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!

You want to become a senior developer. A CTO, maybe. Start your own company, perhaps. Or maybe you just want to land your first role in tech.

You will not get there from raw engineering skill alone.

There’s a skill that’s quietly essential to technical leadership and yet consistently overlooked: public speaking.

If you’re anything like I used to be, you’re already listing reasons not to. “I got into this to code, not to give presentations.” “I don’t want to lead.” “I’m too junior to speak about anything.” No, no, and no again. There’s a ceiling on the return from technical skill alone.

I was terrified of public speaking for the first three years of my career. I wanted to hide behind code, and for the most part it worked. I did my job and did it well.

Then I joined a startup where hiding wasn’t an option. The whole company was five people. I was one of two developers. I had to form opinions on our technical direction and defend them, and the CTO told me directly that I needed to speak up more.

A few things happened once I did. I took more pride in my work. I said some cringe-worthy stuff, lived through the mini-anxiety attacks, and got better. To my own disbelief, I’m now an engineering manager whose job is largely speaking to groups of developers and leading presentations, online and in person.

Here’s why this is worth your time:

Leadership. Communicating ideas clearly, influencing decisions, and aligning your team are core leadership functions, and they matter more the further you climb.

Visibility. Speaking lets you show your expertise, build a reputation, and connect with people who open doors to better roles.

Durability. As automation absorbs more routine technical work, skills rooted in human interaction and judgment are far harder to replace.

The good news is you can build this deliberately, in low-stakes steps.

Record yourself. Use a screen-recording tool to walk through your work, explain a concept, or narrate your code. You can edit, re-record, and over-think it as much as you want. That’s the point. It gets you comfortable on camera before the stakes are real.

Volunteer for demos. Next time you ship a feature or fix a bug, ask your manager for a short time slot to walk the team through it. No format for that on your team? Suggest a monthly lunch-and-learn and kick it off with a 15-minute lightning talk on something you know.

Start small—really small. If your anxiety is spiking, don’t jump into the deep end. In your next meeting, ask one question. Write it down beforehand if you have to. Then be the first to break the awkward silence when someone else asks one. Developers are a famously quiet bunch, so it doesn’t take much to stand out.

The further you grow, the more you’ll be expected to hold opinions and voice them publicly. So start now. Record yourself, ask questions, get uncomfortable, and notice that it gets easier every time you do it.

—Brian

War Taught this Ukrainian Entrepreneur the Value of Resilience

Salome Mikadze-Struk built her tech company Movadex as an undergraduate student at the height of the COVID-19 pandemic—then kept it running during the outbreak of war in her native Ukraine. Now, she’s channeling what she learned into mentoring tech founders and speaking about the importance of resilience as AI upends the software industry.

Read more here.

IEEE Rolls Out Large Language Models Virtual Training Course

LLMs are now part of many engineers’ daily workflow, and the demand for technical expertise in implementing and securing the models is rising. But to build tools that work consistently, developers must have a strong understanding of the core principles that govern how the models work. IEEE is now offering a five-course program to teach how to use LLMs effectively, starting with the fundamental engineering behind the technology.

Read more here.

Make an Origami Circuit Board

Two researchers at the City University of Hong Kong developed a method to make a circuit trace by simply bending a piece of paperlike material. With the right ingredients—isopropanol and liquid metal—you can make your own origami circuit board. The researchers also created a toolkit, called LiqMetCraft, with software tools and instructions to make it easy for beginners, whether in papercraft or electronics.

Read more here.

I started my professional journey as an engineer before moving into product strategy and innovation leadership roles for several global technology organizations. Over the years, I have served as a mentor for a variety of programs including Products That Count’s strategic product management, Women in Product mentorship initiatives, and Alchemist accelerator programs.

In 2024 and 2025 I led Walmart’s Women in Product mentorship program. I was responsible for designing and implementing the programs, including managing participant registration, matching mentors with mentees, and establishing clear standards for how they would interact.

Yet for much of my own early career, I never really had a mentor.

As an individual contributor engineer, I was focused on solving problems, delivering results, and figuring things out independently. I was hesitant to ask for help for fear of being judged for what I didn’t know.

Part of that was also temperament. I am naturally introverted.

That mindset rewarded me well. It made me self-reliant, resilient, and deeply driven. But it also had limits. Looking back, I now realize that believing I had to navigate everything alone was not always a strength. I sometimes wonder how many opportunities I missed simply because I never asked for help.

As I moved into product management and later strategy roles, I began collaborating with larger teams, departments, and organizations. The work itself became more cross-functional and people-centered. Over time, I started recognizing the value of mentorship, sponsorship, and collaborative growth in ways I had not appreciated earlier in my career.

I received valuable advice from different people at important moments throughout my career. Some helped me navigate conflict with more clarity. Others helped me communicate my contributions more effectively. And others gave me perspective on how to approach uncertainty, deal with organizational complexity, and avoid burnout.

But those moments were not the same as mentorship. They were valuable but infrequent interactions, not sustained relationships. No one consistently guided me through difficult decisions, advocated for me with decision-makers and senior leadership, or actively invested in my long-term growth.

My understanding of mentorship changed not as a mentee but as a mentor.

A leadership multiplier

Mentorship is often seen as an act of goodwill: admirable but optional. In reality, effective mentorship can be a competitive advantage for everyone involved.

For mentees, it can accelerate career growth, strengthen decision-making, and create access to opportunities that hard work alone does not always unlock.

Mentorship strengthens an individual’s leadership skills, empathy, and the ability to develop future talent.

For organizations, mentorship builds stronger leadership pipelines, more resilient teams, and healthier cultures of growth and trust.

By getting involved, I began to understand that meaningful mentorship is not simply occasional advice or career guidance. At its best, it is an active investment in another person’s growth. It includes advocacy, sponsorship, honest feedback, visibility, and sometimes helping people access opportunities they may not have reached on their own.

That is why mentorship should not be treated as kindness or incidental support. It is one of the most practical, hands-on, and personal forms of leadership.

Advocacy changes careers

Advice can help someone improve, but advocacy and sponsorship can change the direction of a career.

In many organizations, career growth depends not only on talent but also on access to honest feedback, influential networks, and sponsors willing to speak about someone’s potential when opportunities are discussed. Access also includes introductions to people who can recognize the value and impact of a person’s work.

Sometimes the difference between advice and true sponsorship is illustrated more clearly through stories rather than through leadership frameworks. In The Devil Wears Prada and its sequel Nigel’s relationship with Andy evolves far beyond workplace advice. In the 2006 movie, he helps her grow professionally, pushes her to envision a more expansive future, and guides her through an unfamiliar industry.

In the sequel—set two decades later—his investment in her success continues even though their careers diverge. When Andy (played by Anne Hathaway) is laid off during a difficult job market and struggles to find meaningful opportunities, Nigel (Stanley Tucci) quietly recommends her for a role at his firm. She is arguably overqualified for the position, but Nigel recognizes that it is the right opportunity at the right time. His recommendation helps her transition from a career in the news back into working in fashion. She can regain stability and ultimately rebuild career momentum. Over time, the opportunity becomes a turning point, reshaping her professional trajectory.

What makes it meaningful is not just the recommendation itself. It is that Nigel continued paying attention to her career growth over the years, believed in her potential, and supported her when she needed it.

That is what meaningful mentorship and sponsorship often look like in practice: not surface-level guidance but genuine investment in someone’s long-term growth and success.

When mentors provide that kind of support intentionally, mentorship becomes more than guidance. It becomes a competitive advantage—not only for the mentee but also for the mentor and the organization.

Why inclusive mentorship matters

Mentorship matters because talent alone does not shape a career. Access is important. In many workplaces, advancement depends not only on capability but on guidance, sponsorship, visibility, and informal knowledge about upcoming job opportunities.

Not everyone has equal access to such advantages. Research from McKinsey and Lean In suggests that women often receive less mentorship, sponsorship, and career support than men do, even in organizations that publicly emphasize inclusion and leadership development.

When mentorship is left entirely to informal networks, opportunity often becomes uneven. And when it’s left to chance, opportunity also is uneven.

That’s why inclusive mentorship matters. It creates a more intentional way to support people who might otherwise be overlooked.

What great mentors require

“A mentor is someone who allows you to see the hope inside yourself,” Oprah Winfrey once said.

Great mentorship is not about having all the answers. It’s about showing up with intention. It means listening closely, being candid, and helping someone grow with more confidence and clarity.

The best mentors respect their mentees’ time. They come prepared and listen for what is needed rather than rushing to give advice. They are open about their successes and failures because honesty builds trust faster than polished stories do. Great mentors tailor their guidance to the individual and encourage growth while also creating accountability.

Above all, good mentors create a psychologically safe space. They make it easier for mentees to ask difficult questions, test or pitch ideas, and talk openly about issues without fear of being judged. Growth usually starts at that point.

Organizations have a role to play as well. If mentorship matters, the program should be visible and supported.

That can mean including it in stated expectations of leaders, creating ways to connect mentors and mentees, providing mentorship training, and recognizing outcomes that go beyond performance metrics.

It also can mean broadening the understanding of mentorship. Peer mentorship, cross-functional mentorship, and even cross-industry mentorship can play important roles.

The leadership gap many organizations ignore

Promoting mentorship should not involve forcing artificial relationships or turning an employee’s growth into a line on someone’s to-do list. Organizations ought to promote the idea that leaders should invest in others, helping to build stronger teams, more capable leaders, and more organizational resiliency.

At a minimum, organizations should ask mentors whether they helped their mentee grow in their career and whether the mentee became more confident, capable, or prepared as a result of the relationship. Did they help junior employees navigate the organization more effectively? What opportunities did they create or find to give the mentees more visibility? Did they help mentees develop communication, leadership, or decision-making skills?

Those questions might be hard to quantify, but they get close to the substance of leadership.

Legacy is built through people

People might remember the strategies a leader shaped, the products the leader created, or the financial targets that were hit. Such accomplishments matter, of course. But another part of leadership lasts longer. It lives in the coworkers whose careers were advanced because someone took the time to invest in them.

This article is brought to you by Melbourne Convention Bureau (MCB) supported by Business Events Australia.

As artificial intelligence accelerates global demand for compute, a parallel constraint is emerging with equal urgency: energy.

From hyperscale data centers to electrified industries, AI is driving a step change in electricity demand. This is not a future challenge, it is a present, system-level issue requiring coordinated action across energy, infrastructure, and engineering disciplines.

Around the world, the question is no longer whether AI will scale, but whether energy systems can scale with it.

Melbourne, Australia is moving beyond participation to become a globally connected leader helping define how these challenges are addressed.

A national challenge with global implications

Australia’s ambition to lead in artificial intelligence is sharpening focus on the infrastructure required to support it. Data centers are projected to account for up to 11 percent of the nation’s electricity consumption by 2035, placing increasing pressure on generation, transmission, and system reliability.

At the same time, insight from the IEEE Power and Energy Society (PES) highlights that meeting energy demand from AI and digital infrastructure is one of the most significant challenges facing engineers over the next decade.

The implications are clear. In addition to computing challenges, AI poses major energy systems challenges.

“As artificial intelligence continues to scale globally, the challenge is no longer just computational power, it is the energy systems required to support it” —Professor Thas (Ampalavanapillai) Nirmalathas, University of Melbourne

Why Melbourne is leading on the global stage

Victoria has developed one of the most advanced and integrated energy ecosystems in Australia and globally, spanning renewable generation, battery storage, grid modernization, and advanced materials.

What distinguishes Melbourne globally is how these capabilities are connected and applied at system scale.

The city brings together world class engineering research, a rapidly evolving clean energy sector, advanced digital infrastructure, and strong alignment between government, industry, and academia. This convergence is critical in the AI era, where energy, networks and computing systems must be designed together.

Victoria’s coordinated investment across these areas is positioning Melbourne not only as a national leader, but also as a reference point in the global energy system transformation.

Engineering the systems behind the AI economy

The challenge ahead is that generating more power won’t be enough, as engineers need to design systems that respond dynamically to new patterns of demand.

Three priorities are emerging globally:

• Aligning data center development with grid capacity and renewable supply
• Embedding flexibility through storage, demand response, and system optimization
• Balancing digital growth with decarbonization and long-term reliability

Addressing these priorities requires engineering expertise to be embedded earlier in planning ensuring energy systems, digital infrastructure, and policy are designed in parallel.

Melbourne’s strength lies in its ability to integrate this expertise across research, infrastructure, and real-world application.

Melbourne Connect is a University of Melbourne–led innovation precinct, supported by government and industry, designed to bring together research, business and policy to deliver real-world solutions.Atlantic Group

Research leadership shaping global solutions

At the centre of this capability is the University of Melbourne, where interdisciplinary research is advancing the systems required to support AI driven energy demand.

Through the Melbourne Energy Institute, for example, researchers are examining how energy technologies interact across entire systems from generation and networks through to end use.

“As artificial intelligence continues to scale globally, the challenge is no longer just computational power, it is the energy systems required to support it,” says Professor Thas (Ampalavanapillai) Nirmalathas, Dean of the Faculty of Engineering and Information Technology at the University of Melbourne.

“This is driving a new level of convergence between digital infrastructure and power systems engineering, where integrated, system level thinking is essential.”

Converging energy, networks and AI

Melbourne’s leadership is further strengthened by world-class interdisciplinary facilities such as the Smart Grid Lab in the Department of Electrical and Electronic Engineering, which enables real-time simulation of power systems, allowing engineers to test how solar, batteries, electric vehicles and other distributed resources interact within future grids. This supports the design of more resilient, efficient energy systems before they are deployed at scale.

Melbourne’s Smart Grid Lab in the Department of Electrical and Electronic Engineering enables real-time simulation of power systems. University of Melbourne

These capabilities will become increasingly important as data centers are integrated into the grid.

“AI driven demand is not only increasing computing requirements, but also placing new pressures on underlying energy systems,” says Glen Farivar, Senior Lecturer in Power Electronics at the University of Melbourne. “Designing these systems together is essential to achieving both performance and sustainability outcomes.”

This reflects a critical shift. Future infrastructure must be co designed across energy and digital systems, not developed in isolation.

A living ecosystem delivering real-world outcomes

Victoria’s broader energy ecosystem is translating these insights into practice.

Investment in renewable energy, grid infrastructure and storage is enabling higher levels of clean energy while maintaining reliability. Battery deployment is supporting the flexibility needed to manage both renewable variability and growing AI-driven demand.

At its core, Melbourne offers an integrated environment where research, industry and government collaborate to solve complex system challenges.

Why engineering collaboration matters

Solving the energy demands of the AI era cannot be achieved in isolation.

