoid valves I used constantly clogged up. Designed for water purifiers, the valves didn’t prevent paint from entering the mechanism, where the paint would harden. Moreover, when the valves were turned off, they could stop backflow only if the inlet remained pressurized. So disconnecting a paint can from the system would cause instant leaking. Other off-the-shelf valves I tried couldn’t cycle fast enough and were too expensive. I had some spectacular failures along the way of the sort that only pressurized paint can provide. So I created my own mechanism: a high-speed, electronically controlled, rotary pinch valve. It has a stepper motor that rotates a lever with a rolling bearing to constrict fluid flow inside a flexible tube. This concept isn’t new—there’s something like them in every peristaltic pump. But I added a spring to firmly hold the lever in the closed position against any back pressure when the motor isn’t powered, making it a normally closed valve that isolates the attached can. Additionally, the valve is fast enough to be open for as little as 30 milliseconds. I went through four major prototypes of the system before reaching a working version, and I had some spectacular failures along the way of the sort that only pressurized paint can provide. The final version uses four base colors—red, yellow, blue, and white—with the color mix controlled by four knobs attached to an Arduino Nano and a small display. The flow of paint is triggered by a push button placed above the spray head, similar to a spray can’s nozzle. Cans holding base colors (A) are attached to valves (B). An Arduino-based control panel (C) opens and closes valves to mix paint before it is aerosolized (E). By quickly opening and closing valves with varying durations in sequence (D), you can mix paint in specific ratios to create desired colors.James Provost The length of time a base color’s paint valve can be open is one of eight values between 30 and 250 ms. This means that the entire system—which I coincidentally dubbed Spectrum—can create hundreds of distinct spray-paint colors instantly. It produces less than 84 (or 4,096) colors because duration ratios that are a multiple of each other will produce the same color—for example, 2:3 and 4:6. I added a force sensor to the push button, which allows for a gradient: Two color mixes can be dialed in, and as I increase my thumb’s pressure on the button, the paint mix shifts from one color to the other. Spectrum’s various fixtures are 3D-printed, and project files and videos are available through my website at https://www.sandeshmanik.com/projects/spectrum. Preprints of technical descriptions of the rotary pinch valve and mixing methodology are available on TechRxiv. The total cost for the bill of materials is less than US $150. Working on and off on the side for about seven years, I finally finished developing my system and writing the documentation in late 2025. After I posted a video to social media, I was heartened by the immediate positive response from spray-paint artists around the world. I’m now creating step-by-step instructions so that nontechnical people can build their own Spectrum paint sprayer. I look forward to seeing what creations artists out in the wild make!
This sponsored article is brought to you by NYU Tandon School of Engineering. Within a 6 mile radius of New York University’s (NYU) campus, there are more than 500 tech industry giants, banks, and hospitals. This isn’t just a fact about real estate, it’s the foundation for advancing quantum discovery and application. While the world races to harness quantum technology, NYU is betting that the ultimate advantage lies not solely in a lab, but in the dense, demanding, and hyper-connected urban ecosystem that surrounds it. With the launch of its NYU Quantum Institute (NYUQI), NYU is positioning itself as the central node in this network; a “full stack” powerhouse built on the conviction that it has found the right place, and the right time, to turn quantum science into tangible reality. Proximity advantage is essential because quantum science demands it. Globally, the quest for practical quantum solutions — whether for computing, sensing, or secure communications — has been stalled, in part, by fragmentation. Physicists and chemical engineers invent new materials, computer scientists develop new algorithms, and electrical engineers build new devices, but all three often work in isolated academic silos. Gregory Gabadadze, NYU’s dean for science, NYU physicist and Quantum Institute Director Javad Shabani, and Juan de Pablo, Anne and Joel Ehrenkranz Executive Vice President for Global Science and Technology and executive dean of the Tandon School of Engineering.Veselin Cuparić/NYU NYUQI’s premise is that breakthroughs happen “at the interfaces between different domains,” according to Juan de Pablo, Executive Vice President for Global Science and Technology at NYU and Executive Dean of the NYU Tandon School of Engineering. The Institute is built to actively force those necessary collisions — to integrate the physicists, engineers, materials scientists, computer scientists, biologists, and chemists vital to quantum research into one holistic operation. This institutional design ensures that the hardware built by one team can be immediately tested by software developed by another, accelerating progress in a way that isolated departments never could. NYUQI’s premise is that breakthroughs happen at the interfaces between different domains. —Juan de Pablo, NYU Tandon School of Engineering NYUQI’s integrated vision is backed by a massive physical commitment to the city. The NYUQI is not just a theoretical concept; its collaborators will be housed in a renovated, million-square-foot facility in the heart of Manhattan’s West Village, backed by a state-of-the-art Nanofabrication Cleanroom in Brooklyn serving as a high-tech foundry. This is where the theoretical meets physical devices, allowing the Institute to test and refine the process from materials science to deployment. NYUQI will be housed in a renovated, million-square-foot facility in the heart of Manhattan’s West Village.Tracey Friedman/NYU Leading this effort is NYUQI Director Javad Shabani, who, along with the other members, is turning the Institute into a hub for collaboration with private and public sector partners with quantum challenges that need solving. As de Pablo explains, “Anybody who wants to work on quantum with NYU, you come in through that door, and we’ll send you to the right place.” For New York’s vast ecosystem of tech giants and financial institutions, the NYUQI offers a resource they can’t build on their own: a cohesive team of experts in quantum phenomena, quantum information theory, communication, computing, materials, and optics, and a structured path to applying theoretical discoveries to advanced quantum technologies. Solving the Challenge of Quantum Research The NYUQI’s integrated structure is less about organizational management, and more about scientific requirement. The challenge of quantum is that the hardware, the software, and the programming are inherently interconnected — each must be designed to work with the other. To solve this, the Institute focuses on three applications of quantum science: Quantum Computing, Quantum Sensing, and Quantum Communications. For Shabani, this means creating an integrated environment that bridges discovery with experimentation, starting with the physical components all the way to quantum algorithm centers. That will include a fabrication facility in the new building in Manhattan, as well as the NYU Nanofab in Brooklyn directed by Davood Shahjerdi. New York Senators Charles Schumer and Kirsten Gillibrand recently secured $1 million in congressionally-directed spending to bring Thermal Laser Epitaxy (TLE) technology — which allows for atomic-level purity, minimal defects, and streamlined application of a diverse range of quantum materials — to NYU, marking the first time the equipment will be used in the U.S. NYU Nanofab manager Smiti Bhattacharya and Nanofab Director Davood Shahjerdi at the nanofab ribbon-cutting in 2023. The nanofab is the first academic cleanroom in Brooklyn, and serves as a prototyping facility for the NORDTECH Microelectronics Commons consortium.NYU WIRELESS Tight control over fabrication, and can allow researchers to pivot quickly when a breakthrough in one area — say, finding a cheaper, more reliable material like silicon carbide — can be explored for use across all three applications, and offers unique access to academics and the private sector alike to sophisticated pieces of specialty equipment whose maintenance knowledge and costs make them all-but-impossible to maintain outside of the right staffing and environment. The NYU Nanofab is Brooklyn’s first academic cleanroom, with a strategic focus on superconducting quantum technologies, advanced semiconductor electronics, and devices built from quantum heterostructures and other next-generation materials.NYU Nanofab That speed and adaptability is the NYUQI’s competitive edge. It turns fragmented challenges into holistic solutions, positioning the Institute to solve real-world problems for its New York neighbors—from highly secure data transmission to next-generation drug discovery. Testing Quantum Communication in NYC The integrated approach also makes the NYUQI a testbed for the most critical near-term applications. Take Quantum Communications, which is essential for creating an “unhackable” quantum internet. In an industry first, NYU worked with the quantum start-up Qunnect to send quantum information through standard telecom fiber in New York City between Manhattan and Brooklyn through a 10-mile quantum networking link. Instead of simulating communication challenges in a lab, the NYUQI team is already leveraging NYU’s city-wide campus by utilizing existing infrastructure to test secure quantum transmission between Manhattan and Brooklyn. The NYUQI team is already leveraging NYU’s city-wide campus by utilizing existing infrastructure to test secure quantum transmission between Manhattan and Brooklyn. This isn’t just theory; it is building a functioning prototype in the most demanding, dense urban environment in the world. Real-time, real-world deployment is a critical component missing in other isolated institutions. When the NYUQI achieves results, the technology will be that much more readily available to the massive financial, tech, and communications organizations operating right outside their door. NYUQI includes a state-of-the-art Nanofabrication Cleanroom in Brooklyn serving as a high-tech foundry.NYU Tandon While the Institute has built the physical infrastructure and designed the necessary scientific architecture, its enduring contribution will be the specialized workforce it creates for the new quantum economy. This addresses the market’s greatest deficit: a lack of individuals trained not just in physics, but in the integrated, full-stack approach that quantum demands. By creating a pipeline of 100 to 200 graduate and doctoral students who are encouraged to collaborate across Computing, Sensing, and Communications, the NYUQI is narrowing the skills gap. These will be future leaders who can speak the language of the physicist, the materials scientist, and the engineer simultaneously. This commitment to interdisciplinary talent is also fueled by the launch of the new Master of Science in Quantum Science & Technology program at NYU Tandon, positioning the university among a select group worldwide offering such a specialized degree. Interdisciplinary education creates the shared language and understanding poised to make graduates coming from collaborations in the NYUQI extremely valuable in the current landscape. Quantum challenges are not just technical; they are managerial and philosophical as well. An engineer working with the NYUQI will understand the requirements of the nanofabrication cleanroom and the foundations of superconducting qubits for quantum computing, just as a physicist will understand the application needs of an industry partner like a large financial institution. In a field where the entire team must be able to communicate seamlessly, these are professionals truly equipped to rapidly translate discovery into deployable technology. Creating a talent pipeline at scale will provide a missing link that converts New York’s vast commercial energy into genuine quantum advantage. NYUQI: Building Talent, Technology, and Structure The vision for the NYUQI is an act of strategic geography that plays directly into the sheer volume of opportunity and demand right outside their new facility. By building the talent, the technology, and the structure necessary to capitalize on this dense environment, NYU is not just participating in the quantum race, it is actively steering it. Attendees of NYU’s 2025 Quantum Summit.Tracey Friedman/NYU The initial hypothesis for the NYUQI was simple: the ultimate advantage lies in pursuing the science in the right place at the right time. Now, the institute will ensure that the next wave of scientific discovery, capable of solving previously intractable problems in finance, medicine, and security, will be conceived, built, and tested in the heart of New York City.
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! Engineers Aren’t Bad at Communication. They’re Just Speaking to the Wrong Audience. There’s a persistent myth that engineers are bad communicators. In my experience, that’s not true. Engineers are often excellent communicators—inside their domain. We’re precise. We’re logical. We structure arguments clearly. We define terms. We reason from constraints. The breakdown happens when the audience changes. We’re used to speaking in highly technical language, surrounded by people who share our vocabulary. In that environment, shorthand and jargon are efficient. But outside that bubble, when talking to executives, product managers, marketing teams, or customers, that same precision can be confusing. The problem isn’t that we can’t communicate. It’s that we forget to translate. If you’ve ever explained a critical issue or error to a non-technical stakeholder, you’ve probably experienced this: You give a technically accurate explanation. They leave either more confused than before, or more alarmed than necessary. Suddenly you’re spending more time clarifying your explanation than fixing the issue. Under pressure, we default to what we know best—technical detail. But detail without context creates cognitive overload. The listener can’t tell what matters, what’s normal, and what’s dangerous. That’s when the “engineers can’t communicate” narrative shows up. In reality, we just skipped the translation step. The Writing Shortcut One of the simplest ways to improve written communication today is surprisingly easy: Run your explanation through an AI model and ask, “would this make sense to a non-technical audience? Where would someone get confused?” You can also say: “Rewrite this for an executive audience.” “What analogy would help explain this?” “Simplify this without losing accuracy.” Large language models are particularly good at identifying jargon and offering alternative framings. They’re essentially translation assistants. Analogies are especially powerful. If you’re explaining system latency, compare it to traffic congestion. If you’re describing technical debt, compare it to skipping maintenance on a house. If you’re explaining distributed systems, try using supply chain examples. The goal isn’t to “dumb it down.” It’s to map the unfamiliar onto something familiar. Before sending an email or report, ask yourself: Does this audience need to understand the mechanism, or just impact? Does this explanation help them make a decision? Have I defined terms they might not know? Translation When Speaking When speaking—especially in meetings or presentations—most engineers have one predictable habit: We speak too fast. Nerves speed us up. Speed causes filler words. Filler words dilute authority. To prevent that, follow a simple rule: Speak 10 to 15 percent slower than feels natural. Slowing down cuts down the number of times you say “um” and “uh”, gives you time to think, makes you sound more confident, and gives the listener time to process. Another rule: Say only what the audience needs to move forward. Explain just enough for the person to make a decision. If you overload someone with implementation details when they only need tradeoffs, you’ve made their job harder. The Real Skill The key skill in communication is audience awareness. The same engineer who can clearly explain a concurrency bug to a peer can absolutely explain system risk to an executive. The difference is framing, vocabulary, and context. Not intelligence. In the age of AI, where code generation is increasingly commoditized, the ability to translate complexity into clarity is becoming a defining advantage. Engineers aren’t bad communicators. We just have to remember that outside our bubble, translation is part of the job. —Brian How Robert Goddard’s Self-Reliance Crashed His Dreams Robert Goddard launched the first liquid-fueled rocket 100 years ago, but his legacy still has relevant lessons for today’s engineers. Although Goddard’s headstrong confidence in his ideas helped bring about the breakthrough, it later became an obstacle in what systems engineer Guru Madhavan calls “the alpha trap.” Madhavan writes: “We love to celebrate the lone genius, yet we depend on teams to bring the flame of genius to the people.” Read more here. Redefining the Software Engineering Profession for AI For Communications of the ACM, two Microsoft engineers propose a model for software engineering in the age of AI: Making the growth of early-in-career developers an explicit organizational goal. Without hiring early-career workers, the profession’s talent pipeline will eventually dry up. So, they argue, companies must hire them and develop talent, even if that comes with a short-term dip in productivity. Read more here. IEEE Launches Global Virtual Career Fairs Looking for a job? Last year, IEEE Industry Engagement hosted its first virtual career fair to connect recruiters and young professionals. Several more career fairs are now planned, including two upcoming regional events and a global career fair in June. At these fairs, you can participate in interactive sessions, chat with recruiters, and experience video interviews. Read more here.
