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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 cyber-threat, 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, 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 AnswerThe 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 US-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 likeGitHub 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, are still subject to a range of cyber vulnerabilities, too. 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 DefensesThe 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 even well-sandboxed software cannot. Sandboxes contain 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.Cloudflare, AWS, 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 RoboticsBehind 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 RoboticsThe Dataset InitiativeThis 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 RoboticsThis 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 RoboticsFrom VLA to VTLA: Why Tactile Sensing Changes the EquationThe 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 RoboticsThe Technology: Monochromatic Vision-based Tactile SensingYou 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 RoboticsLast 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 StartupAfter 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 RoboticsBusiness Model and Commercial StrategyWhat 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 MomentYou’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 RoboticsAI 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 DeploymentToday, 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 sparsitySparse 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 CPUsUnfortunately, 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 PéterffyWhen 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.OnyxInstead 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 HsuOnyx 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 sparsityOur 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 tradeoffFPGAs 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 FPGAThe 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 memoryEarlier 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/AMDSemiconductor economics changed the equationThe 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 computingA 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 ASICsFPGAs 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 deviceThis 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 UniversityThe 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 TandonWhich 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/NYUNYU 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 TandonThe 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 LearnProgrammatic 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 southern China to parents with limited formal education. Today the IEEE member and associate editor of IEEE Transactions on Visualization and Computer Graphics is an assistant professor in the College of Computing and Data Science atNanyang Technological University, in Singapore. He studies how people can employdata visualization techniques to get more out of large-scale datasets as well as advanced artificial intelligence techniques.“Visualization helps people understand complex ideas,” he says. “If we design these tools well, they can make advanced technologies accessible to everyone.”For his work in the field, theIEEE 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 data visualization, human-computer interaction and human-AI collaboration—areas becoming more important as the world generates more data than humans can easily interpret.YONG WANGEMPLOYER Nanyang Technological University, in SingaporePOSITION Assistant professor of computing and data scienceIEEE MEMBER GRADE MemberALMA 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.”Growing up in rural HunanWang was born in in a small farming village in southern China. 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 engineeringHis 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 then pursued a master’s degree in pattern recognition and image processing from the Huazhong University of Science and Technology, in Wuhan, China.In 2014 he took a position as a research intern working at a technology company 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.In 2014 he took a position as a research intern working a technology company 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 AIHe 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.He and his students, collaborators have developed a series of approaches to recommend or automatically generate appropriate visualizations, including infographics.It allows nontechnical people to create 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“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 his 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,” he 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 communitiesTeaching 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 cloudSo 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 its AI safety mission. There’s hype and counterhype, 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 CybersecurityWe’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 PracticesThis 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 RoboticistAs 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 GamboaWhen 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 ProvostTeletext 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 AirI 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!

Examining how a U.S. Interregional Transmission Overlay could address aging grid infrastructure, surging demand, and renewable integration challenges.What Attendees will LearnWhy the current regional grid structure is approaching its limits — Explore how coal-fired generation retirements, renewable integration, aging infrastructure past its 50-year lifespan, and exponential large-load growth from data centers and manufacturing reshoring are creating unprecedented pressure on the U.S. transmission system.How an Interregional Transmission Overlay (ITO) would work — Understand the architecture of a high-capacity overlay using HVDC and 765 kV EHVAC technologies, how it would bridge the East/West/ERCOT seams, integrate renewable generation from resource-rich regions to demand centers, and potentially reduce electric system costs by hundreds of billions of dollars through 2050.The five major challenges facing interregional transmission — Examine the obstacles of cross-state planning coordination, investment barriers including permitting and cost allocation, energy market harmonization across regions, supply chain limitations for specialized equipment, and political and regulatory uncertainties that must be navigated.Actionable steps to begin building the ITO roadmap — Learn how utilities and developers can identify strategic corridors, form multi-stakeholder oversight entities, coordinate regional studies, secure state and federal support through FERC Order 1920 and DOE programs, and develop equitable cost allocation frameworks to move from vision to implementation.Download this free whitepaper now!