It requires engineers, researchers, utilities, and policymakers to work together earlier and more often. More than ever, engineering collaboration is a critical enabler of future energy systems.

Environments that bring together global expertise are becoming essential to how solutions are designed and delivered.

“Developing future energy systems that are affordable, sustainable, and resilient is a truly grand challenge” —Professor Pierluigi Mancarella, University of Melbourne

In this context, the University of Melbourne is co-leading, alongside Johns Hopkins University and Imperial College London, one of only seven Global Centres in Climate Change and Clean Energy. Through the Electric Power Innovation for a Carbon Free Society (EPICS) Centre, the University is also the Australian technical lead in advancing future energy systems, with EPICS the only Global Centre focused on future energy infrastructure.

The new Electric Power Innovation for a Carbon-Free Society (EPICS) Centre will address challenges in clean energy production and storage.University of Melbourne

“Developing future energy systems that are affordable, sustainable, and resilient is a truly grand challenge,” says Professor Pierluigi Mancarella, Chair Professor of Electrical Power Systems at the University of Melbourne and Australian director and international co-director of EPICS.

“As electricity grids are increasingly becoming the backbone of future energy systems, optimizing their interactions with other sectors, including AI and digitalization, and fostering interdisciplinary and international collaborations are essential,” he adds.

Global conferences as part of the solution

International conferences are increasingly recognized as critical platforms for advancing engineering solutions at scale. Melbourne’s ability to convene global expertise is central to its leadership.

In 2027, the city will host the IEEE PES Generation Transmission and Distribution (GTD) Asia 2027 Conference and Exposition, bringing together engineers, utilities, researchers and policymakers from across the world to address the challenges shaping the future of power systems.

IEEE PES GTD Asia 2027 Melbourne Committee (left to right): Dr. Mehdi Ghazavi Dozein (Monash University), Dr. Glen Farivar & Professor Pierluigi Mancarella (University of Melbourne) , Dr. Mohammad Mohammadi (Australian Energy Market Operator (AEMO)).MCB

“Melbourne offers a unique environment where world-class research, industry capability and policy leadership come together,” notes the IEEE PES GTD Asia 2027 Local Organising Committee, which includes Professor Pierluigi Mancarella and Dr. Glen Farivar from the University of Melbourne, as well as Dr. Mehdi Ghazavi Dozein of Monash University and Dr. Mohammad Mohammadi of the Australian Energy Market Operator.

“Hosting this event creates an opportunity to advance global collaboration on the systems and technologies required to deliver the energy transition at scale.”

These forums enable knowledge exchange, standards development and interdisciplinary collaboration, accelerating progress on complex engineering challenges.

Attendees view a digital installation at AIME 2025 at Melbourne Connect.MCB

Why Melbourne, and why now

As AI, electrification and digital infrastructure converge, the future of global energy systems will depend on the ability of engineers to collaborate and innovate at scale.

Melbourne provides a proven platform for that collaboration, combining world-class research, a rapidly evolving energy ecosystem, and the infrastructure to connect global expertise.

Melbourne Convention Bureau, IEEE Communications Society, and University of Melbourne Representatives.University of Melbourne

For IEEE members, hosting a conference in Melbourne is more than an event decision.

It is an opportunity to engage with a globally connected engineering community and contribute directly to solving one of the most significant challenges facing the profession today.

Through the support of the Melbourne Convention Bureau, professionals can access tailored, free support to bid for and deliver international conferences, bringing global expertise together in a city actively shaping the future of energy systems.

To explore hosting your next conference in Melbourne, contact the Melbourne Convention Bureau at info@melbournecb.com.

“The lowest-cost place to put AI will be in space, and that will be true within two years, maybe three at the latest,” SpaceX founder Elon Musk told the World Economic Forum in Davos this past January, as his company was preparing to go public.

Later that month, SpaceX filed an application with the Federal Communications Commission for an orbital data center constellation of up to 1 million satellites in low Earth orbit, 500 to 2,000 kilometers above Earth. And just three days before the IPO, he discussed some initial design specifications for a new AI-1 satellite data center in a video interview.

Musk is prone to hyperbole when it comes to timelines. Full self-driving cars by 2017. First human mission to Mars in 2024. Ten thousand Optimus humanoid robots by the end of 2025. Et cetera. For orbital data centers, which he says will be a cost-effective alternative to terrestrial data centers within three years, the math won’t make sense for several years, if ever.

Consider this: There are roughly 14,500 active satellites in orbit. Musk’s Starlink constellation accounts for about two thirds of those. Both the launch cadences and satellite-manufacturing capacity would have to scale up astronomically to deploy a million orbital data center satellites.

For context, there have been roughly 7,000 orbital launches in all of human history. To loft 1 million satellites into low Earth orbit on SpaceX’s Starship, which is designed to carry up to 60 satellites per vehicle, would require 16,666 launches exclusively devoted to satellite deployments. Considering that SpaceX launched a record 165 orbital missions in 2025, even at 10 times that cadence, it would take a decade. And how long would it take to build 1 million satellites, given Starlink’s current pace of around 4,000 per year and a generous tenfold increase in capacity? Short of a manufacturing revolution, try 25 years.

The reality is that the vision of massive constellations of orbital data centers is nowhere close to being realized.

As this month’s cover story, “Why Orbital Data Centers Are So Hard” by Andrew Cavalier of ABI Research, makes clear, the reality is that the vision of massive constellations of orbital data centers is nowhere close to being realized.

Dina Genkina, IEEE Spectrum’s computing and hardware editor, put the idea into perspective: “Starcloud (a startup that has applied to the FCC for an 88,000 orbital data center satellite constellation) sent one Nvidia H100 GPU in space so far. Their radiator was too weak to let the chip run at full power.”

As Cavalier shows, cooling even a single Nvidia H100 GPU in space is difficult: It draws 700 watts, which will require 1.4 square meters of radiator at 60 °C. A 40-kilowatt rack of servers will need an 80-m² radiator; a 100-megawatt data center will require 2,500 of those radiators. Some astronomers are understandably concerned that a million satellites with giant radiative wings would blot out the stars.

So if the economics doesn’t make sense, if the chips are at the mercy of the radiative ravages of space, and if humanity will lose its view of the stars, not to mention increasing the risk of triggering the Kessler syndrome, why are the hyperscalers hyping orbital data centers?

Genkina offered the obvious answer: sweet, sweet moolah. “The Elon Musk part of it is honestly genius because he’s got xAI building the data centers, SpaceX sending them to space, and Tesla building solar panels,” Genkina says. “It’s almost like he’s paying himself.”

Two Analyst’s Views of SpaceX’s Proposed AI1 Data Center Satellite

Michael Pierce, Principal at Technology Strategy Partners

Musk’s timelines are notoriously overly ambitious, but I think SpaceX’s orbital data centers might reach cost parity with terrestrial data centers in 5 to 10 years. The Starlink laser-link network already exists as the communication backbone for any SpaceX compute constellation, and that infrastructure is what no new entrant can replicate quickly. The chip-agnostic payload design probably reflects their disclosed difficulty securing AI silicon as much as any modularity philosophy. My view is that the only realistic near-term application is a SpaceX mega-constellation for inference. Training workloads likely cannot tolerate the synchronization and latency constraints of a distributed orbital system.

Our report analyzed the market from the integrator’s vantage point, but AI1 is what it looks like when one player has assembled all the necessary advantages simultaneously. The question is whether the terrestrial data center industrial base will degrade or improve on economics. I don’t have insight into SpaceX’s internal costs, as opposed to public pricing, on all their components, so it’s hard to say if they’ll completely dominate or not. Even if they are not cost competitive with terrestrial data centers for another 5 to 10 years, it may simply be faster to get new compute that just happens to be in space.

Matt Hasan, AI strategist and independent consultant

My initial view is that AI1 does not fundamentally change the rationale for space-based data centers as much as it changes the timeline and scale. The underlying drivers remain the same: escalating AI compute demand, growing power constraints on terrestrial grids, and the desire to colocate energy generation with computation.

What AI1 does signal is that the concept is beginning to move from theoretical discussion toward engineering and capital allocation decisions. The announcement adds credibility to the idea that hyperscale computing infrastructure may eventually expand beyond terrestrial constraints rather than simply competing for increasingly scarce grid capacity on Earth.

That said, significant economic and technical questions remain. Launch costs, maintenance, hardware replacement cycles, thermal management, latency-sensitive workloads, and overall system economics will ultimately determine whether space-based data centers become a mainstream extension of AI infrastructure or remain a niche capability for specialized applications. The key development is not that these questions have been resolved, but that major industry players now appear willing to invest resources toward answering them.

In the 1970s, American Fireworks, a family-run pyrotechnics company in Hudson, Ohio, used a “home run box” to offer quick and easy fireworks displays for the Cleveland Indians (now the Cleveland Guardians) baseball games.

The red wooden crate had metal silos to store the rockets. Each switch on the control panel allowed the operator to set off a different firing sequence. This setup instantly triggered the display whenever a Cleveland batter hit a home run. Before computerized firing systems became common, panels like this represented the state of the art. But they did not eliminate human error. On 15 September 2015, the technician in charge of the Indians’ pyrotechnics accidentally set off the fireworks when the opposing team hit a home run. The embarrassed technician was caught on camera holding his head in his hands.

This home run box and control panel [left] were used to launch fireworks during Cleveland Indians games. The rockets were housed in metal silos within the box.Left: Jahna Auerbach/Science History Institute; Right: American Fireworks

The Early History of Fireworks

Fireworks are one of the many Song Dynasty inventions that migrated from China through the Middle East and into Europe by way of trade routes. Around 200 B.C.E, the Chinese invented small firecrackers by simply tossing pieces of bamboo into a fire. The air inside the bamboo would expand and crack the wood, and the pop supposedly scared away evil spirits. After the invention of gunpowder—a mixture of sulfur, charcoal, and potassium nitrate—about a thousand years later, some clever person thought to pack the powder into the bamboo tubes and ignite them, launching the first fireworks—and the first rockets—into the sky.

John Bate’s popular 1634 book on fireworks described fire wheels [left] and a flying dragon [right], consisting of a dragon-shaped rocket that sped along a rope. SSPL/Getty Images

By the Renaissance, specialized schools for pyrotechnics had emerged across Italian city-states, and European craftsmen began creating large spectacles for royal occasions and religious celebrations. In 1634, John Bate published the four-volume series The Mysteries of Nature and Art, the second of which described how to create all manner of fireworks. Woodcut illustrations showed fire wheels (now called pinwheels or Catherine wheels), as well as the more ambitious flying dragon—a rocket shaped like a dragon that emitted sparks while speeding across a rope strung between two buildings.

During the 18th and 19th centuries, chemists and alchemists discovered new chemical compounds and isolated new elements that expanded the palette for fireworks. Adding barium nitrate produced green, for example, and strontium nitrate produced red. Chemists also mixed in metal particles to create sparkles.

The 1880s saw the introduction of the loud screech or whistle that precedes the exploding boom. Amédée Denisse, a graphic artist by trade and a fireworks hobbyist, discovered that a cardboard tube containing potassium picrate added that satisfying auditory effect to his fireworks display.

How Did Fireworks Become a 4th of July Tradition?

British colonists brought fireworks to the Americas. In 1608, Captain John Smith set them off to celebrate the founding of Jamestown, Virginia, the first permanent English settlement in what would become the United States. More than a century and a half later, while the Continental Congress was meeting in Philadelphia in July 1776, future U.S. president John Adams speculated in a letter to his wife that Independence Day would be celebrated “with pomp and parade, with shews, games, sports, guns, bells, bonfires and illuminations from one end of this continent to the other.”

Although Adams got the day wrong—he mistakenly thought the committee would complete the revisions to the Declaration of Independence by the 2nd of July—he was correct in foreseeing that Independence Day would be celebrated with lots and lots of fireworks. Just a year later, on 5 July 1777, the Pennsylvania Evening Post reported on the grand exhibition of fireworks the previous night, which began and concluded with 13 rockets representing the 13 colonies.

It’s safe to say that the United States is still obsessed with fireworks. According to the American Pyrotechnics Association, the country spends about US $3 billion on fireworks each year; it’s also the leading importer of fireworks. As the U.S. gears up to celebrate its 250th birthday this 4th of July, expect to see fireworks displays everywhere, from kids with sparklers running in backyards to ambitious professional displays for huge crowds.

Modern fireworks displays like the Macy’s 4th of July celebration in New York City are computer choreographed and controlled. Roy Rochlin/Getty Images

Fireworks today are an engineering marvel. State-of-the-art displays are computer controlled with precise digital timing, often tied to musical accompaniment. Designers can spend weeks choreographing complicated patterns and assigning launch times, shell types, and colors. The completed script is uploaded to an electronic firing system, which consists of the control panel and hundreds or thousands of firing modules that connect to the rockets. It can take days to set up the launch site for a large-scale display that lasts just minutes.

For example, last year more than 60 licensed pyrotechnicians worked for 12 days to arrange more than 80,000 shells for the Macy’s 4th of July Fireworks in New York City. Each of the firework shells measured up to 25 centimeters in diameter and weighed more than 13 kilograms—a far cry from their bamboo ancestors. More than 120 kilometers of wire connected the bundles of explosives to twelve computers. All that for a 25-minute display.

As much as I unabashedly love fireworks, they’re not for everyone and they do have a downside. The explosions can trigger PTSD for military veterans, and they can also upset animals. Every year, thousands of people are injured by mishandled or damaged fireworks. Known to set off wildfires, fireworks are often banned during droughts. Scientists who’ve studied the environmental impact of fireworks displays have noted their tendency to disperse airborne metallic particles and other harmful particulates.

A drone light show over Busan, South Korea, shows a member of the K-pop band BTS.Hwawon Ceci Lee/Anadolu/Getty Images

Perhaps to counter those drawbacks, or maybe it’s just the next technological evolution in aerial display, companies are now offering drone light shows. Fleets of hundreds or thousands of LED-toting drones can be programmed to hover in the air and fly in formation, forming logos and other designs that are more stable than exploding fireworks.

These exquisitely choreographed light shows are truly impressive. And yet I relish the full sensory experience of fireworks, including the booms, the smoke, and the smell. So whether you’re celebrating your country’s birth, Guy Fawkes Day, Saint Sylvester’s Night, New Year’s, Diwali, or simply cheering a home run from your favorite team, I hope you get to enjoy this millennia-old technological marvel.

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 July 2026 print issue as “Rooting for the Home Team.”

References

The American Pyrotechnics Association is a professional organization that encourages safety in design and use of all types of fireworks, provides industry support, and promotes responsible regulation.

