By 2028, genetic engineering and gene therapy will be used frequently to treat a variety of diseases, thanks to advances in artificial intelligence, according to the recently released 2030 Technology Megatrends Report from IEEE.

“We are seeing a fundamental shift in how technology evolves,” says IEEE Fellow Dejan Milojicic, chair of the IEEE Future Directions Committee’s Industry Advisory Board, the group responsible for the report. “Artificial intelligence is no longer operating in a vacuum; it is now deeply connected to our energy, infrastructure, and physical systems.” Milojicic, a Hewlett Packard Enterprise Fellow, is a vice president at HPE Labs in Milpitas, Calif.

AI underpins many of the report’s 30 technologies and five “megatrends,” which are technology shifts with the potential to reshape industries and everyday life for decades. A megatrend isn’t a single breakthrough but a collection of related technologies.

For the 2030 report, 166 experts—from 38 countries across six continents—drawn from industry, academia, and government identified 30 breakthrough technologies across the five megatrends: AI, energy, health, space, and physical AI. The experts graded the trends against five criteria: likelihood of success, impact to humanity, maturity, market adoption, and horizon to adoption.

AI as a common thread

Interconnected systems drive the megatrends, and the connection among them is AI.

The report notes that AI technology is advancing faster than any previous revolution, including industrial, electrical, and digital. The pace has turned the technology into general-purpose infrastructure, Milojicic says.

That evolution carries risks, though, the experts say. The report cites the lack of computing infrastructure and energy sources as constraints on the ability to scale up AI, and it says the technology’s growth will require reshaping the workforce.

Workers have always needed to adapt to stay competitive, Milojicic says, adding that AI adoption isn’t an all-or-nothing approach. Businesses should decide when and how to apply it, he says.

The experts say long-term success will require both technical capabilities and attention to the human-AI relationship. Conditions including trust, security, explainability, and policymaking will determine which AI-supported technologies are most likely to scale, they say.

“This report shifts the conversation to something far more important: how these advances will intersect with one another to shape human lives,” Mary Ellen Randall, 2026 IEEE president and CEO, said in a news release about the report. “The data makes clear that trust, safety, and human connection must guide every major breakthrough in the decade ahead.”

Health care is a high-impact bet

The experts scored personalized medicine as the study’s highest-impact technology, at 4.93 out of 5, for its potential to improve human life.

The report credits AI-driven breakthroughs in personalized diagnostics and therapeutic treatments, driven by innovative biotech startups. Within two years, the experts predict, genetic engineering and gene therapy will be used in therapeutic areas.

Within five years, earlier disease diagnoses will move out of the lab and into day-to-day medical practice, the report predicts. Meanwhile, it says, researchers likely will be able to manufacture simple synthetic proteins—molecules engineered from scratch, rather than found in nature—to perform a specific job in the body, such as targeting a tumor or supplying a missing enzyme.

Energy wakes up for AI

Increased power demand by AI is outpacing the energy sector’s ability to keep up. Tech infrastructure—data centers, networks, and computing hardware—already uses an estimated 10 percent of electricity globally, according to the news release.

The experts predict that within two to three years, energy storage will double in developed countries.

The energy industry has been relatively dormant for years, Milojicic says, with little breakthrough innovation since the times of Thomas Edison and Nikola Tesla.

“Data centers can ramp up within milliseconds, yet it takes months for the power and energy sector to ramp up,” he says. “The governments of various countries will have to rethink their approach to power generation to stay ahead of demand.”

The experts cite the Jevons paradox—the idea that efficiency gains tend to increase overall consumption. As AI chip efficiency improves, organizations will run more hardware to build ever-larger frontier models—those advanced AI systems currently in development—the experts say. Data centers will draw as much power as grids can supply, leaving no surplus, the experts predict.

Physical AI moves the fastest

Physical AI refers to systems embedded within machines that sense and act in the world, including robots and autonomous vehicles. In the report, the category originally was called “hyper-automation and robotics,” a broad, software-driven process automation paired with physical machines. The Industry Advisory Board later folded it into the single physical AI megatrend.

The experts found that human-AI interaction has the shortest adoption horizon of any of the 30 scored technologies, at two to three years. They also say physical AI has the biggest chance of technical advancement of any megatrend during the next four years.

“The data makes clear that trust, safety, and human connection must guide every major breakthrough in the decade ahead.” —Mary Ellen Randall, 2026 IEEE president and CEO

Smarter, more efficient robots will substantially cut energy consumption in the next two years, the experts say.

Communications between humans and AI—whether in an android or another device—will shift from text-based commands to predominantly audio and video instructions within two to three years, the experts predict.

Androids used in factories will use tactile and haptic feedback within two to five years, letting them sense what they’re touching and adjust their grip or force in response, the experts say. Bio-inspired skin, tissues, and sensors will advance in the same timeframe, the experts predict, giving robots perception that better mirrors human touch.

Space is just getting started

Space technology, as the report defines it, covers more than exploration. It spans satellite communications, in-space manufacturing, launch technology, and other infrastructure. It is one of the lower-profile megatrends in the report, not for lack of promise but because of where the sector presently sits.

“It’s not lagging,” Milojicic says. “It’s the current state of maturity. People are talking about satellite communication right now because it’s very cool and practical. But it’s a small segment compared to the attention AI gets.”

He says he believes the space sector will be more prominent in the coming years: “This is an area of technology that will become increasingly important, especially as terrestrial communication and transportation push the boundaries for space as well.”

The report highlights two technologies on the near horizon: semiconductor manufacturing that uses parts built in space and more affordable reusable rockets. It also sees clean energy generation in space and the commercialization of orbital garbage collection as potential technologies to watch for in the next decade.

What’s next?

The report recommends that academia, industry, governments, and professional associations work together.

To build a well-trained workforce, the report says, industry should partner with professional organizations on training programs and revive apprenticeships in chemistry, physics, and other physical sciences. It also calls on businesses to treat AI infrastructure—computing, data, and energy—as a long-term strategic asset.

Other recommendations include expanding access to affordable, quality education in STEM coursework and AI competence.

Governments should lead on policy and implement reintegration programs for workers displaced by AI, the report says.

The experts say professional organizations including IEEE should develop new products and services using AI, strengthen safety and data governance standards, and serve as a collaboration platform for industry, academia, and policymakers.

The report also offers recommendations for end users, content creators, investors, and company leadership on strategies for succeeding in an AI-focused future.

The same thread runs through all the report’s recommendations: Don’t lose sight of the human-AI relationship.

“As a public charity dedicated to advancing technology for the benefit of humanity, IEEE believes progress must be measured by more than technology development and implementation speed,” Sophia A. Muirhead, IEEE executive director and chief operating officer, said in the news release. “Whether retraining the global workforce or integrating AI responsibly into daily life, the success of these megatrends depends on ensuring innovation strengthens human well-being and public trust.”

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!

When I changed careers at 30 to become a software engineer, I expected it to be hard. I expected to struggle to fit in, and I expected to bomb a lot of interviews (I did). What I didn’t expect was how few people would support me.

As a parent, I get it now.

A career change is a risk, and the people who care about you tend to project their fears onto you. They don’t want to watch you struggle or fail, so they discourage you or just don’t offer much enthusiasm.

Right now is a strange time to be in engineering, or knowledge work in general. Plenty of us are excited about what AI means for our jobs. More of us are nervous, and I think that fear is justified. Employment for entry-level software developers has fallen nearly 20 percent since late 2022, according to Stanford’s 2026 AI Index, and it can feel like the knowledge we spent our careers building is being commoditized.

Lately I’ve seen a flood of social media content telling software developers to go into the trades. YouTubers are telling you to become a plumber or a goat herder.

But before you do that, I want to warn you, there is no EASY path. Even if you want to become a goat herder.

I’ve changed careers once so far, but my business has forced me through several pivots that felt a lot like career changes—new audiences, new skills, new identity. Here’s what I’d tell anyone considering a change, whether you’re moving into engineering, leaving it, or finding a new speciality.

Find someone who’s done it and ask how

This is exactly what I told my oldest son, who’s getting ready to enter the job market full-time. Outside of the primary social media feeds, the internet is full of free and paid communities on Facebook, LinkedIn, and other sites. Reddit works, too, if you can stomach the trolls, because there’s valuable information buried in there.

Many of these spaces are flooded with people trying to break in, so look for the ones full of people who already do the work and talk openly about what is and isn’t going well. Then find one or two people in your own circle who’ve done it and ask what the job takes.

You’ll often find the degree matters less than you assumed. Indeed Hiring Lab found the share of U.S. postings requiring a bachelor’s degree fell from 20.4 percent in 2019 to 17.8 percent in early 2024, and more than half listed no education requirement at all.

Build proof of work, even if it’s unpaid

Yes, you’ll probably need to do some work for free, or at least far cheaper than you’re used to. I’ve had to do this in my business over and over. Nobody cares that I’ve been a software engineer for 12 years. They want to know whether I can do the thing they need.

When I started pitching businesses, I offered to build something for free as a case study, then used it to land clients who paid well. You can copy this in most fields. Say you want to get into marketing: Learn how Facebook ads work, find a friend or small business that needs help, and run the campaigns at a low price. Now you’ve picked up the jargon, and you have something concrete to show a hiring manager.

Stability is a myth

It may be corny, but change is the only constant. If you’re a software engineer, you could argue you’re in the middle of a career change already, whether you opted in or not. Programmers in the 1990s had the same experience when the web showed up and they had to quickly learn new tools just to stay relevant.

If you’re looking for stability, you’re in the wrong place, and I think it will get scarcer as AI and other forces reshape work. The best move is to build a strong foundation so you can adapt when the ground shifts. I wrote about the foundational skills to focus on if you’re interested.

—Brian

Barbara Mazzolai Wants to Build a New Field of Robotics

Roboticist Barbara Mazzolai is a prime example of someone who advanced her career by switching fields—and now, she’s helping to create a new one. Learn how her background in biology led her to co-author a manifesto on “sustainability robotics.”

Read more here.

Turning Tech Talent Into Leadership Legacy

The transition from a technical role to a leadership position can be challenging. To help seasoned professionals navigate the shift, IEEE is hosting its first International Leadership Conference in Budapest.

Read more here.

IEEE Celebrates Innovators Shaping the Future

Every year, IEEE recognizes engineers whose technical achievements and leadership have made a lasting impact with its Technical Field Awards. See who’s on the 2027 list of awardees, which was announced in late September.

Read more here.

This White Paper gives robotics researchers and engineers an overview of a new large-scale motion capture dataset built to close the data gap limiting humanoid robot learning. It also shows how policies trained on the dataset transfer to a real humanoid robot.

What you will learn about:

• Why humanoid robot learning, a central problem in embodied AI and Physical AI, needs data that internet video and existing motion capture datasets cannot provide.
• How FrameNet, a linguistic framework for human action, can guide motion capture collection to systematically cover a broad range of whole-body motion.
• Why synchronized object trajectories and meshes make human-object interaction data useful for teaching robots real-world tasks such as carrying, pushing, and pulling.
• How reinforcement learning policies trained on this motion capture data improve with scale, and how sim-to-real transfer carries them onto a physical humanoid robot.

Happy IEEE Day 2026!

IEEE Day is an annual event that celebrates the first time in history when engineers worldwide gathered to share their technical ideas in 1884.

One of the IEEE Day’s objectives is to show the ways IEEE members, in local communities, join together to collaborate on ideas that leverage technology for a better tomorrow.

IEEE Day 2026 - October 6, 2026. Stay tuned as we continue this journey of innovation, connection, and global celebration.IEEE

Celebrate IEEE Day with colleagues from IEEE Sections, Student Branches, Affinity groups, and Society Chapters. Events happen both virtually and in person all around the world.

IEEE Day 2026 Events

Explore IEEE Day 2026 events taking place around the world from 4 - 17 October 2026. The event listing is dynamically updated as new events are registered.

View events →

Compete in contests and win prizes!

Have some fun and compete in the photo and video contests. Get your phone and camera ready when you attend one of the events. This year we will have both Photo and Video Contests. You can submit your entries in technovation, raise your flag, and social categories.

View contests →

Special Offers & Activities

Check out our special offers and activities for IEEE members and future members. And share these with your friends and colleagues.

View offers →

For the first 10 years of my career, I worked in product management and data analytics by myself. I wrote database queries that pulled numbers out of corporate systems, built statistical models to predict what customers would buy, and shipped data pipelines that moved information between business systems.

I built and scaled analytics teams at Best Buy and Target, studying how customers shop and what stores should stock. Today I lead enterprise AI transformation at Lowe’s, the Fortune 100 home improvement retailer.

The goal is not to sell artificial intelligence; it is to use it to deliver useful expertise at the moment a customer needs it. In retail and other customer-facing industries, virtual assistants can help people address everyday questions—such as how to repair a leaky faucet—while guiding them toward relevant products, services, or next steps. As these capabilities become more common, technology roles are changing. The work is no longer limited to building AI systems; it also includes defining how they operate: which decisions they can make autonomously, when they must escalate to a person, and which actions must remain off-limits.

That shift—from building AI systems to governing them—is coming for anyone who is accountable for what such systems produce. Not the casual user typing into a chatbot but the engineers, product managers, analysts, and business operators who sign off on work a machine drafted.

It is the subject of the book I recently coauthored, The Enterprise Brain. I call the change the “governor shift,” from executing tasks yourself to setting the intent, principles, and boundaries within systems that execute them for you.

Business operators might not write code; they will decide which pricing exceptions an agent may approve and which it must escalate.

That is governing.

A 2025 report from MIT Media Lab’s Project NANDA found that, despite an estimated US $30 billion to $40 billion in enterprise generative-AI investment, the vast majority of organizations in its dataset had not yet demonstrated measurable profit-and-loss impact. The report estimated that only about 5 percent of integrated pilots were generating substantial value, underscoring how difficult it remains to move from experimentation to scaled business outcomes.

Researchers named the pattern the GenAI Divide, the term I adopted for the book.

The companies rarely lack technology; they use the same models as the 5 percent that are winners. But they lack people who can direct the systems and stand behind the results.

Guidelines to follow

Here are six guidelines.

• Recognize when you have become “human middleware.” In software, “middleware” is the code that sits between two systems and passes information back and forth. Many of us have become its human version. Take an honest look at your week. How much time is spent pulling data out of one tool, reformatting it, and routing it to another team? I call this the “administrator trap,” which is set by the architecture, not by the people caught in it.
  
  Relaying is what AI agents now do well. But they cannot judge which numbers deserve attention, which risks are real, or which compromises are worth making.

• Trade rules for principles. For many years, workers used rules to manage their work. Refunds for a product over a certain amount needed a signature from upper management, for example. Writing code needed two reviewers. Rules work at human speed. But rules break when a system makes thousands of decisions per hour and meets situations no rulebook anticipated, such as a complaint covered by three different policies. A rule says to do exactly this specific thing; a principle says to achieve the outcome without crossing certain lines.
  
  Governing AI means writing those principles in priority order so the system settles its own conflicts the way a well-led team does when the manager is not available. Never harm the customer. Tell the truth even if the company loses a sale. Protect the economics, and then move quickly. Underneath sits a question of decision rights: the formal authority over who or what may make a given call. Writing down the answers in what I call a “library of principles” is now core leadership work, whether you’re a technologist or a business owner.

• Write your culture into your code. Many companies have turned their values into posters that hang on office walls. But an AI agent cannot read the posters. Instead, write your governance as code. Include your values and policies as machine-readable instructions that the AI agent will follow automatically.
  
  Do so in three layers. The top is the constitution, which states the rules an agent may never break, and never state a fact it cannot support. The second layer is the doctrine: how the business competes and the acceptable trade-offs to get there, such as protecting a long-term relationship over a short-term sale. At the bottom sits the playbook, which has the tactics used for one task.

• Install a trust thermostat, not a trust switch. The question that stalls nearly every company’s AI deployment is some version of: “What if it tells our biggest customer something wrong, or quotes a price we will not honor?” It might. Treating trust as a switch leaves two bad options: an unsupervised system or a human reviewing every transaction—which would cost more than the automation would save.
  
  The alternative is a thermostat. Every decision an agent makes carries a confidence score measured against the principles set. Above an agreed threshold, it proceeds alone; below it, a human decides. That person’s answer is fed back into the learning loop so the next similar case clears the threshold on its own. Every decision stays transparent, auditable, and explainable—which is what I call a glass box.

• Fix context before you govern. You cannot govern a system that cannot see the whole picture. Ask your best employee about a project, and they will pull together the budget, the contract clause, and the customer’s last complaint because they know it all by heart. Most enterprise AI fails that test, because the information sits scattered across applications that store it in incompatible formats. I describe the full loop as Connections, Context, Reasoning, Actions, and Governance (CCRAG).
  Connections feed in raw information such as transactions and service records. Context weaves it into a context graph, which is a single connected picture of the business that gives agents something close to memory. Reasoning makes the decisions. Actions carry them back into the business systems. Governance keeps things aligned with the company’s intent.
  
  Most organizations obsess over the reasoning in the middle and underinvest in context and governance, which is exactly where humans play a role. Context compounds: Every interaction makes the graph richer and harder to reproduce.

• Learn to lead by exception. The most important change in habit comes last. Most of us have been trained to check every report and every number because we never knew where an error could surface. In a governed system, the machine tells you which cases it could not resolve confidently. Routine workflows go untouched, and your attention goes to the small portion that is ambiguous, unfamiliar, or high stakes.
  