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 PrioritiesThere’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 ImpactThe 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 ICUltimately, 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 ChooseBefore 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.—Brian12 Graphs That Explain the State of AI in 2026Stanford 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 EngineeringMuch 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 PerksKyle 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 ArchivesThe 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 TownsThe 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 ImagesIn 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 ArchivesThe 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 ImagesA 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.”ReferencesIn 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 MatarićEmployer University of Southern California, Los AngelesJob Title Professor of computer science, neuroscience, and pediatricsMember gradeFellowAlma maters University of Kansas and MITThe 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 engineerMatarić 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 roboticsOne 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 CaliforniaOver 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 IEEEMatarić 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. Francis Bacon wrote The Advancement and Proficience of Learning.Public DomainBacon 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 wayDrebbel 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 Francis Bacon also wrote Novum Organum.Public DomainThat 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 LearnHow 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 competitionEach 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 NawareStartups 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/IEEEPositive feedback from attendeesIn 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 techStartup 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 InternetI 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 ImagesMy 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 TransmissionsToosheh’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 ImagesToosheh, 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 PhilpotA 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 InterferenceBecause 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 ImagesThe 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 AccessToosheh 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 TooshehUnlike 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 CensorshipToosheh’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 ImagesLooking 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.This article appears in the May 2026 print issue as “The Stealth Signals Bypassing Iran’s Internet Blackout.”

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 20 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 reevaluation 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 five, six, or 10 years, one has to seriously consider a probability, maybe 5 percent or 10 percent 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 U.S. 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 trade-offs 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 Ph.D. 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 GuptaEmployerOpenAI in San FranciscoJobData science staff memberMember gradeSenior memberAlma maters The Hong Kong University of Science and Technology; ColumbiaBy 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 lensGupta’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 scaleAfter 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 pointAfter 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 connectionsGupta 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 BurkhartThe 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 PioneersEase 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. BattelleGetting 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 ChicagoOne 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 BCIAnother 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.” This article appears in the May 2026 print issue as “Life With an Experimental Brain Implant.”

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 switchThe 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 toward 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 RoomMy 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 ItYou’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 MissesProximity 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 AboutThere’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.—BrianAre 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 CafeLast 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.

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. Ng’s current efforts are focused on his company 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. Andrew Ng on... 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. Having said that, a lot of what’s happened over the past decade is that deep learning has happened in consumer-facing companies that have large user bases, sometimes billions of users, and therefore very large data sets. While that paradigm of machine learning has driven a lot of economic value in consumer software, I find that that recipe of scale doesn’t work for other industries. 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. “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.” —Andrew Ng, CEO & Founder, Landing AI 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. Over the past year as I’ve been speaking to people about the data-centric AI movement, I’ve been getting flashbacks to when I was speaking to people about deep learning and scalability 10 or 15 years ago. In the past year, I’ve been getting the same mix of “there’s nothing new here” and “this seems like the wrong direction.” 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. When I started speaking about this, there were many practitioners who, completely appropriately, raised their hands and said, “Yes, we’ve been doing this for 20 years.” This is the time to take the things that some individuals have been doing intuitively and make it a systematic engineering discipline. 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. “Collecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity.” —Andrew Ng 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. One of the powerful tools that data-centric AI gives us is the ability to engineer a subset of the data. Imagine training a machine-learning system and finding that its performance is okay for most of the data set, but its performance is biased for just a subset of the data. If you try to change the whole neural network architecture to improve the performance on just that subset, it’s quite difficult. But if you can engineer a subset of the data you can address the problem in a much more targeted way. 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. For example, I once figured out that a speech-recognition system was performing poorly when there was car noise in the background. Knowing that allowed me to collect more data with car noise in the background, rather than trying to collect more data for everything, which would have been expensive and slow. 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. “In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In manufacturing, you might have 10,000 manufacturers building 10,000 custom AI models.” —Andrew Ng 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. One of the foci of Landing AI is to empower manufacturing companies to do the machine learning work themselves. A lot of our work is making sure the software is fast and easy to use. Through the iterative process of machine learning development, we advise customers on things like how to train models on the platform, when and how to improve the labeling of data so the performance of the model improves. Our training and software supports them all the way through deploying the trained model to an edge device in the factory. 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. In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In manufacturing, you might have 10,000 manufacturers building 10,000 custom AI models. The challenge is, how do you do that without Landing AI having to hire 10,000 machine learning specialists? 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 GorrMathWorksThen, 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/MITIn 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.