Barry Sturman and David Garrioch’s 2023 article “Amateur Science and Innovation in Fireworks in Nineteenth-Century Europe,” in the journal Ambix, provides a detailed history of the development of fireworks. Kathy De Antonis’s 2010 article “Fireworks!” for a publication of the American Chemical Society explains the colors, shapes, and packaging of modern fireworks.

If you happen to find yourself in Philadelphia before the end of July, check out the Science History Institute’s exhibit Flash! Bang! Boom! A History of Fireworks, which is part of the U.S. celebrations around the semiquincentennial. The home run box shown in this article is part of the institute’s collections.

He was born into a storm, lightning split the summer sky, in a
village the world had not yet heard of.
The midwife called it a bad omen, his mother called it a sign. Your first
life began in a storm, under open sky.

One winter night you ran your hand along a cat’s back, and the
darkness cracked open with sparks.
Your mother warned the house could burn.
You were already chasing what you learned: Light would return.

Your second life came underwater, in the current deep. No light,
no air, the river pulling you under,
the surface closing above you without a sound, and
something in you refused to sink or sleep.

Your third life came at the dam.
The water rose. The wall held you in place.
One flash, you turned your body and rose back into air, and left
the weight of water without a trace.

Your fourth life came in stone and dark. Entombed for a
night in a mountain chapel,
visited by no one. Only silence and the memory of a spark. You called
it an awful experience and left it there, untold.

Your fifth life came in fever,
nine months cholera held you down,
until your father said: Survive, and choose your own ground. You rose.
Not from the prayer, but from the promise he made.

Your sixth life came in silence, and it stayed.
Every sound cut through you, a clock three rooms away,
a ringing that would not leave, a noise you learned to bear, until you
lived inside that noise and made a home in there.

Your seventh life burned on Fifth Avenue, not your body, but your work. Not a thief
of fire, but one who stayed with the blaze.
A modern Prometheus, your life’s work turned to ash,
“I must begin again,” you said, and turned to new ways.

Your eighth life came in the street.
No storm. No warning. A taxi struck without a sign. A
sudden impact: ribs breaking, breath gone.
No diagram this time. Only the body, slow to keep up.

The ninth life came on quiet wings.
That dove found you in the dark, and your spirit rose. She did
not move. A beam of light fell from above.
The life you would not return from, the one you loved.

Your mother thought you had nine lives, nine close
brushes with death.
Each close call, a lesson. A hand that would lead you out of the
darkness and into the dynamo of eternal light. The world profits
from the mystery of your mind,
Upon your imagination we stand.

Today, you probably asked a question of a large language model, or accepted a connection suggestion on LinkedIn, or watched a recommended video on YouTube, or took a different route to work based on a traffic prediction from Google Maps. In other words, you probably used artificial intelligence. But what you might not know is how much energy that interaction consumed or why.

AI requires processing massive amounts of data, which is usually done in large data centers populated by thousands of GPUs capable of executing up to trillions of operations per second. But each of those GPUs achieves that by consuming as much as 1,000 watts apiece. For comparison, if you’ve got a newer smartphone, it probably uses less than 1 W. That kilowatt figure puts GPUs on the same level as vacuum cleaners, dishwashers, and stoves, but with the big difference that data-center processors are operating uninterrupted around the clock.

Fundamentally, a lot of this inefficiency is because GPUs are trying to simulate the workings of artificial neural networks using software and billions of transistors, which requires using energy to move massive amounts of data. What’s more, the simulated artificial neurons that make up these networks lack even a fraction of the complex computing behavior of the biological neurons that comprise the most energy-efficient computing system that we know, the human brain.

The brain is roughly one million times as energy efficient at many of the comparable tasks we set for AI. To try to approach these efficiencies, a radically different way of computing called neuromorphic engineering is seeking to build electronic components and circuits that act more like the brain’s neurons and the synapses that connect them.

Huge amounts of work have gone into making electronics operate more like biological neurons and synapses. Some research has focused on developing new, experimental devices, but they aren’t yet reliable enough to be used in large systems. Other efforts aim to implement neurons and synapses by interconnecting many complementary metal-oxide-semiconductor (CMOS) transistors—the workhorses of digital logic—to simulate a single neuron and synapse. But this approach requires so many transistors (and a few bulky capacitors) that it greatly limits the size of the system that can be constructed, making it unclear how such brain-inspired hardware could ever scale up and compete with state-of-the-art GPUs.

But all along there was an artificial neuron and a synapse—each a single device—hiding in plain sight. We found them last year. They were each made possible by an ordinary CMOS transistor—and not even a very good one at that. This is the story of their accidental discovery and their great promise for lowering the environmental footprint of AI.

Biological and artificial neurons

Modern digital electronics is based on producing and manipulating the ones and zeros of the binary code through the operation of metal-oxide-semiconductor field-effect transistors. MOSFETs have evolved in recent years, but their classic form consists of a piece of silicon that has been doped to contain an excess of either positive (p-type) or negative (n-type) charge carriers. (CMOS logic contains transistors of both types.) The device has two terminals connected to the silicon through regions highly doped with the opposite polarity of the rest of the silicon—the source and the drain. Another terminal, the gate, sits atop the silicon that separates the source from the drain. The gate itself doesn’t connect directly to this silicon, instead resting above a thin layer of insulating dielectric.

Notably, there is a fourth terminal that attaches to the bulk of the silicon; think of this bulk terminal as connecting to the underside of the chip. It doesn’t typically get much attention, but it’s very important to our story.

When voltage is applied at the gate and the bulk terminal is grounded, charge carriers of the same polarity as the source and drain are attracted to the channel region. In the case of an n-type source and drain, that will be electrons; for p-type it will be holes. The presence of these charges forms a conductive channel that reduces the resistance between the source and the drain by several orders of magnitude, and the device switches on. As the voltage at the gate increases, this physical phenomenon produces a current signal that, when plotted against the gate voltage, rises steadily. This response is ideal for logic gates, converters, multiplexers, memories, and other digital circuits. But it is not a good fit for mimicking the behavior of a neuron.

In real neural tissue, brain cells, called neurons, consist of a cell body, a long projection called an axon, and short branching projections called dendrites. The suite of behaviors and computing this collection of components is capable of is rich and broad, but the portion that artificial neural networks hope to copy is this: When the cell body’s voltage is perturbed enough to reach a particular threshold, a self-propagating pulse of voltage, called an action potential, shoots down the axon. The axon terminates in a synapse, an electrochemical connection between the axon and another neuron’s dendrites. The action potential will then temporarily boost the voltage of this next neuron, by an amount that depends on the strength of the synaptic connection. If enough action potentials reach these dendrites in a given time—from this neuron or from others that might also form synapses there—the cell body’s voltage will surpass the threshold and trigger its own action potential.

The MOSFET Neuron

The unusual action the authors discovered is understandable if you consider that a MOSFET contains a hidden bipolar-junction transistor.

TRANSISTOR BEHAVIOR

Under normal operation, with the bulk terminal grounded, increasing voltage at the drain leads to current that increases steadily. When the voltage decreases, current follows the same sloped path. Although some pairs of electrons and holes are created by current crashing into silicon atoms, these are swept away before they can accumulate.

NSRAM BEHAVIOR

Adding resistance to the bulk terminal means these extra holes pile up, increasing the bulk voltage relative to the source. Once that voltage reaches a certain value, the hidden transistor activates, causing current to spike. Current remains high until the drain voltage drops past a certain point.

To get closer to the behavior of real neurons, artificial neurons should produce a current spike when a critical voltage threshold is crossed and then quickly relax back to a resting state on their own. This spike needs to be sudden—nonlinear. It should also exhibit some hysteresis; that is, the activation and relaxation voltages should be different from each other to ensure that current flows only for a certain amount of time.

What’s wanted from an artificial synapse, the thing that connects two artificial neurons, is less complicated, but equally important. The main thing is that its conductance can be electronically adjustable. The device’s conductive states should increase and decrease in a linear pattern and remain stable over time.

No single MOSFET working under the standard operation mechanism can reproduce either of these neural properties. Instead, it’s been done by combining them into complex circuits. Until now, each neuron and each synapse has been implemented by interconnecting dozens and sometimes even hundreds of MOSFETs, which is highly inefficient in terms of area, performance, and cost. To limit the amount of space needed, chips can multiplex their signals, sending them to neurons and synapses serially, but such sequential processing introduces additional delays.

Despite these area-and-time penalties on tasks such as audio processing, computer vision, or health monitoring, state-of-the-art brain-inspired microchips have achieved power reductions up to a thousandfold compared with those of GPUs or CPUs on the same task. If we could create neurons and synapses from individual devices that are readily manufacturable instead, we might target more massive implementations while maintaining energy efficiency.

Reinventing the MOSFET for AI

Working in our laboratory in 2024, one of my students was measuring a memory circuit that consisted of one transistor and one memristor—a type of nonvolatile memory device first fabricated in 2008. The student’s memristor circuit was built from two-dimensional material atop a silicon microchip containing MOSFETs. The MOSFETs were created in a commercial foundry using fabrication technology called the 180-nanometer node, which was cutting-edge in the year 2000.

One day the student forgot to connect the bulk terminal of the transistor. What he observed was a sudden increase in current with high nonlinearity that self-relaxed when the voltage was ramped down (a phenomenon called a hysteresis loop). This was a very promising neuronlike behavior!

After a fruitless week of trying to think of an explanation for this behavior, I (Lanza) asked Pazos, then my postdoctoral fellow, to try to observe and control this phenomenon in chips without memristors. This time, we applied pulses of voltage—like the spikes a neuron would produce—instead of the ramped voltage that my student used when he first saw the peculiar behavior.

Pazos’s new data helped us understand what was going on. The key was that oft-ignored fourth, or bulk, terminal of a MOSFET. Under ordinary operation, many mobile charge carriers flitting through the channel collide with the silicon atoms, producing free pairs of electrons and holes—a process known as impact ionization. The electric field created by the potential difference between the source and the drain causes these new free electrons to drift toward the positively biased drain and the holes to move toward the bulk terminal, which is usually grounded, removing the charge without any drama.

However, when the bulk terminal of the transistor is floating—unconnected as it was in my student’s experiment—the holes produced by impact ionization cannot be driven to the ground. Instead, they accumulate in the bulk of the silicon, increasing its voltage. Then things start to get interesting.

It helps here to imagine a MOSFET as two different kinds of transistors occupying the same physical space—the intentionally constructed MOSFET and a hidden, bipolar junction transistor. A bipolar device transmits a current signal across two p-n junctions, in this case the interfaces between the source and the channel region and the channel and the drain. This signal is in proportion to a smaller current at a third terminal in between, called the base. In our experiment, that third terminal is the bulk.

To get current flowing through a bipolar transistor, you need a big enough potential difference between the base and one of the other terminals, so that current can get across the p-n junction. Let’s say this “threshold voltage” is 0.7 volts, although the real number depends on device geometry and silicon doping. In our device, that potential difference comes from those holes that were accumulating in the bulk, because it was not connected to ground. Once it reaches the threshold voltage, the device becomes sharply conductive, producing an abrupt increase of current. This sharp current increase eventually falls off once the drain voltage is lowered, because that lowering reduces the rate at which holes are generated in the bulk. The remaining excess holes recombine with stray electrons or leak away, and finally the bulk voltage falls. This cycle of hole accumulation, current spike, and hole removal gives rise to a hysteresis loop, very much like the electrical behavior of a biological neuron as it integrates ionic currents, fires a spike, and relaxes back to its resting voltage.

Initially, we observed this behavior only in a few transistors, and the relaxation time was very different for each of them. So, to try to control it better, we adjusted the resistance of the bulk terminal using a second MOSFET. Simply setting that resistance suddenly caused all the transistors to fire at the same voltage with hardly any variability. In other words, we found we could create perfect electronic neuron behavior in a single silicon transistor by controlling the bulk contact resistance. Setting the resistance can be done by doping the silicon during fabrication, but we think the two-transistor cell—where one acts as the bulk resistance—offers much greater versatility because it allows for electronic control.

We had to make sure the phenomenon would last, otherwise such a device would be useless. To our delight, every single one of the devices we tested worked over 10 million cycles. Not even one of them failed during our tests.

The MOSFET Synapse

To be honest, we were amazed. Dozens of research groups and companies all around the world have spent many millions of U.S. dollars over the past 20 years trying to emulate these neural behaviors using experimental memristor-like devices and other things, with limited success, mainly due to reliability and cost issues. We managed it in the cheapest and most industry-standard device: the MOSFET. This result was so shocking that we decided to confirm it using microchips from a different foundry. It was successful: All the behaviors could be reproduced, and perfect yield was achieved once again.

We were happy with the results and had started the process of filing for a patent and writing up our findings for the journal Nature, when our lab made another astonishing discovery: The same kind of MOSFET could act as a synapse, too!

Recall that in ordinary operation some electrons crash into silicon atoms to create pairs of electrons and holes. We noticed that at specific values of bulk resistance a significant amount of the charge from this impact ionization would get trapped in the gate dielectric. This trapped charge interferes with the flow of current through the MOSFET, effectively changing the device’s conductance. Importantly, this new conductance is stable and adjustable at will. It was then that we realized the MOSFET could also be used as an electronic synapse.

As it was in the neuron transistor, the bulk terminal was the key. A negative bulk-source voltage drives electrons into the dielectric, decreasing conductance. A positive one pushes holes in, increasing it.

From neuromorphic device to circuit to system

Here’s how the MOSFET synapse and the MOSFET neuron, together called a neurosynaptic random-access memory, or NSRAM, could work together to achieve a simple neural circuit: Say you had a circuit consisting of three synapse MOSFETs and a neuron MOSFET. The synapses have already been programmed as we’ve described, so that each has a different conductance. Spikes of voltage with different patterns and frequencies are applied to the gate of each of these transistors. What emerges from their drains are spikes of current with amplitudes modulated by the synapses conductance values.

The spikes converge at the drain of the neuron MOSFET. With each spike, impact ionization causes charge to build in the bulk of the silicon. Some of it will drain away, but if enough spikes arrive in a short enough period of time, the bulk voltage will reach a value at which the “hidden” transistor triggers a spike of current through the MOSFET. This current would then go on to become the input to other MOSFET synapses, and so on. The behavior is exactly the kind of integrate-and-fire action real neural circuits deliver.

The competitive advantage of our single-MOSFET electronic neurons and synapses is straightforward: We can produce with only one or two transistors the electronic signals that today require, at an industrial level, dozens and sometimes even hundreds of components. And moreover, unlike other emerging technologies, our solution is fully compatible with today’s silicon manufacturing lines and exhibits a yield of 100 percent in key figures of merit with near-zero variability.