  At first, that might feel like losing control, but it is the opposite. It is what makes a self-scaling enterprise possible, an organization whose output grows without its head count growing in proportion. People were not removed from the loop; they were raised above it.

The identity question

When I talk with people about the shift, their resistance is rarely about technical issues. More often it is about identity: If the AI does the doing, what do I do?

I have watched capable people freeze on that question. I’ve also asked myself the question.

Doing was never really the job, though. Judgment was. Doing was just how we expressed it.

AI has not made judgment less valuable. It has made it the scarcest resource in the organization because, for the first time, one person’s judgment, written down well, can guide thousands of decisions each day.

Judgment has a twin we talk less about: taste. Judgment tells you whether an answer is sound. Taste tells you whether the question was worth asking and which of a hundred defensible options to offer the customer. A machine will happily generate all 100 options, but it cannot tell you which is best.

The AI transition rewards instincts many IEEE members already have: systems thinking, precision about requirements, and honesty about failure modes. The tools have changed, but the discipline has not.

Governing is where taste and judgment stop being soft words and become the work itself.

The people who treat it that way, rather than as a step away from engineering, will define the profession in the age of AI.

Christos Cassandras has spent more than 40 years studying how computers and machines make decisions. Lately, his work has focused on a problem nearly every driver knows well: sitting at a red light with no cross traffic in sight, wondering why it takes so long for the light to turn green.

Cassandras, an IEEE Life Fellow, is a professor at Boston University, where he headed its systems engineering division, a graduate program he helped create.

Christos Cassandras

Employer

Boston University

Title

Professor

Member grade

Life Fellow

Alma maters

Yale, Stanford, and Harvard

He is the recipient of this year’s IEEE Intelligent Transportation Systems Society Outstanding Research Award for his work on autonomous vehicles and the systems that help them drive—work that could one day get rid of traffic lights altogether.

That idea—proving with mathematical models and intelligent algorithms using cars’ relative speeds, mass, and distance as inputs that self-driving cars will behave safely before they are ever let loose on the streets—has become the foundation of Cassandras’s career and the reason his name keeps coming up in conversations about transportation.

In the 1990s he theorized that a machine such as a car could be understood as two things at once: an object obeying the laws of physics and a computer processing information and making decisions. This led to what we now call cyber physical systems. That framework is still the standard way engineers describe a car’s mechanical behavior.

“Ultimately, the key word is safety,” Cassandras says. “You would not buy a self-driving car unless the manufacturer could guarantee that it’s safe for you.”

Growing up in Greece during a dictatorship

Born in Athens, Cassandras was a teenager during Greece’s military dictatorship, a period he says scarred him and many others but also taught him some hard lessons. His father was a partner in an insulation manufacturing company, and his mother was a homemaker.

His friends pulled Cassandras toward engineering. As a teenager, he ran with a group of classmates who read about philosophy, history, and science. They were especially struck by the 1969 Apollo 11 moon landing and the questions it raised about the technology that made it possible.

That same year also marked the first time a piece of data was sent from one computer to another—an early step toward the creation of the Internet. Several inspiring teachers pushed his curiosity about technology even further.

He was awarded a scholarship to study in the United States, and he enrolled at Yale, where he first studied physics and philosophy before switching to engineering. He earned his bachelor’s degree in engineering and applied science in 1977. At the time, Yale didn’t yet offer separate degrees in fields such as electrical or mechanical engineering.

From there, Cassandras went to Stanford, earning a master’s degree in electrical engineering in 1978. There, he became interested in game theory, the mathematical study of decision-making and strategy.

A mentor pointed him to Harvard, home to a leading game theory researcher at the time. His new mentor, Professor Yu-Chi “Larry” Ho, now an IEEE Life Fellow, advised him to abandon game theory, which Ho considered a scientific dead end at the time. Ho suggested a newer, less-explored area of research, that of emerging dynamic systems in modern technology whose behavior could be understood and managed through occurrences of discrete events.

Cassandras took the advice. He earned a second master’s degree from Harvard the following year and completed his Ph.D. in applied mathematics there in 1982. He and Ho remain friends and colleagues.

Fiat assembly line births of a new kind of engineering

Ask people in intelligent transportation systems circles why Cassandras’s name carries so much weight, and the answer traces back to a theoretical shift he helped pioneer starting in the 1980s. Cassandras recognized that human-made systems—computers, factory machines and, eventually, vehicles—operate on a different kind of logic than the one that governs the natural world.

The physical world runs on time-driven physics, the same math Isaac Newton used centuries ago. But machines built by people, he realized, are better understood as event-driven: They mostly sit still until something happens, like a button click or a part arriving on a conveyor belt.

That insight didn’t come from a textbook. It took shape during Cassandras’s early graduate work, when he was handed a real-world problem. He was assigned to figure out how to manage the buffers—the temporary holding areas for car parts—on an assembly line for Fiat, the Italian carmaker.

Solving that concrete, practical puzzle helped him see how event-driven thinking could describe an entire class of systems that classical physics-based math couldn’t handle well. This came to be known as discrete event systems.

By the early 1990s, that insight had matured into a formal framework: hybrid systems theory, now known as cyber physical systems theory. The idea is that a machine can be understood as an object obeying the laws of physics and as a computer obeying event-driven dynamics.

Building “event-driven” systems

After completing his doctorate, Cassandras wanted to test himself outside of academia. He spent about a year and a half as a systems engineer at Information and Technology for Production (ITP), a small manufacturing-automation startup in the Boston area. The experience convinced him that the “real world” wasn’t so different from academic life, and he returned to research while continuing to consult for the company for roughly a decade. He has kept up the habit of staying connected to industry throughout his career.

In 1984 he joined the University of Massachusetts Amherst as a faculty member in the electrical and computer engineering department. Building on the ideas born out of the Fiat project, he helped pioneer research into event-driven systems.

That was a shift away from the traditional math used to describe the physical world toward systems built around discrete events. The transition, driven by the rise of computers, became the backbone of Cassandras’s research for the next three decades.

In 1997, after years of commuting weekly between Amherst and Boston for his consulting work, he joined Boston University as a tenured full professor in what was then its manufacturing engineering department. As the field evolved in the early 2000s with the rise of the Internet and sensor technology, Cassandras helped establish the university’s systems engineering division. In 2008 he became the research program’s first director, a role he held until stepping down two months ago.

Using math to show that self-driving cars are safe

Cassandras’s recent research has focused on mathematically proving vehicle safety before a car is road-tested. He has developed models built around measurable quantities: a vehicle’s position, speed, and distance to nearby vehicles. That lets connected and automated vehicles cooperate during tricky maneuvers such as changing lanes on a highway in traffic.

He has extended the approach to a bigger idea: eliminating traffic lights. One of his more striking projects involves a chronically congested intersection near his office in Boston, where Commonwealth Avenue meets the Boston University Bridge. Using computer simulations, he and his team modeled what would happen if they removed all the traffic lights, and cars simply coordinated with each other instead. The results, he says, showed improvements not only in how smoothly traffic would flow but also in safety and energy efficiency.

Cassandras acknowledges that widespread adoption is still far off. For now, only a small percentage of vehicles on the road are capable of that kind of communication. But he’s testing similar coordination systems using small robots in his BU lab, working to bring the idea closer to reality and to exploit the intelligence of cooperating autonomous vehicles even when they are only a small fraction of the actual cars on the road.

He says his perspective has evolved in recent years from viewing humans as obstacles to smooth automation toward designing technology that actually works for people.

Through projects with Boston on smart city initiatives, he learned that not everyone can afford or access new technology—a lesson that reshaped how he approaches his research.

“It’s not just about technology,” he says. “It’s really about technology and people.”

A legacy passed on through his students

His influence in the field has been amplified by some of his students. Several of his Ph.D. students have developed safety algorithms at companies including Aptiv and Zoox, both of which build technology for autonomous and connected vehicles. He wrote a book, titled Safe Autonomy with Control Barrier Functions: Theory and Applications, with one of his former students. It lays out the mathematical foundations of autonomous-vehicle safety, with specific applications to intelligent transportation.

A long relationship with IEEE

Cassandras joined IEEE as a student member while he was a graduate student. He was drawn in by the discounted student conference registration fees, he says.

That early, practical decision grew into something bigger: a professional community with which he has stayed connected for more than 40 years.

He has served as president of the IEEE Control Systems Society, his primary professional home within IEEE. He has served on the conference publications committee and is currently involved with IEEE Publishing Ethics.

IEEE’s greatest value to his career, he says, has come through the relationships he has built at its conferences and serving on committees. Connections with colleagues and IEEE leadership have shaped his thinking and opened doors throughout his career, he says.

Looking back on more than four decades of work, Cassandras says the goal has never really been about the technology for its own sake.

“After all,” he says, “we’re doing all this to make society better and facilitate the comfortable lives of all of humanity.”

Dielectric metasurfaces have moved to the forefront of nanophotonics, offering flat, low-loss alternatives to conventional bulk optical elements for controlling the amplitude, phase, and polarization of light. These structures are of growing interest to researchers and engineers working on sensing, energy harvesting, and flat optics, as their performance hinges on precisely engineered optical resonances. Full-wave finite element simulation, combined with semianalytical multipole decomposition in the COMSOL Multiphysics® software, gives us a way to not only predict these resonances but also uncover their underlying physical origins.

In this webinar, Dr. Pavel Terekhov, postdoctoral researcher at National Institute of Standards and Technology, will trace how a single quadrumer meta-atom, originally studied for its magnetic octupole response, evolves into two distinct light manipulation regimes. He will first revisit the foundational single-particle results that motivated this work, then show how arranging quadrumers into a periodic crystalline silicon metasurface produces anomalous absorption enhancement, governed by two independent multipole mechanisms coexisting in the same structure. Building on this, he will then introduce ongoing work on a gallium nitride metasurface, where the complex interplay of four different multipoles is used to sculpt reflection and transmission spectra including the quasi-bound-states-in-the-continuum (q-BIC) manipulation.

Attendees will see how COMSOL Multiphysics® and multipole decomposition connect full-wave simulation and analytical insight, turning abstract resonance behavior into physically interpretable design rules. The broader takeaway is that multipole-based simulation is not just a diagnostic tool but a design strategy: It enables the on-demand tailoring of absorption, reflection, and transmission in dielectric metasurfaces, with direct relevance to sensing, energy harvesting, and future optical device applications.

Key Takeaways:

• Learn about modeling multipole resonances in dielectric metasurfaces using full-wave finite element simulation and semianalytical multipole decomposition in COMSOL Multiphysics®.
• See how multipole-based simulation can be used to understand and control absorption, reflection, and transmission in silicon and gallium nitride metasurfaces.
• Explore how different multipole mechanisms, including quasi-bound states in the continuum (q-BICs), can be engineered to tailor optical responses for sensing, energy harvesting, and flat optics.
• Gain insights into how simulation and multipole decomposition can help researchers and engineers turn complex resonance behavior into practical design strategies for future optical devices.

It’s disheartening how much power gets generated and then promptly lost as it travels through grid networks. This leaking of electricity happens when it vanishes as heat as well as when it is pilfered by thieves and nonpaying customers.

More than half of the countries that track these metrics lost at least 10 percent of their electricity in 2023, according to the World Bank. Losses topped 20 percent for 24 of those nations. Two countries lost more than half of what they generated.

With numbers this high, cutting down on losses makes sense. Electricity demand is rising beyond what many grid operators can supply; reducing waste would help meet some of that demand without having to build new power plants. Plus, when the power comes from fossil fuels, any loss means emitting even more greenhouse gases into the atmosphere. And electric losses hit the bottom lines of power providers, which ultimately pass those costs on to everyone else.

The trouble is, reducing electricity losses is a hard and expensive process that takes a long time. Typically, the less maintained the grid infrastructure, the more electricity that’s lost. And the more fragile the region’s law enforcement and government, the more prevalent the power theft. Natural disasters and war make things worse.

Delhi’s Power Grid Comeback

Fixing a power grid requires a systemic approach across many sectors. There’s no one technology that will solve the problem. At the outset, the obstacles to success may feel insurmountable. Equipment across entire grid networks must be updated. Multiple arms of government must agree to reforms and coordinate to ensure power providers are set up to succeed. Regulations must be written or revised, investments made, cultures changed.

The city of Delhi did all those things. Over the past 25 years, it cut its electricity losses from about 50 to 5 percent. How the city pulled off that impressive feat is the focus of “The Epic Comeback of Delhi’s Power Grid” by Mini Shaji Thomas, an electrical engineer at the university Jamia Millia Islamia who has lived in Delhi since the 1990s. She gives us a view from the inside—as a resident and a power systems expert.

Delhi is a shining example, but some other regions have significantly lowered electricity losses over the last quarter century too. The country of Georgia went from losses of over 16 percent in 2002 to about 8 percent in 2023. In Singapore, losses dropped from 6.6 percent to a nearly nonexistent 0.2 percent over the same time period.

Global Electricity Theft Crisis

But there are many parts of the world where electricity losses remain a problem or have gotten worse. In Jamaica, where power theft is rampant, losses have hovered between 21 and 28 percent for years. Argentina’s losses nearly doubled between 2015 and 2023, going from an all-time low of about 12 percent to an all-time high of nearly 24 percent. The main problem: Transmission and distribution companies lacked the capital to maintain and upgrade their networks, which left equipment operating under stress. A delay in the installation of smart meters has allowed thieves to more easily siphon power and tamper with meters.

Thomas says she hopes her account of Delhi’s grid comeback will serve as a blueprint for others. It’s possible to replicate the sweeping changes Delhi made, she says. But it “requires a concerted effort from all stakeholders, customers, the utility, the government, and their employees.”

Every year, IEEE recognizes individual researchers, innovators, and collaborative teams whose technical achievements and leadership have made a lasting impact on technology, engineering, and society worldwide with its Technical Field Awards.

The honorees are selected by the IEEE Awards Board based on their extraordinary technical achievements, societal impact, and leadership in specialized engineering disciplines and are approved by the IEEE Board of Directors.

“The 2027 IEEE Technical Field Award recipients embody the relentless spirit of innovation and ingenuity that defines our global community,” said Mary Ellen Randall, 2026 IEEE president and CEO in a news release. “Across microelectronics, artificial intelligence, medical robotics, wireless networks, energy conversion, and technical education, their groundbreaking accomplishments continue to push the boundaries of science and solve critical challenges for the benefit of humanity.”

The recipients are:

IEEE Biomedical Engineering Award

Sponsors: IEEE Engineering in Medicine and Biology, IEEE Circuits and Systems, and IEEE Signal Processing societies

IEEE Life Fellow Kullervo Hynynen

Sunnybrook Research Institute in Toronto, and the University of Toronto

“For pioneering contributions to the development, advancement, and clinical translation of therapeutic ultrasound.”

IEEE Cledo Brunetti Award

Sponsor: The Brunetti Bequest

IEEE Member Edward Charles Gage

IEEE Fellow Ganping Ju

IEEE Fellow Jan-Ulrich Thiele

Seagate Technology in Cupertino, Calif.

“For contributions to heat-assisted magnetic technology powering ultra-high-capacity hard disk drives and enabling greater storage of digital information.”

IEEE Roger W. Brockett Control Systems Award

Sponsors: IEEE Control Systems Society and Friends and Family of Roger W. Brockett

IEEE Life Fellow Arjan van der Schaft

Bernoulli Institute at the University of Groningen in the Netherlands (Retired)

“For contributions to robust and geometric approaches to nonlinear control, hybrid systems, and the control of physical systems.”

IEEE Electromagnetics Award

Sponsors: IEEE Antennas and Propagation, IEEE Electromagnetic Compatibility, IEEE Geoscience and Remote Sensing, and IEEE Microwave Theory and Technology societies

IEEE Fellow Zoya Popović

University of Colorado Boulder

“For contributions to microwave and millimeter-wave circuit designs, including power amplifiers, power-combining, and wireless power systems.”

IEEE James L. Flanagan Speech and Audio Processing Award

Sponsor: Mitsubishi Electric Research Laboratories (MERL)

IEEE Life Fellow Chin-Hui Lee

Georgia Tech

“For contributions to and leadership in technologies that enable automatic speech and speaker recognition, including innovations in adaptive learning and discriminative training.”

IEEE Fourier Award for Signal Processing

Sponsor: Mitsubishi Electric Research Laboratories (MERL)

IEEE Fellow Nicholas Dimitrios Sidiropoulos

University of Virginia in Charlottesville

“For contributions to signal processing methods for analyzing complex data and identifying patterns in multiple dimensions.”

IEEE Andrew S. Grove Award

Sponsor: IEEE Electron Devices Society

IEEE Fellow John Robert Brews

University of Arizona in Tucson (Retired)

“For foundational contributions to the physics and technology underlying MOS devices and circuits.”

IEEE Masaru Ibuka Consumer Technology Award

Sponsor: Sony Group Corporation

IEEE Honorary Member Mike Lazaridis

Quantum Valley Investments in Waterloo, Ont., Canada

“For transformative contributions to mobile communication through visionary leadership that reshaped global consumer technology.”

IEEE Internet Award

Sponsor: Google LLC

IEEE Fellow Matthew Roughan

Adelaide University in Australia

“For foundational contributions to analyzing Internet traffic and network structures, enabling better network management and understanding.”

IEEE Richard Harold Kaufmann Award

Sponsor: IEEE Industry Applications Society

IEEE Fellow Akshay Kumar Rathore

National University of Singapore

“For developing innovative current-fed and multilevel power electronics converters.”