This is a sponsored article brought to you by General Motors. Visit their new Engineering Blog for more insights. Autonomous driving is one of the most demanding problems in physical AI. An automated system must interpret a chaotic, ever-changing world in real time—navigating uncertainty, predicting human behavior, and operating safely across an immense range of environments and edge cases. At General Motors, we approach this problem from a simple premise: while most moments on the road are predictable, the rare, ambiguous, and unexpected events — the long tail — are what ultimately defines whether an autonomous system is safe, reliable, and ready for deployment at scale. (Note: While here we discuss research and emerging technologies to solve the long tail required for full general autonomy, we also discuss our current approach or solving 99% of everyday autonomous driving in a deep dive on Compound AI.) As GM advances toward eyes-off highway driving, and ultimately toward fully autonomous vehicles, solving the long tail becomes the central engineering challenge. It requires developing systems that can be counted on to behave sensibly in the most unexpected conditions. GM is building scalable driving AI to meet that challenge — combining large-scale simulation, reinforcement learning, and foundation-model-based reasoning to train autonomous systems at a scale and speed that would be impossible in the real world alone. Stress-testing for the long tail Long-tail scenarios of autonomous driving come in a few varieties. Some are notable for their rareness. There’s a mattress on the road. A fire hydrant bursts. A massive power outage in San Francisco that disabled traffic lights required driverless vehicles to navigate never-before experienced challenges. These rare system-level interactions, especially in dense urban environments, show how unexpected edge cases can cascade at scale. But long-tail challenges don’t just come in the form of once-in-a-lifetime rarities. They also manifest as everyday scenarios that require characteristically human courtesy or common sense. How do you queue up for a spot without blocking traffic in a crowded parking lot? Or navigate a construction zone, guided by gesturing workers and ad-hoc signs? These are simple challenges for a human driver but require inventive engineering to handle flawlessly with a machine. Autonomous driving scenario demand curve Deploying vision language models One tool GM is developing to tackle these nuanced scenarios is the use of Vision Language Action (VLA) models. Starting with a standard Vision Language Model, which leverages internet-scale knowledge to make sense of images, GM engineers use specialized decoding heads to fine-tune for distinct driving-related tasks. The resulting VLA can make sense of vehicle trajectories and detect 3D objects on top of its general image-recognition capabilities. These tuned models enable a vehicle to recognize that a police officer’s hand gesture overrides a red traffic light or to identify what a “loading zone” at a busy airport terminal might look like. These models can also generate reasoning traces that help engineers and safety operators understand why a maneuver occurred — an important tool for debugging, validation, and trust. Testing hazardous scenarios in high-fidelity simulations The trouble is: driving requires split-second reaction times so any excess latency poses an especially critical problem. To solve this, GM is developing a “Dual Frequency VLA.” This large-scale model runs at a lower frequency to make high-level semantic decisions (“Is that object in the road a branch or a cinder block?”), while a smaller, highly efficient model handles the immediate, high-frequency spatial control (steering and braking). This hybrid approach allows the vehicle to benefit from deep semantic reasoning without sacrificing the split-second reaction times required for safe driving. But dealing with an edge case safely requires that the model not only understand what it is looking at but also understand how to sensibly drive through the challenge it’s identified. For that, there is no substitute for experience. Which is why, each day, we run millions of high-fidelity closed loop simulations, equivalent to tens of thousands of human driving days, compressed into hours of simulation. We can replay actual events, modify real-world data to create new virtual scenarios, or design new ones entirely from scratch. This allows us to regularly test the system against hazardous scenarios that would be nearly impossible to encounter safely in the real world. Synthetic data for the hardest cases Where do these simulated scenarios come from? GM engineers employ a whole host of AI technologies to produce novel training data that can model extreme situations while remaining grounded in reality. GM’s “Seed-to-Seed Translation” research, for instance, leverages diffusion models to transform existing real-world data, allowing a researcher to turn a clear-day recording into a rainy or foggy night while perfectly preserving the scene’s geometry. The result? A “domain change”—clear becomes rainy, but everything else remains the same. In addition, our GM World diffusion-based simulator allows us to synthesize entirely new traffic scenarios using natural language and spatial bounding boxes. We can summon entirely new scenarios with different weather patterns. We can also take an existing road scene and add challenging new elements, such as a vehicle cutting into our path. High-fidelity simulation isn’t always the best tool for every learning task. Photorealistic rendering is essential for training perception systems to recognize objects in varied conditions. But when the goal is teaching decision-making and tactical planning—when to merge, or how to navigate an intersection—the computationally expensive details matter less than spatial relationships and traffic dynamics. AI systems may need billions or even trillions of lightweight examples to support reinforcement learning, where models learn the rules of sensible driving through rapid trial and error rather than relying on imitation alone. To this end, General Motors has developed a proprietary, multi-agent reinforcement learning simulator, GM Gym, to serve as a closed-loop simulation environment that can both simulate high-fidelity sensor data, and model thousands of drivers per second in an abstract environment known as “Boxworld.” By focusing on essentials like spatial positioning, velocity and rules of the road while stripping away details like puddles and potholes, Boxworld creates a high-speed training environment for reinforcement learning models at incredible speeds, operating 50,000 times faster than real-time and simulating 1,000 km of driving per second of GPU time. It’s a method that allows us to not just imitate humans, but to develop driving models that have verifiable objective outcomes, like safety and progress. From abstract policy to real-world driving Of course, the route from your home to your office does not run through Boxworld. It passes through a world of asphalt, shadows, and weather. So, to bring that conceptual expertise into the real world, GM is one of the first to employ a technique called “On Policy Distillation,” where engineers run their simulator in both modes simultaneously: the abstract, high-speed Boxworld and the high-fidelity sensor mode. Here, the reinforcement learning model—which has practiced countless abstract miles to develop a perfect “policy,” or driving strategy—acts as a teacher. It guides its “student,” the model that will eventually live in the car. This transfer of wisdom is incredibly efficient; just 30 minutes of distillation can capture the equivalent of 12 hours of raw reinforcement learning, allowing the real-world model to rapidly inherit the safety instincts its cousin painstakingly honed in simulation. Designing failures before they happen Simulation isn’t just about training the model to drive well, though; it’s also about trying to make it fail. To rigorously stress-test the system, GM utilizes a differentiable pipeline called SHIFT3D. Instead of just recreating the world, SHIFT3D actively modifies it to create “adversarial” objects designed to trick the perception system. The pipeline takes a standard object, like a sedan, and subtly morphs its shape and pose until it becomes a “challenging”, fun-house version that is harder for the AI to detect. Optimizing these failure modes is what allows engineers to preemptively discover safety risks before they ever appear on the road. Iteratively retraining the model on these generated “hard” objects has been shown to reduce near-miss collisions by over 30%, closing the safety gap on edge cases that might otherwise be missed. Even with advanced simulation and adversarial testing, a truly robust system must know its own limits. To enable safety in the face of the unknown, GM researchers add a specialized “Epistemic uncertainty head” to their models. This architectural addition allows the AI to distinguish between standard noise and genuine confusion. When the model encounters a scenario it doesn’t understand—a true “long tail” event—it signals high epistemic uncertainty. This acts as a principled proxy for data mining, automatically flagging the most confusing and high-value examples for engineers to analyze and add to the training set. This rigorous, multi-faceted approach—from “Boxworld” strategy to adversarial stress-testing—is General Motors’ proposed framework for solving the final 1% of autonomy. And while it serves as the foundation for future development, it also surfaces new research challenges that engineers must address. How do we balance the essentially unlimited data from Reinforcement Learning with the finite but richer data we get from real-world driving? How close can we get to full, human-like driving by writing down a reward function? Can we go beyond domain change to generate completely new scenarios with novel objects? Solving the long tail at scale Working toward solving the long tail of autonomy is not about a single model or technique. It requires an ecosystem — one that combines high-fidelity simulation with abstract learning environments, reinforcement learning with imitation, and semantic reasoning with split-second control. This approach does more than improve performance on average cases. It is designed to surface the rare, ambiguous, and difficult scenarios that determine whether autonomy is truly ready to operate without human supervision. There are still open research questions. How human-like can a driving policy become when optimized through reward functions? How do we best combine unlimited simulated experience with the richer priors embedded in real human driving? And how far can generative world models take us in creating meaningful, safety-critical edge cases? Answering these questions is central to the future of autonomous driving. At GM, we are building the tools, infrastructure, and research culture needed to address them — not at small scale, but at the scale required for real vehicles, real customers, and real roads.
When you hear the term humanoid robot, you may think of C-3PO, the human-cyborg-relations android from Star Wars. C-3PO was designed to assist humans in communicating with robots and alien species. The droid, which first appeared on screen in 1977, joined the characters on their adventures, walking, talking, and interacting with the environment like a human. It was ahead of its time. Before the release of Star Wars, a few androids did exist and could move and interact with their environment, but none could do so without losing its balance. It wasn’t until 1996 that the first autonomous robot capable of walking without falling was developed in Japan. Honda’s Prototype 2 (P2) was nearly 183 centimeters tall and weighed 210 kilograms. It could control its posture to maintain balance, and it could move multiple joints simultaneously. In recognition of that decades-old feat, P2 has been honored as an IEEE Milestone. The dedication ceremony is scheduled for 28 April at the Honda Collection Hall, located on the grounds of the Mobility Resort Motegi, in Japan. The machine is on display in the hall’s robotics exhibit, which showcases the evolution of Honda’s humanoid technology. In support of the Milestone nomination, members of the IEEE Nagoya (Japan) Section wrote: “This milestone demonstrated the feasibility of humanlike locomotion in machines, setting a new standard in robotics.” The Milestone proposal is available on the Engineering Technology and History Wiki. Developing a domestic android In 1986 Honda researchers Kazuo Hirai, Masato Hirose, Yuji Haikawa, and Toru Takenaka set out to develop what they called a “domestic robot” to collaborate with humans. It would be able to climb stairs, remove impediments in its path, and tighten a nut with a wrench, according to their research paper on the project. “We believe that a robot working within a household is the type of robot that consumers may find useful,” the authors wrote. But to create a machine that would do household chores, it had to be able to move around obstacles such as furniture, stairs, and doorways. It needed to autonomously walk and read its environment like a human, according to the researchers. But no robot could do that at the time. The closest technologists got was the WABOT-1. Built in 1973 at Waseda University, in Tokyo, the WABOT had eyes and ears, could speak Japanese, and used tactile sensors embedded on its hands as it gripped and moved objects. Although the WABOT could walk, albeit unsteadily, it couldn’t maneuver around obstacles or maintain its balance. It was powered by an external battery and computer. To build an android, the Honda team began by analyzing how people move, using themselves as models. That led to specifications for the robot that gave it humanlike dimensions, including the location of the leg joints and how far the legs could rotate. Once they began building the machine, though, the engineers found it difficult to satisfy every specification. Adjustments were made to the number of joints in the robot’s hips, knees, and ankles, according to the research paper. Humans have four hip, two knee, and three ankle joints; P2’s predecessor had three hip, one knee, and two ankle joints. The arms were treated similarly. A human’s four shoulder and three elbow joints became three shoulder joints and one elbow joint in the robot. The researchers installed existing Honda motors and hydraulics in the hips, knees, and ankles to enable the robot to walk. Each joint was operated by a DC motor with a harmonic-drive reduction gear system, which is compact and offered high torque capacity. To test their ideas, the engineers built what they called E0. The robot, which was just a pair of connected legs, successfully walked. It took about 15 seconds to take each step, however, and it moved using static walking in a straight line, according to a post about the project on Honda’s website. (Static walking is when the body’s center of mass is always within the foot’s sole. Humans walk with their center of mass below their navel.) The researchers created several algorithms to enable the robot to walk like a human, according to the Honda website. The codes allowed the robot to use a locomotion mechanism, dynamic walking, whereby the robot stays upright by constantly moving and adjusting its balance, rather than keeping its center of mass over its feet, according to a video on the YouTube channel Everything About Robotics Explained. “P2 was not just a technical achievement; it was a catalyst that propelled the field of humanoid robotics forward, demonstrating the potential for robots to interact with and assist humans in meaningful ways.” —IEEE Nagoya Section The Honda team installed rubber brushes on the bottom of the machine’s feet to reduce vibrations from the landing impacts (the force experienced when its feet touch the ground)—which had made the robot lose its balance. Between 1987 and 1991, three more prototypes (E1, E2, and E3) were built, each testing a new algorithm. E3 was a success. With the dynamic walking mechanism complete, the researchers continued their quest to make the robot stable. The team added 6-axis sensors to detect the force at which the ground pushed back against the robot’s feet and the movements of each foot and ankle, allowing the robot to adjust its gait in real time for stability. The team also developed a posture-stabilizing control system to help the robot stay upright. A local controller directed how the electric motor actuators needed to move so the robot could follow the leg joint angles when walking, according to the research paper. During the next three years, the team tested the systems and built three more prototypes (E4, E5, and E6), which had boxlike torsos atop the legs. In 1993 the team was finally ready to build an android with arms and a head that looked more like C-3PO, dubbed Prototype 1 (P1). Because the machine was meant to help people at home, the researchers determined its height and limb proportions based on the typical measurements of doorways and stairs. The arm length was based on the ability of the robot to pick up an object when squatting. When they finished building P1, it was 191.5 cm tall, weighed 175 kg, and used an external power source and computer. It could turn a switch on and off, grab a doorknob, and carry a 70 kg object. P1 was not launched publicly but instead used to conduct research on how to further improve the design. The engineers looked at how to install an internal power source and computer, for example, as well as how to coordinate the movement of the arms and legs, according to Honda. For P2, four video cameras were installed in its head—two for vision processing and the other two for remote operation. The head was 60 cm wide and connected to the torso, which was 75.6 cm deep. A computer with four microSparc II processors running a real-time operating system was added into the robot’s torso. The processors were used to control the arms, legs, joints, and vision-processing cameras. Also within the body were DC servo amplifiers, a 20-kg nickel-zinc battery, and a wireless Ethernet modem, according to the research paper. The battery lasted for about 15 minutes; the machine also could be charged by an external power supply. The hardware was enclosed in white-and-gray casing. P2, which was launched publicly in 1996, could walk freely, climb up and down stairs, push carts, and perform some actions wirelessly. P2, which was launched publicly in 1996, could walk freely, climb up and down stairs, push carts, and perform some actions wirelessly.King Rose Archives The following year, Honda’s engineers released the smaller and lighter P3. It was 160 cm tall and weighed 130 kg. In 2000 the popular ASIMO robot was introduced. Although shorter than its predecessors at 130 cm, it could walk, run, climb stairs, and recognize voices and faces. The most recent version was released in 2011. Honda has retired the robot. Honda P2’s influence Thanks to P2, today’s androids are not just ideas in a laboratory. Robots have been deployed to work in factories and, increasingly, at home. The machines are even being used for entertainment. During this year’s Spring Festival gala in Beijing, machines developed by Chinese startups Unitree Robotics, Galbot, Noetix, and MagicLab performed synchronized dances, martial arts, and backflips alongside human performers. “P2’s development shifted the focus of robotics from industrial applications to human-centric designs,” the Milestone sponsors explained in the wiki entry. “It inspired subsequent advancements in humanoid robots and influenced research in fields like biomechanics and artificial intelligence. “It was not just a technical achievement; it was a catalyst that propelled the field of humanoid robotics forward, demonstrating the potential for robots to interact with and assist humans in meaningful ways.” To learn more about robots, check out IEEE Spectrum’s guide. Recognition as an IEEE Milestone A plaque recognizing Honda’s P2 robot as an IEEE Milestone is to be installed at the Honda Collection Hall. The plaque is to read: In 1996 Prototype 2 (P2), a self-contained autonomous bipedal humanoid robot capable of stable dynamic walking and stair-climbing, was introduced by Honda. Its legged robotics incorporated real-time posture control, dynamic balance, gait generation, and multijoint coordination. Honda’s mechatronics and control algorithms set technical benchmarks in mobility, autonomy, and human-robot interaction. P2 inspired new research in humanoid robot development, leading to increasingly sophisticated successors. Administered by the IEEE History Center and supported by donors, the Milestone program recognizes outstanding technical developments around the world.
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“Can I get an interview?” “Can I get a job when I graduate?” Those questions came from students during a candid discussion about artificial intelligence, capturing the anxiety many young people feel today. As companies adopt AI-driven interview screeners, restructure their workforces, and redirect billions of dollars toward AI infrastructure, students are increasingly unsure of what the future of work will look like. We had gathered people together at a coffee shop in Auburn, Alabama, for what we called an AI Café. The event was designed to confront concerns about AI directly, demystifying the technology while pushing back against the growing narrative of technological doom. AI is reshaping society at breathtaking speed. Yet the trajectory of this transformation is being charted primarily by for-profit tech companies, whose priorities revolve around market dominance rather than public welfare. Many people feel that AI is something being done to them rather than developed with them. As computer science and liberal arts faculty at Auburn University, we believe there is another path forward: one where scholars engage their communities in genuine dialogue about AI. Not to lecture about technical capabilities, but to listen, learn, and co-create a vision for AI that serves the public interest. The AI Café Model Last November, we ran two public AI Cafés in Auburn. These were informal, 90-minute conversations between faculty, students, and community members about their experiences with AI. In these conversational forums, participants sat in clusters, questions flowed in multiple directions, and lived experience carried as much weight as technical expertise. We avoided jargon and resisted attempts to “correct” misconceptions, welcoming whatever emotions emerged. One ground rule proved crucial: keeping discussions in the present, asking participants where they encounter AI today. Without that focus, conversations could easily drift to sci-fi speculation. Historical analogies—to the printing press, electricity, and smartphones—helped people contextualize their reactions. And we found that without shared definitions of AI, people talked past each other; we learned to ask participants to name specific tools they were concerned about. Organizers Xaq Frohlich, Cheryl Seals, and Joan Harrell (right) held their first AI Café in a welcoming coffee shop and bookstore. Well Red Most important, we approached these events not as experts enlightening the masses, but as community members navigating complex change together. What We Learned by Listening Participants arrived with significant frustration. They felt that commercial interests were driving AI development “without consideration of public needs,” as one attendee put it. This echoed deeper anxieties about technology, from social media algorithms that amplify division to devices that profit from “engagement” and replace meaningful face-to-face connection. People aren’t simply “afraid of AI.” They’re weary of a pattern where powerful technologies reshape their lives while they have little say. Yet when given space to voice concerns without dismissal, something shifted. Participants didn’t want to stop AI development; they wanted to have a voice in it. When we asked “What would a human-centered AI future look like?” the conversation became constructive. People articulated priorities: fairness over efficiency, creativity over automation, dignity over convenience, community over individualism. The three organizers, all professors at Alabama’s Auburn University, say that including people from the liberal arts fields brought new perspectives to the discussions about AI. Well Red For us as organizers, the experience was transformative. Hearing how AI affected people’s work, their children’s education, and their trust in information prompted us to consider dimensions we hadn’t fully grasped. Perhaps most striking was the gratitude participants expressed for being heard. It wasn’t about filling knowledge deficits; it was about mutual learning. The trust generated created a spillover effect, renewing faith that AI could serve the public interest if shaped through inclusive processes. How to Start Your Own AI Café The “deficit model” of science communication—where experts transmit knowledge to an uninformed public—has been discredited. Public resistance to emerging technologies reflects legitimate concerns about values, risks, and who controls decision-making. Our events point toward a better model. We urge engineering and liberal arts departments, professional societies, and community organizations worldwide to organize dialogues similar to our AI Cafés. We found that a few simple design choices made these conversations far more productive. Informal and welcoming spaces such as coffee shops, libraries, and community centers helped participants feel comfortable (and serving food and drinks helped too!). Starting with small-group discussions, where people talked with neighbors, produced more honest thinking and greater participation. Partnering with colleagues in the liberal arts brought additional perspectives on technology’s social dimensions. And by making a commitment to an ongoing series of events, we built trust. Facilitation also matters. Rather than leading with technical expertise, we began with values: We asked what kind of world participants wanted, and how AI might help or hinder that vision. We used analogies to earlier technologies to help people situate their reactions and grounded discussions in present realities, asking participants where they have encountered AI in their daily lives. We welcomed emotions constructively, transforming worry into problem solving by asking questions like: “What would you do about that?” Why Engineers Should Engage the Public Professional ethics codes remain abstract unless grounded in dialogue with affected communities. Conversations about what “responsible AI” means will look different in São Paulo than in Seoul, in Vienna than in Nairobi. What makes the AI Café model portable is its general principles: informal settings, values-first questions, present-tense focus, genuine listening. Without such engagement, ethical accountability quietly shifts to technical experts rather than remaining a shared public concern. If we let commercial interests define AI’s trajectory with minimal public input, it will only deepen divides and entrench inequities. AI will continue advancing whether or not we have public trust. But AI shaped through dialogue with communities will look fundamentally different from AI developed solely to pursue what’s technically possible or commercially profitable. The tools for this work aren’t technical; they’re social, requiring humility, patience, and genuine curiosity. The question isn’t whether AI will transform society. It’s whether that transformation will be done to people or with them. We believe scholars must choose the latter, and that starts with showing up in coffee shops and community centers to have conversations where we do less talking and more listening. The future of AI depends on it.