Building functional circuits for brain-inspired computing and AI based on this technology is as exciting as it is laborious. It will require us to improve our computer models to resemble the behavior of both devices more accurately and to do so with computational efficiency. We must also perform accurate circuit- and system-level simulations to validate computing architectures, design peripheral circuitry to drive and convert signals, and undergo multiple fabrication rounds to optimize performance.

But all that will be worthwhile, because it could result in brain-inspired microchips for AI with better energy efficiencies than what we have now. These chips will first be a fit for smaller-scale, “edge-AI” tasks, such as bringing greater intelligence to battery-powered systems. But if we can scale up such chips, maybe in the long run they can compete with state-of-the-art GPUs.

In 1839, J.M.W. Turner painted The Fighting Temeraire. The old warship, once a hero of the Battle of Trafalgar in 1805, glides like a ghost across the canvas, towed by a small steam tug belching smoke on its final voyage to the ship-breakers. The image shows a clear moment of change: sail giving way to steam, and with it, a major shift in power. The ship relied on timber, rope, canvas, and Britain’s seafaring towns. The tug depended on coal mines and iron foundries that supplied machine shops in the Midlands. Turner showed the tension of this time, when new technology changed who held power.

By Turner’s time, the United States had already defeated Britain’s navy in two wars—one for liberty on land, another for freedom of the seas. The 13 colonies used new technology in creative ways to win their freedom, and by keeping up with innovation, they managed to defend their freedom. Now, as the U.S. celebrates its 250th anniversary, we can ask: What does it really mean for a country to be independent?

We tend to focus on how nations and individuals defend freedom but rarely turn that focus to the tools and systems that sustain freedom. Declaring independence is only the beginning: Independence must still be engineered.

Forging freedom

Long before the first shots were fired at Lexington and Concord in 1775, Britain had drawn the lines of conflict through technology. The Wool Act of 1699 choked colonial textile exports. The Hat Act of 1732 crushed local hat-making. The Iron Act of 1750 forbade finished iron goods. Each statute tightened the knot: Colonial capability existed only at Britain’s discretion. The Boston Tea Party may have been a loud response, but resistance also took subtler, more empowering forms. At a 1769 Virginia ball, more than a hundred women arrived in homespun gowns. Every thread was defiance.

When war came, everyday tradespeople pivoted to the fight. Farmers turned plowshares into gun barrels, while clockmakers turned their precision skills to making firing mechanisms. By 1777, two weapons production models had emerged—centralized sites like the Springfield Armory that could produce high-quality guns in large quantities, and household workshops that were more agile and could meet local needs. In parallel, the new nation developed an equally important source of supplies and support: France sent gunpowder and loans and eventually opened a second naval front in 1781, which proved as decisive as any weapon.

After the war, the young republic pursued industrial strength with the same resolve it had shown in battle. In 1789, Samuel Slater arrived from England with textile-spinning technology that he’d memorized, sowing the seeds of U.S. manufacturing, whose early growth rested on domestic cotton, slave labor, and copied techniques. By 1816, gun manufacturer Simeon North’s milling machines were producing interchangeable metal parts, allowing the armed forces to cannibalize parts. In 1822, Thomas Blanchard’s copying lathe automated the shaping of gunstocks. In the 1830s, the federal government imposed tariffs that shielded infant industries, fulfilling Alexander Hamilton’s vision for industrial policy: Build capacity first, then compete.

At the 1851 Great Exhibition in London, American revolvers and reapers with swappable parts stunned international observers. By the 1860s, land-grant colleges were spreading technical education across the nation. Engineering moved into the mainstream, from niche to national necessity, driving broad, though uneven, prosperity. As the Industrial Revolution bloomed, the early U.S. focus on industrial capacity via farms, factories, and formidable wealth positioned the country to compete with the most advanced industrial powers in the world.

The right and responsibility to repair

For nearly two centuries, that ethos endured, with government-guided infrastructure and markets deciding the details. But around the U.S. bicentennial, in 1976, a conviction took hold across party lines. Finance began to outrank fabrication, and Wall Street prioritized futures contracts over companies owning the factories that made up their supply chains. Domestic factories closed or moved offshore, and companies turned to just-in-time manufacturing and shipping, ostensibly as a way to save on costs. Shipbuilding felt this shift as much as any industry. Shipyards closed, and suppliers of specialized castings and components disappeared along with them, as did skilled technical workers who retired without replacement. Now the U.S. Navy struggles to build submarines fast enough to replace its aging fleet.

Other changes took hold, among them the idea that the company that builds your tractor or medical equipment could prevent you from fixing it yourself. Invasive “terms of service” prevented customers from reaching for a wrench, instead allowing companies to keep reaching into customers’ pockets. These changes are symptoms of both structural and infrastructural fragility. When we lose the ability to understand and sustain the systems we rely on, we lose control—bit by bit.

RELATED: Why We Must Fight for the Right to Repair Our Electronics

No nation can build everything alone, of course. From hand-forged muskets to finely printed microchips, the sovereignty etched into our tools demands a prudent calculus: what to make at home, what and with whom to trade. Engineering is how a nation keeps its independence alive. Independence requires both the courage to innovate and the stewardship to maintain what has been built. The American Revolution was itself an act of engineering—daring in vision and deliberate in pairing anvil and alliance. Generations later, can a nation that cannot see its own dependencies, build and maintain its critical tools, or repair what breaks still call itself free?

Turner’s Snow Storm—Steam-Boat off a Harbour’s Mouth, completed three years after The Fighting Temeraire, captures this part of the story. Sea and sky dissolve into a churning vortex around the ship. Turner claimed he had himself lashed to the ship’s mast for four hours so that he could paint the sensation of standing inside a system too vast and tangled to comprehend. A nation that loses sight of what it depends on stands there too: lashed to nothing except the churn.

What do an instinct to fix things and the 1999 global panic over whether computers would survive the date change to 2000, known as the Y2K bug, have in common? Both helped shape IEEE Senior Member Ajay Prasad’s career.

Prasad is an industry process director at Dassault Systèmes in Detroit. His focus is global oversight of industry process experts specializing in Enovia, a product lifecycle management (PLM) solution and one of the company’s flagship products.

Ajay Prasad

Employer

Dassault Systèmes in Detroit

Title

Industry process director

Member grade

Senior member

Alma maters

Bangalore University, in Bengaluru, India; and the University of Birmingham, England

As a child growing up in Bangalore, India, his curiosity to build real-world solutions was ignited by his father, a mechanical engineer. Prasad’s father often fixed things around the house, including cars and bicycles. His ability to take something broken and return it to working order laid the groundwork for his son’s career in engineering.

Prasad was in his final year of undergraduate studies when the Y2K panic hit its peak.

“Nobody knew what would happen when the year turned to 2000,” he says, “and it was almost projected like the end of the world was coming.”

The phenomenon left him with the desire to fix computer problems, but he wasn’t sure how he would go about it, as he had no background in computer science.

As it turned out, computer systems didn’t crash when the 1900s ended. The world did not end on Jan. 1, 2000, and neither did his interest in how computers worked.

The consulting pivot that changed his career

Prasad graduated in 2000 with a bachelor’s degree in industrial engineering and management from the RV College of Engineering, in Bengaluru. It was at a time when tech companies were heavily recruiting engineers, regardless of their specialization.

“They were mainly looking for problem-solving skills,” Prasad says.

His parents expected him to immediately enroll in a master’s degree program, he says, but a job offer from Tata Consultancy Services in Bengaluru to work as an assistant systems engineer trainee changed that plan.

“My dad was actually out of town for work when the job offer came in,” he says. “I knew he wanted me to stay in school, but honestly, I was done studying for a while. I wanted to get some work experience.”

He accepted the offer, then broke the news to his father. His parents were supportive of his decision, but his dad offered one piece of advice: Keep the idea of an advanced degree in the back of his mind.

Several months of working on mainframes helped him understand algorithms and how to code to achieve outcomes, he says, and the more he learned about computer systems, the more he wanted to pursue a computer science career. With a solid engineering foundation, he says, he knew the pivot made sense. But he also wanted the academic credentials to back up his tech skills.

Heeding his father’s advice, he paused his career at Tata and enrolled in the master’s degree program in computer science at the University of Birmingham in England. At the time, it was one of the few schools offering the program to students who had no undergraduate computer science degree. When he graduated in 2002, he briefly considered pursuing a Ph.D., but he returned to India and a new role at Tata.

Building a global perspective

As a systems engineer, he worked on the MatrixOne platform, a PLM software solution that helped manufacturers oversee products from design to launch. He spent a lot of time customizing the MatrixOne software to meet customer needs. The experience gave him insights into the pain points that different users of the platform faced, such as managing complex product data across large teams and keeping track of complicated supply chains.

In 2004 Tata transferred him to Minneapolis, where he continued working on the MatrixOne platform.

During that time, Dassault acquired MatrixOne and folded it into its existing Enovia product line. He remained involved with the product until he left Tata in 2008. To scratch an entrepreneurial itch, he became a consultant for the product, helping customize the platform for U.S. clients.

The move also forced him to make a decision: He needed to choose between settling in the United States or returning to India. Inclement weather made up his mind, he says.

“I was heading to my next project across the country, and it was winter,” he says. “During the entire drive, I was trying, unsuccessfully, to outrun a massive snowstorm. I was young, and it was an adventure, but it helped clarify where I wanted to be at that point in my life.”

He returned to India in 2010, armed with a more global perspective and expertise with Enovia. As he looked for a job, he focused on a role with the company that owned the platform he’d worked on for years.

“Dassault Systèmes has continuously pioneered new technologies and concepts and set benchmarks in the PLM space,” he says. “When an opportunity opened up there for me, I jumped at it.”

Instead of a programming role, though, he was hired as an Enovia technical sales specialist, working in Dassault’s Bengaluru location. It was an eye-opening experience, he says.

“It put me on the other side of the table: trying to sell software to customers,” he says. “This was the opposite of my experience customizing software after the sale was complete.”

The role of technical sales

The position involved both presale and postsale duties. Technical salespeople bring subject-matter expertise that bridges the gap between a product’s functionality and the customer’s needs. The role works directly with the sales team to craft a presentation that showcases the value of the software as a solution.

On the postsale side, technical sales professionals work with service teams to customize software solutions to ensure customer goals are met. If needed functionality doesn’t exist, they work with the R&D group to create it. They also offer suggestions to customers on how to improve their processes.

When Prasad stepped into his new role, a senior colleague described technical sales as an “exam syndrome” because customers are judging you and your presentation against competitors. The analogy didn’t land well with him.

Recalling all his years of formal education, he had a different perspective: “I wanted to think of it more as an opportunity to fully understand a customer’s problem, then solve it better than anybody else could.

“Every customer has unique pain points. When I can offer solutions that deliver value, they’ll buy the software.”

It’s his belief that the position is best served by professionals with both engineering and computer science backgrounds. He advocates that engineering students consider adding computer science to their studies, and he draws on his own educational experiences to support the position.

Combining engineering and computer science

Dassault recognized the value in his approach. In 2015 he was hand-picked to be part of the company’s new Worldwide Enovia Center of Excellence team in Auburn Hills, Mich. As an industry process expert, he was able to put his Enovia expertise into action.

He’s now a senior leader managing a global technical sales team. One of his objectives, he says, is advocating to engineers that technical sales is a viable career move.

“The moment an engineer hears the word sales, they tend to stop listening,” he says. “They don’t want to be a salesperson in the traditional sense.”

That’s too narrow a view, he says, adding: “I think everyone is a salesperson to some degree.”

If engineers looked at technical sales differently, they’d see an exciting opportunity, he contends.

“In this role, they have the ability to not only develop solutions but also explore the why behind the need for a solution at all,” he says.

“As engineers, sometimes we are so focused on engineering concepts and principles that we get bogged down in the details and don’t focus on what the problem really is,” he says. “I learned with technology that even before you try and create a solution, you need to understand the logic of the problem first.”

From problems to patents

His approach has delivered measurable results. He holds one patent and has a second under consideration. His combination of engineering and computer science expertise played a crucial role in each, he says.

His first patent, granted in 2023 by the U.S. Patent and Trademark Office, was for his solution to improve product benchmarking for clients with large-scale data management issues. It replaces traditional spreadsheets with powerful databases and a user-friendly interface, ensuring information is up to date, accessible, and shareable.

“I think that being part of the IEEE community is a huge value for folks in the engineering space. It’s a great way to collaborate and to understand what’s happening, especially in your local ecosystem.”

His second patent, pending with the USPTO, is designed to help customers manage large projects that involve a high volume of engineering design tasks. Instead of relying on ambiguous communication between engineers and project managers, his solution would draw data from the work management system and update the project management dashboard automatically. It would replace guesswork with real-time data.

Prasad has authored the peer-reviewed technical paper “Transforming Product Development With a Platform-Based Approach to Product Lifecycle Management,” which was published by SAE International. His writings on the use of data tracking and AI in product lifecycle management have appeared on Engineering.com and in Wavelengths, a monthly publication from the IEEE Southeastern Michigan Section.

In February, Dassault marked Prasad’s success by promoting him to worldwide Enovia industry process director. The title reflects a career built on the belief that engineering and computer science are stronger together, and that technical sales is where the combination delivers its greatest value, he says.

The value of IEEE

Prasad first encountered IEEE at a student branch meeting he attended at Bangalore University in 2000, shortly before graduation. The meeting featured engineers from industry discussing the work they did—which sparked his interest in joining, he says. But with his first job waiting for him, the timing wasn’t right to become active with the organization.

It took nearly 25 years, he says, before he felt he had enough spare time and professional experience to contribute actively and meaningfully to IEEE. He joined the Southeastern Michigan Section in 2024, was quickly elevated to senior member, and then took on a leadership role.

He was nominated to be conference chair for this year’s Innovative Applications of AI in Industry event. Together with a team of eight, he led the planning and execution of the in-person conference, the first time it was held since the COVID-19 pandemic shelved it.

The event explored how AI is permeating practically every aspect of our lives. Speakers came from Amazon, Torc Robotics, academia, and health care.

The event was a success, he says, and he hopes to parlay its momentum into a multiday conference in the coming years.

As a representative from the section, he served as a technical judge at this year’s Robofest, a competition held in May for students in Grades 4 through 12. Since the annual event’s inception, more than 40,000 students from 35 countries have participated. He says his involvement helps him understand how students use robotics to solve problems.

“I think that being part of the IEEE community is a huge value for folks in the engineering space,” he says. “It’s a great way to collaborate and to understand what’s happening, especially in your local ecosystem. There’s always something going on in terms of a conference or a talk where you can listen, gain knowledge, and network. It’s also an invaluable opportunity to discover where you can add value at IEEE.”