IEEE Joseph F. Keithley Award in Instrumentation and Measurement

Sponsors: Tektronix Inc. and the IEEE Instrumentation and Measurement Society

IEEE Life Fellow Pasquale Daponte

University of Sannio in Benevento, Italy

“For contributions to the measurement and standardization of analog-to-digital and digital-to-analog converters.”

IEEE Gustav Robert Kirchhoff Award

Sponsor: IEEE Circuits and Systems Society

IEEE Life Fellow Janusz Rajski

Siemens Digital Industries Software in Wilsonville, Ore.

“For contributions to testing digital integrated circuits at the silicon level.”

IEEE Leon K. Kirchmayer Graduate Teaching Award

Sponsor: The IEEE Leon K. Kirchmayer Memorial Fund

IEEE Life Fellow Rama Chellappa

Johns Hopkins University in Baltimore

“For contributions to multi-disciplinary graduate education and mentoring of doctoral students in image processing, computer vision, and pattern recognition.”

IEEE Koji Kobayashi Computers and Communications Award

Sponsor: NEC Corporation

IEEE Member Amin Vahdat

Google in Mountain View, Calif.

“For developing network systems that scale data centers to support modern cloud computing and machine learning.”

IEEE Arun N. Netravali Video Analytics, Technology, and Systems Award

Sponsor: Nokia Bell Labs

IEEE Life Member Jens-Rainer Ohm

RWTH Aachen University in Germany (Professor Emeritus)

IEEE Life Fellow Gary J. Sullivan

Dolby Laboratories in San Francisco

IEEE Member Benjamin Bross

Fraunhofer Institute for Telecommunications Heinrich-Hertz-Institut in Berlin

“For leadership in and contributions to developing the High Efficiency Video Coding (HEVC) international standard, which improves the efficiency of digital video compression.”

IEEE William E. Newell Power Electronics Award

Sponsor: IEEE Power Electronics Society

IEEE Fellow Paolo Mattavelli

University of Padova in Padua, Italy

“For contributions to advanced modeling and control of power electronics converters.”

IEEE Donald O. Pederson Award in Solid-State Circuits

Sponsor: IEEE Solid-State Circuits Society

IEEE Life Fellow Wanda Kay Gass

Texas Instruments in Bellevue, Wash. (Retired)

“For pioneering contributions to programmable digital signal processing systems through innovations in circuit design, system architecture, and silicon compilers.”

IEEE Frederik Philips Award

Sponsor: The IEEE Frederik Philips Award Fund

Recipient: IEEE Member Ann B. Kelleher

Intel in Portland, Ore. (Retired)

“For leading the historic advancement of CMOS process technology, culminating in the first node with backside power delivery.”

IEEE Photonics Award

Sponsor: IEEE Photonics Society

IEEE Life Fellow Martin David Dawson

University of Strathclyde in Glasgow

“For contributions to microstructured and wide-bandgap semiconductor optoelectronics and for leadership in technological innovation.”

IEEE Robotics and Automation Award

Sponsor: IEEE Robotics and Automation Society

IEEE Life Fellow Russell H. Taylor

Johns Hopkins University in Baltimore

“For contributions to, leadership in, and the clinical translation of surgical and medical robotics.”

IEEE Frank Rosenblatt Award

Sponsor: IEEE Computational Intelligence Society

IEEE Life Fellow Shun-ichi Amari

Teikyo University in Tokyo

“For foundational contributions to neural network theory and information geometry.”

IEEE Marie Sklodowska-Curie Award

Sponsor: IEEE Nuclear and Plasma Sciences Society

Victor Malka

Weizmann Institute of Science in Rehovot, Israel

“For leadership in the development of laser-driven particle sources and advancing the technology toward practical applications.”

IEEE Innovation in Societal Infrastructure Award

Sponsors: Hitachi, Ltd. and the IEEE Computer Society

IEEE Fellow Jaideep Vaidya

Rutgers University in New Brunswick, N.J.

IEEE Senior Member Lucila Ohno-Machado

Yale School of Medicine

IEEE Senior Member Xiaoqian Jiang

The University of Texas Health Science Center at Houston

“For leadership in designing and deploying privacy-preserving distributed clinical research infrastructures that enable secure, large-scale collaboration using health data.”

IEEE Charles Proteus Steinmetz Award

Sponsor: IEEE Standards Association

IEEE Life Fellow Mehmet Ulema

Manhattan University in New York City (Professor Emeritus)

“For leadership in the development and standardization of architecture, management, and protocols used in wireless communications and data networks.”

IEEE Eric E. Sumner Award

Sponsor: Nokia Bell Labs

IEEE Fellow Gerhard Fettweis

Technische Universität Dresden Barkhausen Institut in Germany

“For contributions to physical-layer communication technology that have had an impact on the global wireless industry.”

IEEE Nikola Tesla Award

Sponsors: Wolong Electric Group Co. Ltd. and the IEEE Industry Applications and IEEE Power & Energy societies

IEEE Life Fellow Pragasen Pillay

Concordia University in Montreal

“For contributions to the modeling, design, and application of electric machines in industrial, transportation, and renewable energy applications.”

IEEE Kiyo Tomiyasu Award

Sponsors: The late Dr. Kiyo Tomiyasu and the IEEE Geoscience and Remote Sensing and the IEEE Microwave Theory and Technology societies

IEEE Fellow Yun Raymond Fu

Northeastern University in Boston

“For contributions to computer vision and augmented human-machine interaction.”

IEEE Transportation Technologies Award

Sponsors: IEEE Industry Applications, IEEE Industrial Electronics, IEEE Intelligent Transportation Systems, IEEE Microwave Theory and Technology, IEEE Power Electronics, IEEE Power & Energy, and IEEE Vehicular Technology societies

IEEE Fellow Chunting Chris Mi

San Diego State University

“For contributions to advancing electric and hybrid electric vehicle technologies.”

IEEE Rao R. Tummala Electronics Packaging Award

Sponsors: IEEE Electronics Packaging Society and Friends of Rao R. Tummala

IEEE Fellow Ravi Mahajan

Intel in Chandler, Ariz.

“For contributions to the development of technology that connects multiple chips within advanced electronic packages.”

IEEE Undergraduate Teaching Award

Sponsor: IEEE Education Society

IEEE Fellow Rhonda Rene Franklin

University of Minnesota in Minneapolis

“For leadership in advancing inclusive workforce development initiatives that create clear and accessible career pathways via undergraduate engineering education.”

IEEE Lotfi A. Zadeh Award for Emerging Technologies

Sponsor: IEEE Systems, Man, and Cybernetics Society

IEEE Life Fellow Peng Shi

Adelaide University in Australia

“For contributions to artificial intelligence-based methods for robust control and filtering in complex systems.”

Nominate a colleague

Nominations are now open for the 2028 Technical Field Awards. The deadline to submit a nomination is 15 January.

For more information on the awards, please visit the IEEE Awards webpage.

Financial markets have always relied on timely information, and the drive for timeliness has always adapted to the latest technology. From clipper ships transiting the oceans to telegraph wires connecting cities to fiber-optic cables conducting trades in microseconds, traders have embraced any advantage to get the most up-to-date information. Indeed, the history of finance is really a story about how fast you can move information and who controls the interface.

It’s only natural that people also figured out a way to profit by supplying that market intel. In 1841, for example, the Mercantile Exchange (predecessor to Dun & Bradstreet) began selling proprietary business information to its U.S. clients. The following decade, Paul Julius Reuter began selling news services and stock price information. To supplement the company’s telegraph dispatches, he sent pigeons between Aachen, Germany, and Brussels; each bird carried a cylinder containing slips of paper with that day’s stock prices. In 1867, an inventor named Edward Calahan introduced the first telegraphic ticker-tape machine, which spooled out stock price information in near real time; Thomas Edison improved upon the design with his patented version in 1871.

The Dow Jones Industrial Average debuted in 1896 as an index of 12 key businesses listed on U.S. stock exchanges. It included gas, oil, coal, and electric companies, as well as enterprises dealing in leather, rubber, and tobacco. Messengers delivered quotes from the trading floor to brokerage offices, while stock tickers kept investors informed of prices. By the time New York City held its first official ticker-tape parade, in 1919, telegraphy in Western Europe and the United States had become the chief means for quick transmission of vital stock information.

In 1960, the first paperless financial service debuted, when Quotron introduced its electronic screens for displaying market quotes. Over the next two decades, other companies rolled out similar innovations for distributing financial news and data.

So when Michael Bloomberg decided to enter this well-established industry in 1981, the big question was: How would his new company stand out?

The Birth of the Bloomberg Terminal

Bloomberg had cofounded Innovative Market Systems (IMS) after being fired from the investment bank Salomon Brothers. Landing on his feet with his US $10 million equity payout and joined by former Salomon colleagues Thomas Secunda, Duncan MacMillan, and Charles Zegar, Bloomberg pursued his belief that Wall Street would pay a premium for specialized financial data. He’d earned an electrical engineering degree from Johns Hopkins University and an MBA from Harvard, and he’d built computerized financial systems for Salomon. IMS focused on developing a computer terminal that not only provided up-to-date information but could also do instant quantitative analysis based on historical data.

Michael Bloomberg believed Wall Street would pay a premium for access to specialized financial data. Karjean Levine/Getty Images

At the time, most financial data still circulated through telephone calls, printed price sheets, and specialist publications, and analysis involved a fair amount of gut instinct guided by human expertise. Companies such as Reuters and Dow Jones provided subscription-based services for access to business news. But traders still had to assemble information from multiple sources and perform their own calculations and analysis.

IMS proposed an integrated system with a single interface. Its Market Master terminal consisted of a monochrome CRT monitor, a custom keyboard, and a communications/controller unit that connected to the company’s private network. At launch, it provided only U.S. government bond prices and bond-calculation tools, but the dream was much bigger: a dedicated terminal that would sit on a trader’s desk and run different market scenarios, produce yield curves, and support investment calculations.

IMS initially had just one client, Merrill Lynch, which invested $30 million (about $110 million today) in exchange for a 30 percent stake in the company and exclusive rights to the terminals for five years; Merrill waived that right in 1984. The first 22 Market Master terminals were delivered to Merrill in 1982, in the middle of a global recession. The timing was fortuitous. Worldwide, stock markets were transitioning to electronic trading, and the U.S. Federal Reserve was allowing more freely floating interest rates. Bond prices were more volatile, and investors were eager to figure out how to value them accurately. Bloomberg’s specialized financial terminals provided the data and the analytical tools to process and comprehend those sweeping changes.

Five years after its launch, IMS rebranded as Bloomberg LP and expanded its clientele, and the Market Master became known as the Bloomberg Terminal.

How Did the Bloomberg Terminal Work?

The Bloomberg Terminal’s keyboard was designed with traders and analysts in mind. The function keys were color-coded and given labels specifying their usage, so that users didn’t have to remember. The original keyboard, affectionately referred to as “the Chiclet,” was hand assembled. A cable ran from the keyboard to the Bloomberg Controller, which had a dedicated phone line to connect to a local hub. The internet wasn’t commercially available yet, so the company basically built its own closed network, with centralized computers that maintained large databases and performed most of the calculations. Commands entered on the keyboard sent a request to the hub, which processed the information and sent back the result.

The Chiclet keyboard for the Bloomberg Terminal was introduced around 1983. Although it looks like a generic keyboard, its function keys were finance-specific hot keys.National Museum of American History/Smithsonian Institution

Hot keys let traders easily call up data on government securities, corporate debt, and currency markets, among other things. A series of keystrokes would pull up other historical and real-time data, run an analysis, or place a trade. Learning how to use the terminal and digest the vast amount of information, which was presented mostly in tabular form, became a rite of passage for users.

In 1990, Bloomberg added a trackball to the keyboard, which helped the user navigate the multiple windows and menus typically displayed on screen. Two years later, the keyboard gained a built-in speaker, to support multimedia information; this design also included telephone, headphone, and microphone jacks. One of the most popular features was Instant Bloomberg, which allowed users to chat directly with fellow Bloomberg Terminal users over the proprietary network. By 1996, Bloomberg had keyboards that supported 23 different languages. In the early 2000s, the company began incorporating biometric authentication for terminal login, via a fingerprint reader on the keyboard.

As the company’s business model evolved, the Bloomberg Terminal added services well beyond its initial offerings. In 1990, for example, worried that Dow Jones would stop providing access to its news stories, Bloomberg set up its own news service. It recruited Wall Street Journal reporter Matthew Winkler to oversee a dozen reporters; their stories on market and securities movements used graphs and calculations that served as advertisements for the terminal’s capabilities. These days, the Bloomberg news empire includes Bloomberg Businessweek, Bloomberg Radio, and Bloomberg Television.

Bloomberg’s subscription-based financial model included the leasing of a Bloomberg Terminal with its specialized keyboard and other hardware, access to a dedicated private network, and a suite of services. In 1999, a subscription to a single Bloomberg Terminal cost $1,600 per month with a minimum two-year contract and a discount on each additional terminal. Today the annual price is upwards of $32,000 (trending a little below inflation). In 1995, the company launched a suite of “Open Bloomberg” software products that ran on the customer’s own PC; five years later, it stopped leasing dedicated terminals. Current customers also have access to mobile applications that allow terminal functions to run on phones and tablets. Today, “Bloomberg Terminal” has come to refer to the integrated data, analytics, news, communications, and trading environment.

The Legacy of the Bloomberg Terminal

Although the shift away from dedicated terminals was a logical response to the rise of the internet and publicly available market data, it altered the material culture of financial work. For nearly two decades, Bloomberg Terminals commanded an aura of power and financial prowess. They were emblems of market mastery, with a brand that was distinct from other office computers. With Open Bloomberg, users were no longer tied to a single desk or a fixed set of monitors.

And so, cast-off Bloomberg Terminals found their way into museum collections. They’re a physical embodiment of the ethereal nature of financial markets, and a manifestation of mathematical calculations, network infrastructure, and business culture.

The Bloomberg keyboard used by “Bond King” Bill Gross has his login and password taped on the front.National Museum of American History/Smithsonian Institution

The Smithsonian Institution’s National Museum of American History has a number of Bloomberg keyboards in its collection, but my favorite is object number 2014.0012.02, which was used by “Bond King” Bill Gross during the 1990s and 2000s at Pacific Investment Management. Gross had cofounded PIMCO in 1971 and built it into a $2 trillion bond investment firm. I especially love that Gross taped his login and password directly on his keyboard, which makes the object more relatable. I may never know what it’s like to manage billions in assets from a Bloomberg Terminal, but I absolutely understand the trial of remembering my passwords.

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 October 2026 print issue as “The Keyboard That Moved Markets.”

References

Bloomberg Professional Services has a nice timeline showing the evolution of its keyboards.

For an economic analysis of how computers like the Bloomberg Terminal changed financial markets, see Gerben Bakker’s 2025 paper “The Terminal Revolution: Reuters and Bloomberg as global providers of financial and economic news, 1960–2020,” published by the London School of Economics and Political Science.

Bloomberg Terminals are in the collections of many museums, including the Computer History Museum and the National Museum of American History.

Seventeen years ago, cell biologist Donald Ingber and his colleagues at Harvard University’s Wyss Institute for Biologically Inspired Engineering submitted a paper to the journal Science describing their model human lung. It was smaller than a USB stick and made of a clear polymer slab containing narrow channels, which were lined with the type of cells that line a lung’s air sacs and blood vessels. When air was pumped through hollow chambers beside the channels, the device rhythmically expanded and contracted—it “breathed.”

This lifelike movement was a dramatic change from previous generations of lung models, which typically used static cultures of lung tissue that were unable to simulate the movements essential to lung function. When exposed to inflammatory proteins and bacteria, Ingber’s artificial lung reacted much as living lungs would. And exposure to silica nanoparticles used to model the effects of ultrafine particulates revealed that movement affected how tissues absorbed them.

Donald Ingber led the team that developed the first human lung-on-a-chip at Harvard University’s Wyss Institute. Sam Ogden

It was a powerful proof-of-principle demonstration of a system that could be used to test drugs and other chemicals, providing a complement and even an alternative to testing in tissue cultures or in animals. Even so, the editors at Science were hesitant. They rejected the paper and suggested that Ingber’s team also run the tests in mice.

It wasn’t an unreasonable request: Harvard’s lung system was new and comparing the results it generated to results from mice would help validate it. Ingber’s team ran the suggested experiments and resubmitted their study a year later, in 2010, at which point it was published. (It has since been cited by nearly 5,400 other papers.) Still, the incident spoke to how animal models have been the default of modern biomedical research.

An early lung-on-a-chip developed at Harvard’s Wyss Institute used microfluidic channels lined with human cells to reproduce key features of lung function. Wyss Institute at Harvard University

A recent story told by Ilka Maschmeyer, a translational toxicology researcher and executive at the German biotech company TissUse, shows how much things have changed. TissUse specializes in building organ-on-a-chip systems—the conversational name for systems like Ingber’s lung—that are used by pharmaceutical companies for research. A few months ago, says Maschmeyer, a pharmaceutical company approached TissUse after being denied permission by the U.S. Food and Drug Administration to run a clinical trial of a new drug. The problem: It had presented animal data, but the FDA wanted data from organs-on-a-chip or some comparable alternative. The standards had come full circle.

A Breathing Lung-on-a-Chip

The moment spoke to a trend, perhaps even the early days of a fundamental shift, away from the use of animals in toxicology and drug development. “It’s rare still,” says Maschmeyer, “but I think it’s going to be more and more frequent.”