U.S. doctoral programs in electrical engineering form the foundation of technological advancement, training the brightest minds in the world to research, develop, and design next-generation electronics, software, electrical infrastructure, and other high-tech products and systems. Elite institutions have long served as launchpads for the engineers behind tomorrow’s technology. Now that foundation is under strain. With U.S. universities increasingly entangled in political battles under the second Trump administration, uncertainty is beginning to ripple through doctoral admissions for electrical engineering programs. While some departments are reducing the number of spots available in anticipation of potential federal funding cuts, others are seeing their applicant pools shrink, particularly among international students, who make up a significant portion of their programs. In 2024 alone, U.S. universities awarded more than 2,000 doctorates in electrical and computer engineering, according to data from the National Center for Science and Engineering Statistics. The number of computing Ph.D.s grew significantly in the 2010s, according to data from the National Academies, but there is still high demand for those with advanced degrees across academia, government, and industry. Now, some universities point to warning signs of waning enrollment. Though not all engineers have Ph.D.s, if enrollment continues to shrink, fewer doctoral students could mean fewer engineers developing cutting-edge technology and training the next generation, potentially exacerbating existing labor shortages as global competition for tech talent intensifies. Federal funding cuts affect admissions Public universities in particular are feeling the strain because they rely heavily on federal grants to support doctoral students. The University of California, Los Angeles, for instance, must fund Ph.D. students for the duration of a degree—typically five years. In August 2025, the U.S. government pulled more than US $580 million in federal grants over allegations that the university failed to adequately address antisemitism on campus during student protests. A federal judge has since ordered the funding to be restored, but faculty began to worry that research support could be clawed back without notice, says Subramanian Iyer, distinguished professor at UC Los Angeles’s department of electrical and computer engineering. According to Iyer, departments across UC Los Angeles, including engineering, plan to scale back Ph.D. admissions this year. “The fear is that at some point, all this government money will be taken away,” Iyer says. “Lowering the admissions rate is just a way to prepare for that reality.” In response to a request for comment, a spokesperson for the U.S. National Science Foundation—a major source of federal research funding at UC Los Angeles and elsewhere—said, “NSF recognizes the essential role doctoral trainees play in the nation’s engineering and STEM enterprise” and noted several of the foundation’s awards and programs that support graduate research. Funding shocks may also force Pennsylvania State University to reshape future admissions decisions, according to Madhavan Swaminathan, head of Penn State’s electrical engineering department and director of the Center for Heterogeneous Integration of Micro Electronic Systems (CHIMES), a semiconductor research lab. In 2023, the Defense Advanced Research Projects Agency (DARPA) and industry partners awarded CHIMES a five-year $32.7 million grant. But in late 2025, the agency pulled its final year of funding from the center, citing a shift in priorities from microelectronics to photonics, Swaminathan says. As a result, CHIMES’ annual budget, which supports research assistantships for roughly 100 engineering graduate students, the majority pursuing Ph.D.s, will fall from $7 million in 2026 to $3.5 million in 2027. If these constraints persist, Penn State’s engineering department may reduce the number of doctoral students it supports. In a statement, a DARPA spokesperson told IEEE Spectrum: “Basic research is central to identifying world-changing technologies, and DARPA remains committed to engaging academic institutions in our program research. By design, a DARPA program typically lasts about 3 to 5 years. Once we establish proof of concept, we transition the technology for further development and turn our attention to other challenging areas of research.” Penn State’s enrollment numbers reflect Swaminathan’s caution. He says the electrical engineering Ph.D. cohort shrank from 28 students in 2024 to 15 students in 2025. Applications show a similar pattern. After rising from 195 in 2024 to 247 in 2025, Ph.D. applications fell roughly 30 percent to 174 for the upcoming 2026 cohort, a sign that prospective students may be wary of applying to U.S. programs. Immigration restrictions and application declines In late January, the Trump administration announced it had paused visa approvals for citizens of 75 countries. Months earlier, the administration proposed new restrictions on student visas, including a four-year cap. For Texas A&M University’s graduate electrical and computer engineering programs, up to 80 percent of applicants each year are international students, according to Narasimha Annapareddy, professor and head of the department. Annapareddy says applications for the fall 2026 Ph.D. cohort have dropped by roughly 50 percent. Annapareddy says the United States is “sending a message that migration is going to be more difficult in the future.” Foreign students often pursue degrees in the U.S. not only for academic training, he says, but to build long-term careers and lives in the country. Fewer applications from international students mean that the university forgoes a “driven and hungry” segment of the applicant pool who are highly qualified in technical fields. “The fear is that at some point, all this government money will be taken away.”— Subramanian Iyer, UC Los Angeles At the University of Southern California, the decline is more moderate. The freshman Ph.D. class fell from about 90 students in 2024 to roughly 70 in 2025, a reduction of 22 percent, according to Richard Leahy, department chair of USC’s Ming Hsieh Department of Electrical and Computer Engineering. While Leahy says applications are down modestly overall, domestic applications have increased by roughly 15 percent. Beyond immigration restrictions, international students, particularly from countries such as India and China, may be staying in their home countries as their technology sectors expand. “A lot of those students that would normally have come to the U.S. are now taking very good jobs working in the AI industry and other areas,” Leahy says. “There are a lot more opportunities now.” Workforce pipeline strains Some faculty say shrinking cohorts could erode the tech workforce if the pattern continues. At UC Los Angeles, Iyer describes a doctoral ecosystem built on a chain of mentorship. Among the roughly 25 students in his lab, senior doctoral students mentor junior Ph.D. candidates, who in turn guide master’s students and undergraduates. The system depends on overlapping cohorts. Reducing the number of students hired weakens that overlap and the trickle-down benefits of the mentorship model that keeps labs functioning. The real benefit of the university system isn’t just the teaching but also “the community that you build,” Iyer says. “As you decrease admissions, this will disappear.” At Penn State, Swaminathan points to specialization as key to a strong workforce. Many doctoral students train in semiconductor engineering, feeding expert talent into the domestic chip industry. If enrollment continues to shrink over the next few years, Swaminathan says, companies may need to hire students with bachelor’s or master’s degrees, who might lack the necessary skills required to design and innovate new chips. “Without that specialization, there’s only so much one can do,” Swaminathan says. The industry–academia gap Not all departments are shrinking. At the University of Texas at Austin, overall enrollment has remained relatively steady, according to Diana Marculescu, chair of UT Austin’s Chandra Family Department of Electrical and Computer Engineering. While she says recent fluctuations aren’t raising alarms, her concern lies more with alignment between research and industry. Doctoral students often train according to current grant priorities, she says. But by the time graduates enter the job market four to six years later, their specialization may not align neatly with open roles. That creates friction in the talent pipeline. “That lack of connection might be problematic,” Marculescu says. She argues that closer collaboration between universities and the private sector could help create stronger feedback loops between hiring needs and academic research priorities. For now, USC’s Leahy says Ph.D. graduates remain in high demand, and the current shifts have not yet translated into measurable workforce shortages. “We should be concerned about the number of Ph.D.s,” he says. “But there isn’t a crisis at this point.”
Last week’s Nvidia GTC conference highlighted new chip architectures to power AI. But as the chips become faster and more powerful, the remainder of data center infrastructure is playing catch-up. The power-delivery community is responding: Announcements from Delta, Eaton, and Vertiv showcased new designs for the AI era. Complex and inefficient AC-to-DC power conversions are gradually being replaced by DC configurations, at least in hyperscale data centers. “While AC distribution remains deeply entrenched, advances in power electronics and the rising demands of AI infrastructure are accelerating interest in DC architectures,” says Chris Thompson, vice president of advanced technology and global microgrids at Vertiv. AC-to-DC Conversion Challenges Today, nearly all data centers are designed around AC utility power. The electrical path includes multiple conversions before power reaches the compute load. Power typically enters the data center as medium-voltage AC (1 to 35 kilovolts), is stepped down to low-voltage AC (480 or 415 volts) using a transformer, converted to DC inside an uninterruptible power supply (UPS) for battery storage, converted back to AC, and converted again to low-voltage DC (typically 54 V DC) at the server, supplying the DC power computing chips actually require. “The double conversion process ensures the output AC is clean, stable, and suitable for data center servers,” says Luiz Fernando Huet de Bacellar, vice president of engineering and technology at Eaton. That setup worked well enough for the amounts of power required by traditional data centers. Traditional data center computational racks draw on the order of 10 kW each. For AI, that is starting to approach 1 megawatt. At that scale, the energy losses, current levels, and copper requirements of AC-to-DC conversions become increasingly difficult to justify. Every conversion incurs some power loss. On top of that, as the amount of power that needs to be delivered grows, the sheer size of the convertors, as well as the connector requirements of copper busbars, becomes untenable. According to an Nvidia blog, a 1-MW rack could require as much as 200 kilograms of copper busbar. For a 1-gigawatt data center, it could amount to 200,000 kg of copper. Benefits of High-Voltage DC Power By converting 13.8-kV AC grid power directly to 800 V DC at the data center perimeter, most intermediate conversion steps are eliminated. This reduces the number of fans and power-supply units, and leads to higher system reliability, lower heat dissipation, improved energy efficiency, and a smaller equipment footprint. “Each power conversion between the electric grid or power source and the silicon chips inside the servers causes some energy loss,” says Bacellar. Switching from 415-V AC to 800-V DC in electrical distribution enables 85 percent more power to be transmitted through the same conductor size. This happens because higher voltage reduces current demand, lowering resistive losses and making power transfer more efficient. Thinner conductors can handle the same load, reducing copper requirements by 45 percent, a 5 percent improvement in efficiency, and 30 percent lower total cost of ownership for gigawatt-scale facilities. “In a high-voltage DC architecture, power from the grid is converted from medium-voltage AC to roughly 800-V DC and then distributed throughout the facility on a DC bus,” said Vertiv’s Thompson. “At the rack, compact DC-to-DC converters step that voltage down for GPUs and CPUs.” A report from technology advisory group Omdia claims that higher voltage DC data centers have already appeared in China. In the Americas, the Mt. Diablo Initiative (a collaboration among Meta, Microsoft, and the Open Compute Project) is a 400-V DC rack power distribution experiment. Innovations in DC Power Systems A handful of vendors are trying to get ahead of the game. Vertiv’s 800-V DC ecosystem that integrates with Nvidia Vera Rubin Ultra Kyber platforms will be commercially available in the second half of 2026. Eaton, too, is well advanced in its 800-V DC systems innovation courtesy of a medium-voltage solid-state transformer (SST) that will sit at the heart of DC power distribution system. Meanwhile Delta, has released 800-V DC in-row 660-kW power racks with a total of 480 kW of embedded battery backup units. And, SolarEdge is hard at work on a 99%-efficient SST that will be paired with a native DC UPS and a DC power distribution layer. But much of the industry is far behind. Patrick Hughes, senior vice president of strategy, technical, and industry affairs for the National Electrical Manufacturers Association, says most innovation is happening at the 400-V DC level, though some are preparing 800-V DC. He believes the industry needs a complete, coordinated ecosystem, including power electronics, protection, connectors, sensing, and service‑safe components that scale together rather than in isolation. That, in turn, requires retooling manufacturing capacity for DC‑specific equipment, expanding semiconductor and materials supply, and clear, long‑term demand commitments that justify major capital investment across the value chain. “Many are taking a cautious approach, offering limited or adapted solutions while waiting for clearer standards, safety frameworks, and customer commitments,” said Hughes. “Building the supply chain will hinge on stabilizing standards and safety frameworks so suppliers can design, certify, manufacture, and install equipment with confidence.”
The undying thirst for smarter (historically, that means larger) AI models and greater adoption of the ones we already have has led to an explosion in data-center construction projects, unparalleled both in number and scale. Chief among them is Meta’s planned 5-gigawatt data center in Louisiana, called Hyperion, announced in June of 2025. Meta CEO Mark Zuckerberg said Hyperion will “cover a significant part of the footprint of Manhattan,” and the first phase—a 2-GW version—will be completed by 2030. Though the project’s stated 5-GW scale is the largest among its peers, it’s just one of several dozen similar projects now underway. According to Michael Guckes, chief economist at construction-software company ConstructConnect, spending on data centers topped US $27 billion by July of 2025 and, once the full-year figures are tallied, will easily exceed $60 billion. Hyperion alone accounts for about a quarter of that. For the engineers assigned to bring these projects to life, the mix of challenges involved represent a unique moment. The world’s largest tech companies are opening their wallets to pay for new innovations in compute, cooling, and network technology designed to operate at a scale that would’ve seemed absurd five years ago. At the same time, the breakneck pace of building comes paired with serious problems. Modern data-center construction frequently requires an influx of temporary workers and sharply increases noise, traffic, pollution, and often local electricity prices. And the environmental toll remains a concern long after facilities are built due to the unprecedented 24/7 energy demands of AI data centers which, according to one recent study, could emit the equivalent of tens of millions of tonnes of CO2 annually in the United States alone. Regardless of these issues, large AI companies, and the engineers they hire, are going full steam ahead on giant data-center construction. So, what does it really take to build an unprecedentedly large data center? AI Rewrites Building Design The stereotypical data-center building rests on a reinforced concrete slab foundation. That’s paired with a steel skeleton and poured concrete wall panels. The finished building is called a “shell,” a term that implies the structure itself is a secondary concern. Meta has even used gigantic tents to throw up temporary data centers. Still, the scale of the largest AI data centers brings unique challenges. “The biggest challenge is often what’s under the surface. Unstable, corrosive, or expansive soils can lead to delays and require serious intervention,” says Robert Haley, vice president at construction consulting firm Jacobs. Amanda Carter, a senior technical lead at Stantec, said a soil’s thermal conductivity is also important, as most electrical infrastructure is placed underground. “If the soil has high thermal resistivity, it’s going to be difficult to dissipate [heat].” Engineers may take hundreds or thousands of soil samples before construction can begin. GPUs Modern AI data centers often use rack-scale systems, such as the Nvidia GB200 NVL72, which occupy a single data-center rack. Each rack contains 72 GPUs, 36 CPUs, and up to 13.4 terabytes of GPU memory. The racks measure over 2.2 meters tall and weigh over one and a half tonnes, forcing AI data centers to use thicker concrete with more reinforcement to bear the load. A single GB200 rack can use up to 120 kilowatts. If Hyperion meets its 5-gigawatt goals, the data-center campus could include over 41,000 rack-scale systems, for a total of more than 3 million GPUs. The final number of GPUs used by Hyperion is likely to be less than that, though only because future GPUs will be larger, more capable, and use more power. Money According to ConstructConnect, spending on data centers neared US $27 billion through July of 2025 and, according to the latest data, will tally close to $60 billion through the end of the year. Meta’s Hyperion project is a big slice of the pie, at $10 billion. Data-center spending has become an important prop for the construction industry, which is seeing reduced demand in other areas, such as residential construction and public infrastructure. ConstructConnect’s third quarter 2025 financial report stated that the quarter’s decline “would have been far more severe without an $11 billion surge in data center starts.” There’s apparently no shortage of eligible sites, however, as both the number of data centers under construction, and the money spent on them, has skyrocketed. The spending has allowed companies building data centers to throw out the rule book. Prior to the AI boom, most data centers relied on tried-and-true designs that prioritized inexpensive and efficient construction. Big tech’s willingness to spend has shifted the focus to speed and scale. The loose purse strings open the door to larger and more robust prefabricated concrete wall and floor panels. Doug Bevier, director of development at Clark Pacific, says some concrete floor panels may now span up to 23 meters and need to handle floor loads up to 3,000 kilograms per square meter, which is more than twice the load international building codes normally define for manufacturing and industry. In some cases, the concrete panels must be custom-made for a project, an expensive step that the economics of pre-AI data centers rarely justified. Simultaneously, the time scale for projects is also compressed: Jamie McGrath, senior vice president of data-center operations at Crusoe, says the company is delivering projects in “about 12 months,” compared to 30 to 36 months before. Not all projects are proceeding at that pace, but speed is universally a priority. That makes it difficult to coordinate the labor and materials required. Meta’s Hyperion site, located in rural Richland Parish, Louisiana, is emblematic of this challenge. As reported by NOLA.com, at least 5,000 temporary workers have flocked to the area, which has only about 20,000 permanent residents. These workers earn above-average wages and bring a short-term boost for some local businesses, such as restaurants and convenience stores. However, they have also spurred complaints from residents about traffic and construction noise and pollution. This friction with residents includes not only these obvious impacts, but also things you might not immediately suspect, such as light pollution caused by around-the-clock schedules. Also significant are changes to local water tables and runoff, which can reduce water quality for neighbors who rely on well water. These issues have motivated a few U.S. cities to enact data-center bans. Data Centers Often Go BYOP (bring your own power) Meta’s Richland Parish site also highlights a problem that’s priority No. 1 for both AI data centers and their critics: power. Data centers have always drawn large amounts of power, which nudged data-center construction to cluster in hubs where local utilities were responsive to their demands. Virginia’s electric utility, Dominion Energy, met demand with agreements to build new infrastructure, often with a focus on renewable energy. The power demands of the largest AI data centers, though, have caught even the most responsive utilities off guard. A report from the Lawrence Berkeley National Laboratory, in California, estimated the entire U.S. data-center industry consumed an average load of roughly 8 GW of power in 2014. Today, the largest AI data-center campuses are built to handle up to a gigawatt each, and Meta’s Hyperion is projected to require 5 GW. “Data centers are exasperating issues for a lot of utilities,” says Abbe Ramanan, project director at the Clean Energy Group, a Vermont-based nonprofit. Ramanan explains that utilities often use “peaker plants” to cope with extra demand. They’re usually older, less efficient fossil-fuel plants which, because of their high cost to operate and carbon output, were due for retirement. But Ramanan says increased electricity demand has kept them in service. Meta secured power for Hyperion by negotiating with Entergy, Louisiana’s electric utility, for construction of three new gas-turbine power plants. Two will be located near the Richland Parish site, while a third will be located in southeast Louisiana. Entergy frames the new plants as a win for the state. “A core pillar of Entergy and Meta’s agreement is that Meta pays for the full cost of the utility infrastructure,” says Daniel Kline, director of power-delivery planning and policy at Entergy. The utility expects that “customer bills will be lower than they otherwise would have been.” That would prove an exception, as a recent report from Bloomberg found electricity rates in regions with data centers are more likely to increase than in regions without. CO2 Research published in Nature in 2025 projects that data-center emissions will range from 24 million to 44 million CO2-equivalent metric tonnes annually through 2030 in the United States alone. While some materials used in data centers, such as concrete, lead to significant emissions, the majority of these emissions will result from the high energy demands of AI servers. Estimating the carbon emissions of Hyperion is difficult, as the project won’t be completed until 2030. Assuming that the three new natural gas plants that are planned for construction as part of the project produce emissions typical for their type, however, the plants could lead to full life-cycle emissions of between 4 million