This article is brought to you by Capital One.

After five years leading natural language understanding and eventually the entire Alexa AI organization at Amazon, Prem Natarajan made a nontraditional move: He became Chief Scientist at a bank. Not just any bank: Capital One, a financial institution serving over 100 million customers, helping everyday Americans manage their financial lives.

For Natarajan, a veteran of DARPA-funded research and academia who had watched machine learning evolve from task-specific applications to foundation models, the logic was clear. Some of the most interesting advances in AI research and deployment were shifting from big tech’s horizontal platforms to industry verticals like finance, where the most complex problems aren’t just building models but making AI work under the constraints of real-world customer problems, contextual business knowledge, continuous learning, with an incredibly high bar for accuracy and privacy.

That’s also what made Capital One the right place to do it. For decades, the company has been recognized as one of the most data- and analytics-driven financial institutions in the industry. Its business model from the very beginning was built around using data and technology to personalize financial products for customers. A decade ago, Capital One went all in on the cloud and rebuilt its data ecosystem, creating a unified environment for data, compute, and AI and machine learning experimentation. Today, its modern infrastructure, disciplined approach to governance, and deep bench of talent form the foundation that allows it to lead in enterprise AI.

Advances in AI research and deployment are shifting from big tech’s horizontal platforms to industry verticals like finance.

So, why does a bank need a Chief Scientist? The answer lies in a fundamental misconception about AI in financial services. Most financial institutions still view AI as a technology to deploy – leveraging the latest large language model, deploying it through APIs, and integrating it into existing workflows – rather than a scientific discipline. Capital One is doing something different: building a scientific community and research organization to solve real-world customer problems and invent impactful AI solutions that don’t yet exist.

While widely available foundation models can handle general tasks, they can’t yet solve many domain-specific challenges, such as detecting fraud in real-time across billions of transactions, or providing state-of-the-art conversational tools so customers can engage when, how, and where they want to.

These challenges of making AI reliable, scalable, and well governed require original research and scientific innovation that is funneled back into the business to create real-world applications to address customer needs.

The Constraints That Demand Innovation

Prem Natarajan, an IEEE Fellow, is Chief Scientist at Capital One. “If you want to solve really important problems in AI and see your work come to life, this is one of the few places you can do that,” he says.Capital One

Because banks are dealing with people’s finances, there is an incredibly high bar for getting it right when it comes to AI. Take fraud, for example. Even a minor fraud event can have a devastating impact on certain customers. The best fraud models and platforms can detect and help mitigate fraud in the time it takes someone to tap their card, which is table stakes for protecting customers and their financial information with accuracy and speed. Looking at these types of challenges, Capital One and Natarajan saw that serving millions of customers meant solving AI problems at a scale and complexity that many enterprises don’t encounter. These same constraints create a unique research environment.

At Capital One, the approach to building AI is to provide value to customers in ways never possible before, improving their financial lives and meeting them where they are with services they actually need. That focus, combined with massive scale and world-class risk management requirements, makes the scientific problems both harder and just as consequential as those found in most big tech labs.

Advancing AI Through “Destination-Back Thinking”

Capital One’s approach to AI research and innovation starts with what Natarajan calls “destination-back thinking.” Rather than asking what’s possible with current technology, the team envisions the customer experience they want to deliver – perhaps a car buyer who works long days and can only research the options at 10 p.m., or a customer facing an unexpected expense who needs immediate, personalized guidance – and then works backward to identify the scientific breakthroughs required to get there.

“You’re thinking back from where you’re providing incredibly valuable services,” Natarajan explains. “Once you have that vision clearly, you work back and say, what are the gaps? What are the things we need to invent?” This ensures that when problems are solved, the impact is essentially guaranteed, because the team has already identified what will make a tangible difference in customers’ lives.

But methodology alone isn’t enough. Capital One’s nearly 15-year bet on cloud-first architecture created something rare in financial services: a unified data and compute ecosystem that can support the kind of scientific experimentation typically seen in big tech research labs. As the only major U.S. bank to go all-in on public cloud infrastructure, Capital One eliminated the legacy systems that can constrain AI research at most financial institutions. This modern tech stack enables rapid iteration, large-scale model training, and what Natarajan calls “continuous learning,” systems that improve after deployment rather than degrading over time. This unique approach to infrastructure is a critical component in making new categories of research possible.

Agentic AI: From Research to Production

The research agenda manifests in systems already serving customers. Early last year, Capital One launched what may be the first fully agentic AI customer service experience built entirely in-house by a bank: a car buying tool that takes actions on behalf of customers based on their requests, not just answers questions. Behind it lies extensive research into multi-agentic AI reasoning systems that can navigate real-time data, business knowledge, constraints, and guardrails, with various agents that can work together to accomplish complex tasks.

Capital One has launched a fully agentic AI customer service experience powered by extensive research into multi-agentic reasoning systems that can navigate real-time data.

The team is also working on solving things like tokenization challenges, protecting sensitive data while enabling model training. To accelerate this cutting-edge work, Capital One has established partnerships with Columbia University, the University of Southern California, and the University of Illinois, and became the only bank funding NSF’s national AI research centers in 2025, investing millions in initiatives that span mental health, materials discovery, science, technology, engineering, and mathematics education, human-AI collaboration, and drug development.

In the spring of 2026, the company hosted its inaugural AI Symposium to deepen connections and foster insight-sharing between the scientific AI community, leading AI labs, startups, and its own technology, science, and AI leaders and partners.

Building a World-Class AI Organization

Capital One is building the next generation of AI talent. Join the team inventing impactful AI solutions to shape the future of finance. Learn more at https://capitalone.science/

External validation suggests the strategy is working. Evident AI ranked Capital One as the leading bank in AI talent and a global leader in AI innovation for three consecutive years, noting the bank accounted for 38 percent of all AI patents filed by the top 50 financial institutions. Capital One was also recognized by IFI Insights as the only financial institution among the top U.S. patent leaders in agentic and generative AI in 2025, alongside the likes of Google, NVIDIA, DeepMind, IBM, Microsoft, Intel, Adobe and Samsung. Capital One’s AI team – which has experience from leading AI labs and top universities – represents expertise rarely found outside Silicon Valley.

But recruitment requires a mission. “If you want to solve really important problems in AI and see your work come to life, this is one of the few places you can do that,” Natarajan says. The pitch is consistent: Capital One isn’t just optimizing algorithms for niche financial applications like high frequency trading, it’s using science to enhance financial experiences for over 100 million everyday Americans, expanding engagement and real-time insights, personalization, and access to their personal finances and products like never before.

Capital One was recognized as the only financial institution among the top U.S. patent leaders in agentic and generative AI in 2025, alongside the likes of Google, NVIDIA, DeepMind, and Microsoft.

The frontiers Natarajan is most excited about – agentic AI systems that can dramatically improve performance by reframing how problems are solved, and domain-specific reasoning that understands contextual and financial nuance – represent the next phase of innovation. “By just casting the problem in an agentic framework, you can actually get way more performance” from the same underlying models, he explains.

It’s this kind of applied research, like translating general capabilities into production systems for millions of customers, that defines the Chief Scientist’s mandate. When recruiting talent to his AI team, a group comparable only to the most sophisticated tech companies in caliber, Natarajan frames the opportunity around a mission. He invokes Steve Jobs’ famous challenge to John Sculley: “Do you want to spend the rest of your life selling sugared water, or do you want to change the world?” For Natarajan, the parallel is clear. Building AI systems that transform financial services for millions of everyday Americans – that’s changing the world. And it requires the kind of scientific rigor that only a Chief Scientist can lead.

In the mid-noughties, when music by the Killers and Franz Ferdinand blared out of every pub and nightclub I passed, I spent my days and nights struggling through a Ph.D. in applied mathematics. My research focused on simulating how special light waves interact in liquid crystals and using simple equations to approximate and understand those interactions. When I look back at my thesis now, liquid crystal technology is old hat, and I imagine my work could be completed with AI assistance in a matter of days—maybe hours.

But the same cannot be said for the work of the pure mathematics Ph.D. students with whom I shared a cramped office at the University of Edinburgh. At the time, I felt sorry for these colleagues, who day after day sat at their desks, seemingly tearing their hair out and making no progress. (Though I was struggling too, I was at least always making some headway.) When we finished and went our separate ways, some hadn’t even published a paper.

Now, in hindsight, I finally understand why they toiled for years on abstract mathematical problems that only a handful of people in the world care about. It wasn’t arrogance, as I thought at the time; they weren’t trying to prove their superior intelligence by being the first to solve a seemingly intractable mathematical problem. It wasn’t even a form of masochism (which was my second guess)—penance for some imagined inadequacy. I realized they derived joy, satisfaction, and meaning from the long journey toward understanding.

“Sometimes, understanding just strikes you as being very beautiful.” —Jeremy Avigad, Carnegie Mellon University

“Sometimes, understanding just strikes you as being very beautiful. Sometimes it’s a feeling of accomplishment, like completing a marathon,” muses Carnegie Mellon University mathematician Jeremy Avigad. “But it’s not quite either of those: It’s just a wonderful feeling when you’ve been thinking long and hard about something complex, difficult, and then—all of a sudden—it just comes together.”

This feeling has driven mathematicians throughout history. Likewise, the way mathematicians pursue that feeling has changed little over the centuries. They notice or imagine links, patterns, or properties in numbers, shapes, or logical structures. From this, they write conjectures—unproven statements of their speculation. They or other mathematicians then use logical reasoning and the tools of mathematics in often creative ways to prove or disprove those conjectures. Finally, yet other mathematicians verify (or challenge) the proofs.

Invariably, this process requires a whole heap of thinking time. “I went to a pure maths camp with classes where we would sit with hard maths problems for half an hour and no one would say anything—everyone was just thinking,” says Krystal Maughan, a mathematician and computer scientist about to get her Ph.D. at the University of Vermont. “But then we would work together and kind of tease out the problem.”

This is the age-old joy of math in action. But today’s AI systems are starting to make inroads into bypassing this slow, deliberative process. Taking this trend to its logical conclusion, what happens if AI makes the mathematician’s struggle completely unnecessary? Might AI even sideline humanity completely?

AI’s Growing Role in Mathematics

For decades, computation has accelerated mathematical progress. This began 50 years ago, when mathematicians used a computer to prove the four-color theorem, which asks whether any map can be colored using no more than four colors, with no adjacent regions sharing the same color. The answer is yes, and the computer proved it, controversially, by checking 1,936 cases in a way no human could realistically verify.

Yet throughout this computational era, even in proofs relying on massive computational resources, the role of the human mathematician has remained central. Humans propose conjectures, guided by intuition. They devise strategies to prove them, guided by creativity and experience. And humans verify whether those proofs are correct.

Now AI is challenging the status quo. In just a few years, large language models (LLMs) have evolved from “stochastic parrots,” capable of little more than regurgitating basic mathematics scraped from the internet, into advanced mathematical reasoning machines.

Last summer, systems from Google DeepMind and OpenAI reached a level equivalent to the world’s most mathematically gifted high school students, achieving gold-medal status at the International Mathematical Olympiad. In this annual competition, contestants must solve six notoriously difficult problems from various areas of mathematics.

Earlier this year, Google DeepMind’s experimental AI system Aletheia achieved an even more significant milestone when it autonomously produced publishable Ph.D.-level research results. While the work itself is obscure mathematically—calculating structure constants in arithmetic geometry—the significance lies in the complex reasoning it displayed in tackling an unsolved mathematical problem. And more recently, a new general-purpose AI system from OpenAI disproved an important conjecture in combinatorial geometry. This result would have been worthy of publication in a major mathematics journal if humans had been the authors, and top mathematicians hailed the feat as a milestone for AI in mathematics, demonstrating independent, original, and sophisticated thinking.

Another shift has come from combining LLMs with mathematical tools known as proof assistants, which have been around for more than a decade. These systems—such as Isabelle, Lean, and Rocq—are specialized programming languages that check mathematical proofs step-by-step, verifying their logical correctness. Traditionally, mathematicians have had to translate their theorems and proofs into this machine-readable format by hand, a laborious process known as formalization. Now, LLMs are starting to remove this bottleneck, automating the translation of informal proofs into formal code that proof assistants can verify.

From Human Proof to Formal Proof

Euclid’s famous proof that there are infinitely many prime numbers appears very different when formalized in Lean, a proof assistant. Human mathematicians routinely skip steps and rely on shared understanding; formalization makes every assumption and inference explicit so a computer can verify the proof.

❶ Definitions must be explicit. The proof formally defines p as the smallest prime factor of n! + 1 before it can use that quantity.

❷ Formal proofs build on earlier formal proofs. Here Lean invokes a previously verified theorem showing that p is prime.

❸ Hidden logical steps become explicit. A human mathematician can write that p “clearly” divides 1. Lean requires the proof to invoke a formal theorem about divisibility and show exactly why that conclusion follows.

With technical assistance from Sidharth Hariharan

Versions of such systems, sometimes called reasoning agents, are becoming highly sophisticated. In February, for example, the AI company Math, Inc. used its aspirationally named reasoning agent Gauss to formalize a proof that had earned the mathematician Maryna Viazovska, of EPFL, in Switzerland, a Fields Medal in 2022. Gauss first helped human mathematicians complete the formalization of Viazovska’s solution to the 8-dimensional sphere-packing problem in a matter of days, and then autonomously formalized the more complicated 24-dimensional case in just two weeks.

Such achievements suggest that AI is already capable of handling some mathematical tasks long considered uniquely human. As the technology advances, more of the day-to-day work of human mathematicians is likely to become fair game for AI.

Mathematicians Debate AI’s Role in Discovery

Human mathematicians could become “priests to oracles.” —Yang-Hui He, London Institute for Mathematical Sciences

In September 2025, I attended the 12th Heidelberg Laureate Forum—an annual conference that brings hundreds of young mathematicians and computer scientists together with their intellectual idols. AI dominated the conversation and, from the get-go, tension was in the air.

Speakers described a future in which superhuman AI mathematicians transcend human knowledge and capabilities: forming conjectures, searching solution spaces, proving conjectures, and finally verifying the proofs and generalizing the results, all without human involvement. If this future comes to pass, Yang-Hui He of the London Institute for Mathematical Sciences memorably declared, human mathematicians could become “priests to oracles.”

While such startling predictions were being voiced on stage, my gaze was drawn to the audience. Frowning, fidgeting, and exchanging furtive glances—the crowd’s unease was palpable. Trill White, a student at Australia’s Deakin University, later recalled sitting in that hall and thinking: “ ‘That’s devastating. What will people have to contribute to mathematics? Will it become something that no one understands?’ I did get a sense that this is going to change everything.”