A host of these kinds of alternatives to experiments on animals have been developed over the years. Collectively they’re known as NAMs, an acronym that stands, depending on whom you’re talking to, for new approach methodologies, novel alternative methods, or nonanimal methods. Most NAMs have yet to be rigorously tested, but early studies suggest their potential.

As NAMs have become more sophisticated, the question of how they will be implemented has become less about their technical qualities and more about the practical next steps needed to realize their potential. Validating NAMs—standardizing the systems, conducting head-to-head comparisons with animal experiments—is an enormous challenge. Moreover, simply outperforming animal models is necessary but not sufficient. The adoption of NAMs will require changes in policy, training, and culture.

“This transition process is much more complicated than you would think,” says Thomas Hartung, a toxicologist and director of the Center for Alternatives to Animal Testing at Johns Hopkins University. “It is more about change management than it is about the technology.”

The Technologies Replacing Animal Testing

For decades, animal advocates and many scientists have criticized both the morality and usefulness of experimenting on animals. An estimated 92 percent of all drugs that enter U.S. clinical trials fail to reach the market, sometimes for business reasons but often because the drugs prove ineffective or unsafe in ways that were not predicted by animal experiments. Failure rates are even higher in drugs for heart disease, cancer, and diseases of the brain.

These statistics don’t automatically mean that a reliance on animals is to blame. Flawed study designs are a problem too, and also the sheer confounding complexity of disease. But there’s little question that animals have made poor surrogates for many conditions. And just as animal experiments may mistakenly suggest efficacy or fail to predict harm in humans, they might also erroneously suggest that drugs are ineffective or harmful when they could actually work in humans. Some researchers argue that if aspirin or acetaminophen had been discovered after the advent of modern testing requirements, they might have been abandoned.

TissUse’s Humimic systems use microfluidic chips to culture human tissues and model interactions between organs. A researcher images tissues in a chip during an experiment [top], Humimic chips sit in a temperature-controlled unit [center], and a researcher prepares chips for use [bottom].TissUse (3)

Researchers developing NAMs have pushed these systems far beyond old-fashioned tissue cultures. The new technologies include organoids that more closely mimic the structure, composition, and function of human organs. More humanlike still are organ-on-a-chip systems; alongside Ingber’s lung-on-a-chip are brains, hearts, kidneys, and even placentas on a chip. As many as 10 such organs have been linked together, yielding multi-organ systems that promise to recapitulate many aspects of human physiology—not perfectly, but better than a mouse or a monkey would. Supporting these systems are computational simulations of organs and organisms, and also artificial intelligence tools that analyze data generated by other systems and inform future experiments in a high-powered iterative loop.

Yet even as studies piled up and some pharmaceutical companies started using NAMs in-house, the U.S. regulatory system governing drug developing and testing remained an obstacle to their wider use. NAM proponents were overjoyed, then, when in late 2022 the FDA Modernization Act 2.0 passed into law. It explicitly authorized the use of NAMs in the preclinical studies required of new drugs before they could enter human trials. Previous regulations had mandated animal testing; now the door was open to alternatives. It was a landmark moment. “That was something I didn’t expect to see in my life,” says Maschmeyer.

Although immediate in-the-lab impact was limited, the FDA’s decision was a harbinger of things to come. In 2025, the FDA pledged “to make animal studies the exception rather than the norm” for drug safety testing. Then, in September 2026, the agency followed up by issuing a rule that, if it takes effect, will replace references to “animal tests” in its drug-development regulations with the broader term “nonclinical tests.” The change makes explicit that validated alternatives such as human-cell systems, organs-on-chips, and computer models can be used when appropriate.

Also in 2025, the U.S. National Institutes of Health, the world’s largest public biomedical research funder, announced that researchers applying for grants to study animal models would also need to incorporate nonanimal research, such as real-world data or studies of NAMs. Meanwhile, the European Commission and United Kingdom have announced their own plans to phase out animal testing, and the intergovernmental Organisation for Economic Co-operation and Development updated its influential guidelines to allow for expanded use of NAMs.

NAM proponents say these shifts were essential: If regulators won’t accept NAM results, there’s less incentive to adopt them, especially for researchers already working with animals. Maschmeyer says TissUse’s clients increasingly include scientists whose research has been focused on animals. “I see, within the last year, a change,” says Maschmeyer. “It’s more people who are working with animal models who now have to also add in vitro models.” She traces it mainly to the regulatory shift—a trend Ingber calls “game-changing.”

Proving That NAMs Work

It’s not enough for regulators to say that NAMs can or should be used, though. Even more important is the regulatory apparatus dedicated to assessing how they should be used. This begins with their validation: the process by which experimental methodologies and devices are determined to be reliable and trustworthy. A prototype brain-on-a-chip designed to model a rare neurological disease might work fine in the lab that developed it—but to be validated, the system needs to work in the real world.

“You read about all the organ chips that come out of academic labs, which is great—but that’s not going to change their uptake by the FDA, because you have to get the same results anywhere in the world. It has to be a commercial product. It has to be mass-produced and meet very fine performance criteria,” says Ingber. For example, even minute variations in the hydrogels used as tissue scaffolds in organ chips can produce very different growth patterns.

Emulate’s Organ-Chips are connected to the company’s automated culture system, which supplies the chips with nutrients and controls the flow of fluid through them. Emulate

Workflows and procedures need to be uniform, too. One obstacle to wider use of vascularized tumor-on-a-chip platforms in developing cancer therapies, for example, is the different metrics used by different research groups to characterize blood-vessel function and geometry. Experimental guidelines, workflows, checkpoints, metrics, reporting criteria: All need to be standardized in order for researchers to compare their work and collaborate across platforms. Members of Ingber’s lab coach industry researchers on how to use chips developed by Emulate, a company founded by Ingber. But even with instructions, they still need help with the finer points of tending to stem-cell cultures.

When a NAM is ready for commercial use and researchers know how to use it, the most important test—whether it provides clinical benefit—still remains. A rare-disease organ chip might be reliable, but are the biomarkers it measures actually relevant? If so, are the algorithms that extrapolate chip results to the drug’s in-body effects truly predictive?

Microscopic images reveal the human tissues grown inside Emulate’s Organ-Chips. Bacteria, shown in magenta, interact with mucus and airway cells in a LungChip [top]; an IntestineChip develops structures resembling those that absorb nutrients in the small intestine [center]; and tiny hairlike cilia grow on cells in another LungChip, where they help move mucus and trapped particles out of the airway [bottom].Emulate (3)

Such questions have been answered for some NAMs. For example, a liver-on-a-chip system from Emulate correctly flagged about seven out of every eight drugs that had safely passed animal trials but proved toxic to human livers. A similar study was conducted by researchers from Oxford University and Janssen Pharmaceutica (later renamed Johnson & Johnson Innovative Medicine). That team showed that their computational simulations of human heart cells flagged compounds that caused a type of dangerous heart arrhythmia with 89 percent accuracy, compared to animal studies that were 75 percent accurate.

Such studies, however, are complicated and costly. Emulate’s study required 870 chips and the labor equivalent of 16 full-time employees working for 16 weeks—efforts far beyond the reach of the average lab. If the researchers wanted regulatory approval to use their chip to predict large-molecule drugs rather than the small-molecule drugs they tested, they would have needed to run another such study for that particular use. And comparable studies ostensibly need to be conducted for every commercially available NAM and every context in which they would be used—a vast undertaking. “That’s a challenge,” says Kathy Archibald, founder of Safer Medicines Trust, a United Kingdom–based group that considers animals to be poor models of human biology. “It takes too long and costs too much, and small companies can’t afford to do it.”

Ingber thinks that academic scientists need to collaborate more with industry researchers on NAMs, and that governments should fund those projects. He and other NAM proponents also stress the importance of having access to the necessary data: Without information from preclinical animal studies and human clinical trials, comparisons are difficult, but much of that data is now proprietary. Pharmaceutical companies and regulators need to share it, they say, and the FDA has called for an open-access repository of drug toxicity data. Hartung of Johns Hopkins also suggests that new animal experiments be run in parallel with NAMs, producing side-by-side comparisons.

To Christian Maass, a computational biologist at the German biotechnology company ESQlabs, NAMS are overdue for a showdown with animal models. His company makes “digital twin” systems in which data from organ chip systems inform whole-human simulations of drug outcomes and disease progression. “I love what we are doing,” says Maass, speaking not only of his company but of the whole field. But he adds that researchers have not yet provided “the evidence and the proof that we are doing better or as good as the animal models.”

Maass thinks that head-to-head comparisons are essential to good science. After all, if a NAM doesn’t outperform an animal model, or works best as a complement rather than a replacement, that needs to be known. He also believes such studies could convince skeptics. Maass mentions the debut of the iPhone, when people saw for the first time how well a phone could work without buttons. “That was an ‘aha!’ moment,” he says. But for NAMs, “that moment is still lacking.”

Changing Scientific Habits

Even when those head-to-head comparisons are made, though, and regulations are appropriately changed, adoption can be slow. In the mid-1990s, researchers developed and validated the monocyte activation test—an assay that uses human blood cells to predict immune response—to replace the rabbit pyrogen test, which involves injecting a compound into a rabbit’s ear and monitoring the animal’s rectal temperature. But it wasn’t until 2010 that the European Pharmacopeia—the official Europe-wide standards for such testing—accepted the monocyte activation test as a replacement. And rabbits are still widely used for this test worldwide.

Why the slow pace of change? In part because updates to guidance documents referring to animal tests lagged behind, but also because of inertia within the culture and institutions of science. “The formal requirement may disappear, but the informal expectation persists,” says Kathrin Herrmann, a veterinary scientist and colleague of Hartung’s at the Center for Alternatives to Animal Testing. Regulators, grant reviewers, peer reviewers, journal editors—the human infrastructure of science—often still expect to see animal data and are unfamiliar with NAMs.

Herrmann is now overseeing a survey of early-career researchers working with, or trying to make the switch to, NAMs. “We consistently hear concerns that NAM-only proposals are perceived as risky by funders, that there is pressure to ‘add an animal experiment’ for credibility, that access to NAM infrastructure is limited, and that career trajectories become uncertain when departing from established animal models,” says Herrmann.

The vast majority of drugs entering clinical trials in the United States fail to reach FDA approval [failure rates in pink], with particularly high failure rates in some therapeutic areas.

Animal models are embedded in databases, training programs, and the very culture of research. Scientists who use animals may be reluctant to change; their identities as researchers are tied to animals and, more practically, they’ve spent their careers learning the techniques. A toxicologist who has used rats for decades might understandably look askance when asked to take a chance on unfamiliar chunks of polymer and stem cells—especially when human well-being, or millions of dollars, may ride on the choice. Likewise, an academic scientist whose career was built on animal models may not welcome NAMs; a switch may represent the loss of jobs for lab members whose expertise is no longer relevant. “I could see why it’s a hard thing for people to take it up,” says Ingber.

Education and training is vital, say NAM proponents. The NIH and FDA now offer resources for researchers interested in NAMs, as do their counterparts in other countries embracing the technologies. Herrmann helps run webinars where researchers and regulators learn to use and evaluate NAMs; Hartung’s modules on Coursera, the online learning platform, have been taken by about 12,000 students so far. “These trainees will set up their own labs. They will go to industry. They will replace the old guard,” says Joseph Wu, director of Stanford University’s Cardiovascular Institute.

Wu is also a cofounder of Greenstone Biosciences, a company that uses stem-cell-derived human tissues and AI to model disease and predict drug responses. He’s used that position to introduce researchers to NAMs, helping convince the company’s directors to freely share Greenstone’s large library of stem-cell lines with any academic researchers who want to use them. “I really believe that people should understand how this platform works,” says Wu. “At the end of the day, we’re just trying to advance science.”

With enough time—and funding, incentives, training, education, collaboration, and generational turnover—the research culture of drug development and safety testing may shift. Whether NAMs will be used in other areas of science, though, is an open question. Early-stage drug development and regulatory testing account for roughly 30 percent of animals used in experiments; the rest are used in basic biological research. Replacing those animals is less straightforward, but it may be possible: Ingber describes organ-on-a-chip-based insights into inflammatory bowel disease, preterm birth, and treating viral infections that couldn’t have been made in animals. Hartung calls the adoption of NAMs in toxicology a “lighthouse function,” helping guide the way for other types of research.

“Suddenly, all the dams have opened,” he says.

Learn how engineers can shift from manually managing tools and handoffs to defining objectives and supervising AI-driven execution across the silicon development lifecycle.

Key Takeaways

• Explore four critical technologies enabling silicon design autonomy
• Understand how autonomous AI accelerates complex engineering workflows
• See real-world impact across semiconductor design and verification
• Gain practical insights from Ambiq’s deployment of autonomous AI in production

Building useful robots starts with understanding the people who use them. For Charlie Kemp, cofounder and chief technology officer of Hello Robot, that means developing assistive robots that can help people with everyday tasks and support greater independence.

In this Robots Guide profile, Kemp shares his path from studying artificial intelligence at MIT to building Stretch, explains how working with people with disabilities has shaped his approach, and offers advice for aspiring roboticists. Read the full profile on IEEE’s Robots Guide.

IEEE TryEngineering is dedicated to inspiring intellectual curiosity in children.

The technologies shaping our world, including in the realms of artificial intelligence, electric vehicles, and ocean exploration, are evolving rapidly. Helping young learners understand the concepts is essential to preparing the next generation of problem-solvers, creators, and engineers.

TryEngineering has introduced a STEM book series for youngsters ages 8 to 12 through the Lerner Publishing Group.

The series, Tomorrow’s Technology With TryEngineering, Powered by IEEE, makes complex topics more approachable and engaging, with each book combining age-appropriate explanations, real-world examples, and design challenges that encourage curiosity and critical thinking. The series is based on ebooks and videos available at tryengineering.org.

For the series, TryEngineering partnered with several other IEEE groups including the Communications, Computer, and Oceanic Engineering societies and the Transportation Electrification Council.

Whether used in the classroom, a library, or at home, the books can help pupils connect STEM concepts to the technologies they encounter every day, including computers and smartphones.

Six topics in the collection

Here are the books in the new collection:

Artificial Intelligence: The Future of Smart Technology explores the systems behind streaming services, search engines, and health care. Readers learn how AI works while exploring ethical concerns such as bias, deepfakes, hallucinations, and privacy. Pupils can better understand one of the most influential technologies of our time as it evolves.

Communication Technology: From Morse Code to Smartphones teaches readers about smoke signals, semaphore towers, telephones, and wireless networks. The pupils can gain an understanding of how engineers are changing communications technology through innovations such as 6G and space-based networks. The book highlights career opportunities in the aerospace and telecommunications fields.

Electric Vehicles: Powering the Future of Transportation covers how e-cars, e-bikes, e-scooters, and electrified trains are transforming the way people travel. Readers can discover how hybrid and fully electric vehicles operate and how the batteries that power them work. They also can learn about the roles engineers play in developing smarter transit systems.

Ocean Engineering: Protecting Our Ocean Environments highlights the vital ecosystem role played by the world’s oceans, which require careful stewardship. play in our ecosystem. Readers can learn how engineers study the underwater world using submersibles and floats, how they address pollution, and how they protect marine environments.

Semiconductors: The Building Blocks of Modern Electronics focuses on the technology behind nearly every electronic tool we use. Microchips power smartphones, computers, and countless other products, all thanks to semiconductors. Readers can learn about insulators and conductors, how microchips are manufactured, and why semiconductor engineering is a fertile field for innovation.

Signal Power: The Hidden Waves Behind Modern Tech explores how engineers analyze and manipulate signals to make technologies work more effectively. Whether it is a phone call reaching the correct person despite background noise or a medical device monitoring a patient, signal processing plays crucial roles in modern life. The book introduces different wave types, the signal processing workflow, and careers in the field.

The book series can help children understand the technologies shaping the world around them while encouraging them to think like engineers and innovators. By connecting STEM concepts to real-world applications, the series can make learning more meaningful and engaging.

The Tomorrow’s Technology With TryEngineering series is available through Amazon, Bookshop, and Lerner. More about the collection may be found here.

It’s 6 a.m. on a cold January morning in 2002 in New Delhi. It’s still dark outside, and I’m in the kitchen preparing breakfast, packing lunches, and getting my two children ready to catch the school bus when, for the third time in a week, the power goes out. No lights, no mixer to finish my daughter’s puttu—her favorite rice dish—no kettle, no toaster. The bathroom is dark, and the kids are upset.

It will probably be hours before the power comes back on, so I grab a flashlight and light the candles that are set up around the house for these occasions. We’re behind schedule now. We pack the food we have, bundle up as the house turns chilly, and head outside, leaving a mess in the kitchen. We make our way to the bus stop in the dark—the streetlights are out, too—only to discover my daughter has missed her ride. Again. I’ll be late for work at Jamia Millia Islamia, a university where I am a professor of electrical engineering and teach power systems and smart grids. I just hope the power is on there.

This was a common scene for my family and all of Delhi in the early 2000s. Power outages happened almost daily and lasted hours. When the power was on, the quality was so poor that it would dim lights, flicker screens, and wreak havoc on appliances. Customer service at the power utilities essentially didn’t exist.