and 10 million metric tons of CO2 annually—roughly equivalent to the annual emissions of a country like Latvia. Concrete Data centers are typically built from concrete, with steel used as a skeleton to reinforce and shape the concrete shell. While the foundation is often poured concrete, the walls and floors are most often built from prefabricated concrete panels that can span up to 23 meters. Floors use a reinforced T-shape, similar to a steel girder, measuring up to 1.2 meters across at its thickest point. The largest data centers include hundreds of these concrete panels. The America Cement Association projects that the current surge in building will require 1 million tonnes of cement over the next three years, though that’s still a tiny fraction of the overall cement industry, which weighed in at roughly 103 million tonnes in 2024. The plants, which will generate a combined 2.26 GW, will use combined-cycle gas turbines that recapture waste heat from exhaust. This boosts thermal efficiency to 60 percent and beyond, meaning more fuel is converted to useful energy. Simple-cycle turbines, by contrast, vent the exhaust, which lowers efficiency to around 40 percent. Even so, total life-cycle emissions for the Hyperion plants could range from 4 million to over 10 million tonnes of CO2 each year, depending on how frequently the plants are put in use and the final efficiency benchmarks once built. On the high end, that’s as much CO2 as produced by over 2 million passenger cars. Fortunately, not all of Meta’s data centers take the same approach to power. The company has announced a plan to power Prometheus, a large data-center project in Ohio scheduled to come online before the end of 2026, with nuclear energy. But other big tech companies, spurred by the need to build data centers quickly, are taking a less efficient approach. xAI’s Colossus 2, located in Memphis, is the most extreme example. The company trucked dozens of temporary gas-turbine generators to power the site located in a suburban neighborhood. OpenAI, meanwhile, has gas turbines capable of generating up to 300 megawatts at its new Stargate data center in Abilene, Texas, slated to open later in 2026. Both use simple-cycle turbines with a much lower efficiency rating than the combined-cycle plants Entergy will build to power Hyperion. Demand for gas turbines is so intense, in fact, that wait times for new turbines are up to seven years. Some data centers are turning toward refurbished jet engines to obtain the turbines they need. AI Racks Tip the Scales The demand for new, reliable power is driven by the power-hungry GPUs inside modern AI data centers. In January of 2025, Mark Zuckerberg announced in a post on Facebook that Meta planned to end 2025 with at least 1.3 million GPUs in service. OpenAI’s Stargate data center plans to use over 450,000 Nvidia GB200 GPUs, and xAI’s Colossus 2, an expansion of Colossus, is built to accommodate over 550,000 GPUs. GPUs, which remain by far the most popular for AI workloads, are bundled into human-scale monoliths of steel and silicon which, much like the data centers built to house them, are rapidly growing in weight, complexity, and power consumption. Memory In addition to raw compute performance, Nvidia GB200 NVL72 racks also require huge amounts of memory. An Nvidia GB200 NVL72 rack may include up to 13.4 terabytes of high-bandwidth memory, which implies a data-center campus at Hyperion’s scale will require at least several dozen petabytes. The immense demand has sent memory prices soaring: The price of DRAM, specifically DDR5, has increased 172 percent in 2025. Power Hyperion is expected to use 5 gigawatts of power across 11 buildings, which works out to just under 500 megawatts per building, assuming each will be similar to its siblings. That’s enough to power roughly 4.2 million U.S. homes. Just one Hyperion data center built at the Richland Parish site will consume twice as much power as xAI’s Colossus which, at the time of its completion in the summer of 2024, was among the largest data centers yet built. Nvidia’s GB200 NVL72—a rack-scale system—is currently a leading choice for AI data centers. A single GB200 rack contains 72 GPUs, 36 CPUs, and up to 17 terabytes of memory. It measures 2.2 meters tall, tips the scales at up to 1,553 kilograms, and consumes about 120 kilowatts—as much as around 100 U.S. homes. And this, according to Nvidia, is just the beginning. The company anticipates future racks could consume up to a megawatt each. Viktor Petik, senior vice president of infrastructure solutions at Vertiv, says the rapid change in rack-scale AI systems has forced data centers to adapt. “AI racks consume far more power and weigh more than their predecessors,” says Petik. He adds that data centers must supply racks with multiple power feeds, without taking up extra space. The new power demands from rack-scale systems have consequences that are reflected in the design of the data center—even its footprint. In 2022 Meta broke ground on a new data center at a campus in Temple, Texas. According to SemiAnalysis, which studies AI data centers, construction began with the intent to build the data center in an H-shaped configuration common to other Meta data centers. LAND Meta CEO Mark Zuckerberg kicked off the buzz around Hyperion by saying it would cover a large chunk of Manhattan. Many took that to mean Hyperion would be a single building of that size, which isn’t correct. Hyperion will actually be a cluster of data centers—11 are currently planned—with over 370,000 square meters of floor space. That’s a lot smaller even than New York City’s Central Park, which covers 6 percent of Manhattan. Meta has room to grow, however. The Richland Parish site spans 14.7 million square meters in total, which is about a quarter the area of Manhattan. And the 370,000 square meters of floor space Hyperion is expected to provide doesn’t include external infrastructure, such as the three new combined-cycle gas power plants Louisiana utility Entergy is building to power the project. Construction was paused midway in December of 2022, however, as part of a company-wide review of its data-center infrastructure. Meta decided to knock down the structure it had built and start from scratch. The reasons for this decision were never made public, but analysts believe it was due to the old design’s inability to deliver sufficient electricity to new, power-hungry AI racks. Construction resumed in 2023. Meta’s replacement ditches the H-shaped building for simple, long, rectangular structures, each flanked by rows of gas-turbine generators. While Meta’s plans are subject to change, Hyperion is currently expected to comprise 11 rectangular data centers, each packed with hundreds of thousands of GPUs, spread across the 13.6-square-kilometer Richland Parish campus. Cooling, and Connecting, at Scale Nvidia’s ultradense AI GPU racks are changing data centers not only with their weight, and power draw, but also with their intense cooling and bandwidth requirements. Data centers traditionally use air cooling, but that approach has reached its limits. “Air as a cooling medium is inherently inferior,” says Poh Seng Lee, head of CoolestLAB, a cooling research group at the National University of Singapore. Instead, going forward, GPUs will rely on liquid cooling. However, that adds a new layer of complexity. “It’s all the way to the facilities level,” says Lee. “You need pumps, which we call a coolant distribution unit. The CDU will be connected to racks using an elaborate piping network. And it needs to be designed for redundancy.” On the rack, pipes connect to cold plates mounted atop every GPU; outside the data-center shell, pipes route through evaporation cooling units. Lee says retrofitting an air-cooled data center is possible but expensive. The networking used by AI data centers is also changing to cope with new requirements. Traditional data centers were positioned near network hubs for easy access to the global internet. AI data centers, though, are more concerned with networks of GPUs. These connections must sustain high bandwidth with impeccable reliability. Mark Bieberich, a vice president at network infrastructure company Ciena, says its latest fiber-optic transceiver technology, WaveLogic 6, can provide up to 1.6 terabytes per second of bandwidth per wavelength. A single fiber can support 48 wavelengths in total, and Ciena’s largest customers have hundreds of fiber pairs, placing total bandwidth in the thousands of terabits per second. This is a point where the scale of Meta’s Hyperion, and other large AI data centers, can be deceptive. It seems to imply the physical size of a single data center is what matters. But rather than being a single building, Hyperion is actually a set of buildings connected by high-speed fiber-optics. “Interconnecting data centers is absolutely essential,” says Bieberich. “You could think about it as one logical AI training facility, but with geographically distributed facilities.” Nvidia has taken to calling this “scale across,” to contrast it with the idea that data centers must “scale up” to larger singular buildings. The Big but Hazy Future The full scale of the challenges that face Hyperion, and other future AI data centers of similar scale, remain hazy. Nvidia has yet to introduce the rack-scale AI GPU systems it will host. How much power will it demand? What type of cooling will it require? How much bandwidth must be provided? These can only be estimated. In the absence of details, the gravity of AI data-center design is pulled toward one certainty: It must be big. New data-center designers are rewriting their rule book to handle power, cooling, and network infrastructure at a scale that would’ve seemed ridiculous five years ago. This innovation is fueled by big tech’s fat wallet, which shelled out tens of billions of dollars in 2025 alone, leading to questions about whether the spending is sustainable. For the engineers in the trenches of data-center design, though, it’s viewed as an opportunity to make the impossible possible. “I tell my engineers, this is peak. We’re being engineers. We’re being asked complicated questions,” says Stantec’s Carter. “We haven’t got to do that in a long time.” This article appears in the April 2026 print issue.
WHEN KYIV-BORN ENGINEER Yaroslav Azhnyuk thinks about the future, his mind conjures up dystopian images. He talks about “swarms of autonomous drones carrying other autonomous drones to protect them against autonomous drones, which are trying to intercept them, controlled by AI agents overseen by a human general somewhere.” He also imagines flotillas of autonomous submarines, each carrying hundreds of drones, suddenly emerging off the coast of California or Great Britain and discharging their cargoes en masse to the sky. “How do you protect from that?” he asks as we speak in late December 2025; me at my quiet home office in London, he in Kyiv, which is bracing for another wave of missile attacks. Azhnyuk is not an alarmist. He cofounded and was formerly CEO of Petcube, a California-based company that uses smart cameras and an app to let pet owners keep an eye on their beloved creatures left alone at home. A self-described “liberal guy who didn’t even receive military training,” Azhnyuk changed his mind about developing military tech in the months following the Russian invasion of Ukraine in February 2022. By 2023, he had relinquished his CEO role at Petcube to do what many Ukrainian technologists have done—to help defend his country against a mightier aggressor. It took a while for him to figure out what, exactly, he should be doing. He didn’t join the military, but through friends on the front line, he witnessed how, out of desperation, Ukrainian troops turned to off-the-shelf consumer drones to make up for their country’s lack of artillery. Ukrainian troops first began using drones for battlefield surveillance, but within a few months they figured out how to strap explosives onto them and turn them into effective, low-cost killing machines. Little did they know they were fomenting a revolution in warfare. The Ukrainian robotics company The Fourth Law produces an autonomy module [above] that uses optics and AI to guide a drone to its target. Yaroslav Azhnyuk [top, in light shirt], founder and CEO of The Fourth Law, describes a developmental drone with autonomous capabilities to Ukrainian President Volodymyr Zelenskyy and German Chancellor Olaf Scholz.Top: THE PRESIDENTIAL OFFICE OF UKRAINE; Bottom: THE FOURTH LAW That revolution was on display last month, as the U.S. and Israel went to war with Iran. It soon became clear that attack drones are being extensively used by both sides. Iran, for example, is relying heavily on the Shahed drones that the country invented and that are now also being manufactured in Russia and launched by the thousands every month against Ukraine. A thorough analysis of the Middle East conflict will take some time to emerge. And so to understand the direction of this new way of war, look to Ukraine, where its next phase—autonomy—is already starting to come into view. Outnumbered by the Russians and facing increasingly sophisticated jamming and spoofing aimed at causing the drones to veer off course or fall out of the sky, Ukrainian technologists realized as early as 2023 that what could really win the war was autonomy. Autonomous operation means a drone isn’t being flown by a remote pilot, and therefore there’s no communications link to that pilot that can be severed or spoofed, rendering the drone useless. By late 2023, Azhnyuk set out to help make that vision a reality. He founded two companies, The Fourth Law and Odd Systems, the first to develop AI algorithms to help drones overcome jamming during final approach, the second to build thermal cameras to help those drones better sense their surroundings. “I moved from making devices that throw treats to dogs to making devices that throw explosives on Russian occupants,” Azhnyuk quips. Since then, The Fourth Law has dispatched “more than thousands” of autonomy modules to troops in eastern Ukraine (it declines to give a more specific figure), which can be retrofitted on existing drones to take over navigation during the final approach to the target. Azhnyuk says the autonomy modules, worth around US $50, increase the drone-strike success rate by up to four times that of purely operator-controlled drones. And that is just the beginning. Azhnyuk is one of thousands of developers, including some who relocated from Western countries, who are applying their skills and other resources to advancing the drone technology that is the defining characteristic of the war in Ukraine. This eclectic group of startups and founders includes Eric Schmidt, the former Google CEO, whose company Swift Beat is churning out autonomous drones and modules for Ukrainian forces. The frenetic pace of tech development is helping a scrappy, innovative underdog hold at bay a much larger and better-equipped foe. All of this development is careening toward AI-based systems that enable drones to navigate by recognizing features in the terrain, lock on to and chase targets without an operator’s guidance, and eventually exchange information with each other through mesh networks, forming self-organizing robotic kamikaze swarms. Such an attack swarm would be commanded by a single operator from a safe distance. According to some reports, autonomous swarming technology is also being developed for sea drones. Ukraine has had some notable successes with sea drones, which have reportedly destroyed or damaged around a dozen Russian vessels. The Skynode X system, from Auterion, provides a degree of autonomy to a drone.AUTERION For Ukraine, swarming can solve a major problem that puts the nation at a disadvantage against Russia—the lack of personnel. Autonomy is “the single most impactful defense technology of this century,” says Azhnyuk. “The moment this happens, you shift from a manpower challenge to a production challenge, which is much more manageable,” he adds. The autonomous warfare future envisioned by Azhnyuk and others is not yet a reality. But Marc Lange, a German defense analyst and business strategist, believes that “an inflection point” is already in view. Beyond it, “things will be so dramatically different,” he says. “Ukraine pretty rapidly realized that if the operator-to-drone ratio can be shifted from one-to-one to one-to-many, that creates great economies of scale and an amazing cost exchange ratio,” Lange adds. “The moment one operator can launch 100, 50, or even just 20 drones at once, this completely changes the economics of the war.” Drones With a View For a while, jammers that sever the radio links between drones and operators or that spoof GPS receivers were able to provide fairly reliable defense against human-controlled first-person-view attack drones (FPVs). But as autonomous navigation progressed, those electronic shields have gradually become less effective. Defenders must now contend with unjammable drones—ones that are attached to hair-thin optical fibers or that are capable of finding their way to their targets without external guidance. In this emerging struggle, the defenders’ track records aren’t very encouraging: The typical countermeasure is to try to shoot down the attacking drone with a service weapon. It’s rarely successful. A truck outfitted with signal-jamming gear drives under antidrone nets near Oleksandriya, in eastern Ukraine, on 2 October 2025.ED JONES/AFP/GETTY IMAGES “The attackers gain an immense advantage from unmanned systems,” says Lange. “You can have a drone pop up from anywhere and it can wreak havoc. But from autonomy, they gain even more.” The self-navigating drones rely on image-recognition algorithms that have been around for over a decade, says Lange. And the mass deployments of drones on Ukrainian battlefields are enabling both Russian and Ukrainian technologists to create huge datasets that improve the training and precision of those AI algorithms. A Ukrainian land robot, the Ravlyk, can be outfitted with a machine gun. While uncrewed aerial vehicles (UAVs) have received the most attention, the Ukrainian military is also deploying dozens of different kinds of drones on land and sea. Ukraine, struggling with the shortage of infantry personnel, began working on replacing a portion of human soldiers with wheeled ground robots in 2024. As of early 2026, thousands of ground robots are crawling across the gray zone along the front line in Eastern Ukraine. Most are used to deliver supplies to the front line or to help evacuate the wounded, but some “killer” ground robots fitted with turrets and remotely controlled machine guns have also been tested. In mid-February, Ukrainian authorities released a video of a Ukrainian ground robot using its thermal camera to detect a Russian soldier in the dark of the night and then kill the invader with a round from a heavy machine gun. So far these robots are mostly controlled by a human operator, but the makers of these uncrewed ground vehicles say their systems are capable of basic autonomous operations, such as returning to base when radio connection is lost. The goal is to enable them to swarm so that one operator controls not one, but a whole herd of mesh-connected killer robots. But Bryan Clark, senior fellow and director of the Center for Defense Concepts and Technology at the Hudson Institute, questions how quickly ground robots’ abilities can progress. “Ground environments are very difficult to navigate in because of the terrain you have to address,” he says. “The line of sight for the sensors on the ground vehicles is really constrained because of terrain, whereas an air vehicle can see everything around it.” To achieve autonomy, maritime drones, too, will require navigational approaches beyond AI-based image recognition, possibly based on star positions or electronic signals from radios and cell towers that are within reach, says Clark. Such technologies are still being developed or are in a relatively early operational stage. How the Shaheds Got Better Russia is not lagging behind. In fact, some analysts believe its autonomous systems may be slightly ahead of Ukraine’s. For a good example of the Russian military’s rapid evolution, they say, consider the long-range Iranian-designed Shahed drones. Since 2022, Russia has been using them to attack Ukrainian cities and other targets hundreds of kilometers from the front line. “At the beginning, Shaheds just had a frame, a motor, and an inertial navigation system,” Oleksii Solntsev, CEO of Ukrainian defense tech startup MaXon Systems, tells me. “They used to be imprecise and pretty stupid. But they are becoming more and more autonomous.” Solntsev founded MaXon Systems in late 2024 to help protect Ukrainian civilians from the growing threat of Shahed raids. A Russian Geran-2 drone, based on the Iranian Shahed-136, flies over Kyiv during an attack on 27 December 2025.SERGEI SUPINSKY/AFP/GETTY IMAGES First produced in Iran in the 2010s, Shaheds can carry 90-kilogram warheads up to 650 km (50-kg warheads can go twice as far). They cost around $35,000 per unit, compared to a couple of million dollars, at least, for a ballistic missile. The low cost allows Russia to manufacture Shaheds in high quantities, unleashing entire fleets onto Ukrainian cities and infrastructure almost every night. The early Shaheds were able to reach a preprogrammed location based on satellite-navigation coordinates. Even one of these early models could frequently overcome the jamming of satellite-navigation signals with the help of an onboard inertial navigation unit. This was essentially a dead-reckoning system of accelerators and gyroscopes that estimate the drone’s position from continual measurements of its motions. In the Donetsk Region, on 15 August 2025, a Ukrainian soldier hunts for Shaheds and other drones with a thermalimaging system attached to a ZU23 23-millimeter antiaircraft gun.KOSTYANTYN LIBEROV/LIBKOS/GETTY IMAGES Ukrainian defense forces learned to down Shaheds with heavy machine guns, but as Russia continued to innovate, the daily onslaughts started to become increasingly effective. Today’s Shaheds fly faster and higher, and therefore are more difficult to detect and take down. Between January 2024 and August 2025, the number of Shaheds and Shahed-type attack drones launched by Russia into Ukraine per month increased more than tenfold, from 334 to more than 4,000. In 2025, Ukraine found AI-enabling Nvidia chipsets in wreckages of Shaheds, as well as thermal-vision modules capable of locking onto targets at night. “Now, they are interconnected, which allows them to exchange information with each other,” Solntsev says. “They also have cameras that allow them to autonomously navigate to objects. Soon they will be able to tell each other to avoid a jammed region or an area where one of them got intercepted.” These Russian-manufactured Shaheds, which Russian forces call Geran-2s, are thought to be more capable than the garden variety Shahed-136s that Iran has lately been launching against targets throughout the Middle East. Even the relatively primitive Shahed-136s have done considerable damage, according to press accounts. Those Shahed successes may accrue, at least in part, from the fact that the United States and Israel lack Ukraine’s long experience with fending them off. In just two days in early March, upward of a thousand drones, mostly Shaheds, were launched against U.S. and Israeli targets, with hundreds of them reportedly finding their marks. One attack, caught on videotape, shows a Shahed destroying a radar dome at the U.S. navy base in Manama, Bahrain. U.S. forces were understood to be attempting to fend off the drones by striking launch platforms, dispatching fighter aircraft to shoot them down, and by using some extremely costly air-defense interceptors, including ones meant to down ballistic missiles. On 4 March, CNN reported that in a congressional briefing the day before, top