“We certainly started realizing AI has the potential to replace us.” —Jessica Randall, Google Developer Groups

Jessica Randall, a South African mathematician for Google Developer Groups, says she sensed a collective existential dread rising among the young mathematicians. “I could feel everyone was worried, because they hadn’t thought that far ahead,” she says. “It was like a big bombshell that hit us, and we certainly started realizing AI has the potential to replace us.”

Some established mathematicians, including He, seem comfortable with AI taking on tasks that are currently the preserve of human mathematicians. That’s because they just want to know the answers to the biggest questions in mathematics—such as the six remaining Millennium Prize Problems—even if AI does it all. “A lot of mathematicians are pragmatic and just want to understand. They would sell their soul for the solution to a problem,” jokes Avigad. “Whatever it takes, right?”

But this “just want to know” camp is by no means the only faction: Most mathematicians do not hope or expect AI to replace them entirely. Instead, two broad alternatives are emerging. The first is a human-centric aspiration that prioritizes human understanding of mathematics and treats AI as a tool, much like a calculator. The second is a collaborative “teamwork makes the dream work” vision, where humans and AI work together to tackle problems neither could solve alone.

The Human Role in Mathematics

Numbers are “a way of bringing us to agreement.” —Akshay Venkatesh, Princeton University

Fields Medalist and Princeton mathematician Akshay Venkatesh has been thinking about this topic from the human-centric viewpoint for years. In 2022, he used his Fields Medal Symposium to implore the mathematics community to deeply consider what AI might mean for the practice of mathematics. At the time, the idea that AI could replace mathematicians seemed far-fetched. Now, he says, “we’re reaching the point where, for at least some tasks with abstract mathematical reasoning, computers are becoming competitive with humans.”

For Venkatesh, the question is not just what computers can do, but what mathematics is for. “Sometimes I think when we use numbers, it’s not so much that we are describing phenomena that are intrinsically numerical, but that we can all agree exactly what the numbers mean,” he says. “It’s a way of bringing us to agreement.”

Mathematician and machine learning expert Maia Fraser, of the University of Ottawa, shares this sentiment. She says the joy she derives from mathematics is something distinctly human that integrates the subconscious and conscious mind. She describes starting with an intuitive sense that a certain thing should be true and gradually bringing out something that she can express in a rigorous proof. Communicating and sharing these deep-born thoughts is “a form of collective intelligence that is something beautiful about the human spirit,” she says.

By these arguments, an AI proof of a mathematical conjecture that has stubbornly resisted human efforts would be useful only if comprehensible to humans. “That the statement can be proved by AI is already useful information,” concedes Fraser. “But then it’s still an open problem to come up with an elegant, beautiful human proof.” Even if no such proof exists, she says, searching for it “is still a valuable endeavor.”

AI and the Future of Mathematical Collaboration

A more collaborative approach to AI in mathematics comes from Terence Tao, who first competed in the math Olympiad at the age of 10. In 1986, 1987, and 1988, he won bronze, silver, and gold medals, respectively, making him the youngest winner of each of the three medals in Olympiad history. Now a Fields Medalist and professor at the University of California, Los Angeles, he has earned a reputation as one of the most gifted mathematicians alive.

Unlike some of his peers, Tao is neither dismissive of AI nor fearful. Instead, he sees it as the catalyst for a fundamental shift in the discipline—a transition toward what he calls “big mathematics.” He envisions a future of large-scale, decentralized collaborations between humans and machines, where complex mathematical tasks can be diced and sliced, with humans claiming the creative parts and AI doing the lion’s share of the technical grunt work.

Three Futures for AI in Mathematics 

Already, Tao is experimenting with this concept, working on problems alongside scores of online collaborators, some using AI tools. “A hundred years ago, almost every mathematics paper was single author,” he says. “But now I collaborate with people I’ve never met—and maybe in the future, I won’t even know if they are AI or real people.”

The key to Tao’s vision is uniquely mathematical: formalization. When a proof is translated into code and checked step-by-step by proof assistants, it removes any chance of human error or dishonesty. This approach changes how collaboration works, because trust is established through verification rather than reputation or rapport. An idea from an unknown researcher or even an amateur can be taken seriously if it has a formal proof.

“If it wasn’t for this formal verification layer, opening projects up without any safeguards would just be a disaster,” adds Tao. “But in math, we can completely check and verify outputs, and this really filters out a lot of the rubbish.”

The Risks of AI in Mathematics

From the young researchers at the Heidelberg Laureate Forum to some of the biggest names in the field, mathematicians all seem to agree on one point: AI has the potential to transform their discipline. But there’s far less consensus on what that transformation will mean in practice.

Some worry about the accessibility of AI tools. Traditionally, mathematicians have required little more than intuition, training, and a pen and paper to advance their field. If this slow, deliberative process is no longer valued by society, and particularly by research funders, then mathematics could become an elitist activity, only practiced by select organizations that can afford to work with proprietary AI models.

Another concern is motivation. As AI systems take on more of the work, the incentive to engage deeply with difficult problems may weaken. Princeton’s Venkatesh says that the long human process of formulating and understanding a proof may be hard to justify, not just to funders, but even to mathematicians themselves. “There have been times where I’ve spent years thinking about something, and I’ve slowly struggled to understand it,” he says. “If your computer can do large chunks of that for you, will you have the motivation to spend that time?”

That concern extends to the next generation. If students can use AI to jump straight to answers, they most likely will. But every time they skip the struggle, they miss an opportunity to build the foundations of their own unique intuition. Over time, some worry, the next generation of mathematicians may suffer from a form of intellectual atrophy, unable to think outside the AI box that trained them.

In response to such fears, the mathematics community is taking action. Individuals are writing essays, organizing workshops, and debating in journals, while institutions and community groups are developing guidelines for how AI should be used in research and publication. Indeed, mathematicians are applying the same rigor and curiosity that they use every day to reckon with the challenges of AI. Taken together, these efforts reflect a broad effort to try to retain control over the direction of mathematics in the era of AI.

So, is AI sucking the soul out of math? In one way, it is doing the opposite. It is forcing mathematicians to confront deep questions about what mathematics is, why they have devoted their lives to it, and the purpose math serves in society. At the same time, though, it is reshaping the practice of mathematics in a way that may be difficult to reverse.

“Mathematics makes me a better problem solver at normal problems, because it frames my mind to think in a very logical, rational way,” says Randall, who noted the existential dread at the Heidelberg Forum. “It helps with every aspect of my life.” As AI transforms mathematics, many researchers wonder whether future mathematicians will be able to say the same.

When considering the 1960s sitcoms Bewitched and I Dream of Jeannie, both of which featured women with supernatural powers navigating life with mortals, most people wouldn’t connect them with pursuing an engineering career. But Karen Panetta did. The sitcoms’ main characters—Samantha Stevens, a witch; and Jeannie, a genie—were “strong, empowered female leads using magic,” Panetta says, and they inspired her to become an engineer, as it was like sorcery to her.

Panetta, an IEEE Fellow, is dean of graduate education at the Tufts University engineering school, in Medford, Mass., outside of Boston.

Karen Panetta

Employer

Tufts University, in Medford, Mass.

Title

Dean of the engineering school’s graduate education

Member grade

IEEE Fellow

Alma maters

Boston University and Northeastern University in Boston

Like Samantha and Jeannie, Panetta has made magic happen, such as when she helped to invent the first CPU digital-twin simulator. Digital twins are computer simulation programs that track and adjust the operations of a physical device in detail. Her simulator has been adapted for several industrial uses, including by NASA to help design spacecraft.

Panetta also mentors young women to encourage them to pursue a STEM career through the Nerd Girls program she launched at Tufts in 2000. Engineering undergraduate students work on technology for socially conscious projects such as environmental cleanup, renewable energy, and the development of assistive devices to improve mobility for people with disabilities.

Panetta received this year’s IEEE Mildred Dresselhaus Medal for “contributions to computer vision and simulation algorithms, and for leadership in developing programs to promote STEM careers.” The award, sponsored by Google, was presented at the IEEE Honors Ceremony on 24 April in New York City.

Receiving the medal is particularly special to Panetta, she says, because she knew its namesake: Mildred Dresselhaus, an IEEE Life Fellow who pioneered the study of carbon nanostructures at a time when researching physical and material properties of commonplace atoms was unpopular. She was a MIT professor of physics and electrical engineering, and died in 2017.

Panetta nominated Dresselhaus for the IEEE Medal of Honor, which she received in 2015.

“Millie was a rock star,” Panetta says. “I can’t think of another medal that really encapsulates her spirit and what I’ve dedicated my life to.”

Finding a creative outlet in engineering

As a child growing up in Boston, Panetta built trapdoors and other features in her treehouse, she says.

“I also explored fashion and sewed my own clothes,” she adds. “I wasn’t very successful, but I was very creative.”

She was a top performer in math and science classes in high school, so her father encouraged her to pursue civil engineering.

“I didn’t know what an engineer was, and my father, who was a mechanic working on heavy construction equipment, only knew about civil engineers,” Panetta says. “I started taking computer programming classes at school, but knowing how to type on a keyboard and make a software program wasn’t good enough for me. I wanted to know what was inside the box.”

Her thirst for knowledge inspired her to pursue a bachelor’s degree in computer engineering at Boston University.

“My father was very disappointed that I didn’t pick civil engineering,” she says, laughing.

She commuted to school, and she struggled to find study groups for her classes, so she joined IEEE to connect with peers.

She became active in the university’s student branch, organizing events including the IEEE Student Professional Awareness Conference, which helps students learn practical career skills including résumé building, interviewing, and networking. She organized a SPAC for her branch, and IEEE Life Senior Member Jim Watson volunteered to speak at the event. It changed her life, she says.

Watson was the director of commercial and industrial marketing at Ohio Edison in Akron, where he worked for 36 years.

“He flew to Boston to speak at our event, but fewer than 20 students attended. I was embarrassed,” Panetta says. But Watson told her the important lesson was that she showed up and organized the event.

“He said I would be successful because of that,” she says. “He didn’t care about the attendees’ grade point averages, only that we were professional enough to organize the talk.

“That encouragement was the first time anyone outside of my family ever told me that I would succeed, so it was reaffirming. To this day, I still use some of the techniques that I learned in his presentation in my own classroom to teach students.”

Panetta graduated in 1986. Her IEEE membership helped her get hired for her first dream job: a diagnostic engineer at Digital Equipment Corp.

While attending the IEEE Computer Society’s annual symposium on very large-scale integration in Boston, she handed her résumé to a DEC representative, who hired her to work in Hudson, Mass.

While working full time, Panetta attended Northeastern University, in Boston, as a part-time graduate student. She earned a master’s degree in electrical engineering in 1988.

Developing the first CPU digital twin

In the early 1990s, Panetta was assigned to work with Ernst Ulrich, one of DEC’s most respected consulting engineers, she says. He was developing a new CPU using millions of CMOS transistors.

“I thought, ‘Wow, what a great opportunity,’” she says, “not realizing they assigned it to me because no one else wanted to work with him, as he set rigorous standards, expecting those who worked with him to think outside of the box and hold their own to bullet-proof new concepts.”

Panetta and Ulrich wanted the ability to test the CPU while still designing the hardware and software. That way, both would be ready to use at the same time. Typically, the hardware was developed before the software was written.

“We decided that we were going to simulate the machine to see how it was going to run—which was unheard of,” she says.

During a meeting with the company’s top engineers, Panetta shared her idea for an algorithm that could accomplish the team’s goal. She was met with silence.

“It’s going to be the engineers who better society because we know how to work together. We’ve proven that IEEE members know how to work across geographic boundaries, ethnic boundaries, and gender boundaries. And that’s a good model for the world.”

“I thought to myself, ‘Did I just say something stupid?’” she says. “But then, the top engineer looked at me and said, ‘I have been doing this for 50 years, and you, a kid just out of school, comes up with this [solution] like it’s obvious.’”

Her idea became the basis for the digital twin simulator. It used behavioral models to run software on a CPU simulation. The software passes information through the system, she says, just like it would pass information through wires or interconnects.

“We did successfully have a complete model of millions of transistors,” Panetta says. “I efficiently simulated hundreds of thousands of experiments and ran the software on this simulated model so that we knew exactly how it was going to perform on the real machine. That had never been done before.”

Her groundbreaking work led to a promotion: from computer analyst to principal software engineer.

When she began managing a team and hiring staff members, Panetta noticed the younger employees knew the theory but didn’t have the technical skills to hit the ground running, she says.

“It took the company two years to train somebody before they could really contribute technically to a team,” she says. She decided she wanted to help prepare students for jobs in industry.

In 1995 she was accepted into DEC’s Engineers and Education program, in which full-time employees who wanted to teach could take a leave of absence to complete a degree while still being paid. Participants were then placed in academic institutions for two-year stints to help students bridge the gap between classroom theory and real-world problem-solving.

After earning a Ph.D. in electrical engineering from Northeastern in 1994, Panetta began her teaching assignment at Tufts. After one year, she left her job at DEC to join the university as its first female electrical engineering professor. At the time, the department had only one female undergraduate EE student.

“I showed up to work dressed in an all-pink suit,” she says, laughing. “Other professors looked at me like I didn’t belong there because I looked different.”

She didn’t let that stand in the way of reaching her goals: preparing the next generation of students for jobs and mentoring young women who were interested in becoming engineers but who felt they wouldn’t be accepted and therefore couldn’t pursue a career in the field.

Launching the Nerd Girls program

When Panetta began teaching, she noticed that students weren’t getting any hands-on engineering experience, so in 1996 she created an internship program. It was the precursor to Nerd Girls.

At the time, she was consulting for NASA’s data visualization and animation lab in Langley, Va., translating complex information into a user-friendly animated form. The programs visualized Earth’s atmosphere and identified pollutants, their origins, and their effects on people and the environment.

Panetta needed a larger team to help conduct the research, so she asked her undergraduate students if they wanted to participate.

“Female students flocked to me because they could relate to the work I was doing, loved how their skills could benefit humanity, and didn’t see me as the classic nerd professor with no life,” Panetta said in a 2008 interview with The Institute about the program. “Eventually, the girls outnumbered the boys.”

“The research project ended up winning awards,” she added. “Tufts couldn’t believe that undergrads had a hand in it. That’s when things really turned around.”

Nerd Girls officially launched at Tufts in 2000 as a class where students work closely with industry on engineering projects. Examples have included building a solar-powered car, developing a battery for the last functioning twin lighthouse in the United States, and creating devices to help people train service animals.