A child walks in July 2007 past a store in New Delhi specializing in reconditioned generators. The fear of power cuts during summer heat spurs demand for these generators so that residents can produce their own power.Nicholas Bradley/AFP/Getty Images

These problems had been getting worse through the 1980s and 1990s. The cause: an aging distribution grid bereft of crucial technologies, and electricity providers with little accountability. The situation became so bad that the city was losing more than half of its power through obsolete equipment and theft. These staggering losses meant that utilities got paid for only a fraction of the electricity they were trying to deliver. And the lack of funds prevented them from investing in better grid infrastructure.

But over the last quarter century, a remarkable effort by the government and the city’s distribution utilities has turned Delhi’s grid into a reliable, modern system. Power losses have shrunk from over 50 percent in 2002 to 5 to 6 percent in 2026—on par with France and Belgium, and better than Greece and Serbia. Delhi’s grid reliability index, a measure of how often electricity can be counted on, stood at around 70 percent in 2002 and has now topped 99.9 percent.

The bustling Main Bazar in the Paharganj neighborhood of Delhi increasingly uses more nighttime electricity, but reductions in electricity loss help counter demand. iStock

With reliable power, businesses across the city have blossomed. The streetlights are bright. The number of electric vehicles, including city buses, is growing daily. Quality of life has improved. Today, my family is comfortable year-round in our home despite Delhi’s scorching summers and cold winters. The chaos of losing power no longer hinders me from getting to work. The city still has problems—pollution, overcrowding, noise—but thankfully, reliable power is no longer among them.

The transformation of Delhi’s grid can serve as a model for other cities that suffer from decrepit power infrastructure. Regions of Albania, Argentina, Bangladesh, Brazil, Estonia, India, Kenya, Pakistan, Sri Lanka, Uganda, and Venezuela are reeling from heavy losses in their distribution grids. Their problems look like Delhi’s 25 years ago. I believe it’s possible to improve electricity in these places by adapting the changes Delhi made. Here’s an inside look at how the city accomplished it.

Delhi’s Power Grid and Energy Mix

The city of Delhi hosts the capital of the Republic of India, and sits along the Yamuna River in the northern part of the country. It’s home to about 23 million people and is one of the most densely populated areas in the world. Delhi’s grid includes thousands of kilometers of power lines, and peak electricity demand reached an all-time high this year of 8,748 megawatts. The city currently buys 76 percent of its power from central generating companies and private players from neighboring states. Energy generation within the city is restricted to natural gas and renewable sources. Nearly 48.5 percent of the city’s power comes from coal, about 26.5 percent from natural gas, and the rest from carbon-free sources, led by hydropower at 15.6 percent.

Tata Power-DDL replaced about 5 kilometers of overhead lines with underground cables, which reduced electricity loss and improved the aesthetics of Delhi’s streets, such as the Janta Flats in the Shalimar Bagh neighborhood.Tata Power-DDL

By the early 2000s, Delhi’s nearly 100-year-old power distribution system was in serious disrepair. Everything was old—lines, transformers, circuit breakers, switches. New grid technologies were needed to keep up with new kinds of electricity loads, but there was little money to upgrade components.

The shabby state of the grid caused many problems, most notably high electricity losses, where electricity vanishes primarily as heat. The cause of the losses was a classic electrical problem: too much current flowing through a network that wasn’t designed to carry it efficiently.

To understand the problem, it helps to understand how modern power grids work. Typically, they include generation, transmission, and distribution. After power is generated, transformers convert the electricity to high voltage levels—typically 132, 220, 400, or 765 kilovolts in India. Transmission lines then carry the power over long distances to receiving substations that are closer to where customers need electricity. Transformers then step down the voltage (to 66, 33, or 11 kV in India) and distribution lines branch out, carrying the power to customers. The whole grid works primarily on alternating current.

Electricity Losses by Country, 2002 vs. 2023

Distribution networks carry both active and reactive power. Active power is the energy used to perform useful work (and is measured in watts). Reactive power is the power that flows back and forth in an electric circuit, building electric and magnetic fields (measured in volt-ampere-reactive, or VAR). Although it doesn’t perform useful work, reactive power is necessary for many devices, such as induction motors, transformers, and computers (typically any circuit or device with inductance or capacitance elements).

When there are a lot of devices consuming reactive power on the same line, the overall current carried by the line—the sum of the active and reactive current—must increase. The more current in the line, the more the line heats up and the more energy that’s wasted as heat.

In addition to current, resistance in the line will increase losses as well. Resistance is when electrons encounter opposition as they move through the conductive material (typically aluminum in a power grid). Longer lines with many branches and connection points will increase resistance. The rule of thumb is that line loss equals the square of the current multiplied by the resistance.

Reactive power creates a second problem: It causes the voltage along the line to drop. And when the voltage falls, many modern electrical devices try to maintain roughly the same level of performance by drawing more current. That higher current produces even greater losses in the line and causes the voltage to fall further.

Meter Technology Impacts Electricity Losses

In a healthy grid, the utility will take compensatory measures to lower the current and maintain the voltage all the way to the ends of the lines. But in Delhi, this wasn’t happening. The result was a vicious cycle. Reactive loads increased the current, the higher current increased energy losses and lowered the voltages, lower voltages forced devices to draw more current and further increased the losses.

In some parts of Delhi, the effect was so severe that residents took matters into their own hands. A colleague of mine who lived in a different part of the city constantly experienced voltage that was too low for her appliances to operate reliably, so she had to install her own voltage stabilizer. At my home, we bought an inverter and battery system to keep a fan and a few lights running during the many outages.

Electricity Loss and Theft in Delhi

The losses in Delhi weren’t caused solely by technical problems. Theft of electricity was rampant, by both the powerful and the powerless (in both senses of the word). Businesses, residential customers, and utility employees with vested interests would siphon electricity from the grid. It was easy to illegally hook into a streetlight or a distribution line running close to one’s house or factory. Utilities didn’t have the resources to identify theft or penalize offenders. Even if they could, the courts were already overburdened, and an electricity regulatory commission that could push for reforms had not yet fully formed.

Updated meters have made billing easier and more accurate. Tata Power-DDL

Making matters worse, the utilities and their employees were rarely held accountable for their actions, and so corruption plagued the system. Junior engineers and line workers, many of them lacking appropriate technical skills, were tasked with handling nearly every issue, including outages, flickering, and bill payment. This was too much authority in the hands of people with too little training.

On top of that, customers didn’t pay their bills. Meters were old, frequently faulty, and easily tampered with. Utility employees would take a meter reading by visiting the customer’s property, noting the reading in a book, entering it in a ledger or on a computer back at the office, and converting it into an electricity bill that would get dropped off at the customer’s property. This process left a lot of room for incorrect billing.

To pay a bill, customers had to stand in long queues at the utility offices, which had limited business hours. Not wanting to take off a half day of work for this, many customers simply didn’t pay. And there was no penalty for not paying—there were no regulations allowing the utilities to cut off a customer’s power. (I paid my bill by having a family member stand in line for me.)

The combined commercial and technical losses left Delhi’s utilities collecting payment for less than half of the electricity they were supplying in the early 2000s.

India’s Electricity Act and Power Reforms

Such problems weren’t unique to Delhi. On average in 2002, state utilities across India experienced electricity losses of nearly 37 percent. My country desperately needed systemic reforms, but authority over electricity was split between the central and state governments so any decision-making was fractured. States managed most of the generation, as well as transmission and distribution, while the central government oversaw generation that supplied multiple states, such as hydropower, fossil fuel plants, and nuclear plants. The central government could push reforms, but the states determined whether those reforms would succeed. Making matters worse, most states put a single organization in charge of generation, transmission, and distribution, giving that entity too much control and reducing transparency and competition.

A team of technicians with BSES Rajdhani Power maintains an insulator string on a large power transformer in 2011. BSES Rajdhani Power

In 2001, India’s central government began writing some historic legislation that became the landmark Electricity Act, 2003. Among the grand reforms aimed at transforming the country’s power industry, it unbundled state oversight of grid networks, creating separate entities for generation, transmission, and distribution. It also opened up the power sector to privatization. It allowed large electricity customers to bypass local distribution companies and purchase electricity from competitors or build their own power plants. It created a central regulatory agency responsible for determining interstate tariffs and promoting market competition in the power sector. And it created mechanisms for prosecuting electricity theft.

Hundreds of capacitor banks have been installed in Delhi to supply reactive power at strategic locations and help stabilize voltage.Tata Power-DDL

In 2002, Delhi was already taking drastic action to fix its grid. The organization overseeing Delhi’s distribution, the Delhi Vidyut Board, was broken up and two private companies—BSES (now part of Reliance Infrastructure), and North Delhi Power Limited (now known as Tata Power-DDL)—took over distribution. They faced a Herculean task. Tata Power-DDL, serving the northern half of Delhi, would have to tackle a combined commercial and technical electricity loss of 53.5 percent. BSES, whose territory was split between two subsidiaries, was facing 51.5 percent losses in South Delhi and 63.1 percent losses in East Delhi.

“The company inherited a deteriorated and overloaded network, massive power theft, weak billing and collection systems, inaccurate consumer records, and an aging, largely untrained workforce,” Dwijadas Basak, CEO of Tata Power-DDL, told me. There were over 100,000 unresolved billing complaints, 20,000 pending connection applications, and frequent supply failures, which had severely eroded consumer trust, he added. Both Tata Power-DDL and BSES devised sweeping reforms and human resource development initiatives. The companies followed their own paths over the years, but ultimately implemented similar changes, with similar results.

Delhi’s Electricity System Overhaul

Fixing Delhi’s grid was a journey that involved all stakeholders, including customers, city authorities, and utility employees at all levels. The utilities revamped their organizational structures, diminishing the power of junior staff and creating separate teams to focus on specific tasks. Long-term employees of the erstwhile Delhi Vidyut Board received training from the up-and-comers at the new companies.

On the technical side, both companies installed digital control systems that let them monitor and operate the grid from a central location. Known as SCADA, or supervisory control and data acquisition, the systems offered a bird’s-eye view of the infrastructure, including the status of equipment, voltage, current, power flow, and switch positions, with updates in seconds. This helped the companies identify areas of high loss and theft and make faster decisions based on accurate information.

The SCADA (supervisory control and data acquisition) system at Balaji Estate in Delhi’s Kalkaji neighborhood serves as the nerve center of BSES Rajdhani Power’s distribution network in South and West Delhi. It enables real-time visibility, remote control of grid operations, fault identification and isolation, and load management. BSES Rajdhani Power

The utilities also replaced aging transformers and circuit breakers and created extensive maintenance plans for equipment. In 2002, 11 percent of the transformers in the region were failing at any given time. That rate is less than 1 percent today, according to Tata Power-DDL. Crucially, the companies installed hundreds of capacitor banks, including some mobile ones, to supply reactive power at strategic locations. This improvement reduced the total current flowing in the distribution lines and helped stabilize the voltage. They also installed voltage regulators at points in the system where voltage tends to drop.

To reduce theft, the companies replaced bare distribution wires with insulated lines—a single cable for three phases—which made it harder to tap into the lines. The cables also reduced outages because they’re better at preventing ground faults, which can occur when, say, a tree branch falls on the line.

Workers received better sensors and tools to do their jobs safely and accurately. For instance, they were given helmet-mounted voltage sensors, which are safer than handheld ones, and thermal scanning tools to detect hidden defects in the insulation of high-voltage equipment that could otherwise have led to catastrophic failures.

To reduce inaccurate billing and meter tampering, the companies replaced the old electromechanical meters with digital ones that are read with handheld devices. In some locations, radio-frequency-based group metering systems were installed by Tata Power-DDL to consolidate multiple customers’ meters into one. The data is then wirelessly transmitted to a central database, eliminating the need for individual meter readings. The companies are now trying smart meters, which give consumers more control over their electricity bills and give utilities remote control of some equipment (with the customer’s consent).

To encourage people to pay their bills, the utilities installed kiosks that are available 24 hours a day, and they created a web-based payment system and mobile app. Incentives for early bill payment and community-engagement programs also helped. Assistance from Delhi’s law enforcement considerably reduced electricity theft.

Tata Power-DDL hired women living in the 223 slums it serves in the northern parts of the city to knock on neighbors’ doors and remind them to pay their power bills. These payment collectors [left and center], known as abhas, were photographed while speaking with a customer [right] in the Sanjay Basti area of New Delhi in 2017. Prashanth Vishwanathan/Bloomberg/Getty Images

In areas where theft was particularly rampant and losses were as high as 83 percent, according to Tata Power-DDL, the companies took a different strategy. These pockets of Delhi were predominantly occupied by low-income families. Tata Power-DDL, and later BSES, worked to improve the water supply for these residents and provide educational opportunities, such as instruction in reading and writing in Hindi as well as financial literacy. These efforts focused on the women, who were at home more, and paid them to collect electricity payments from their neighbors. Bill payment rates from these areas are now on par with those of other parts of Delhi.

In recent years, some customers have been installing rooftop solar panels to take advantage of subsidies and incentives. This trend can reduce electricity losses further because the energy generated at the customer end reduces current in the distribution lines. Customers are also installing more LED lights and energy-efficient appliances, reducing the load in the system.

BSES is using AI to help detect theft. The algorithms analyze consumption patterns in pockets where losses are higher than they should be. The company is also using AI to forecast demand, fine-tune operational efficiency, and provide chatbots for customers.

Quality of Life Improves in Delhi

Life in Delhi is better than it was 25 years ago. I’m not worried that the power may go out and force me to reschedule my activities. My uninterrupted Wi-Fi gives me peace of mind, and my heating and cooling systems keep me and my family comfortable. I rarely need to use our old inverter and battery.

The sharp rise of e-rickshaws in Delhi has increased demand on the power grid. Sajjad Hussain/AFP/Getty Images

The number of businesses in Delhi has increased substantially, in part because of the access to quality power. People can confidently buy products that depend on electricity. In fact, the city’s peak electricity demand has tripled since 2002 due to the increase in population, commercial activity, and use of electrical gadgets.

And then there’s the benefits to the planet. One unit of electricity that isn’t frittered away is one less unit that must be generated, not to mention the reductions in carbon emissions.

Still, there’s work to do. Some areas of Delhi continue to have high losses, driven partly by the illegal charging of e-rickshaws. Elsewhere in India, the states of Himachal Pradesh, Madhya Pradesh, Maharashtra, and Telangana still experience losses of about 17 to 23 percent despite the sweeping Electricity Act, 2003. There are many reasons for the ongoing losses: long distribution lines to remote villages, less digitization, and inefficiencies in billing and collection of payments.

These regions, and others around the world, can learn from Delhi’s grid comeback. Recently, power losses have increased substantially in countries such as Argentina, Greece, Jamaica, and Morocco, according to the World Bank, and some of the causes are similar to those that Delhi faced back in 2002.

Meanwhile, Australia, most countries in North America and Europe, and a few countries in Asia and Africa experience low electricity losses as they invest regularly in their distribution infrastructure and the ethical enforcement of rules. In China, for example, losses have gradually been cut in half, from 7.1 to 3.4 percent. In Latvia, losses plummeted from 25 to 5.8 percent.

What’s important is a comprehensive approach. Technologies like smart metering, AI, and analytics certainly help, but equally important is that people in the field are trained and take responsibility for their jobs, and that laws are enforced and payments collected.

“Sustainable loss reduction cannot happen through technology alone,” Abhishek Ranjan, CEO of BSES Rajdhani Power told me. “Technology is an important enabler, but long-term success comes from combining it with disciplined execution, operational accountability, and strong consumer engagement.”

This article was updated on 30 September, 2026 with clarification about the full name Tata Power-DDL.

My job is to translate
dry and unrelenting code
into a user interface of surpassing beauty.
With my mouse, I roll one pixel after another
up the vast anthill of the internet.

My dream is to translate
the visions of the holy ones
into a communication protocol
of universal wonderment.
I want to launch shreds of light into the air
to fall like a layer of diamonds
on the endless mountains of the Web.

Don’t imagine these dreams are limited
by the LANs of the software lab.
Between here and the ultimate
unlimited interface of my aspirations
lives a dazzling darkness,
wide as the universe and thin as a hair.

Even before France approved legislation banning social media for children under 15 last January, 13-year-old Benjamin was already wondering what life without social media would look like. “If we want to play football, we won’t be able to organize it. What will we do? Send letters instead?” he joked in an interview for Le Monde.

His reaction captured the central challenge behind the growing wave of youth social media bans: Removing access is one thing; understanding what those platforms mean in children’s lives is another.

Within weeks of Australia’s similar ban, the country’s eSafety Commissioner reported that platforms had restricted access to 4.7 million under-16 accounts. Two months later, though, one in five Australian teenagers under 16 was still using TikTok and Snapchat, according to a parental-control data company. But even if all children’s social media accounts were to disappear, do such bans actually make children safer online?

Governments are moving ahead without answering that question as they follow Australia’s lead. Indonesia’s child-safety framework, which took effect in March, bars children under 16 from holding accounts on “high-risk” platforms. The U.K. government has announced plans to ban social media for under-16s, add default overnight social media curfews for 16- and 17-year-olds, and extend child-safety rules to cover risky AI features. And on 17 September, the European Commission proposed the EU KIDS Act, which would bar children under 13 from social media, set 15 as the EU-wide minimum age for opening an account independently, and require platforms to show that their services are age appropriate and safe by design.

But based on my experience working on Child Online Protection initiatives with the International Telecommunication Union (ITU) across Southeast Asia and the Pacific, I know the bans don’t address the real problems. Instead, we should be paying more attention to the systems that generate harm in the first place—namely, recommender algorithms, engagement-maximizing design, opaque moderation, and extractive data practices.