U.S. defense officials, including Secretary of Defense Pete Hegseth, acknowledged that U.S. air defenses weren’t keeping up with the onslaught of Shahed drones. Russian V2U attack drones are outfitted with Nvidia processors and run computer-vision software and AI algorithms to enable the drones to navigate autonomously.GUR OF THE MINISTRY OF DEFENSE OF UKRAINE Russia is also starting to field a newer generation of attack drones. One of these, the V2U, has been used to strike targets in the Sumy region of northeastern Ukraine. The V2U drones are outfitted with Nvidia Jetson Orin processors and run computer-vision software and AI algorithms that allow the drones to navigate even where satellite navigation is jammed. The sale of Nvidia chips to Russia is banned under U.S. sanctions against the country. However, press reports suggest that the chips are getting to Russia via intermediaries in India. Antidrone Systems Step Up MaXon Systems is one of several companies working to fend off the nightly drone onslaught. Within one year, the company developed and battle-tested a Shahed interception system that hints at the sci-fi future envisioned by Azhnyuk. For a system to be capable of reliably defending against autonomous weaponry, it, too, needs to be autonomous. MaXon’s solution consists of ground turrets scanning the sky with infrared sensors, with additional input from a network of radars that detects approaching Shahed drones at distances of, typically, 12 to 16 km. The turrets fire autonomous fixed-winged interceptor drones, fitted with explosive warheads, toward the approaching Shaheds at speeds of nearly 300 km/h. To boost the chances of successful interception, MaXon is also fielding an airborne anti-Shahed fortification system consisting of helium-filled aerostats hovering above the city that dispatch the interceptors from a higher altitude. “We are trying to increase the level of automation of the system compared to existing solutions,” says Solntsev. “We need automatic detection, automatic takeoff, and automatic mid-track guidance so that we can guide the interceptor before it can itself flock the target.” An interceptor drone, part of the U.S. MEROPS defensive system, is tested in Poland on 18 November 2025.WOJTEK RADWANSKI/AFP/GETTY IMAGES In November 2025, the Ukrainian military announced it had been conducting successful trials of the Merops Shahed drone interceptor system developed by the U.S. startup Project Eagle, another of former Google CEO Eric Schmidt’s Ukraine defense ventures. Like the MaXon gear, the system can operate largely autonomously and has so far downed over 1,000 Shaheds. What Works in the Lab Doesn’t Necessarily Fly on the Battlefield Despite the progress on both sides, analysts say that the kind of robotic warfare imagined by Azhnyuk won’t be a reality for years. “The software for drone collaboration is there,” says Kate Bondar, a former policy advisor for the Ukrainian government and currently a research fellow at the U.S. Center for Strategic and International Studies. “Drones can fly in labs, but in real life, [the forces] are afraid to deploy them because the risk of a mistake is too high,” she adds. Ukrainian soldiers watch a GOR reconnaissance drone take to the sky near Pokrovsk in the Donetsk region, on 10 March 2025.ANDRIY DUBCHAK/FRONTLINER/GETTY IMAGES In Bondar’s view, powerful AI-equipped drones won’t be deployed in large numbers given the current prices for high-end processors and other advanced components. And, she adds, the more autonomous the system needs to be, the more expensive are the processors and sensors it must have. “For these cheap attack drones that fly only once, you don’t install a high-resolution camera that [has] the resolution for AI to see properly,” she says. “[You install] the cheapest camera. You don’t want expensive chips that can run AI algorithms either. Until we can achieve this balance of technological sophistication, when a system can conduct a mission but at the lowest price possible, it won’t be deployed en masse.” While existing AI systems are doing a good job recognizing and following large objects like Shaheds or tanks, experts question their ability to reliably distinguish and pursue smaller and more nimble or inconspicuous targets. “When we’re getting into more specific questions, like can it distinguish a Russian soldier from a Ukrainian soldier or at least a soldier from a civilian? The answer is no,” says Bondar. “Also, it’s one thing to track a tank, and it’s another to track infantrymen riding buggies and motorcycles that are moving very fast. That’s really challenging for AI to track and strike precisely.” Clark, at the Hudson Institute, says that although the AI algorithms used to guide the Russian and Ukrainian drones are “pretty good,” they rely on information provided bysensors that “aren’t good enough.” “You need multiphenomenology sensors that are able to look at infrared and visual and, in some cases, different parts of the infrared spectrum to be able to figure out if something is a decoy or real target,” he says. German defense analyst Lange agrees that right now, battlefield AI image-recognition systems are too easily fooled. “If you compress reality into a 2D image, a lot of things can be easily camouflaged—like what Russia did recently, when they started drawing birds on the back of their drones,” he says. Autonomy Remains Elusive on the Ground and at Sea, Too To make Ukraine’s emerging uncrewed ground vehicles (UGVs) equally self-sufficient will be an even greater task, in Clark’s view. Still, Bondar expects major advances to materialize within the next several years, even if humans are still going to be part of the decision-making loop. A mobile electronic-warfare system built by PiranhaTech is demonstrated near Kyiv on 21 October 2025.DANYLO ANTONIUK/ANADOLU/GETTY IMAGES “I think in two or three years, we will have pretty good full autonomy, at least in good weather conditions,” she says, referring to aerial drones in particular. “Humans will still be in the loop for some years, simply because there are so many unpredictable situations when you need an intervention. We won’t be able to fully rely on the machine for at least another 10 or 15 years.” Ukrainian defenders are apprehensive about that autonomous future. The boom of drone innovation has come hand in hand with the development of sophisticated jamming and radio-frequency detection systems. But a lot of that innovation will become obsolete once the pendulum swings away from human control. Ukrainians got their first taste of dealing with unjammable drones in mid-2024, when Russia began rolling out fiber-optic tethered drones. Now they have to brace for a threat on a much larger scale. An experimental drone is demonstrated at the Brave1 defense-tech incubator in Kyiv.DANYLO DUBCHAK/FRONTLINER/GETTY IMAGES “Today, we have a situation where we have lots of signals on the battlefield, but in the near future, in maybe two to five years, UAVs are not going to be sending any signals,” says Oleksandr Barabash, CTO of Falcons, a Ukrainian startup that has developed a smart radio-frequency detection system capable of revealing precise locations of enemy radio sources such as drones, control stations, and jammers. Last September, Falcons secured funding from the U.S.-based dual-use tech fund Green Flag Ventures to scale production of its technology and work toward NATO certification. But Barabash admits that its system, like all technologies fielded in Ukrainian war zones, has an expiration date. Instead of radio-frequency detectors, Barabash thinks, the next R&D push needs to focus on passive radar systems capable of identifying small and fast-moving targets based on the signal from sources like TV towers or radio transmitters that propagate through the environment and are reflected by those moving targets. Passive radars have a significant advantage in the war zone, according to Barabash. Since they don’t emit their own signal, they can’t be that easily discovered by the enemy. “Active radar is emitting signals, so if you are using active radars, you are target No. 1 on the front line,” Barabash says. Bondar, on the other hand, thinks that the increased onboard compute power needed for AI-controlled drones will, by itself, generate enough electromagnetic radiation to prevent autonomous drones from ever operating completely undetectably. “You can have full autonomy, but you will still have systems onboard that emit electromagnetic radiation or heat that can be detected,” says Bondar. “Batteries emit electromagnetic radiation, motors emit heat, and [that heat can be] visible in infrared from far away. You just need to have the right sensors to be able to identify it in advance.” She adds that that takeaway is “how capable contemporary detection systems have become and how technically challenging it is to design drones that can reliably operate in the Ukrainian battlefield environment.” There Will Be Nowhere to Hide from Autonomous Drones When autonomous drones become a standard weapon of war, their threat will extend far beyond the battlefields of Ukraine. Autonomous turrets and drone-interceptor fortification might soon dot the perimeter of European cities, particularly in the eastern part of the continent. A fixed-wing drone is tested in Ukraine in April 2025.ANDREWKRAVCHENKO/BLOOMBERG/GETTY IMAGES Nefarious actors from all over the world have closely watched Ukraine and taken notes, warns Lange. Today, FPV drones are being used by Islamic terrorists in Africa and Mexican drug cartels to fight against local authorities. When autonomous killing machines become widely available, it’s likely that no city will be safe. “We might see nets above city centers, protecting civilian streets,” Lange says. “In every case, the West needs to start performing similar kinetic-defense development that we see in Ukraine. Very rapid iteration and testing cycles to find solutions.” Azhnyuk is concerned that the historic defenders of Europe—the United States and the European countries themselves—are falling behind. “We are in danger,” he says. While Russia and Ukraine made major strides in their drones and countermeasures over the past year, “Europe and the United States have progressed, in the best-case scenario, from the winter-of-2022 technology to the summer-of-2022 technology. “The gap is getting wider,” he warns. “I think the next few years are very dangerous for the security of Europe.” This article appears in the April 2026 print issue as “Rise of the AUTONOMOUS Attack Drones.”
Mel Olken Former executive director of the IEEE Power & Energy Society Fellow, 92; died 9 January Olken became the first executive director of the IEEE Power & Energy Society (PES) in 1995. In 2002 he left the position to serve as founding editor in chief of the society’s Power & Energy Magazine. Olken led the publication until 2016, when he retired. After receiving a bachelor’s degree in engineering from the City College of New York, Olken was hired as an electrical engineer by American Electric Power, a utility based in Columbus, Ohio. He helped design coal, hydroelectric, and nuclear power plants. While at AEP, he was promoted to manager of the electrical generation department. He joined IEEE in 1958 and became a PES member in 1973. An active volunteer, he chaired the society’s energy development and power generation committee and its technical council. Olken was elected an IEEE Fellow in 1988 for “contributions to innovative design of reliable generating stations.” He became an IEEE staff member in 1984 as society services director for IEEE Technical Activities. From 1990 to 1995 he served as managing director of Regional Activities group (now IEEE Member and Geographic Activities), before becoming PES executive director. He received a PES Lifetime Achievement Award in 2012 for his “broad and sustained technical contributions to the development of power engineering and the power engineering profession.” Stephanie A. Huguenin Research scientist IEEE member, 48; died 1 October Huguenin was an administrative assistant in the physics and biophysics department at Augusta University, in Georgia. According to her Augusta obituary, she died of an illness acquired during her volunteer work in India. She received a bachelor’s degree in engineering in 1999 from the College of Charleston, in South Carolina. During her senior year, she worked as a mathematics and science tutor at the Jenkins Orphanage (now the Jenkins Institute for Children), in North Charleston. After graduating, Huguenin traveled to India to volunteer at an orphanage run by the Mother Teresa Foundation. Upon returning to the United States in 2001, Huguenin worked as a freelance research consultant. Three years later she was hired as a systems administrator and archivist by photographer Ebet Roberts in New York City. In 2010 she left to work as an operations strategist and technical consultant. She earned a master’s degree in communication and research science in 2016 from New York University. While at NYU, she conducted experimental and theoretical research in Internet Protocol design and implementation as well as network security and management. From 2020 to 2024 she was a research scientist at businesses owned by her family. She joined Augusta University in 2023. She was a member of the IEEE Geoscience and Remote Sensing Society and the IEEE Systems Council. Huguenin volunteered for the Internet Engineering Task Force, a standards development organization, and the American Registry for Internet Numbers. ARIN manages and distributes internet number resources such as IP addresses and autonomous system numbers. The nonprofits she supported included the Coastal Conservation League, the Longleaf Alliance, the Lowcountry Land Trust, the Nature Conservancy, and Women in Defense.
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An in-depth examination of how rising power density, 3D integration, and novel materials are outpacing legacy thermal measurement — and what advanced metrology must deliver. What Attendees will Learn Why heat is now the dominant constraint on semiconductor scaling — Explore how heterogeneous integration, 3D stacking, and AI-driven power density have shifted the primary bottleneck from lithography to thermal management, with heat flux projections exceeding 1,000 W/cm² for next-generation accelerators. How extreme material properties are redefining thermal design requirements —Understand the measurement challenges posed by nanoscale thin films where bulk assumptions fail, engineered ultra-high-conductivity materials (diamond, BAs, BNNTs), and devices operating above 200 °C in wide-band gap systems. Why interfaces and buried layers now govern reliability — Examine how thermal boundary resistance at bonded interfaces, TIM layers, and dielectric stacks has become a first-order reliability accelerator. What a thermal-first design workflow looks like in practice — Learn how measured, scale-appropriate thermal properties can be integrated early in the design cycle to calibrate models, reduce uncertainty, and prevent costly late-stage failures across advanced packaging and 3D architectures. Download this free whitepaper now!
Most people who regularly use AI tools would say they’re making their lives easier. The technology promises to streamline and take over tasks both professionally and personally—whether that’s summarizing documents, drafting deliverables, generating code, or even offering emotional support. But researchers are concerned AI is making some tasks too easy, and that this will come with unexpected costs. In a commentary titled Against Frictionless AI, published in Communications Psychology on 24 February, psychologists from the University of Toronto discuss what might be lost when AI removes too much effort from human activities. Their argument centers on the idea that friction—difficulty, struggle, and even discomfort—plays an important role in learning, motivation, and meaning. Psychological research has long shown that effortful engagement can deepen understanding and strengthen memory, sometimes described as “desirable difficulties.” The authors worry that AI systems capable of instantly producing polished answers or highly responsive conversation may bypass these processes of learning and motivation. By prioritizing outcomes over effort, AI could weaken the experiences that help people develop skills, build relationships, and find meaning in their work. IEEE Spectrum spoke with the paper’s lead author, Emily Zohar, an experimental psychology Ph.D. student, about why she and her coauthors (psychologists Paul Bloom and Michael Inzlicht) argue that friction matters—and what a more human-centered approach to AI design could look like. When you say “friction,” what do you mean, from both a cognitive and an interpersonal standpoint? Zohar: We define friction as any difficulty encountered during goal pursuit. In the context of work, it involves mental effort—rumination and persistence, staying on a problem for some time, and this helps solidify the idea and the creative process. In relationships, friction involves disagreement, compromise, misunderstanding, a back and forth that is natural where you don’t always see eye to eye, and it helps you broaden your horizons. Even the feeling of loneliness is important. It motivates you to find social interactions. So having these negative feelings and difficulty is important in the social context. Given that definition, what do you mean by “frictionless” AI? Zohar: Frictionless AI refers to the excessive removal of effort from cognitive and social tasks. With AI, as we typically use it, it’s really easy to go from ideation right to the end product. You ask AI to solve something with one prompt, and it completes the whole thing. This is a problem because it takes away the intermediate steps that really drive motivation and learning, and it prioritizes outcome over process. Rather than working through the steps, AI does that meaningful work for you. There’s a lot of research showing work products are better with AI. That makes sense, it has all this knowledge, but it does worry us as it may be eroding something essential that will have long-term consequences. If you’re faced with the same problem and AI is removed, you don’t have the required knowledge to know how to face the problem next time. You argue that removing friction can harm learning and relationships. What role do effort and struggle play in human development? Zohar: In learning, the term is “desirable difficulties.” It’s the idea of effort and work, not just any effort but manageable effort. Facing problems that you can overcome, but you have to work at them a bit, that’s the key idea of friction. We don’t want you to face insurmountable problems. We want you to work hard, but still be able to overcome it. This helps you really digest information and learn from it. In interpersonal relationships, you have to face some difficulties to see other perspectives and learn from them, and learn to be accepting of others. If you’re used to an AI reinforcing all your ideas and being sycophantic, you’ll come into the real world and you won’t be used to seeing other ideas. You won’t know how to interact socially because you’ll expect people to always be on your side and agree with you. You won’t learn that life doesn’t always go exactly how you expect it to, and conversations don’t always go the way you want them to. AI’s Impact on Creative Processes A lot of technologies have historically aimed to reduce effort: calculators, washing machines, spell-check. What’s different about AI? Zohar: Past technologies have mostly focused on reducing physical effort. We don’t have to go down to the lake to wash our laundry anymore. [Past technologies] took away the mundane tasks that weren’t driving our learning and growth, they were just adding unneeded obstacles and taking away time from more important tasks. But AI is taking away effort from creative and cognitive processes that drive meaning, motivation, and learning. That’s a key difference, because it’s not taking away friction from tasks that don’t serve us. It’s taking away friction from experiences that are really important and integral to our development. Are there contexts where AI is already removing beneficial friction? How might the impacts of reduced friction show up over time? Zohar: One clear example is writing. People increasingly rely on AI to draft everything from emails to essays, removing many instances of beneficial friction. Research shows that people trust responses less when they learn they were written by AI, judge AI-generated products as less creative and less valuable, and have greater difficulty remembering their own work products when they were produced with AI assistance. Outsourcing writing to AI strips away both social and cognitive friction. Vibe coding is another good example. If you’re a programmer, coding is integral to what drives your meaning. People get meaning out of their work, and if you’re substituting that with AI, it could be detrimental. The negative impact of frictionless AI is that it takes away friction from things that are really important to who you are as a person, and your skills. One area I worry about a lot is adolescents using AI in general. It’s a really important developmental period to learn and grow and find the path you’ll follow. So if you don’t have these effortful interactions with work and relationships that teach you how to think, this will have long-term detrimental impacts. They might not be able to think critically in the same way, because they never had to before. If they’re turning to AI for social relationships at such a young age, that could really erode important skills they should be learning at that age. What is productive friction? Zohar: Friction goes along a continuum. With too little friction, you’re not getting learning and motivation. Too much friction and the task becomes overwhelming. Productive friction falls right in the middle, where struggle leads to achievement. It’s effortful but possible, and it requires you to think critically and work on a problem for some time or face some difficulty in the process. An example we used in the paper is the difference between taking a chairlift and hiking up a mountain. They both get to the top, but with the chairlift, you don’t get any growth benefits, while the hiker’s climb involves difficulties and a sense of achievement. It becomes much more of an experience and a learning opportunity versus the person who just went up the chairlift effortlessly. Do you envision AI that sometimes deliberately slows people down or asks them to do part of the work themselves? Zohar: It’s important in behavioral science to think about the default option, because people don’t usually change their default. So right now, the default in AI is to give you your answer and probe you to keep going down the rabbit hole. But I think we could think about AI in a different way. Maybe we can make the default more constructive. Instead of just jumping to the answer, it’s more of a process model where it helps you think about the problem and teaches you along the way, so it’s more collaborative rather than a one-stop shop for the answer. How might users of these systems and the companies developing them feel about such a design shift? Zohar: For the makers of these systems, the biggest concern is the pushback. People are used to going in and just getting the answer, and they might be really resistant to a design that makes them work more for it. But it might feed more engagement, because you have to go back and forth and find the answer together. Ultimately I think it has to come from the companies making these models, if they think [a more friction-full design] would help people. Friction-full AI is more of a long-term product. It’s hard to say if that would motivate companies to change their models to include moderate friction. But in the long term, I think this would be beneficial.
Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion. ICRA 2026: 1–5 June 2026, VIENNA Summer School on Multi-Robot Systems: 29 July–4 August 2026, PRAGUE Enjoy today’s videos! Human athletes demonstrate versatile and highly dynamic tennis skills to successfully conduct competitive rallies with a high-speed tennis ball. However, reproducing such behaviors on humanoid robots is difficult, partially due to the lack of perfect humanoid action data or human kinematic motion data in tennis scenarios as reference. In this work, we propose LATENT, a system that Learns Athletic humanoid TEnnis skills from imperfect human motioN daTa. [ LATENT ] A beautifully designed robot inspired by Strandbeests. [ Cranfield University ] We believe we’re the first robotics company to demonstrate a robot peeling an apple with dual dexterous humanlike hands. This breakthrough closes a key gap in robotics, achieving bimanual, contact-rich manipulation and moving far beyond the limits of simple grippers. Today’s AI models (VLMs) are excellent at perception but struggle with action. Controlling high-degree-of-freedom hands for tasks like this is incredibly complex, and precise finger-level teleoperation is nearly impossible for humans. Our first step was a shared-autonomy system: rather than controlling every finger, the operator triggers prelearned skills like a “rotate apple or tennis ball” primitive via a keyboard press or pedal. This makes scalable data collection and RL training possible. How does the AI manage this? We created “MoDE-VLA” (Mixture of Dexterous Experts). It fuses vision, language, force, and touch data by using a team of specialist “experts,” making control in high-dimensional spaces stable and effective. The combination of these two innovations allows for seamless, contact-rich manipulation. The human provides high-level guidance, and the robot executes the complex in-hand coordination required. [ Sharpa ] Thanks, Alex! It was great to see our name amongst the other “AI Native” companies during the NVIDIA GTC keynote. NVIDIA Isaac Lab helps us train reinforcement learning policies that enable the UMV to drive, jump, flip, and hop like a pro. [ Robotics and AI Institute ] This Finger-Tip Changer technology was jointly researched and developed through a collaboration between Tesollo and RoCogMan LaB at Hanyang University ERICA. The project integrates Tesollo’s practical robotic hand development experience with the lab’s expertise in robotic manipulation and gripper design. I don’t know why more robots don’t do this. Also, those pointy fingertips are terrifying. [ RoCogMan LaB ] Here’s an upcoming ICRA paper from the Fluent Robotics Lab at the University of Michigan featuring an operational PR2! With functional batteries!!! [ Fluent Robotics Lab ] This video showcases the field tests and interaction capabilities of KAIST Humanoid v0.7, developed at the DRCD Lab featuring in-house actuators. The control policy was trained through deep reinforcement learning leveraging human demonstrations. [ KAIST DRCD Lab ] This needs to come in adult size. [ Deep Robotics ] I did not know this, but apparently shoeboxes are really annoying to manipulate because if you grab them by the lid, they just open, so specialized hardware is required. [ Nomagic ] Thanks, Gilmarie! This paper presents a method to recover quadrotor Unmanned Air Vehicles (UAVs) from a throw, when no control parameters are known before the throw. [ MAVLab ] Uh-oh, robots can see glass doors now. We’re in trouble. [ LimX Dynamics ] This drone hugs trees [ Stanford BDML ] Electronic waste is one of the fastest-growing environmental problems in the world. As robotics and electronic systems become more widespread, their environmental footprint continues to increase. In this research, scientists developed a fully biodegradable soft robotic system that integrates electronic devices, sensors, and actuators yet completely decomposes after use. [ Nature ] We developed a distributed algorithm that enables multiple aerial robots to flock together safely in complex environments, without explicit communication or prior knowledge of the surroundings, using only onboard sensors and computation. Our approach ensures collision avoidance, maintains proximity between robots, and handles uncertainties (tracking errors and sensor noise). Tested in simulations and real-world experiments with up to four drones in a dense forest, it proved robust and reliable. [ RBL ] The University of Pennsylvania’s 2025 President’s Sustainability Prize winner Piotr Lazarek has developed a system that uses satellite data to pinpoint inefficiencies in farmers’ fields, conducts real-time soil analysis with autonomous drones to understand why they occur, and generates precise fertilizer application maps. His startup Nirby aims to increase productivity in farm areas that are underperforming and reduce fertilizer in high-performing ones. [ University of Pennsylvania ] The production version of Atlas is a departure from the typical humanoid form factor, favoring industrial utility over human likeness. Intended for purposeful work in an industrial setting, Atlas has a form factor that signals its role as a machine rather than a companion or friendly assistant. Join two lead hardware engineers and our head of industrial design for a technical discussion of how key product requirements, ranging from passive thermal management to a modular architecture, dictated a bold new vision for a humanoid. [ Boston Dynamics ] Dr. Christian Hubicki gives a talk exploring the common themes of modern robotics research and his time on the reality competition show, “Survivor.” [ Optimal Robotics Lab ]
Wheelchair users with severe disabilities can often navigate tight spaces better than most robotic systems can. A wave of new smart-wheelchair research, including findings presented in Anaheim, Calif., earlier this month, is now testing whether AI-powered systems can, or should, fully close this gap. Christian Mandel—senior researcher at the German Research Center for Artificial Intelligence (DFKI) in Bremen, Germany—co-led a research team together with his colleague Serge Autexier that developed prototype sensor-equipped electric wheelchairs designed to navigate a roomful of potential obstacles. The researchers also tested a new safety system that integrated sensor data from the wheelchair and from sensors in the room, including from drone-based color and depth cameras. Mandel says the team’s smart wheelchairs were both semiautonomous and autonomous. “Semiautonomous is the shared control system where the person sitting in the wheelchair uses the joystick to drive,” Mandel says. “Fully autonomous is controlled by natural-language input. You say, ‘Please drive me to the coffee machine.’ ” This is a close-up of the wheelchair’s joystick and camera.DFKI The researchers conducted experiments (part of a larger project called the Reliable and Explainable Swarm Intelligence for People With Reduced Mobility, or REXASI-PRO) using two identical smart wheelchairs that each contained two lidars, a 3D camera, odometers, user interfaces, and an embedded computer. In contrast to semiautonomous mode, where the participant controls the wheelchair with a joystick, in autonomous mode, control involves the open-source ROS2 Nav2 navigation system using natural-language input. The wheelchairs also used simultaneous localization and mapping (SLAM) maps and local obstacle-avoidance motion controllers. One scenario that Mandel and his team tested involved the user pressing a key on the wheelchair’s human-machine interface, speaking a command, then confirming or rejecting the instruction via that same interface. Once the user confirmed the command, the mobility device guided the user along a path to the destination, while sensors attempted to detect obstacles in the way and adjust the mobility device accordingly to avoid them. When Are Smart Wheelchairs Bad Value? According to Pooja Viswanathan, CEO & founder of the Toronto-based Braze Mobility, research in the field of mobile assistive technology should also prioritize keeping these devices readily available to everyday consumers. “Cost remains a major barrier,” she says. “Funding systems are often not designed to support advanced add-on intelligence unless there is very clear evidence of value and safety. Reliability is another barrier. A smart wheelchair has to work not just in ideal conditions, but in the messy, variable conditions of daily life. And there is also the human factors dimension. Users have different cognitive, motor, sensory, and environmental needs, so one solution rarely fits all.” For its part, Braze makes blind-spot sensors for electric wheelchairs. The sensors detect obstacles in areas that can be difficult for a user to see. The sensors can also be added to any wheelchair to transform it into a smart wheelchair by providing multimodal alerts to the user. This approach attempts to support users rather than replace them. According to Louise Devinge, a biomedical research engineer from IRISA (Research Institute of Computer Science and Random Systems) in Rennes, France, the increased complexity of smart wheelchairs demands more sensing. And that requires careful management of communication and synchronization within the wheelchair’s system. “The more sensing, computation, and autonomy you add,” she says, “the harder it becomes to ensure robust performance across the full range of real-world environments that wheelchair users encounter.” In the near term, in other words, the field’s biggest challenge is not about replacing the wheelchair user with AI smarts but rather about designing better partnerships between the user and the technology. This image shows data representations used by the 3D Driving Assistant. These include immutable sensor percepts such as laser scans and point clouds, as well as derived representations like the virtual laser scans and grid maps. Finally, the robot shape collection describes the wheelchair’s physical borders at different heights.DFKI Where Will Smart Wheelchairs Go From Here? Mandel says he expects to see smart wheelchairs ready for the mainstream marketplace within 10 years. Viswanathan says the REXASI-PRO system, while out of reach of present-day smart wheelchair technologies, is important for the longer term. “It reflects the more ambitious end of the smart wheelchair spectrum,” she says. “Its strengths appear to lie in intelligent navigation, advanced sensing, and the broader effort to build a wheelchair that can interpret and respond to complex environments in a more autonomous way. From a research standpoint, that is exactly the kind of work that pushes the field forward. It also appears to take seriously the importance of trustworthy and explainable AI, which is essential in any mobility technology where safety, reliability, and user confidence are paramount.” Mandel says he’s ultimately in pursuit of the inspiration that got him into this field years ago. As a young researcher, he says, he helped develop a smart wheelchair system controllable with a head joystick. However, Mandel says he realized after many trials that the smart wheelchair system he was working on had a long way to go because, as he says, “at that point in time, I realized that even persons that had severe handicaps [traveling through] a narrow passage, they did very, very well. “And then I realized, okay, there is this need for this technology, but never underestimate what [wheelchair users] can do without it.” The DFKI researchers presented their work earlier this month at the CSUN Assistive Technology Conference in Anaheim, Calif. This article was supported by the IEEE Foundation and a Jon C. Taenzer fellowship grant.
The rapid ascent of artificial intelligence and semiconductor manufacturing has created a paradox: Industries are booming yet they face a critical shortage of skilled workers. Demand for data center technicians, fabrication facility workers, and similar positions is growing. There aren’t enough candidates with the right skill sets to fill the in-demand jobs. Although those technical roles are essential, they don’t always require a four-year degree—which has paved the way for skills-based microcredentials. By partnering with higher education institutions and training providers, industry leaders are helping to design targeted skills programs that quickly turn learners into job-ready technical professionals. The new standard for skills validation Because microcredentials are relatively new, consistency is key. Through its credentialing program, IEEE serves as a bridge between academia and industry. Developed and managed by IEEE Educational Activities, the program offers standardized credentials in collaboration with training organizations and universities seeking to provide skills-based qualifications outside formal degree programs. IEEE, as the world’s largest technical professional organization, has more than 30 years of experience offering industry-relevant credentials and expertise in global standardization. IEEE is setting the benchmark for skills-based microcredentials by establishing a framework that includes assessment methods, qualifications for instructors and assessors, and criteria for skill levels. A recent collaboration with the University of Southern California, in Los Angeles, for example, developed microcredentials for USC’s semiconductor cleanroom program. USC heads the CA Dreams microelectronics innovation hub. “The IEEE framework allows us to rapidly prototype training programs and adapt on the fly in a way that building new university courses—much less degree programs—won’t allow.” —Adam Stieg IEEE worked with USC to create standardized skills assessments and associated microcredentials so that industry hiring managers can recognize the newly developed skills. The microcredentials help people with or without four-year degrees join the semiconductor industry as cleanroom technicians or as engineers with cleanroom experience. IEEE also has partnered with the California NanoSystems Institute at the University of California, Los Angeles, to create skills-based microcredentials for its cleanroom protocol and safety program. Best practices for designing microcredentials Based on IEEE’s work designing microcredentials with USC, UCLA, and other leading academic institutions, three best practices have emerged. 1. Align with industry needs before design. Collaborate with industry prior to starting the design process. There isn’t a one-size-fits-all approach. Workforce needs vary based on industry sector, company size, and geography. Higher education institutions and training providers build relationships with companies and industry groups to create effective microcredential programs and methods of assessment. 2. Build for flexibility. Traditional academic cycles can be slow, but technology moves fast. A flexible skills-based microcredentials framework allows programs to create or pivot as new breakthroughs occur. “Setting up a credit-bearing course is not easy. And in a rapidly changing environment, you need to pivot quickly,” says Adam Stieg, research scientist and associate director at UCLA’s CNSI. “IEEE skills-based microcredentials are a flexible way to keep up our curriculum aligned with an evolving technology landscape.” Stieg’s team worked with IEEE to build a framework to create microcredentials for its cleanroom protocol and safety program, ensuring it kept pace with the industry’s evolution. “The IEEE framework allows us to rapidly prototype training programs and adapt on the fly,” he says, “in a way that building new university courses—much less degree programs—won’t allow.” 3. Implement a continuous-feedback loop. Many of the technical roles companies are looking to fill in emerging fields such as AI, cybersecurity, and semiconductors are still being developed or are quickly evolving. The rapidly changing landscape requires continual communications and feedback among higher education, training providers, and industry. “We struggle to have feedback loops through the education system to the industry and back again,” says Matt Francis, president and CEO of Ozark Integrated Circuits, in Fayetteville, Ark. Francis, who has served as IEEE Region 5 director, is an IEEE volunteer who supports workforce development for the semiconductor industry. Creating consistent feedback loops is critical for generating consensus on the skills sets needed for microcredential programs, experts say, and it allows providers to update assessments as new tools and safety protocols enter the workplace. “If we start thinking about having training frameworks used within companies that are essentially on some sort of standard and align with a microcredential, we can start to build consensus,” Francis says. Getting started Through its credentialing program, IEEE is helping higher education and industry work together to bridge the technical workforce skills gap. Contact its team to learn how IEEE skills-based microcredentials can help you fill your workforce pipeline.
A growing number of Nigerian companies are turning to kit-based assembly to bring electric vehicles to market in Africa. Lagos-based Saglev Micromobility Nigeria recently partnered with Dongfeng Motor Corp., in Wuhan, China, to assemble 18-seat electric passenger vans from imported kits. Kit-based assembly allows Nigerian firms to reduce costs, create jobs, and develop local technical expertise—key steps toward expanding EV access. Fully assembled and imported EVs face high tariffs that put them out of reach for many African consumers, whereas kit-based approaches make electric mobility more affordable today. Saglev’s initiative reflects a broader trend: CIG Motors, NEV Electric, and regional players in Côte D’Ivoire, Ghana, and Kenya are also leveraging imported kits to build local EV ecosystems, signaling that parts of West Africa are intent on catching up with global electrification efforts. Expanding the Local EV Ecosystem CIG Motors operates a kit-assembly plant in Lagos producing vehicles from Chinese automakers GAC Motor and Wuling Motors. These vehicles include the Wuling Bingo, a compact five-door electric hatchback, and the Hongguang Mini EV Macaron, a microcar with roughly 200 kilometers of range aimed at ride-share operators looking for ultralow-cost urban transport. NEV Electric focuses on electric buses and three-wheelers for urban transit and last-mile delivery. Saglev’s CEO, Olu Faleye, emphasizes that Nigeria’s EV transition addresses both practical economic needs in addition to environmental goals. Beyond passenger transport, electric vehicles could help reduce one of Nigeria’s persistent agricultural challenges: postharvest spoilage. Nigeria loses an estimated 30 million to 40 million tonnes of food annually because of weak logistics and limited refrigeration infrastructure, according to the Organization for Technology Advancement of Cold Chain in West Africa. Electric vans, minitrucks, and three-wheel cargo vehicles could help close this gap because their batteries can power refrigeration systems during transport without relying on costly diesel fuel. As EV adoption grows and charging infrastructure expands, temperature-controlled transport could become more affordable, reducing spoilage, improving farmer incomes, and helping stabilize food supplies, the organization says. “I don’t believe that the promised land is making a fully built EV on the ground here.” –Olu Faleye, Saglev CEO Beyond Nigeria, Mombasa, Kenya–based Associated Vehicle Assemblers has begun making electric taxis and minibuses from imported kits, and Ghana’s government is spurring kit-car assembly there under its national Automotive Development Plan. In Ghana, assemblers benefit from import-duty exemptions on kits and equipment, corporate tax breaks, and access to industrial infrastructure. Saglev is already availing itself of those benefits, at its kit-assembly plant in Accra, Ghana. The company says it also plans to expand its assembly operations to Côte D’Ivoire. Infrastructure Challenges and Workarounds Despite these signs that West Africa’s EV ecosystem is gaining traction, limited grid reliability and sparse public charging infrastructure remain major barriers to widespread EV adoption. Urban households in Nigeria experience roughly six or seven blackouts per week, each lasting about 12 hours, according to Nigeria’s National Bureau of Statistics. That’s more downtime each day than the average U.S. household experiences in a year. More than 40 percent of households rely on generators, which supply about 44 percent of residential electricity, according to research by Stears and Sterling Bank. Many early EV adopters therefore charge vehicles using gasoline or diesel generators. Faleye notes that Nigerians have long relied on such workarounds and expects fossil fuels to remain part of the EV charging equation for the foreseeable future—at least until falling costs for solar panels and battery storage make cleaner charging viable. He acknowledges that charging EVs using hydrocarbons is fraught from an environmental perspective, but he points out that the practice at least brings other benefits of EVs, including lower maintenance costs and the EVs’ synergies with refrigeration and transportation logistics. And he points to a 2020 peer-reviewed study in the journal Environmental and Climate Technologies that compared the overall efficiency of internal combustion vehicles and electric vehicles across the full well-to-wheel energy chain. The study’s conclusion: Even after accounting for conversion losses, generating electricity with a diesel or gasoline generator to power an electric vehicle can remain just as efficient overall as burning the same fuel directly in a vehicle’s internal combustion engine. Workers at Saglev’s Lagos, Nigeria, EV assembly plant put the finishing touches on partially assembled vehicle kits imported from China. Saglev Scalable EV Adoption in Nigeria The approach taken by Saglev and other Nigerian kit-car builders shows how local assembly can advance EV adoption even where infrastructure remains unreliable. By starting with kits, companies can deploy practical electric mobility solutions now while building the supply chains and technical expertise needed for more resource-intensive localized production. Still, when asked whether Saglev plans to eventually move beyond kit assembly to independent design and manufacturing of EVs, Faleye calls such a move impractical. “I don’t believe that the promised land is making a fully built EV on the ground here,” he says. “For me to do efficient vehicle manufacturing, I’d need a lot of robotics and 3D printing. That expense is unnecessary—it would just increase costs and make EVs more expensive.” In a country where electricity can disappear for days, Nigeria’s kit-based EV strategy highlights a practical truth: Incremental progress and ingenuity may matter more than perfect infrastructure. For Saglev, every kit-based vehicle rolling off the line is not just a van or bus—it’s a step toward an EV ecosystem that works for Nigeria’s realities today.