“Everyone who has participated in the program graduated with a bachelor’s degree,” Panetta says. “I’m also very proud that 98 percent of participants pursue a graduate degree within three years of earning their bachelor’s.”

The program is open to all students, regardless of gender.

Creating a community at IEEE

Panetta became an active IEEE volunteer in 2004 after meeting Arthur Winston, the IEEE president at the time. Winston, an IEEE Life Fellow, was an electrical engineering professor at Tufts. He helped found the Gordon Institute, a leadership-focused engineering school at the university.

“I sat next to him on a bus, and he invited me to attend the IEEE Boston Section meetings,” she says.

Panetta eventually was elected by the section as a member-at-large—which allowed her to attend conferences and other events.

To help spread the word about the Nerd Girls program throughout IEEE, Winston connected Panetta to Mary Ellen Randall, who was chair of IEEE Women in Engineering at the time. Randall is the current IEEE president and CEO. Panetta joined IEEE WIE and was elected as its 2007–2009 chair.

In that position, she worked with Randall and Leah Jamieson, the 2007 IEEE president, to hire more staff to support the program and launch its magazine.

“At that time, we didn’t have any way to connect to members or tell the stories of women in technology,” Panetta says. “I wanted people to read the stories of women from around the globe and how they overcame adversity. So I launched the IEEE Women in Engineering Magazine in 2007.”

Panetta serves as the award-winning publication’s editor in chief, and she is a member of several other IEEE societies and committees.

IEEE is helping to change the world for the better, she says.

“It’s going to be the engineers who better society,” she says, “because we know how to work together.

“We’ve proven that IEEE members know how to work across geographic boundaries, ethnic boundaries, and gender boundaries. And that’s a good model for the world.”

What could you do if you could make a circuit trace by just bending a piece of paper? How about bridging modern technologies and traditional handicrafts while providing opportunities for learning skills in both.

As part of our interdisciplinary research into digital craftsmanship at the MEI Lab at the School of Creative Media, City University of Hong Kong, we came across research that demonstrated how to impregnate paperlike material (technically a “nonwoven textile”) with the kind of liquid metal used to make conductive ink. Initially, the impregnated material is nonconductive because an insulating oxide layer forms that encapsulates microscopic droplets of the liquid metal. However, applying pressure via shaped molds will crack open the insulating layer, allowing neighboring particles to merge, and thus creating conducting regions in the shape of the mold.

Both of us were introduced as children to origami and kirigami (similar to origami, except that cutting is allowed in addition to folding). We, along with our colleagues, decided to see if those traditional techniques could be used on the new material to eliminate the need for molds. Our goal was to allow crafters to make hybrid papercraft creations that contained easily integrated elements such as LEDs and motors.

In particular, we were interested in the possibility of combining the separate stages of creating a papercraft object and adding electrical conductors. Previous approaches to creating electrified papercraft objects relied on adding a separate flexible conductor—such as adhesive copper tape—to the paper. This increases the effort required and runs the risk of creating open circuits as the conductive material conforms to the object’s shape.

Isopropanol and a gallium-indium liquid material are used to impregnate a paperlike material that is 55 percent polyester and 45 percent cellulose. Electronic components such as LEDs and motors are held in place with masking tape. James Provost

Our first step was to see if the pressures involved in bending and cutting alone would be sufficient to create conductive traces. We became frequent visitors to our university’s materials science and engineering department to fabricate samples and then to borrow equipment to characterize their behavior.

We soon confirmed that the pressures involved in folding and cutting—ranging from 2.5 to 100 megapascals—were enough to create conductive traces. We also confirmed that normal handling of the paper didn’t accidentally create conductive paths.

We made a number of changes to the original method for creating the impregnated paper. For example, instead of immersing the paper in a mixture of isopropanol and liquid metal, we used an airbrush to spray the mixture onto the paper. That allowed us to vary how much was deposited on the paper and to use cardboard stencils to mask some areas from being impregnated, allowing folding and cutting in those regions without creating unwanted conductive traces. We also experimented with the ratios of isopropanol and liquid metal.

We became frequent visitors to our university’s materials science and engineering department.

After optimizing the mixing ratios and amount applied via airbrush, we were left with a material that reliably conducts with a resistance of 23.18 ohms per centimeter for cut edges and 4.4 Ω/cm for folded edges. The folded edges retain their conductivity even if later flattened out, and the conductivity is the same on either side of the paper. We estimate the combined cost of the paper and liquid metal (available from many online vendors) is about US $1.80 to make a 10- by 10-cm piece.

The next step was attaching electronic components to the traces. To make the connections more flexible, we cut down the rigid leads of LEDs and attached conductive thread to the stumps. We then held the threads in place using masking tape. Similarly, we connected conductive thread to the terminals of a power supply.

As our goal was to use this material educationally, we now needed to make it easy for a beginner—whether in papercraft or electronics—to try it out. We created a toolkit, dubbed LiqMetCraft. This consists of all the required materials, plus a browser-based software tool that lets the user select or create designs and then gives guidance on physical construction.

We created three versions of LiqMetCraft. The first is based on Chinese papercraft in which a piece of paper is folded into a fanlike segment and then cut to create a radially symmetric design. We provided circles of paper with a doughnot-shape impregnated region, with an untreated region that created a gap in the donut. We attached positive and negative terminals to either side of the gap. The user could specify in the software how many times they wanted to fold the disk and then draw potential cuts, receiving immediate feedback on what the unfolded disk would look like, as well as guidance on how to place LEDs.

To make our paper sample, isopropanol and liquid metal are mixed in specific ratios while being cooled by an ice bath. Sonic waves are used to ensure the liquid metal breaks up into microscopic droplets. The mixture is then applied via airbrush, while stencils prevent some areas being covered for different papercraft templates. James Provost

The second version of LiqMetCraft was based on origami. We supplied rectangular pieces of paper with two conductive regions separated by a border down the middle. The software tool provided templates for 12 origami designs, with step-by-step instructions for folding them. Once the project was completed, the user could add components, such as a motor, by taping them to the folds.

The final version supported 3D paper model making. In this case, the initial paper supplied was a rectangle with an untreated rectangular central area. By cutting this paper in half and then further cutting the halves into patterns separated by a spacer, the user could make various self-standing models. The software allowed the user to draw a pattern on screen, and then have a cutting machine produce a template for cutting the impregnated paper.

We had 42 participants, evenly divided into three groups, try out the different versions. All found it easy to use, and we were pleasantly surprised that some participants moved beyond the supplied designs to their own creations.

For full details of the current process, see our open access LiqMetCraft research paper published in CHI ‘26: Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. In the future, we plan to try different substrates for the impregnating solution, as well as explore further types of papercraft, such as pop-up books. We’re also interested in developing ways to use the material to support inputs as well as outputs by constructing switches and potentiometers directly out of the material. Imagine traditional papercraft creations becoming interactive devices!

This article appears in the July 2026 print issue.

Take a moment and try to imagine your life without the wireless advances of the past three decades.

Have you lost your luggage? What a shame AirTags have not been invented. The airline representative has promised to call with updates, so settle in for a long wait by the kitchen telephone, because there are no affordable cellphones. You’ll be stuck listening to whatever is on the radio while you wait, because there are no streaming services. That’s not even to speak of all the movie plots that would have been ruined.

This is just a tiny sliver of how wireless technology makes itself felt in your day-to-day existence. The effects it has had on supply chains, infrastructure, and how the economy runs have been world-altering.

None of it would be possible without the radio-frequency integrated circuits that allow all our devices to unobtrusively send and receive information.

Now imagine what the further evolution of this technology will bring: Wide-spread autonomous vehicles, quantum communications, 6G mobile service and satellite communications. Continued momentum will depend on newer and more advanced versions of today’s RF chips.

But there’s the rub. Whereas the design of most of the world’s computing chips has been standardized into its own science, RF design has remained stubbornly in the realm of art. A dark art, even, that is mastered only through years of experience. As any sorcerer will tell you, the dark arts keep their own schedule. And that schedule is impeding progress not just in RF chip design but in every other technology that depends on it.

About seven years ago, in the wake of AlphaGo’s victory over world Go champion Lee Sedol, my students at Princeton and I began to wonder: Could AI be taught this art as well? Recent successes suggest that, to a large extent, it can. Over the last few years, our group and other leaders in the field have started to develop machine-learning-driven algorithmic methods for designing RFICs. Some of the resulting chips look more like modern art than circuit layouts. Yet in many cases, the physical prototypes bested state-of-the art circuits in terms of performance. The real achievement, however, is that it took the AI orders of magnitude less time to conceive a working design than it would a human designer.

This is not about one or two RF chips. AI-enabled design could be the future of all RF design, and maybe much more.

The Dark Art of RFIC Design

So why do these chips all have to be crafted by hand? Why aren’t RFICs designed with an algorithmic synthesis process, much as CPUs and GPUs are?

The design of RFICs is an exercise in engineering across multiple physical domains. Maxwell’s equations, operating across different spatial and temporal scales, govern how electromagnetic fields interact with active and passive devices that must be carefully codesigned for the chip to function. Alongside these are the laws of thermodynamics, which determine how heat is generated and removed during operation, as well as the mechanics of thermal expansion and contraction that dictate how reliably the chip and its packaging survive temperature changes.

Simultaneously accounting for all the physical constraints these impose makes the design space almost impossibly large. Every decision involves complex priorities that often compete with one another, preventing the optimization of any of them.

To better understand the issue, let’s walk through the steps involved, after which you’ll better understand why a single new chip design takes years and tens to hundreds of millions of dollars.

Most of the area of radio-frequency integrated circuits is dominated by complex electromagnetic structures. Human-designed RFICs, like this broadband power amplifier [1], start with templates and follow a symmetric, understandable pattern. But freed from the constraints of human-designed templates and the need for humans to even understand the rationale of electromagnetic structures, power amplifier ICs [2–5] and low-noise amplifiers [6] can take on truly wild-looking yet efficient designs. SENGUPTA LAB

Let’s say you’re an engineer assigned to design a new 28-gigahertz power amplifier for a 5G-millimeter-wave handset. (This is the type of RFIC that boosts the 5G signals on your phone and transmits them to the antenna where they can be picked up by a distant base station). Where do you start?

RFIC design has some features in common with house building. Just as the blueprint for a house dictates the number of bedrooms and bathrooms to be built and the hallways connecting them, the blueprint for an RFIC—called the architecture—establishes the kinds of elements the RFIC needs to fulfill its intended function. Instead of rooms, the architecture includes, for example, the number of stages of amplification your power amplifier needs. Instead of hallways, it shows the paths that signals must take to get through those stages.

The blueprint for RFICs is actually mostly hallway; passive elements, like inductors and transmission lines, take up far more real estate than active elements like transistors.

Here’s why. As you have probably experienced yourself, a typical CPU’s transistors overheat when faced with operating frequencies of just a few gigahertz. The frequencies RFICs can operate at are higher by an order of magnitude—28 and 39 GHz for 5G signals, 26.5 to 40 GHz and even higher for satellite communications, and 77 GHz for automotive radar. Under this onslaught, a CPU’s transistors would fail.

RFIC transistors avoid this fate because these chips cleverly manage the signal’s energy with careful electromagnetic design. This takes the form of byzantine networks of metal elements that dominate the chip’s real estate. These structures are geometrically regular, often symmetrical, and so intricately constructed they sometimes resemble lacelike filigree. But while they may look decorative, they are essential to the chip’s functioning.

Electrically speaking, these “hallways” work more like the chip’s plumbing. Like plumbing, this extensive labyrinth of passives confines electromagnetic energy only to the places it should be traveling around the chip.

The major challenge in RFIC design is putting all these elements together to ensure they work, just as constructing a house from its blueprints demands exact specs for load-bearing beams, pipes, and external walls. On an RFIC, the architecture needs to be realized with physically fabricable transistors and passive components that are connected just so, to permit the signal to travel through the chip and be processed. The way these devices are connected locally is what we call the circuit’s topology.

The RFIC Design Process

To make that power amplifier, then, your first step is to identify a candidate circuit template: The combination of structures that will meet the goals of a particular architecture with a specific circuit topology. Over the years, researchers have eased your burden by developing reusable design templates for specific functions. For example, templates suggest how many amplification stages a circuit needs (because sometimes, combining the output of two smaller amplifiers will result in better bandwidth and efficiency than you would get from a single larger one). And they suggest what the general configuration of the passive structures should be. Today there is an extensive library of such templates.

However, these can’t simply be used off-the-shelf, because each comes with trade-offs. Some have better gain at the expense of stability; some better bandwidth at the expense of efficiency; still others are more energy efficient at the expense of output power, and so on. There is rarely a clear best choice.

To arrive at the “sweet spot” where all these different parameters are balanced into optimal harmony, designers will typically lay out several different versions of the circuit, using intuitions and methods they have picked up in their years of training.

The challenge is that the decision around the architecture, circuit topology, or the electromagnetic passives cannot be done separately. One decision influences the others. So, designing an RF circuit can often feel like trying to fit an oversized carpet into too small a room—press down one corner, and another pops up.

At microwave and millimeter-wave frequencies, even the smallest misstep is the difference between a chip that works and one that doesn’t, and any number of things can go wrong. For example, when an electromagnetic wave encounters a transistor—or any other component —the path it travels must be properly “matched” to what comes next. If it isn’t, some of the energy reflects backward instead of flowing forward. Imagine trying to connect a high-pressure fire hose directly to a narrow garden hose. Without the right adapter, water will splash backward at the junction. Very little will make it through. In electronics, this is called the impedance-matching problem.

To prevent those reflections, engineers design special transitions, essentially microscopic adapters, that smooth the handoff between components. On a chip, these adapters can be surprisingly intricate. They don’t just pass the signal along; they can also split it, combine it, or distribute it across multiple paths with carefully controlled timing and strength.

Once you’ve done the architecture, plumbing, and everything in between comes the moment of truth. Have all the choices you have navigated through the enormous design space resulted in an RFIC that meets its specifications? If the specifications are not met, you will have to go back, either redoing the topology or the entire architecture, and repeat the whole process. So get ready for months of time- and resource-heavy simulation and iteration. Perhaps you now see why, for decades, a core belief has persisted in the RFIC community: “RF design is an art.” It was said that only an experienced designer—with an artisanal understanding of how the pieces make up the whole—could master the subtleties of analog and RF design. Unfortunately, this entrenched notion has long held back algorithmic innovations in the field just when we need them most. Traditional, artisanal RFIC design is hitting its limits as the complexity of these systems inexorably grows.