Account removals are not the same as online child safety

My experience working on protecting children’s online safety has taught me three main lessons:

First, the public institutions responsible for child online protection often lack the staff, budget, or technical capacity to enforce complex online safety policies.

Indonesia is illustrative. A 2026 UNICEF evaluation found capacity constraints among service providers, long-term funding uncertainty, and a need for specialized personnel. At the local level, some staff lacked digital skills, while budget constraints left some areas reliant on external support.

Second, many children, and often their parents, lack the digital literacy and critical thinking skills needed to navigate online risks safely. My policy research on child online protection in Indonesia, published earlier this year in Digital Society, found substantial gaps that account removals cannot repair: Many children lacked guidance on navigating the internet safely, and large numbers did not know how to report harmful experiences.

And third, the platforms have limited independent oversight as they identify underage users, design age-verification systems, and report their own compliance. In Indonesia, platforms themselves are responsible for carrying out age verification, while the Ministry of Communication and Digital Affairs oversees compliance. TikTok’s appeals process for users flagged as underage, for instance, can require a government-issued ID and selfies, which is a problem because it involves collecting the additional personal data on an ID card, beyond that needed to confirm age. Will government regulators ensure that TikTok handles that data responsibly?

The privacy paradox of proving age

Every age-based ban creates an engineering problem: How can a platform reliably determine that a user is old enough, without intruding on other information? Governments and companies may use identity documents, parental authorization, app-store checks, or facial age estimation. Each approach has trade-offs among accuracy, privacy, accessibility, and resistance to circumvention.

There are also technical issues. One tool, facial age estimation, draws on enormous databases but it is probabilistic, not exact, because people vary so much. It’s also been shown to misclassify both children and adults.

The challenge should not merely be to “verify age.” It should be to prove that someone is above a threshold, without disclosing their identity, birth date, or other information third parties might use to create a marketing profile. The European Commission’s age-verification blueprint challenges companies to verify ages without collecting all that additional information.

Privacy-preserving technologies offer promising ways to achieve this. Zero-Knowledge Proofs (ZKPs) can confirm that someone meets an age threshold without revealing their identity or exact date of birth. W3C Verifiable Credentials are cryptographically verifiable digital claims that can disclose only the information needed, such as “over 16.” And device-based age signals can allow a phone or app store to share an age range without revealing a user’s exact birth date. But these methods still require rigorous security testing, common standards, independent oversight, and clear limits on data retention. Otherwise, poorly designed child-safety policies risk creating permanent identity infrastructures in which businesses, not people, control personal data.

Where connection goes when a platform closes

Blocking access to a platform redirects some young people, but not always where expected. Early anecdotal reports in Australia pointed to teenagers migrating to smaller, less-regulated platforms like Yope, a pattern the Cato Institute flagged as a “whack-a-mole” problem for regulators. But industry data collected two months later found no broad-based shift of that kind, aside from a small uptick in WhatsApp use. Many teens simply found a way to stay on the banned platforms.

This points to a deeper gap in current society: the erosion of youth “third places“ physical spaces where young people have room to socialize and build identity outside home and school. As those spaces have diminished, commercial communications platforms have absorbed that role.

For many teenagers, social media workarounds are merely inconvenient. But for isolated, marginalized, disabled, or LGBTQ+ youth who depend on online communities for support that’s otherwise unavailable, displacement can mean losing certain kinds of belonging, or having to move to a platform with even weaker oversight.

How to design safer online systems for children

If blanket social media bans don’t work, then what will? The platforms have created many of the conditions that governments are now trying to contain: engagement-optimized recommenders, intrusive data practices, weak safeguards against unwanted contact, and features such as infinite scroll, autoplay, streaks, and persistent notifications.

These design patterns increasingly face regulatory scrutiny, including what’s required under the European Union’s Digital Services Act. A 2026 study from the 5Rights Foundation that tracked children’s device use minute by minute found that the user interfaces shape children’s attention, sleep, and well-being in real time.

A more durable response would regulate those interfaces directly, treating children as legitimate users whose privacy, agency, and well-being are required protections, not afterthoughts. That means designing for safety from the outset. One example would be for children’s apps to have high-privacy defaults, such as private accounts and location sharing switched off for minors. They could also have recommender systems that explain the main factors shaping a feed and give young users more control over personalization. The European Commission has published age-appropriate interaction guidelines that limit unsolicited contact and prevent minors from being added to groups without consent. Rules could also prohibit engagement-maximizing features that demand users’ attention, such as autoplay, infinite scroll, usage streaks, read receipts, and push notifications, by disabling or limiting them by default.

Governments should define measurable outcomes and fund independent evaluation, platforms should give researchers meaningful data access, and engineers should audit age-assurance systems for bias and data leakage. Schools, parents, and children themselves need a seat in designing the technology that’s designed to protect children.

If policymakers still decide to remove an infrastructure for youth connection, they should offer something better in return. Social media bans may reduce some forms of exposure to harmful content and may be justified for particular ages, services, or risks. But they are just one tool, not a comprehensive substitute for safer design, accountable platforms, digital literacy, institutional capacity, and noncommercial digital “third places”—moderated communities, creative spaces, and public-interest platforms designed for youth participation rather than profit.

The first wave of social media restrictions isn’t enough to keep children safe. Governments are still measuring what’s easiest to count, while neglecting harder-to-measure outcomes such as children’s access to safe third places and meaningful social connection, both online and offline. Until governments can show evidence that harm has actually declined, they will keep mistaking account removal for safety.

In Guadalajara, Mexico, many high schools have motivated teachers and talented students with an interest in science, technology, engineering, and mathematics, but they lack access to advanced tools such as robotics laboratories. The resources shortfall limits the students’ opportunities for hands-on learning on cutting-edge applications.

A team from ITESO, Universidad Jesuita de Guadalajara, is working to change that. Through the EPICS in IEEE initiative, a multidisciplinary group of 15 engineering students, faculty advisors, and IEEE Guadalajara Section volunteers developed RoboMeshA. The portable, self-contained educational platform brings robotics and AI experiences into classrooms.

EPICS is administered by IEEE Educational Activities and funded by the IEEE Robotics and Automation Society.

A mobile laboratory

Rather than requiring a school to build a dedicated computer lab or install complex software, RoboMeshA operates as an all-in-one mobile learning network.

“RoboMeshA brings robotics and AI to students who don’t have access to specialized facilities or preinstalled software,” says team member Fernando Vidal Luna, an IEEE student member and a mechatronics engineering major at ITESO.

Students connect directly to the platform from a user-friendly web browser. They can interact with the robot manually or use its control modes to watch it move and detect and avoid obstacles.

“The project combines mechanical design, embedded systems, control engineering, computer vision, and AI into a single robotic system that functions as a mobile learning laboratory,” says faculty advisor Jorge A. Lizarraga.

The team says young students are interested in technology, programming, and robotics but don’t have an opportunity to work with systems that combine mechanics, electronics, software, and control.

“RoboMeshA allows students to see how all these disciplines work together in a tangible and understandable way,” says team member José S. González, who also is studying mechatronics engineering.

The team has built two units and is developing a modular coupling framework to expand the system’s capabilities for research and classroom demonstrations. The structured system design approach connects independent software components while minimizing internal dependencies, enabling four RobotMeshA robots to operate together.

Overcoming design challenges

The team faced significant hurdles while designing the project.

“One key challenge involved the robot’s structural design,” Luna says. “It wasn’t only about making a chassis where all the components fit and the design had sufficient stability, rigidity, and weight distribution. It was also about ensuring that the electronics were protected while still being accessible for maintenance, testing, and modifications.”

“It was also challenging to design a platform that could be used by students with different levels of experience,” González adds.

“When students realize the technology they develop can inspire others and improve lives, engineering becomes far more meaningful.” —Luis Fernando Luque-Vega

The team partnered with the CETI Colomos and Prepa ITESO high schools to validate the platform in classroom settings.

“We wanted the first interactions with the robot to be simple and intuitive,” González says, “such that students could simply power the robot, connect to its network, and begin interacting with it, rather than having to deal with software installation, extensive configuration, or troubleshooting.”

Engineering with social impact

Many of the students who participated were from ITESO’s applied professional projects program. The experience offered them practical training in project management, system integration, and user-centered design.

The team also presented a research paper and a project poster in May at the Engineering Congress of the Jesuit University System.

“Seeing a design move from a digital model to a physical system was invaluable,” González says. “Working with students from different backgrounds taught us to listen to end users and design for their actual needs.”

Project lead Luis Fernando Luque-Vega, an IEEE member, says he’d like the venture to serve as a blueprint for engineering education.

“I hope RoboMeshA is adopted by schools, universities, and IEEE student branches across Mexico and internationally as a model for integrating technical innovation with community engagement,” Luque-Vega says.

By pairing engineering talent with community service, initiatives such as EPICS in IEEE demonstrate how targeted support can turn academic concepts into real-world solutions.

“When students realize the technology they develop can inspire others and improve lives, engineering becomes far more meaningful,” Luque-Vega says.

For more information on service-learning opportunities, visit the EPICS website.

I’ve seen two total solar eclipses and have been duly impressed by what happens as the moon casts its shadow on Earth. But recently I’ve become even more intrigued by a similar phenomenon that doesn’t involve the sun or the moon—something called an asteroid occultation.

That’s what happens when an asteroid orbits around the solar system and blocks the light of a distant star you’re viewing from Earth. Like the moon during a solar eclipse, the asteroid casts a predictable moving shadow on a swath of Earth’s surface—a small silhouette in the dim light bathing us from that one star.

When such a fortuitous alignment occurs, amateur astronomers can discern things about the asteroid that professionals can’t readily measure, even with their giant telescopes on high mountains. That’s because amateurs are nimble: They can be in just the right place at just the right time to measure an asteroid’s fleeting shadow, which could be just a few hundred meters wide and traveling at tens of kilometers per second. With enough observers, they can collectively map that shadow, revealing the asteroid’s shape.

Even folks on a limited budget can do this, because the size of an asteroid you can measure doesn’t scale with the size of your telescope. If the occulted star is relatively bright, you don’t need much of a telescope at all.

How Do You Catch an Asteroid Occultation?

My own efforts along these lines have been with a modest 5.1-inch-aperture (130-millimeter) Newtonian telescope that sells for about US $300. I attach it to a small equatorial mount ($150) that can track the stars by virtue of some added stepper motors driven by an open-source telescope controller called OnStep. (You could save yourself the time, trouble, and expense of all that DIY hacking by purchasing a motorized mount for as little as $300.)

A flasher provides a calibrated time base for light-curve measurements. It relies on a GPS module [top] to provide a high-accuracy pulse once per second, is gated by an Arduino nano [middle] to prevent flashes occurring at the moment of occultation, and is then passed to a LED [bottom].James Provost

I bought an inexpensive astronomy color camera on Amazon for $260 to take images at the video rates required to capture the rapid changes during an occultation. I chose this camera because it has a relatively large sensor, Sony’s IMX585, which provides a large field of view. A monochrome camera would be better for asteroid occultations, but the monochrome version of this camera is harder to come by and more expensive. If you’re looking for a cheaper option, the monochrome ToupTek G3M662M (about $200) would be a good choice, although its sensor is smaller.

Knowing where and when to catch an occultation in your area is of course critical and can be calculated using free PC software found on the International Occultation Timing Association (IOTA) website. If you plan to contribute your observations to IOTA to increase the body of scientific knowledge about asteroids, you will need to calibrate the timing of your images. You can’t just depend on the time stamps your computer adds to the video frames, which can be way off.

For time calibration, many practitioners use a flasher: a red LED driven from the pulse-per-second signal from a GPS receiver. Asteroid observers use such a pulsing LED positioned in front of their telescopes to calibrate the timing of the images they take. With some effort, it’s possible to reduce the uncertainty to just a handful of milliseconds.

The flasher I built uses a GPS module that I had on hand. But I’d recommend you purchase a different one that accepts an external active antenna. HiLetgo’s NEO-7M $12 module might be a good choice—but don’t forget to remove its antenna-coupling capacitor (marked as C2 on the circuit board) if you do attach an active external antenna to it.

How Do You Make a Telescope Flasher?

You can’t let the flasher just blink away every second, though, because its light might stomp on the very signal you’re trying to detect. So alongside the GPS module, my flasher also contains an Arduino Nano, plus two transistors, three resistors, and a switch. I wired these components together so as to drive the LED directly from the pulse-per-second signal coming from the GPS. The signal passes through a transistor controlled by the Arduino so that the flashes can be started and stopped at prescribed times. I can then program the flasher to produce calibrating pulses near the start and end of each recording session, while suppressing the flashing around the occultation itself.

Over time, an asteroid such as Duccio will pass in front of multiple stars [below]. Each time it does, it will block the light from a star [above] for a time that depends on its width along the line of transit. By combining multiple light curves, it is possible to map the shape of the asteroid.James Provost

So far, I’ve managed to record four occultations that have occurred within easy driving distance of my home in North Carolina. The first was quite short, by an asteroid a mere 4 kilometers wide. The star involved was rather dim, so I really had to squint at my laptop screen to see the star momentarily blink out. The star in my second occultation was brighter, and the dimming much longer, so no squinting was required. My third observation tested the limits of my little telescope with a very dim target star, requiring quite long exposures per video frame (about a third of a second). Thankfully, the asteroid was a big one (120 km wide), so the occultation lasted a few seconds, and I could discern it.

The asteroid I targeted last, named Duccio, is about a dozen kilometers wide and orbits in the main asteroid belt between Mars and Jupiter. Its shadow, moving at a clip of some 24 km per second, took about a half second to pass over me. The star this asteroid blocked was bright enough for me to record the event very distinctly at 24 frames per second, providing excellent time resolution.

Asteroid occultations offer a wonderful natural experiment. And unlike a solar eclipse, observable events probably take place near you multiple times each month. So with a little knowledge and the right gear, you can observe them. You just have to wait for the stars—and the asteroids—to align.

This white paper gives distribution engineers and utility teams a practical overview of the substation exit, the short and high-consequence section where station capacity divides into individual feeders. It explains how covered, spacer-supported overhead construction can reduce common contact-driven faults while leaving room for future circuits.

What you will learn about:

• Why a fault near the substation exposes more customers than one farther along the feeder, and why the first spans out of the station carry so much reliability weight.
• How covered conductors and spacer-cable systems differ from bare overhead conductors, and why covered conductor is not treated as touch-safe insulation.
• Which engineering factors shape a sound exit design, including conductor rating, protection coordination, grounding, and structural loading.
• How overhead spacer cable compares with conventional bare overhead and underground shielded cable across footprint, reliability, and lifecycle cost.
• How to move a project from concept to commissioning using staged design gates and a clear performance specification.

This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free!

If you have kids, they’re probably back in school right now after the summer break. Mine are too.

My kids range from 9 to 20 years old, and lately I’ve been thinking a lot about what their future careers are going to look like.

Some schools are embracing AI in the classroom. Others are banning it completely. I’m not sure either side has figured out the right answer yet—I’m not sure any of us have.

What I do know is that AI is already changing how engineers, and nearly every other knowledge worker, does their job. So I’ve been thinking: What skills do I actually want my kids to develop for an AI-augmented workforce?

1. Learn to write

The more I use AI, the more convinced I am that it’s a multiplier, not a substitute.

Give a great researcher AI tools and they can accelerate their research. Give an experienced software engineer the same tools and they can build at an incredible rate. But put those tools in the hands of someone without foundational knowledge and you often get something else you’re used to seeing way too much of: slop.

We’ve all seen the vibe-coded applications that barely work. Our inboxes are filled with emails that sound suspiciously identical. Teachers are reading papers that sound like they were all written by the same person. That “person” has the last name GPT.

When everyone has access to the same tools, having an actual voice becomes a differentiator.

Ironically, strong writing skills might be MORE important because of AI, not less. Text is still the primary way we communicate with these models, so being able to clearly articulate what you want improves what you get back.

But more importantly, writing teaches you to develop ideas of your own.

AI can help you express your opinion. It shouldn’t manufacture one for you.

2. Learn to speak

Like written communication, speaking skills are now at a premium—but for a different reason.

We live in a strange moment. We’re more digitally connected than ever, while often feeling increasingly isolated from one another. At the same time, we’re seeing renewed interest in conferences, meetups, communities, and in-person experiences.

When human interaction feels scarce, communication becomes more valuable.

So learn how to explain an idea in front of a room. Learn how to disagree without being disagreeable. Learn how to tell a story. Learn how to listen. Learn how to persuade someone.

There’s a timeless book, originally published 90 years ago, that teaches these interpersonal skills, and it’s probably more valuable for engineers than another white paper on how neural networks work: How to Win Friends and Influence People.

An AI can generate a presentation for you, but it can’t convincingly deliver it to a skeptical audience. That takes emotional intelligence.

3. Learn to be bored

This might be the hardest one.

We have engineered boredom almost completely out of our lives. There’s always a podcast to listen to, a notification to check, a video to watch, a feed to scroll, or now an AI to talk to.

Go for a walk without headphones. Eat without looking at a phone. Sit in the car without immediately reaching for something to fill the silence, and let your mind wander.

Because boredom isn’t wasted time. It’s a breeding ground for original ideas.

The danger I worry about isn’t that AI becomes too intelligent, but that we become too willing to outsource the uncomfortable parts of thinking.

The students going back to school today will enter a workforce filled with technology that I couldn’t have imagined when I was their age. I have no idea what the dominant AI model will be by then or even what interacting with a computer will look like.

That’s exactly why I don’t want to optimize their education around today’s tools. I want them to learn the skills that will outlive the tools.