One morning in May 2019, a cardiac surgeon stepped into the operating room at Boston Children’s Hospital more prepared than ever before to perform a high-risk procedure to rebuild a child’s heart. The surgeon was experienced, but he had an additional advantage: He had already performed the procedure on this child dozens of times—virtually. He knew exactly what to do before the first cut was made. Even more important, he knew which strategies would provide the best possible outcome for the child whose life was in his hands. How was this possible? Over the prior weeks, the hospital’s surgical and cardio-engineering teams had come together to build a fully functioning model of the child’s heart and surrounding vascular system from MRI and CT scans. They began by carefully converting the medical imaging into a 3D model, then used physics to bring the 3D heart to life, creating a dynamic digital replica of the patient’s physiology. The mock-up reproduced this particular heart’s unique behavior, including details of blood flow, pressure differentials, and muscle-tissue stresses. This type of model, known as a virtual twin, can do more than identify medical problems—it can provide detailed diagnostic insights. In Boston, the team used the model to predict how the child’s heart would respond to any cut or stitch, allowing the surgeon to test many strategies to find the best one for this patient’s exact anatomy. That day, the stakes were high. With the patient’s unique condition—a heart defect in which large holes between the atria and ventricles were causing blood to flow between all four chambers—there was no manual or textbook to fully guide the doctors. The condition strains the lungs, so the doctors planned an open-heart surgery to reroute deoxygenated blood from the lower body directly to the lungs, bypassing the heart. Typically with this kind of surgery, decisions would be made on the fly, under demanding conditions, and with high uncertainty. But in this case, the plan had been tested in advance, and the entire team had rehearsed it before the first incision. The surgery was a complete success. Such procedures have become routine at the Boston hospital. Since that first patient, nearly 2,000 procedures have been guided by virtual-twin modeling. This is the power of the technology behind the Living Heart Project, which I launched in 2014, five years before that first procedure. The project started as an exploratory initiative to see if modeling the human heart was possible. Now with more than 150 member organizations across 28 countries, the project includes dozens of multidisciplinary teams that regularly use multiscale virtual twins of the heart and other vital organs. This technology is reshaping how we understand and treat the human body. To reach this transformative moment, we had to solve a fundamental challenge: building a digital heart accurate enough—and trustworthy enough—to guide real clinical decisions. A father’s concern Now entering its second decade, the Living Heart Project was born in part from a personal conviction. For many years, I had watched helplessly as my daughter Jesse faced endless diagnostic uncertainty due to a rare congenital heart condition in which the position of the ventricles is reversed, threatening her life as she grew. As an engineer, I understood that the heart was an array of pumping chambers, controlled by an electrical signal and its blood flow carefully regulated by valves. Yet I struggled to grasp the unique structure and behavior of my daughter’s heart well enough to contribute meaningfully to her care. Her specialists knew the bleak forecast children like her faced if left untreated, but because every heart with her condition is anatomically unique, they had little more than their best guesses to guide their decisions about what to do and when to do it. With each specialist, a new guess. Then my engineering curiosity sparked a question that has guided my career ever since: Why can’t we simulate the human body the way we simulate a car or a plane? At a visualization center in Boston, VR imagery helps the mother of a young girl with a complex heart defect understand the inner workings of her child’s heart. Dassault Systèmes I had spent my career developing powerful computational tools to help engineers build digital models of complex mechanical systems, using models that ranged from the interactions of individual atoms to the components of entire vehicles. What most of these models had in common was the use of physics to predict behavior and optimize performance. But in medicine today, those same physics-based approaches rarely inform decision-making. In most clinical settings, treatment decisions still hinge on judgments drawn from static 2D images, statistical guidelines, and retrospective studies. This was not always the case. Historically, physics was central to medicine. The word “physician” itself traces back to the Latin physica, which translates to “natural science.” Early doctors were, in a sense, applied physicists. They understood the heart as a pump, the lungs as bellows, and the body as a dynamic system. To be a physician meant you were a master of physics as it applied to the human body. As medicine matured, biology and chemistry grew to dominate the field, and the knowledge of physics got left behind. But for patients like my daughter, that child in Boston, and millions like them, outcomes are governed by mechanics. No pill or ointment—no chemistry-based solution—would help, only physics. While I did not realize it at the time, virtual twins can reunite modern physicians with their roots, using engineering principles, simulation science, and artificial intelligence. A decade of progress The LHP concept was simple: Could we combine what hundreds of experts across many specialties knew about the human heart to build a digital twin accurate enough to be trusted, flexible enough to personalize, and predictive enough to guide clinical care? We invited researchers, clinicians, device and drug companies, and government regulators to share their data, tools, and knowledge toward a common goal that would lift the entire field of medicine. The Living Heart Project launched with a dozen or so institutions on board. Within a year, we had created the first fully functional virtual twin of the human heart. The Living Heart was not an anatomical rendering, tuned to simply replicate what we observed. It was a first-principles model, coupling the network of fibers in the heart’s electrical system, the biological battery that keeps us alive, with the heart’s mechanical response, the muscle contractions that we know as the heartbeat. The Living Heart virtual twin simulates how the heart beats, offering different views to help scientists and doctors better predict how it will respond to disease or treatment. The center view shows the fine engineering mesh, the detailed framework that allows computers to model the heart’s motion. The image on the right uses colors to show the electrical wave that drives the heartbeat as it conducts through the muscle, and the image on the left shows how much strain is on the tissue as it stretches and squeezes. Dassault Systèmes Academic researchers had long explored computational models of the heart, but those projects were typically limited by the technology they had access to. Our version was built on industrial-grade simulation software from Dassault Systèmes, a company best known for modeling tools used in aerospace and automotive engineering, where I was working to develop the engineering simulation division. This platform gave teams the tools to personalize an individual heart model using the patient’s MRI and CT data, blood-pressure readings, and echocardiogram measurements, directly linking scans to simulations. Surgeons then began using the Living Heart to model procedures. Device makers used it to design and test implants. Pharmaceutical companies used it to evaluate drug effects such as toxicity. Hundreds of publications have emerged from the project, and because they all share the same foundation, the findings can be reproduced, reused, and built upon. With each application, the research community’s understanding of the heart snowballed. Early on, we also addressed an essential requirement for these innovations to make it to patients: regulatory acceptance. Within the project’s first year, the U.S Food and Drug Administration agreed to join the project as an observer. Over the next several years, methods for using virtual-heart models as scientific evidence began to take shape within regulatory research programs. In 2019, we formalized a second five-year collaboration with the FDA’s Center for Devices and Radiological Health with a specific goal. That goal was to use the heart model to create a virtual patient population and re-create a pivotal trial of a previously approved device for repairing the heart’s mitral valve. This helped our team learn how to create such a population, and let the FDA experiment with evaluating virtual evidence as a replacement for evidence from flesh-and-blood patients. In August 2024, we published the results, creating the first FDA-led guidelines for in silico clinical trials and establishing a new paradigm for streamlining and reducing risk in the entire clinical-trial process. In 10 years, we went from a concept that many people doubted could be achieved to regulatory reality. But building the heart was only the beginning. Following the template set by the heart team, we’ve expanded the project to develop virtual twins of other organs, including the lungs, liver, brain, eyes, and gut. Each corresponds to a different medical domain, which has its own community, data types, and clinical use cases. Working independently, these teams are progressing toward a breakthrough in our understanding of the human body: a multiscale, modular twin platform where each organ twin could plug into a unified virtual human. How a digital twin of the heart is constructed A cardiac digital twin starts with medical imaging, typically MRI, CT, or both. The slices are reconstructed into the 3D geometry of the heart and connected vessels. The geometry of the whole organ must then be segmented into its constituent parts, so each substructure—atria, ventricles, valves, and so on—can be assigned their unique properties. At this point, the object is converted to a functional, computational model that can represent how the various cardiac tissues deform under load—the mechanics. The complete digital twin model becomes “living” when we integrate the electrical fiber network that drives mechanical contractions in the muscle tissue. Each part of the heart, such as the left ventricle [left], is superimposed with a detailed digital mesh to re-create its physiology. These pieces come together to form an anatomically accurate rendering of the whole organ [right].Dassault Systèmes To simulate circulation, the twin adds computational models of hemodynamics, the physics of blood flow and pressure. The model is constrained by boundary conditions of blood flow, valve behavior, and vascular resistance set to closely match human physiology. This lets the model predict blood flow patterns, pressure differentials, and tissue stresses. Finally, the model is personalized and calibrated using available patient data, such as how much the volume of the heart chambers changes during the cardiac cycle, pressure measurements, and the timing of electrical pulses. This means the twin reflects not only the patient’s anatomy but how their specific heart functions. Building bigger cohorts with generative AI When the FDA in silico clinical trial initiative launched in 2019, the project’s focus shifted from these handcrafted virtual twins of specific patients to cohorts large enough to stand in for entire trial populations. That scale is feasible today only because virtual twins have converged with generative AI. Modeling thousands of patients’ responses to a treatment or projecting years of disease progression is prohibitively slow with conventional digital-twin simulations. Generative AI removes that bottleneck. AI boosts the capability of virtual twins in two complementary ways. First, machine learning algorithms are unrivaled at integrating the patchwork of imaging, sensor, and clinical records needed to build a high-fidelity twin. The algorithms rapidly search thousands of model permutations, benchmark each against patient data, and converge on the most accurate representation. Workflows that once required months of manual tuning can now be completed in days, making it realistic to spin up population-scale cohorts or to personalize a single twin on the fly in the clinic. Second, enriching AI models’ training sets with data from validated virtual patients grounds the AI simulations in physics. By contrast, many conventional AI predictions for patient trajectories rely on statistical modeling trained on retrospective datasets. Such models can drift beyond physiological reality, but virtual twins anchor predictions in the laws of hemodynamics, electrophysiology, and tissue mechanics. This added rigor is indispensable for both research and clinical care—especially in areas where real-world data are scarce, whether because a disease is rare or because certain patient populations, such as children, are underrepresented in existing datasets. Enabling in silico clinical trials On the research side, the FDA-sponsored In Silico Clinical Trial Project that we completed in 2024 opened a new world for medical innovations. A conventional clinical trial may take a decade, and 90 percent of new drug treatments fail in the process. Virtual twins, combined with AI methods, allow researchers to design and test treatments quickly in a simulated human environment. With a small library of virtual twins, AI models can rapidly create expansive virtual patient cohorts to cover any subset of the general population. As clinical data becomes available, it can be added into the training set to increase reliability and enable better predictions. The Living Heart Project has expanded beyond the heart, modeling organs throughout the body. The 3D brain reconstruction [top] shows major pathways in the brain’s white matter connecting color-coded regions of the brain. The lung virtual twin [middle] combines the organ’s geometry with a physics-based simulation of air flowing down the trachea and into the bronchi. And the cross section of a patient’s foot [bottom] shows points of strain in the soft tissue when bearing weight. Dassault Systèmes Virtual twin cohorts can represent a realistic population by building individual “virtual patients” that vary by age, gender, race, weight, disease state, comorbidities, and lifestyle factors. These twins can be used as a rich training set for the AI model, which can expand the cohort from dozens to hundreds of thousands. Next the virtual cohort can be filtered to identify patients likely to respond to a treatment, increasing the chances of a successful trial for the target population. The trial design can also include a sampling of patient types less likely to respond or with elevated risk factors, thus allowing regulators and clinicians to understand the risks to the broader population without jeopardizing overall trial success. This methodology enhances precision and efficiency in clinical research, providing population-level insights previously available only after many years of real-world evidence. Of course, though today’s heart digital twins are powerful, they’re not perfect replicas. Their accuracy is bounded by three main factors: what we can measure (for example, image resolution or the uncertainty of how tissue behaves in real life), what we must assume about the physiology, and what we can validate against real outcomes. Many inputs, like scarring, microvascular function, or drug effects are difficult to capture clinically, so models often rely on population data or indirect estimation. That means predictions can be highly reliable for certain questions but remain less certain for others. Additionally, today’s digital twins lack validation for predicting long-term outcomes years in the future, because the technology has been in use for only a few years. Over time, each of these limitations will steadily shrink. Richer, more standardized data will tighten personalization of the models. AI tools will help automate labor-intensive steps. And the collection of longitudinal data will improve the model’s ability to reliably predict how the body will evolve over time. How virtual twins will change health care Throughout modern medicine, new technologies have sharpened our ability to diagnose, providing ever-clearer images, lab data, and analytics that tell physicians what is presently happening inside a patient’s body. Virtual twins shift that paradigm, giving clinicians a predictive tool. This “Living Lung” virtual-twin simulation shows strain patterns during breathing. Mona Eskandari/UC Riverside Early demonstrations are already appearing in many areas of medicine, including cardiology, orthopedics, and oncology. Soon, doctors will also be able to collaborate across specialties, using a patient-specific virtual twin as the common ground for discussing potential interactions or side effects they couldn’t predict independently. Although these applications will take some time to become the standard in clinical care, more changes are on the horizon. Real-time data from wearables, for example, could continuously update a patient’s personalized virtual twin. This approach could empower patients to understand and engage more deeply in their care, as they could see the direct effects of medical and lifestyle changes. In parallel, their doctors could get comprehensive data feeds, using virtual twins to monitor progress. Imagine a digital companion that shows how your particular heart will react to different amounts of salt intake, stress, or sleep deprivation. Or a visual explanation of how your upcoming surgery will affect your circulation or breathing. Virtual twins could demystify the body for patients, fostering trust and encouraging proactive health decisions. How are virtual twins being used in medicine? Virtual twins have guided cardiovascular surgeries, providing predictions and exposing hidden details that even expert clinicians might miss, such as subtle tissue responses and flow dynamics. Oncologists are modeling tumor growth and the body’s response to different therapies, reducing the uncertainty in choosing the best treatment path for both medical and quality-of-life metrics. Orthopedic specialists are personalizing implants to deliver custom-made solutions, considering not only the local environment but also the overall body kinematics that will govern long-term outcomes. A new era of healing With the Living Heart Project, we’re bringing physics back to physicians. Modern physicians won’t need to be physicists, any more than they need to be chemists to use pharmacology. However, to benefit from the new technology, they will need to adapt their approach to care. This means no longer seeing the body as a collection of discrete organs and considering only symptoms, but instead viewing it as a dynamic system that can be understood, and in most cases, guided toward health. It means no longer guessing what might work but knowing—because the simulation has already shown the result. By better integrating engineering principles into medicine, we can redefine it as a field of precision, rooted in the unchanging laws of nature. The modern physician will be a true physicist of the body and an engineer of health.
A technical examination of the sensing, motion control, power, and thermal challenges facing humanoid robotics engineers — with component-level design strategies for real-world deployment. What Attendees will Learn Why motion control remains the hardest unsolved problem — Explore the modelling complexity, real-time feedback requirements, and sensor fusion demands of maintaining stable bipedal locomotion across dynamic environments. How sensing architectures enable perception and safety — Understand the role of inertial measurement units, force/torque feedback, and tactile sensing in achieving reliable human-robot interaction and collision avoidance. What power and thermal constraints mean for system design — Examine the trade-offs in battery chemistry selection (LFP vs. NCA), DC/DC converter topologies, and thermal protection strategies that determine operational endurance. How the industry is transitioning from prototype to mass production — Learn about the shift toward modular architectures, cost-driven component selection, and supply chain readiness projected for the late 2020s. Download this free whitepaper now!