Write clearly. Speak confidently. Think independently.

And every once in a while, embrace boredom.

—Brian

AI Slop Is Changing How Engineers Review Code

Software engineers have entered a new era of code review. The strategies they’re now testing will determine whether AI can actually provide code that’s faster and more reliable when you factor in the review process. If it can, what does that mean for the entry-level engineers who are still learning to code on their own?

Read more here.

U.S. Tech Firms Change Strategies to Hire International Talent

In response to a barrage of actions by the U.S. federal government to limit legal immigration, tech companies are adapting their search for top talent. IEEE Spectrum looked into the responses to proposed changes, like higher fees for H-1B visa applications.

Read more here.

What It Takes to Be an Adaptable Engineer

As AI changes the job market and day-to-day work of engineers, young professionals are often told they need to be adaptable. But what does adaptability actually look like in practice? The skill has different definitions depending on who you ask, but with the right mindset and support from leadership, adaptability can help keep you afloat.

Read more here.

Many youngsters fascinated by exploring outer space dream of becoming an astronaut, but few do. One who had the right stuff is Pedro Duque, who was Spain’s first astronaut. The aeronautics engineer flew aboard the space shuttle Discovery and the International Space Station.

After retiring as an astronaut, he headed Spain’s Ministry of Science, Innovation, and Universities. Today he is the chairman of HispaSat, a Spanish satellite company.

Pedro Duque

Employer

HispaSat in Madrid

Title

President and chairman of the board

Member grade

Honorary member

Alma mater

The Polytechnic University of Madrid

Nearly 30 years after his first mission, Duque is still Spain’s most famous astronaut. Three public schools have been named after him, and he has received numerous awards. This year, the IEEE Board of Directors made him an IEEE honorary member for “contributions to space exploration, leadership in collaborative science and technology programs, and serving as a role model for younger generations.”

He was unable to attend the 24 April ceremony in New York City, but he expressed his gratitude in recorded acceptance remarks in his award presentation shown during the event.

“We engineers of all specialties recognize the leadership of your institute—the largest and most important engineering society in the world,” he says in the video. “What an honor it is to belong now to an organization whose purpose it is to foster technological innovation and excellence for the benefit of humanity.”

Inspired by Apollo 11

Duque says he knew he wanted to be an engineer from a young age. It’s not surprising that he became interested in aeronautics, as his father was an air traffic controller who explained to him how airplanes worked.

In 1969, when he was 6 years old, he was inspired to become an astronaut after watching the Apollo 11 moon landing.

“I didn’t know anyone who wasn’t attracted to space exploration,” after the moon landing, he says, laughing. “It was presented in such an epic manner, with declarations about its impact on society. The landing made us aware that humanity was exploring new places, and I was keen on knowing more about them.”

His dream of becoming an astronaut was unrealistic at the time, he says, because the country had no space program. Spain was ruled by Francisco Franco, who spent little to no money on scientific innovation, Duque says.

That changed after Franco died in 1975. The country transitioned to a democratic constitutional monarchy and began participating in research and development programs, particularly with the European Space Agency (ESA).

Astronaut duties

Duque earned an aeronautical engineering degree in 1986 from the aeronautical and space engineering school at the Polytechnic University of Madrid.

His first job was as an engineer in the flight dynamics group at GMV, a space and technology company based in Madrid. He was a member of the orbit determination group and worked at ESA’s European Space Operations Centre, in Darmstadt, Germany. He helped develop algorithms, orbit computational software, and computer models.

He was also a member of the space agency’s flight control team for the European Remote Sensing-1 satellite, launched in 1991, and the European Retrievable Carrier, launched in 1992 on the space shuttle Atlantis’s STS-46 mission.

In 1990, ESA recruited candidates for its astronaut program—which Duque says rarely happens. He and several colleagues applied.

“Why not?” he says. “What we thought we wanted to be when we were little was now possible.”

After a considerable selection process, Duque was chosen in 1992 to join the agency’s Astronaut Corps. He trained at the European Astronaut Centre and at the Russian Cosmonaut Training Center (now known as the Gagarin Research and Test Cosmonaut Training Center).

In 1994, he served as the prime crew interface coordinator on the ESA–Russian EuroMir space station. He managed communication between the astronauts on the Mir station and the European scientists to help ensure orbital experiments and operations ran smoothly.

NASA selected Duque to be an alternate payload specialist on the ground for space shuttle Columbia’s STS-78 mission in 1996. In that role, he was trained to operate and manage scientific experiments, equipment, and cargo during a crewed mission. He also supported the flight from the ground as a crew interface coordinator.

His first flight into space was in 1998 as a mission specialist representing the ESA on the space shuttle Discovery’s STS-95 mission. He managed in-orbit tasks, experiments, spacewalks, and equipment operations.

He served as a flight engineer in 2003 onboard the Soyuz TMA-3 Cervantes, a joint mission between Russia and Spain to the International Space Station. He operated the station systems, managed daily maintenance tasks, and performed scientific experiments assisted by the mission commander.

Memories of flying with John Glenn

The years of training and preparation to become an astronaut were rigorous, he says, but the experience was fulfilling. Even though he spent only about 10 days on each mission, he says, “they were very rewarding times because all the preparation paid off, and we got the results we needed.”

Some of his favorite memories were viewing Earth during the day and night for the first time, and watching it pass from one phase to the other. Another was seeing the moon flattened to almost a sliver during the few seconds before it set. Experiencing microgravity was a thrill as well, he says.

“I also cherish the companionship of the people who worked alongside me in space and on the ground,” he adds.

One was John H. Glenn, the first American to orbit the Earth. Duque flew with Glenn on Discovery. They talked about the selection criteria for astronauts. Glenn told him those on board the Apollo and Gemini missions were chosen because they were test pilots, who were thought to be most likely to handle problems or failures effectively and proactively.

“The technology in today’s missions is advanced enough that astronauts won’t be needed to handle probable catastrophic equipment failures,” Duque says. “Instead, they will perform experiments and update or fix the devices used in space capsules. But flights to the moon and even Mars will use new types of spacecraft that might not necessarily work as planned, so those astronauts will have to know how to fix them and possibly solve critical problems on their own.

“Also, future astronauts will have to live with others in a confined space for months, and not everyone can do that.”

The price of sudden fame

As Spain’s first astronaut, Duque became a celebrity. Among his honors are Russia’s Order of Friendship and Spain’s Great Cross of Aeronautical Merit and Princess of Asturias Award. He also received NASA’s Space Flight Medal, which is given to an astronaut who flies aboard a U.S. space mission.

Learning to navigate sudden fame and being treated like a celebrity was challenging, Duque says. Like many engineers, he was accustomed to working behind the scenes and out of the public eye.

“Being famous, both in the profession and the public, was quite difficult in the beginning,” he says. “Being a celebrity doesn’t come easily to me, but after so many years, somehow I learned how to deal with it.”

People might assume that an astronaut’s leadership skills come effortlessly, Duque says, but that’s not always the case.

“Everybody gives so much importance to your opinion, and sometimes I was surprised by that,” he says. “Being an astronaut, you tend to have a certain kind of leadership style because it’s what you have done for years without knowing it.”

Minister of science and other leading roles

His leadership style has served him well. After he retired from the ESA in 2018, he was appointed as Spain’s minister of science, a role he held until 2021. He oversaw the government’s policies on scientific research, technological development, innovation, space programs, and higher education. During his term, Spain committed to contributing US $800 million (€701 million) between 2020 and 2026 to the ESA—which at the time was the largest overall investment in the agency’s history.

After retiring as an astronaut, Pedro Duque headed Spain’s Ministry of Science, Innovation, and Universities. Today, the IEEE honorary member is the chairman of HispaSat, a Spanish satellite company.Isaac Buj/AP

In 2022, he joined Destinus Spain, a European defense manufacturer that develops technologies for aircraft propulsion and auxiliary systems. He advised its strategic committee.

The following year, Spain’s government appointed him president and chairman of the HispaSat board. Headquartered in Madrid, the satellite operator provides broadcasting and broadband services for Europe, North Africa, and the Americas. The public-private partnership was in its origin a joint initiative between the Spanish government and private telecom companies.

The IEEE honor was a surprise

Duque says he was surprised to learn that IEEE added him to its membership ranks. He was aware that a colleague had nominated him, but he deemed it unlikely the nomination would be supported.

“When I started looking into who its members were, I wondered why they selected me,” he says. “Obviously, electrical engineering is not my branch, but it could have been, because growing up, I was just as interested in telecommunications as I was in aeronautics.

“Most engineers know IEEE for its standards and the work it does in achieving consensus in standards development.”

After discussing with several colleagues who were IEEE members about the significance of the IEEE honorary membership—which is bestowed for a significant achievement and impact on society— Duque feels the award is a significant honor.

“I’m still in the early phase of figuring out how I can contribute to IEEE,” he says, “and what I can do for the many hundreds of thousands of members, all whom have impressive qualities.”

Throughout her career, roboticist Barbara Mazzolai has turned to nature for inspiration. Now she wants to ensure the technology she builds gives back to the environment, too.

After starting her career as a biologist, a chance opportunity saw Mazzolai switch streams to engineering and become an early pioneer of bioinspired robotics. Building on her knowledge of biology’s ability to solve a diverse set of problems, she has developed robots based on octopuses, plant roots, and even seeds. “I’ve always been fascinated by living organisms, [and] by the extraordinary variety of solutions in nature, selected by the evolutionary process,” she says.

Barbara Mazzolai

Employer:

Italian Institute of Technology

Occupation:

Associate director for robotics; director of the Bioinspired Soft Robotics Laboratory

Education:

Master’s degree in biology, University of Pisa; master’s degree in eco-management and audit schemes, Scuola Superiore Sant’Anna; Ph.D. in microsystems engineering, University of Rome Tor Vergata

But Mazzolai, now the associate director for robotics at the Italian Institute of Technology, in Genoa, also believes engineering needs to reckon with its own impact on the natural world. That’s why she is advocating for a new field of research she calls “sustainability robotics.”

In a manifesto published in Nature Machine Intelligence in July, she and her collaborators outline a vision for a new approach to designing robots that’s meant to improve the relationship between nature, humanity, and technology.

“We need to reduce the footprint of our technology,” she says. “It’s really about thinking in a different way to open new possibilities for robotics [and] for society.” In this new mode of thinking, Mazzolai considers sustainability a core component of the design.

A child of nature

Mazzolai traces her fascination with the living world back to her childhood growing up on Italy’s Tuscan coast, close to the port city Livorno. Her father was a public-health inspector and a professional mycologist, and the family spent a lot of time exploring forests and learning about the local fungi and plants.

After toying with the prospect of pursuing art, her other major passion, Mazzolai ultimately decided to enroll at the University of Pisa in 1987 to study biology. She was particularly drawn to marine biology, but shortly before graduating with a master’s degree in 1995, she secured a research position at the Italian National Research Council’s Institute of Biophysics studying the cycles of heavy metals like mercury through both living and nonliving parts of the environment.

This involved collecting and analyzing samples from water, soil, vegetables, and even humans to understand the impact these metals have on health and the environment. She balanced this work with studying environmental management at the Scuola Superiore Sant’Anna, in Pisa, graduating with a master’s degree in 1998.

During that time, however, she learned that the university was recruiting biologists to help design new devices for environmental monitoring. She applied for and got the job in 1999 and began working as a research assistant under renowned bioroboticist Paolo Dario, first developing sensors and then robots meant to monitor air, water, and soil.

Even before entering a doctoral program, Mazzolai was promoted to assistant professor in 2004 and shortly afterward made her first foray into bioinspired robotics. In collaboration with colleagues at Sant’Anna, she helped design a soft robot inspired by the octopus. “We proposed it as a paradigm for launching this idea of soft robotics: demonstrating that [robots] can be soft, but at the same time apply strong force to the environment, like the animal does,” she says.

Back to school

In 2007 Mazzolai enrolled in a Ph.D. in microsystems engineering at Tor Vergata University of Rome, which she balanced with her role at Sant’Anna. She was already relying heavily on microfabrication techniques to develop sensors for her robots, and she was keen to push that part of the field forward.

While robots frequently feature sensors designed for perception, such as tactile or proprioceptive sensors, these systems typically focus on understanding the robot’s position in its environment, she says. “But there are few robots that integrate physical or chemical sensors to really understand the environment they move in,” she adds.

“I’ve always been fascinated by living organisms, [and] by the extraordinary variety of solutions in nature.”

Mazzolai was appointed as a team leader at the Center for Micro-BioRobotics of the Italian Institute of Technology in 2009, where she continued her work on the emerging field of bioinspired robotics. Two years later, she completed her Ph.D. and was promoted to director of the center.

Planting the seeds

Around this time Mazzolai says she became interested in using plants as a model for new kinds of robots, expanding bioinspiration beyond just animals. In particular, she was captivated by the ability of roots to efficiently explore the underground environment, and she imagined machines with the same deftness could have applications in both environmental modeling and precision agriculture.

While many bioinspired robots mimic animals, plants also serve as a muse for Mazzolai. This tendril-like bot can coil around other structures like a vine. Italian Institute of Technology

When she first proposed the idea, colleagues were somewhat skeptical of robots based on seemingly static organisms. But in reality, she says, plants move nonstop through a process known as indeterminate growth. “They really grow for their entire life,” she says. “They adapt their morphology, their behavior to the external environment; they repair, they sense, they communicate.”

Trying to mimic a system that operates on such different principles to conventional robotics required some serious thinking, however. Mazzolai says that working in bioinspired robotics sometimes requires you to have “two separate brains”—one of a biologist and one of an engineer.

The process often involves deep study of the target organism to learn the underlying principles that shape how it operates before trying to engineer a robot capable of mimicking them. “It’s not a copy of natural organisms,” says Mazzolai, because a living organism is both difficult to replicate and has different goals.

In the case of plant roots, what makes them so efficient at exploring the soil is that they reduce friction by growing only at the very fine tip of the structure, while the thicker base of the root remains static. This significantly reduces the amount of energy required to push through the earth compared to that of a more conventional drill, which must push the entire structure from above.

To realize this principle in a robot, her team developed a miniaturized 3D printer that sits at the machine’s tip and feeds thermoplastic filament through a heated nozzle to build a snakelike body behind it. This allows the robot to push through the soil efficiently. The tip also contains sensors that allow it to avoid obstacles and detect nearby nutrients or water.

Making robotics sustainable

After spending so much of her career borrowing from nature, Mazzolai is now eager to return the favor. Many modern technologies, including plastics and car batteries, have been developed with little thought about how they will affect the environment at the end of their life cycles, she says.

She wants to ensure that robotics doesn’t follow the same path. This is the inspiration for what she and collaborators now call sustainability robotics. The approach has three central pillars: ensuring that robots have minimal impact on the environment; that they’re available to people from across the world and all socioeconomic backgrounds; and that they’re “symbiotic,” providing benefits to both humans and nature.

More concretely, Mazzolai would like to incorporate the concept of a life cycle into the design of robots, so that at the end of their useful life these machines can be reused, recycled, or even biodegraded.

While that might sound ambitious, she’s confident that all the ingredients to make it a reality are in place. And it’s a vision that she is certain will inspire future roboticists. “There are younger people who want to really work in this field because this is the future, their future,” she says. Facing the threat of ongoing environmental damage, “they want to develop something that can help.”

This article is brought to you by CoolIT, an Ecolab Company.

Beyond 250 kW a server rack can no longer be cooled by a hybrid approach of liquid and air. At this density a 70/30 liquid-air split leaves 75 kW of air load. The air cooling system needed to move it brings cost and complexity few operators will accept. The answer is near-total heat capture. Liquid takes effectively all the heat, air falls below 1 percent of the load, allowing the server to run fanless.

CoolIT builds these loops today from modular coldplate blocks proven across six generations of fanless designs. Processor thermal design power (TDP) keeps climbing generation over generation. This rising heat load is now cascading into the memory, networking, storage, and power components that once ran comfortably on air.

The heat escaped the chip

For years the story stayed simple. Cool the processor and let air handle the rest. That balance has shifted. As TDP climbs, heat spreads outward from the processor and cascades into the components around it. Memory, networking, storage, and power now run hot enough to demand liquid of their own. Engineers designing the next generation of AI servers face a board where heat capture rises with every launch.

Beyond 250 kW per rack, air cooling becomes the bottleneck. Near-total liquid heat capture enables fanless AI server designs built for the next generation of computing.

New parts, new rules

Unlike processors, which are cooled as flat rectangular packages, these peripherals come in a wide range of shapes, sizes, and mounting requirements, each with its own thermal limits. Some run cooler than the processor case temperature, others run hotter, which leaves them sensitive to a design tuned only for CPUs and GPUs. Operators need purpose-built solutions here, matched to the part rather than stretched across the board.

CoolIT engineers meet this with a deep toolkit. Conductive plates, vapor chambers, heat pipes, and thermal transfer plates move heat from components closer to the liquid path. Riding coldplates enable pluggable components. Each solution stays true to the component it serves.

CoolIT Customer Showcase: How GWDG Cools HPC & AI Systems with CoolIT’s Direct Liquid Cooling CoolIT

One loop, one server

Cooling the parts is one challenge. Uniting them is the real work. Full heat capture means folding every one of these solutions into a single server loop that distributes coolant effectively and remains easy to install. Connection reliability, coolant routing, and the time it takes to assemble the loop at rack integration determine whether a design thrives in production or stalls on the bench. CoolIT builds these loops from proven modular blocks, so operators gain performance and deployment speed within the same solution.

Density forces the decision

Rack power continues to climb toward 1 MW, and the case for liquid grows stronger at every step. A 70/30 split of liquid to air holds comfortably at lower density. Past roughly 250 kW it stops working. The 30 percent left to air becomes a 75 kW load inside a single rack, and moving that much heat demands a parallel air system whose cost and footprint few operators will accept. Adding density only widens the gap.

As rack power continues to climb toward 1 MW, CoolIT’s modeling places full heat capture as the standard server design for flagship rack-scale products through 2028.

The simpler, more efficient answer is to capture the heat in liquid and drop air to less than 1 percent of the total load. True 100 percent remains almost impossible to reach in the strictest sense, so the honest and achievable target is near-total capture. That distinction matters to engineers who value precision, and the direction stays clear either way. Full heat capture moves from a premium option to a mainstream requirement as density rises, and CoolIT’s modeling places it as the standard server design for flagship rack-scale products through 2028.

CoolIT delivers it

CoolIT scales heat capture all the way to 100 percent using modular coldplate building blocks proven across six generations of fanless server designs. Engineering teams are already working on designs for the maximum density racks coming next. As the cascade spreads and racks grow denser, near-total heat capture becomes the design that keeps AI running.

Talk to CoolIT about building a server loop engineered for total heat capture.

Shortwave radios offer a way to connect one location on Earth to practically anywhere else with minimal infrastructure. But these radios come with some drawbacks—a significant one being that, unlike satellite communications, their transmission rates for digital data are typically measured in just hundreds of bits per second.

Peter Bloom

Peter Bloom is the founder of Rhizomatica, a nonprofit that works with remote, indigenous, and off-grid communities around the world to build shortwave and cellular-communication infrastructure.

Peter Bloom is the founder of Rhizomatica, a Philadelphia-based nonprofit that has open-sourced a digital shortwave-radio set called the High-frequency Emergency and Rural Multimedia Exchange System, or HERMES. The set operates in the high-frequency (HF) band from 3 to 30 megahertz, as does Mercury, its digital modem. Rhizomatica staff travel around the globe to remote locations in countries like Bangladesh, Brazil, and Ecuador. Wherever they go, they use HERMES to help connect locals to the rest of the world.

Bloom spoke with IEEE Spectrum about how HERMES brings better data rates and encryption to shortwave radios.

How does HERMES connect remote locations?

Peter Bloom: We use the ionosphere as our satellite—or mirror—which helps us move information, voice, and data over really long distances. We’re using small radios that put out about 20 watts of power, and we can pretty reliably do 400- to 600-kilometer links between two radios. We’re talking about places that are not easy to reach, where it’s not simple to put terrestrial infrastructure.

What can HERMES send that a basic voice radio can’t?

Bloom: HERMES is a software stack—it’s a set of different programs that all work together in order to be able to send data over HF. HERMES allows you to send pretty much any file. Depending on what the file is, whether it’s a photo or an email or a voice memo, it just sends it as a file. It’s like a data pipeline over HF.

Why does sending files and data matter more than just voice?

Bloom: In emergency situations, people send their latitude and longitude over HF to say, “Hey, I’m here at this place.” People need to be able to send data over HF if there’s a manifest, a parts list, telemedicine—here’s what we have, here’s what we need. Instead of trying to read that out over the air, it’s much easier to just send the file. Same with a photo—if we need evidence that an area was logged illegally, we can just have someone send that over HF, rather than spending days getting down the river to get the photo where it needs to go.

Why did you build in encryption that amateur-radio regulations in many countries don’t allow?

Bloom: Encryption [regulations] for ham radio operators are different in each country. So it’s all optional—you turn it on, you turn it off. The reason we built the encryption is that some of the partners we work with are in very sensitive areas and don’t want to be sending out information that can be easily captured and used against them.

How has HERMES made an impact?

Bloom: We’ve been working with artisanal fishers in Bangladesh on a pilot project. There’s 10 or 11 boats that have HERMES systems on them. Pretty soon after we installed those, one of the boats had a mechanical issue in the Bay of Bengal, 100 or 200 kilometers offshore. They were able to send their GPS position and an SOS that they were having trouble. They were able to coordinate the rescue of the crew and the boat. So that was a really cool moment of HERMES in action that we’re super happy about.

This article appears in the October 2026 print issue as “Peter Bloom.”

This White Paper gives data engineers and architects a practical overview of how parallel partitioned reads, write-path optimization, and cloud-native bulk loading reduce large-table replication times, and why replication speed has become a business concern as data volumes grow.

Download this free whitepaper now!

Transitioning from years of working in a senior technical or executive role to a leadership position is one of the most challenging phases of a STEM career. It requires moving away from making decisions on your own to mentoring others, making strategic decisions for the organization, and collaborating with coworkers from different generations.

The shift in mindset is known as “legacy leadership,” a philosophy whereby success is no longer measured by personal achievements but by how effectively a senior leader empowers others.

To help seasoned professionals and senior experts navigate the transition, the inaugural IEEE International Leadership Conference (ILC) will provide attendees practical advice on cultivating collaborations and guiding emerging talent.

“Early in our STEM careers, we measure success by what we have achieved,” says Jeewika Ranaweera, cochair of the IEEE ILC program committee. “Later, we should measure success by what we enable, how many people we mentor, how much knowledge we transfer, and how many doors we open for the next generation.”

The ILC is scheduled for 3 and 4 October in Budapest. Registration is open.

Letting go of the “expert” identity

For decades, seasoned technologists have been valued primarily for their technical expertise. Shifting from that identity can feel uncomfortable, but legacy leadership requires measuring success by different standards. They include a leader’s influence on the staff, the ability to uphold the company’s mission, and empowering others to lead and succeed.

The transition requires leaders to find purpose outside their corporate titles, shifting their focus to the long-term sustainability of their teams, their organization, and the broader technical community.

“A professional legacy is not measured only by what we have achieved but also by sharing our knowledge, experience, and opportunities with others,” says Sudhanshu S. Jamuar, another program committee cochair. “The real transition from expert to a legacy builder happens when we stop asking, ‘What more can I accomplish myself?’ and start asking, ‘How many others can I enable to accomplish more?’”

Neeli Rashmi Prasad, IEEE ILC treasurer and sponsorship cochair, adds that the transition transforms a lifetime of technical work into a platform for future innovation.

“The true value of experience is not in how much knowledge we accumulate but in how intentionally we transfer it,” Prasad says. “When we partner with, mentor, and create space for others to lead, our expertise becomes a foundation for progress far beyond our own careers.”

Moving beyond advice-giving

True knowledge transfer requires coaching rather than advising, and mastering the art of active listening. To build deep trust with early-career colleagues and ensure a seamless transfer of leadership to the next generation, senior leaders must avoid offering unsolicited or outdated anecdotes. Effective mentorship is a collaborative loop in which senior experts contribute hard-won industry wisdom while remaining curious and learning from the fresh, cutting-edge perspectives of talented coworkers.

“Knowledge transfer is most powerful when it is a two-way bridge: experience flows from one generation to the next, while new ideas and perspectives flow back,” says Sudeendra Koushik, president of the IEEE Technology and Engineering Management Society and an ILC keynote speaker. “This approach transforms mentorship from simply passing on information into one where the next generation can question, experiment, innovate, and ultimately surpass what came before them.”

Seasoned professionals should encourage independent, disruptive thinkers rather than carbon copies of themselves, Prasad says.

“Legacy leadership is about building continuity,” she says. “We should not simply prepare the next generation to follow the paths we created; we should give them the confidence, knowledge, and networks to challenge those paths, create new ones, and take technology further than we imagined.”

Designing your next chapter

Leadership does not need to stop when one’s job ends; it can evolve. Seasoned, retired professionals can continue contributing meaningful service through pathways that align with their personal passions. They include:

• Advisory boards: steering corporate, technical, or community organizations.
• Civic engagement: applying engineering methodologies to solve community challenges.
• Volunteerism: mentoring the next generation of grassroots innovators through professional networks including IEEE.

Those pathways offer experienced professionals an opportunity to redefine success, not in terms of position, authority, or personal achievement but in terms of sustained impact.

“Retirement from a job should never mean retirement from purpose,” Koushik says. “Our experience becomes even more valuable when we use it in service of the profession, society, and the generation that follows.”

At the same time, the collaborative continuum relies on a proactive younger generation. Emerging leaders need to take responsibility for building their professional connections, Prasad says, advising: “Be bold, stay curious, and build your network early!”

A space for continuity

The conference is designed to combine a drive to cultivate emerging innovators with a deep reservoir of industry stewardship and strategic perspective. Rather than a single classroom session, the conference will feature a dedicated career-readiness and mentorship track where attendees can explore practical frameworks for building strategic professional networks, connecting with peer advocates, and establishing reciprocal knowledge-sharing opportunities across generations.

By participating, seasoned professionals can ensure their decades of expertise continue to yield dividends for generations to come.

“Our professional legacy is not the technology we build, the titles we earn, or the awards we receive,” Ranaweera says. “It is the knowledge we share, the lives we influence, and the future we help others create.”

You can view the agenda, read speaker biographies, and secure your seat at the ILC online.

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 GorrMathWorks

Then, thinking about the logistical modeling that you see in any industry, there is always planned downtime that you want to mitigate; but you also end up having unplanned downtime. So, looking back at that historical data of when you’ve had those moments where maybe it took a bit longer than expected to manufacture something, you can take a look at all of that data and use AI to try to identify the proximate cause or to see something that might jump out even in the processing and design phases. We think of AI oftentimes as a predictive tool, or as a robot doing something, but a lot of times you get a lot of insight from the data through AI.

What are the benefits of using AI for chip design?

Gorr: Historically, we’ve seen a lot of physics-based modeling, which is a very intensive process. We want to do a reduced order model, where instead of solving such a computationally expensive and extensive model, we can do something a little cheaper. You could create a surrogate model, so to speak, of that physics-based model, use the data, and then do your parameter sweeps, your optimizations, your Monte Carlo simulations using the surrogate model. That takes a lot less time computationally than solving the physics-based equations directly. So, we’re seeing that benefit in many ways, including the efficiency and economy that are the results of iterating quickly on the experiments and the simulations that will really help in the design.

So it’s like having a digital twin in a sense?

Gorr: Exactly. That’s pretty much what people are doing, where you have the physical system model and the experimental data. Then, in conjunction, you have this other model that you could tweak and tune and try different parameters and experiments that let sweep through all of those different situations and come up with a better design in the end.

So, it’s going to be more efficient and, as you said, cheaper?

Gorr: Yeah, definitely. Especially in the experimentation and design phases, where you’re trying different things. That’s obviously going to yield dramatic cost savings if you’re actually manufacturing and producing [the chips]. You want to simulate, test, experiment as much as possible without making something using the actual process engineering.

We’ve talked about the benefits. How about the drawbacks?

Gorr: The [AI-based experimental models] tend to not be as accurate as physics-based models. Of course, that’s why you do many simulations and parameter sweeps. But that’s also the benefit of having that digital twin, where you can keep that in mind—it’s not going to be as accurate as that precise model that we’ve developed over the years.

Both chip design and manufacturing are system intensive; you have to consider every little part. And that can be really challenging. It’s a case where you might have models to predict something and different parts of it, but you still need to bring it all together.

One of the other things to think about too is that you need the data to build the models. You have to incorporate data from all sorts of different sensors and different sorts of teams, and so that heightens the challenge.

How can engineers use AI to better prepare and extract insights from hardware or sensor data?

Gorr: We always think about using AI to predict something or do some robot task, but you can use AI to come up with patterns and pick out things you might not have noticed before on your own. People will use AI when they have high-frequency data coming from many different sensors, and a lot of times it’s useful to explore the frequency domain and things like data synchronization or resampling. Those can be really challenging if you’re not sure where to start.

One of the things I would say is, use the tools that are available. There’s a vast community of people working on these things, and you can find lots of examples [of applications and techniques] on GitHub or MATLAB Central, where people have shared nice examples, even little apps they’ve created. I think many of us are buried in data and just not sure what to do with it, so definitely take advantage of what’s already out there in the community. You can explore and see what makes sense to you, and bring in that balance of domain knowledge and the insight you get from the tools and AI.

What should engineers and designers consider when using AI for chip design?

Gorr: Think through what problems you’re trying to solve or what insights you might hope to find, and try to be clear about that. Consider all of the different components, and document and test each of those different parts. Consider all of the people involved, and explain and hand off in a way that is sensible for the whole team.

How do you think AI will affect chip designers’ jobs?

Gorr: It’s going to free up a lot of human capital for more advanced tasks. We can use AI to reduce waste, to optimize the materials, to optimize the design, but then you still have that human involved whenever it comes to decision-making. I think it’s a great example of people and technology working hand in hand. It’s also an industry where all people involved—even on the manufacturing floor—need to have some level of understanding of what’s happening, so this is a great industry for advancing AI because of how we test things and how we think about them before we put them on the chip.

How do you envision the future of AI and chip design?

Gorr: It’s very much dependent on that human element—involving people in the process and having that interpretable model. We can do many things with the mathematical minutiae of modeling, but it comes down to how people are using it, how everybody in the process is understanding and applying it. Communication and involvement of people of all skill levels in the process are going to be really important. We’re going to see less of those superprecise predictions and more transparency of information, sharing, and that digital twin—not only using AI but also using our human knowledge and all of the work that many people have done over the years.

Quantum computing is a devilishly complex technology, with many technical hurdles impacting its development. Of these challenges two critical issues stand out: miniaturization and qubit quality.

IBM has adopted the superconducting qubit road map of reaching a 1,121-qubit processor by 2023, leading to the expectation that 1,000 qubits with today’s qubit form factor is feasible. However, current approaches will require very large chips (50 millimeters on a side, or larger) at the scale of small wafers, or the use of chiplets on multichip modules. While this approach will work, the aim is to attain a better path toward scalability.

Now researchers at MIT have been able to both reduce the size of the qubits and done so in a way that reduces the interference that occurs between neighboring qubits. The MIT researchers have increased the number of superconducting qubits that can be added onto a device by a factor of 100.

“We are addressing both qubit miniaturization and quality,” said William Oliver, the director for the Center for Quantum Engineering at MIT. “Unlike conventional transistor scaling, where only the number really matters, for qubits, large numbers are not sufficient, they must also be high-performance. Sacrificing performance for qubit number is not a useful trade in quantum computing. They must go hand in hand.”

The key to this big increase in qubit density and reduction of interference comes down to the use of two-dimensional materials, in particular the 2D insulator hexagonal boron nitride (hBN). The MIT researchers demonstrated that a few atomic monolayers of hBN can be stacked to form the insulator in the capacitors of a superconducting qubit.

Just like other capacitors, the capacitors in these superconducting circuits take the form of a sandwich in which an insulator material is sandwiched between two metal plates. The big difference for these capacitors is that the superconducting circuits can operate only at extremely low temperatures—less than 0.02 degrees above absolute zero (-273.15 °C).

Superconducting qubits are measured at temperatures as low as 20 millikelvin in a dilution refrigerator.Nathan Fiske/MIT

In that environment, insulating materials that are available for the job, such as PE-CVD silicon oxide or silicon nitride, have quite a few defects that are too lossy for quantum computing applications. To get around these material shortcomings, most superconducting circuits use what are called coplanar capacitors. In these capacitors, the plates are positioned laterally to one another, rather than on top of one another.

As a result, the intrinsic silicon substrate below the plates and to a smaller degree the vacuum above the plates serve as the capacitor dielectric. Intrinsic silicon is chemically pure and therefore has few defects, and the large size dilutes the electric field at the plate interfaces, all of which leads to a low-loss capacitor. The lateral size of each plate in this open-face design ends up being quite large (typically 100 by 100 micrometers) in order to achieve the required capacitance.

In an effort to move away from the large lateral configuration, the MIT researchers embarked on a search for an insulator that has very few defects and is compatible with superconducting capacitor plates.

“We chose to study hBN because it is the most widely used insulator in 2D material research due to its cleanliness and chemical inertness,” said colead author Joel Wang, a research scientist in the Engineering Quantum Systems group of the MIT Research Laboratory for Electronics.

On either side of the hBN, the MIT researchers used the 2D superconducting material, niobium diselenide. One of the trickiest aspects of fabricating the capacitors was working with the niobium diselenide, which oxidizes in seconds when exposed to air, according to Wang. This necessitates that the assembly of the capacitor occur in a glove box filled with argon gas.

While this would seemingly complicate the scaling up of the production of these capacitors, Wang doesn’t regard this as a limiting factor.

“What determines the quality factor of the capacitor are the two interfaces between the two materials,” said Wang. “Once the sandwich is made, the two interfaces are “sealed” and we don’t see any noticeable degradation over time when exposed to the atmosphere.”

This lack of degradation is because around 90 percent of the electric field is contained within the sandwich structure, so the oxidation of the outer surface of the niobium diselenide does not play a significant role anymore. This ultimately makes the capacitor footprint much smaller, and it accounts for the reduction in cross talk between the neighboring qubits.

“The main challenge for scaling up the fabrication will be the wafer-scale growth of hBN and 2D superconductors like [niobium diselenide], and how one can do wafer-scale stacking of these films,” added Wang.

Wang believes that this research has shown 2D hBN to be a good insulator candidate for superconducting qubits. He says that the groundwork the MIT team has done will serve as a road map for using other hybrid 2D materials to build superconducting circuits.