Andrew Ng has serious street cred in artificial intelligence. He pioneered the use of graphics processing units (GPUs) to train deep learning models in the late 2000s with his students at Stanford University, cofounded Google Brain in 2011, and then served for three years as chief scientist for Baidu, where he helped build the Chinese tech giant’s AI group. So when he says he has identified the next big shift in artificial intelligence, people listen. And that’s what he told IEEE Spectrum in an exclusive Q&A. Landing AI, which built a platform called LandingLens to help manufacturers improve visual inspection with computer vision. He has also become something of an evangelist for what he calls the data-centric AI movement, which he says can yield “small data” solutions to big issues in AI, including model efficiency, accuracy, and bias. What’s next for really big models The career advice he didn’t listen to Defining the data-centric AI movement Synthetic data Why Landing AI asks its customers to do the work The great advances in deep learning over the past decade or so have been powered by ever-bigger models crunching ever-bigger amounts of data. Some people argue that that’s an unsustainable trajectory. Do you agree that it can’t go on that way? Andrew Ng: This is a big question. We’ve seen foundation models in NLP [natural language processing]. I’m excited about NLP models getting even bigger, and also about the potential of building foundation models in computer vision. I think there’s lots of signal to still be exploited in video: We have not been able to build foundation models yet for video because of compute bandwidth and the cost of processing video, as opposed to tokenized text. So I think that this engine of scaling up deep learning algorithms, which has been running for something like 15 years now, still has steam in it. Having said that, it only applies to certain problems, and there’s a set of other problems that need small data solutions. When you say you want a foundation model for computer vision, what do you mean by that? Ng: This is a term coined by Percy Liang and some of my friends at Stanford to refer to very large models, trained on very large data sets, that can be tuned for specific applications. For example, GPT-3 is an example of a foundation model [for NLP]. Foundation models offer a lot of promise as a new paradigm in developing machine learning applications, but also challenges in terms of making sure that they’re reasonably fair and free from bias, especially if many of us will be building on top of them. What needs to happen for someone to build a foundation model for video? Ng: I think there is a scalability problem. The compute power needed to process the large volume of images for video is significant, and I think that’s why foundation models have arisen first in NLP. Many researchers are working on this, and I think we’re seeing early signs of such models being developed in computer vision. But I’m confident that if a semiconductor maker gave us 10 times more processor power, we could easily find 10 times more video to build such models for vision. Back to top It’s funny to hear you say that, because your early work was at a consumer-facing company with millions of users. Ng: Over a decade ago, when I proposed starting the Google Brain project to use Google’s compute infrastructure to build very large neural networks, it was a controversial step. One very senior person pulled me aside and warned me that starting Google Brain would be bad for my career. I think he felt that the action couldn’t just be in scaling up, and that I should instead focus on architecture innovation. I remember when my students and I published the first NeurIPS workshop paper advocating using CUDA, a platform for processing on GPUs, for deep learning—a different senior person in AI sat me down and said, “CUDA is really complicated to program. As a programming paradigm, this seems like too much work.” I did manage to convince him; the other person I did not convince. I expect they’re both convinced now. Ng: I think so, yes. Back to top How do you define data-centric AI, and why do you consider it a movement? Ng: Data-centric AI is the discipline of systematically engineering the data needed to successfully build an AI system. For an AI system, you have to implement some algorithm, say a neural network, in code and then train it on your data set. The dominant paradigm over the last decade was to download the data set while you focus on improving the code. Thanks to that paradigm, over the last decade deep learning networks have improved significantly, to the point where for a lot of applications the code—the neural network architecture—is basically a solved problem. So for many practical applications, it’s now more productive to hold the neural network architecture fixed, and instead find ways to improve the data. The data-centric AI movement is much bigger than one company or group of researchers. My collaborators and I organized a data-centric AI workshop at NeurIPS, and I was really delighted at the number of authors and presenters that showed up. You often talk about companies or institutions that have only a small amount of data to work with. How can data-centric AI help them? Ng: You hear a lot about vision systems built with millions of images—I once built a face recognition system using 350 million images. Architectures built for hundreds of millions of images don’t work with only 50 images. But it turns out, if you have 50 really good examples, you can build something valuable, like a defect-inspection system. In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn. When you talk about training a model with just 50 images, does that really mean you’re taking an existing model that was trained on a very large data set and fine-tuning it? Or do you mean a brand new model that’s designed to learn only from that small data set? Ng: Let me describe what Landing AI does. When doing visual inspection for manufacturers, we often use our own flavor of RetinaNet. It is a pretrained model. Having said that, the pretraining is a small piece of the puzzle. What’s a bigger piece of the puzzle is providing tools that enable the manufacturer to pick the right set of images [to use for fine-tuning] and label them in a consistent way. There’s a very practical problem we’ve seen spanning vision, NLP, and speech, where even human annotators don’t agree on the appropriate label. For big data applications, the common response has been: If the data is noisy, let’s just get a lot of data and the algorithm will average over it. But if you can develop tools that flag where the data’s inconsistent and give you a very targeted way to improve the consistency of the data, that turns out to be a more efficient way to get a high-performing system. For example, if you have 10,000 images where 30 images are of one class, and those 30 images are labeled inconsistently, one of the things we do is build tools to draw your attention to the subset of data that’s inconsistent. So you can very quickly relabel those images to be more consistent, and this leads to improvement in performance. Could this focus on high-quality data help with bias in data sets? If you’re able to curate the data more before training? Ng: Very much so. Many researchers have pointed out that biased data is one factor among many leading to biased systems. There have been many thoughtful efforts to engineer the data. At the NeurIPS workshop, Olga Russakovsky gave a really nice talk on this. At the main NeurIPS conference, I also really enjoyed Mary Gray’s presentation, which touched on how data-centric AI is one piece of the solution, but not the entire solution. New tools like Datasheets for Datasets also seem like an important piece of the puzzle. When you talk about engineering the data, what do you mean exactly? Ng: In AI, data cleaning is important, but the way the data has been cleaned has often been in very manual ways. In computer vision, someone may visualize images through a Jupyter notebook and maybe spot the problem, and maybe fix it. But I’m excited about tools that allow you to have a very large data set, tools that draw your attention quickly and efficiently to the subset of data where, say, the labels are noisy. Or to quickly bring your attention to the one class among 100 classes where it would benefit you to collect more data. Collecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity. Back to top What about using synthetic data, is that often a good solution? Ng: I think synthetic data is an important tool in the tool chest of data-centric AI. At the NeurIPS workshop, Anima Anandkumar gave a great talk that touched on synthetic data. I think there are important uses of synthetic data that go beyond just being a preprocessing step for increasing the data set for a learning algorithm. I’d love to see more tools to let developers use synthetic data generation as part of the closed loop of iterative machine learning development. Do you mean that synthetic data would allow you to try the model on more data sets? Ng: Not really. Here’s an example. Let’s say you’re trying to detect defects in a smartphone casing. There are many different types of defects on smartphones. It could be a scratch, a dent, pit marks, discoloration of the material, other types of blemishes. If you train the model and then find through error analysis that it’s doing well overall but it’s performing poorly on pit marks, then synthetic data generation allows you to address the problem in a more targeted way. You could generate more data just for the pit-mark category. Synthetic data generation is a very powerful tool, but there are many simpler tools that I will often try first. Such as data augmentation, improving labeling consistency, or just asking a factory to collect more data. Back to top To make these issues more concrete, can you walk me through an example? When a company approaches Landing AI and says it has a problem with visual inspection, how do you onboard them and work toward deployment? Ng: When a customer approaches us we usually have a conversation about their inspection problem and look at a few images to verify that the problem is feasible with computer vision. Assuming it is, we ask them to upload the data to the LandingLens platform. We often advise them on the methodology of data-centric AI and help them label the data. How do you deal with changing needs? If products change or lighting conditions change in the factory, can the model keep up? Ng: It varies by manufacturer. There is data drift in many contexts. But there are some manufacturers that have been running the same manufacturing line for 20 years now with few changes, so they don’t expect changes in the next five years. Those stable environments make things easier. For other manufacturers, we provide tools to flag when there’s a significant data-drift issue. I find it really important to empower manufacturing customers to correct data, retrain, and update the model. Because if something changes and it’s 3 a.m. in the United States, I want them to be able to adapt their learning algorithm right away to maintain operations. So you’re saying that to make it scale, you have to empower customers to do a lot of the training and other work. Ng: Yes, exactly! This is an industry-wide problem in AI, not just in manufacturing. Look at health care. Every hospital has its own slightly different format for electronic health records. How can every hospital train its own custom AI model? Expecting every hospital’s IT personnel to invent new neural-network architectures is unrealistic. The only way out of this dilemma is to build tools that empower the customers to build their own models by giving them tools to engineer the data and express their domain knowledge. That’s what Landing AI is executing in computer vision, and the field of AI needs other teams to execute this in other domains. Is there anything else you think it’s important for people to understand about the work you’re doing or the data-centric AI movement? Ng: In the last decade, the biggest shift in AI was a shift to deep learning. I think it’s quite possible that in this decade the biggest shift will be to data-centric AI. With the maturity of today’s neural network architectures, I think for a lot of the practical applications the bottleneck will be whether we can efficiently get the data we need to develop systems that work well. The data-centric AI movement has tremendous energy and momentum across the whole community. I hope more researchers and developers will jump in and work on it. Back to top This article appears in the April 2022 print issue as “Andrew Ng, AI Minimalist.”
The end of Moore’s Law is looming. Engineers and designers can do only so much to miniaturize transistors and pack as many of them as possible into chips. So they’re turning to other approaches to chip design, incorporating technologies like AI into the process. Samsung, for instance, is adding AI to its memory chips to enable processing in memory, thereby saving energy and speeding up machine learning. Speaking of speed, Google’s TPU V4 AI chip has doubled its processing power compared with that of its previous version. But AI holds still more promise and potential for the semiconductor industry. To better understand how AI is set to revolutionize chip design, we spoke with Heather Gorr, senior product manager for MathWorks’ MATLAB platform. How is AI currently being used to design the next generation of chips? Heather Gorr: AI is such an important technology because it’s involved in most parts of the cycle, including the design and manufacturing process. There’s a lot of important applications here, even in the general process engineering where we want to optimize things. I think defect detection is a big one at all phases of the process, especially in manufacturing. But even thinking ahead in the design process, [AI now plays a significant role] when you’re designing the light and the sensors and all the different components. There’s a lot of anomaly detection and fault mitigation that you really want to consider. Heather GorrMathWorks Then, thinking about the logistical modeling that you see in any industry, there is always planned downtime that you want to mitigate; but you also end up having unplanned downtime. So, looking back at that historical data of when you’ve had those moments where maybe it took a bit longer than expected to manufacture something, you can take a look at all of that data and use AI to try to identify the proximate cause or to see something that might jump out even in the processing and design phases. We think of AI oftentimes as a predictive tool, or as a robot doing something, but a lot of times you get a lot of insight from the data through AI. What are the benefits of using AI for chip design? Gorr: Historically, we’ve seen a lot of physics-based modeling, which is a very intensive process. We want to do a reduced order model, where instead of solving such a computationally expensive and extensive model, we can do something a little cheaper. You could create a surrogate model, so to speak, of that physics-based model, use the data, and then do your parameter sweeps, your optimizations, your Monte Carlo simulations using the surrogate model. That takes a lot less time computationally than solving the physics-based equations directly. So, we’re seeing that benefit in many ways, including the efficiency and economy that are the results of iterating quickly on the experiments and the simulations that will really help in the design. So it’s like having a digital twin in a sense? Gorr: Exactly. That’s pretty much what people are doing, where you have the physical system model and the experimental data. Then, in conjunction, you have this other model that you could tweak and tune and try different parameters and experiments that let sweep through all of those different situations and come up with a better design in the end. So, it’s going to be more efficient and, as you said, cheaper? Gorr: Yeah, definitely. Especially in the experimentation and design phases, where you’re trying different things. That’s obviously going to yield dramatic cost savings if you’re actually manufacturing and producing [the chips]. You want to simulate, test, experiment as much as possible without making something using the actual process engineering. We’ve talked about the benefits. How about the drawbacks? Gorr: The [AI-based experimental models] tend to not be as accurate as physics-based models. Of course, that’s why you do many simulations and parameter sweeps. But that’s also the benefit of having that digital twin, where you can keep that in mind—it’s not going to be as accurate as that precise model that we’ve developed over the years. Both chip design and manufacturing are system intensive; you have to consider every little part. And that can be really challenging. It’s a case where you might have models to predict something and different parts of it, but you still need to bring it all together. One of the other things to think about too is that you need the data to build the models. You have to incorporate data from all sorts of different sensors and different sorts of teams, and so that heightens the challenge. How can engineers use AI to better prepare and extract insights from hardware or sensor data? Gorr: We always think about using AI to predict something or do some robot task, but you can use AI to come up with patterns and pick out things you might not have noticed before on your own. People will use AI when they have high-frequency data coming from many different sensors, and a lot of times it’s useful to explore the frequency domain and things like data synchronization or resampling. Those can be really challenging if you’re not sure where to start. One of the things I would say is, use the tools that are available. There’s a vast community of people working on these things, and you can find lots of examples [of applications and techniques] on GitHub or MATLAB Central, where people have shared nice examples, even little apps they’ve created. I think many of us are buried in data and just not sure what to do with it, so definitely take advantage of what’s already out there in the community. You can explore and see what makes sense to you, and bring in that balance of domain knowledge and the insight you get from the tools and AI. What should engineers and designers consider when using AI for chip design? Gorr: Think through what problems you’re trying to solve or what insights you might hope to find, and try to be clear about that. Consider all of the different components, and document and test each of those different parts. Consider all of the people involved, and explain and hand off in a way that is sensible for the whole team. How do you think AI will affect chip designers’ jobs? Gorr: It’s going to free up a lot of human capital for more advanced tasks. We can use AI to reduce waste, to optimize the materials, to optimize the design, but then you still have that human involved whenever it comes to decision-making. I think it’s a great example of people and technology working hand in hand. It’s also an industry where all people involved—even on the manufacturing floor—need to have some level of understanding of what’s happening, so this is a great industry for advancing AI because of how we test things and how we think about them before we put them on the chip. How do you envision the future of AI and chip design? Gorr: It’s very much dependent on that human element—involving people in the process and having that interpretable model. We can do many things with the mathematical minutiae of modeling, but it comes down to how people are using it, how everybody in the process is understanding and applying it. Communication and involvement of people of all skill levels in the process are going to be really important. We’re going to see less of those superprecise predictions and more transparency of information, sharing, and that digital twin—not only using AI but also using our human knowledge and all of the work that many people have done over the years.
Quantum computing is a devilishly complex technology, with many technical hurdles impacting its development. Of these challenges two critical issues stand out: miniaturization and qubit quality. IBM has adopted the superconducting qubit road map of reaching a 1,121-qubit processor by 2023, leading to the expectation that 1,000 qubits with today’s qubit form factor is feasible. However, current approaches will require very large chips (50 millimeters on a side, or larger) at the scale of small wafers, or the use of chiplets on multichip modules. While this approach will work, the aim is to attain a better path toward scalability. Now researchers at MIT have been able to both reduce the size of the qubits and done so in a way that reduces the interference that occurs between neighboring qubits. The MIT researchers have increased the number of superconducting qubits that can be added onto a device by a factor of 100. “We are addressing both qubit miniaturization and quality,” said William Oliver, the director for the Center for Quantum Engineering at MIT. “Unlike conventional transistor scaling, where only the number really matters, for qubits, large numbers are not sufficient, they must also be high-performance. Sacrificing performance for qubit number is not a useful trade in quantum computing. They must go hand in hand.” The key to this big increase in qubit density and reduction of interference comes down to the use of two-dimensional materials, in particular the 2D insulator hexagonal boron nitride (hBN). The MIT researchers demonstrated that a few atomic monolayers of hBN can be stacked to form the insulator in the capacitors of a superconducting qubit. Just like other capacitors, the capacitors in these superconducting circuits take the form of a sandwich in which an insulator material is sandwiched between two metal plates. The big difference for these capacitors is that the superconducting circuits can operate only at extremely low temperatures—less than 0.02 degrees above absolute zero (-273.15 °C). Superconducting qubits are measured at temperatures as low as 20 millikelvin in a dilution refrigerator.Nathan Fiske/MIT In that environment, insulating materials that are available for the job, such as PE-CVD silicon oxide or silicon nitride, have quite a few defects that are too lossy for quantum computing applications. To get around these material shortcomings, most superconducting circuits use what are called coplanar capacitors. In these capacitors, the plates are positioned laterally to one another, rather than on top of one another. As a result, the intrinsic silicon substrate below the plates and to a smaller degree the vacuum above the plates serve as the capacitor dielectric. Intrinsic silicon is chemically pure and therefore has few defects, and the large size dilutes the electric field at the plate interfaces, all of which leads to a low-loss capacitor. The lateral size of each plate in this open-face design ends up being quite large (typically 100 by 100 micrometers) in order to achieve the required capacitance. In an effort to move away from the large lateral configuration, the MIT researchers embarked on a search for an insulator that has very few defects and is compatible with superconducting capacitor plates. “We chose to study hBN because it is the most widely used insulator in 2D material research due to its cleanliness and chemical inertness,” said colead author Joel Wang, a research scientist in the Engineering Quantum Systems group of the MIT Research Laboratory for Electronics. On either side of the hBN, the MIT researchers used the 2D superconducting material, niobium diselenide. One of the trickiest aspects of fabricating the capacitors was working with the niobium diselenide, which oxidizes in seconds when exposed to air, according to Wang. This necessitates that the assembly of the capacitor occur in a glove box filled with argon gas. While this would seemingly complicate the scaling up of the production of these capacitors, Wang doesn’t regard this as a limiting factor. “What determines the quality factor of the capacitor are the two interfaces between the two materials,” said Wang. “Once the sandwich is made, the two interfaces are “sealed” and we don’t see any noticeable degradation over time when exposed to the atmosphere.” This lack of degradation is because around 90 percent of the electric field is contained within the sandwich structure, so the oxidation of the outer surface of the niobium diselenide does not play a significant role anymore. This ultimately makes the capacitor footprint much smaller, and it accounts for the reduction in cross talk between the neighboring qubits. “The main challenge for scaling up the fabrication will be the wafer-scale growth of hBN and 2D superconductors like [niobium diselenide], and how one can do wafer-scale stacking of these films,” added Wang. Wang believes that this research has shown 2D hBN to be a good insulator candidate for superconducting qubits. He says that the groundwork the MIT team has done will serve as a road map for using other hybrid 2D materials to build superconducting circuits.