AI for RFIC Design

While RFIC designers continued their battle against their “oversized carpet” problem, a series of interesting developments emerged in allied disciplines. Across a range of other previously intractable problems like protein folding and climate modeling, AI has been able to successfully navigate multidimensional complex spaces. This gave us the incentive to look deeper into AI for RF. After all, the combinatorial complexity of protein folding is not that different from the nature of the design space in our domain.

We were not the first to think of using artificial intelligence to speed up parts of RFIC design. Researchers had previously trained machine learning algorithms on circuit templates in the hope of speeding up the normal optimization processes. While this approach was undoubtedly faster than humans at optimizing templates, it still relied fundamentally on libraries of existing designs invented by humans.

We didn’t want that. We wanted to break free from the restrictions of prefabricated topologies. Because while a designer’s experience and hard-won heuristics are crucial to building a working design, they also place fundamental limits on it. Furthermore, such an approach would necessarily require simulation steps as part of the optimization cycle, and even the fastest simulations use a lot of computing resources. Worse still, in many advanced cases, such as for broadband designs, there are no existing templates.

But if we didn’t start with templates, where could we start?

The goal here was to allow algorithms to determine—entirely from scratch—every parameter for architecture, constituent circuits, and electromagnetic passives. This approach differs fundamentally from conventional optimization, which is limited to determining the parameters—like transistor dimensions and passive component geometries—that optimize structures originally devised by humans.

In our new approach, the architecture begins essentially from nothing and is progressively assembled through successive iterations. The system explores the design space by generating myriad candidate circuit combinations and mapping the resulting performance trade-offs as it navigates this landscape. Because the process is not biased by prior human design choices, it can produce completely novel circuit topologies that look markedly different from those created by human designers.

In some ways, the approach echoes AI systems such as AlphaGo Zero, which achieved superhuman performance not because it was trained on games played by humans but because it explored the rules by playing against itself. Similarly, our algorithm develops new circuit architectures by exploring and evaluating its own design strategies. In so doing, it learns to understand circuits, electromagnetics, and the close codesign they need to achieve the end-to-end design of RFIC.

Inverse Design for RFICs

To realize this capability, we proceeded in two stages. First, we developed a reinforcement-learning (RL) framework that determines the optimal system architecture, circuit topology, device parameters, and even the properties of the electromagnetic interfaces that connect different circuit elements. In this stage, the algorithm effectively defines how signals should propagate and interact across the system.

The algorithm trains very similarly to how a computer learns to play a game. If you let it play enough times, it can learn to play better by observing the relationship between the actions it took and the score it achieves. In a similar way, the RL agent here learns to design effective circuits by playing with a set of combinations, and over time, it can map the space between the circuit performance to its architecture, topology, and parameters. This training takes a few days to a week, but once trained, the agent can design circuits very quickly

The next step was to determine the physical structure of the IC’s electromagnetics—the plumbing—that can create the desired properties of the passive elements, which are characterized by a set of metrics called scattering parameters. These measure if a signal entering a component actually moves forward—or is reflecting backward, being wasted, as in our previous example with the fire hose and the garden hose.

Deriving the structure from the desired scattering parameters is an example of an approach called inverse design, which appears across many areas of engineering. In structural engineering, for example, one might collaborate with an architect on a physical goal—such as creating large interior spaces with high ceilings—and then determine the arrangement of arches or buttresses that can support it.

Generative AI for Electromagnetic Networks

But RF integrated crcuits pose a particular challenge for inverse design: The process must account simultaneously for circuit behavior and the electromagnetic responses of the interconnects and passive elements that link them together. But it has to figure that out without doing a lot of artisanal iterating.

So we replaced our RF circuit simulator with an AI-based emulator. This AI model can predict the behavior of electromagnetic fields going through any structure—even totally arbitrary two-dimensional shapes—without having to compute the underlying physics from scratch, as simulation tools do. It would predict the solution of Maxwell’s equations and tell you the scattering parameters for any structure you showed it, without actually doing the math. With such an AI in hand, what a time-consuming electromagnetic solver normally takes minutes or hours to accomplish is reduced to milliseconds.

We chose to build our emulator around a convolutional neural network—a machine learning model that has been remarkably successful for image processing. Such networks can extract spatial features from any structure, and it turns out that the image of a structure contains a lot of spatial information that can accurately predict its electromagnetic performance. Then we trained it on a vast number of random pixelated structures whose scattering parameters had been labeled.

Once we had our inverse-design RL and suitable AI emulator, we essentially had an end-to-end AI designer. So we asked it to design us a power amplifier.

Unconventional RF Architectures

In 2023, we published this proof of concept—a power amplifier targeting the millimeter-wave band, specifically spanning 30 to 100 GHz, which covers most of the relevant 5G and radar frequencies. The final design achieved the best combination of wide bandwidth, output power, and efficiency then reported for a silicon-based power amplifier—meaning it could amplify a large amount of data across a wide swath of frequencies—while maintaining record efficiency.

The structure of the IC’s electromagnetic pathways was unlike anything any human would ever consider. Since the AI is not trained on human designs, the layout that emerged looked more like an arbitrary pattern or perhaps a QR code than the regular symmetrical structures we are used to seeing.

One unexpected insight revealed by this prototype, and our research generally, is that there’s no evidence that the templates we’ve historically relied on are even close to optimal for modern design goals. It’s not that a human designer can never come up with a better design. But with the removal of the templates and the time to synthesize cycle upon cycle of optimized circuits, it is now clear that AI-driven synthesis could break traditional design barriers and push the limits of RFIC capabilities.

Our 5G amplifier had only one input port and one output port. Adding more inputs and outputs to a design is not straightforward. Every port electromagnetically couples to every other port, so the scattering parameters quickly add up. Two ports give you four scattering parameters. Four ports, 16 scattering parameters. The math gets ugly fast. Could our model keep up?

We next trained our model on larger classes of electromagnetic structures with many input and output ports. In 2024, we published work showing that multiport integrated circuits are no problem for these AI algorithms either. Where previously multiport electromagnetic simulation required days or weeks of toil, this model evolved new structures in minutes. Since then, a plethora of work in the space by research communities across the globe have demonstrated the power of inverse design in RFIC.

Combining the reinforcement learning framework with the inverse design, we now had the ability to create an RFIC from specifications all the way to a fabrication-ready layout. We’ve so far shown this is true for RFICs ranging from low-noise amplifiers to subterahertz and broadband power amplifiers. The hope is that this will work just as well for other circuits.

Making AI Designs Interpretable

Our goal was to make RFIC design better and easier, but we didn’t want to make it beyond human understanding. Chip testing and debugging is a long, arduous process, sometimes even more so than design. Engineers often prefer ICs to have interpretable structures, so that if a problem crops up, they can understand how the chip works well enough to debug it.

To create structures that are more interpretable, we turned to diffusion models, which you may know from their remarkable ability to generate realistic images from text prompts.

AI-driven synthesis could break traditional design barriers and push the limits of RFIC capabilities.

Imagine you go to your favorite image-generation engine and ask it to create a painting of the sky in the style of Picasso, Van Gogh, or Michelangelo. You will get images that capture the essence of their brushstrokes, their use of colors, and their framing. All are pictures of the sky nonetheless, but in different styles.

Electromagnetic design is similar in that multiple structures can have very similar electromagnetic responses. Instead of using text input, we used scattering parameters as our input, and the electromagnetic structure of an RFIC chip as our output. As part of the inputs to the diffusion model, we created a dial that sets the spatial frequency of the final structure. By turning the dial, a designer can direct the model to synthesize structures with low (classical-looking and interpretable), medium (mazelike structures), or high (pixelated or arbitrarily-shaped) spatial frequency.

From prompts to output, the entire process took about 6 minutes. With this diffusion model, algorithms can now both discover novel architectures and accelerate the creation of conventional, so-called classical ones.

All an RFIC designer needs to do is specify virtually any valid set of scattering parameters. As long as they are physically realizable under Maxwell’s equations, the model pops out a corresponding structure as if it were a vending machine.

The Future of AI-Driven RFIC Design

The results of our investigations have drawn the attention of the RF community. The traditional bottom-up design process is clearly beginning to reverse.

But there are still questions: How generalizable are these methods? Can they consistently deliver truly high performance? Can we get to a place where AI produces designs that maximize every conceivable trade-off, holistically optimizing every parameter to its most ideal physical state? We want to take this strategy beyond RFIC design and invent other kinds of circuits that are different from anything humans have ever done.

These are exciting and ambitious prospects, but we are not there yet. AI can hallucinate a design that creates bad circuits that don’t work. This means verification methods need to remain under human oversight. And, while hallucinations are rare, it would still be good to reduce their occurrence.

History suggests that meeting these dreams of the future will take much more data than we’ve been using. Before the creation of the ImageNet repository—a repository of 14 million varied, human-annotated images—image-recognition models didn’t function well in the real world. The datasets they had been trained on were too tiny to be effective. ImageNet’s massive amounts of training data ushered in a revolution that led to AI that can generalize and recognize images in the wild. The rest was history.

If the goal for RFIC and analog design is a universal foundational model—something that learns the governing laws of electromagnetics and circuit behavior—then we also need data.

The good news is that this data is plentiful. Around the world, countless engineers at companies and academic labs simulate nearly identical RF circuits and passive structures every day. The bad news is that it’s all locked away behind nondisclosure agreements.

Open ecosystems have propelled other areas, and we think the RFIC community should do the same. There had been some movement toward this. Natcast, the operator of the U.S. CHIPS and Science Act’s R&D program, would have bolstered shared infrastructure and innovation for the next generation of wireless, sensing, and defense technologies. Unfortunately, both the organization and the program it ran specifically for machine learning and RFICs have been closed.

But the momentum Natcast’s effort sparked hasn’t died out. Building on our early work, groups across the community have already demonstrated remarkable advances. AI-driven IC design is part of a much broader technological shift. From biology and materials science to automotive and aerospace engineering, AI is reshaping how complex systems are conceived and optimized. Deeper collaboration between AI researchers and chip designers will unlock the field’s full potential. It’s by no means a foregone conclusion, but if we get this right, this genie won’t stay in its bottle.

5G telecommunications, according to industry hype when 5G first launched in 2019, was going to be all about buzzy applications like mobile augmented reality and autonomous vehicles. But the surprise plot twist came when replacing home cable internet turned into 5G’s most widely adopted new application.

Fixed wireless access (FWA) now serves over 14 million U.S. customers, and contributes 28 percent of worldwide wireless traffic. Fixed wireless access is what the term sounds like: broadband internet delivered over a cellular radio link to a stationary location—no cable, no fiber, no trenching, no satellite broadband antenna pointed at the sky. What makes FWA distinctive is that it repurposes the same towers, spectrum, and 5G infrastructure that was built for mobile devices.

One U.S. Federal Communications Commission commissioner has called FWA 5G’s killer app. And that’s true not just in the United States either. Jio, India’s largest carrier, is also one of the world’s largest FWA providers, with over 9 million customers as of last year.

Carriers discovered they could repurpose surplus 5G capacity, while also exploiting a usage pattern quirk: mobile traffic starts to drop after 8 p.m., just when home internet usage peaks. The result is broadband, delivered via traditional cellphone towers, at a lower cost than fiber deployment. For these reasons, FWA provides real price competition to cable broadband, while reaching underserved rural and suburban communities.

Fixed Wireless Access Repurposes Ambitious 5G Infrastructure

FWA is cheaper to deploy than fiber, and for most homes and small businesses, fiber’s gigabit speeds are overkill anyway. And since FWA uses the same wireless networks built for cellular service, FWA works anywhere that receives a steady cellular signal.

As cellular networks extend into areas with minimal service, FWA’s coverage map expands with them. In these remote locales, the other main viable broadband alternative typically comes from satellite services like Starlink—which are, compared to FWA, more expensive, with higher delays, and lower bandwidth.

While most FWA deployments use currently underused microwave bands, some FWA deployments use electromagnetic spectrum that 5G launched but that mostly failed with mobile users. Millimeter waves operate at frequencies 10 to 40 times higher than 4G’s spectrum, offering high data rates from their wide available bandwidth.

However, there are good reasons 5G mobile users today don’t generally use millimeter-wave spectrum. Millimeter waves can’t penetrate buildings. Plus, they lose signal strength within a kilometer or two of the transmitter. Millimeter-wave antennas are also a real drain on cellphone batteries compared to microwave and radio-wave tech.

Yet none of these challenges applies to a fixed station with a clear line of sight to a nearby tower. FWA home units (called customer premise equipment or CPEs) outperform 5G handsets by a significant margin. That’s mostly because of hardware. CPEs carry larger, more sensitive antennas than a typical cellphone, paired with more capable transceivers. CPEs also tend to be plugged into wall outlets, making battery concerns a nonissue.

Another 5G technology that did not gain traction in mobile wireless is multi-user multiple-input multiple-output (MU-MIMO). A base station with MU-MIMO uses an array of antennas to serve multiple users on the same frequency simultaneously.

However, maintaining a MU-MIMO signal involves tracking each user individually—a problem that quickly becomes overwhelming with enough mobile users. FWA is different, however. Static CPEs, with their steadier downlink traffic loads, are an ideal match for MU-MIMO technology.

So, FWA internet service not only uses mostly fallow spectrum but also uses 5G spectrum more efficiently than do 5G mobile users—for whom, of course, these 5G technologies were originally designed!

How FWA Became 5G’s Surprise Killer App

Not long ago, the high-bandwidth use cases for 5G made for an impressive list: millisecond latency for autonomous vehicles, mobile augmented reality headsets with extensive high-speed data needs, and massive machine connectivity for an expanding internet of things (IoT).

These applications have all stalled. Autonomous vehicles pose challenging—and still unsolved—problems unrelated to spectrum allocation. Augmented and virtual reality technologies have yet to create meaningful spikes in bandwidth demand. And the IoT has, to date at least, fragmented across an array of competing standards.

Mobile carriers had built dense 5G networks for mobile customers whose needs rarely saturated the network’s capacity. Home broadband usage peaks in the evening hours, precisely when cellular networks are quietest.

FWA sits at cellular networks’ crossroads of supply and demand.

The Advent of 6G Will Only Expand FWA’s Reach

In December, the telecom standards body, the Third Generation Partnership Project (3GPP), issued its latest 5G specification—Release 20, the final “5G only” update. So, although 6G is still years away (its first specifications are expected in early 2029), engineering decisions that will define 6G are being made today. And FWA is not on the margins of that conversation; FWA is currently considered an established day-one use case.

6G wireless technology promises to expand FWA’s reach—not only via spectrum but also via geometry. Instead of following 4G and 5G’s connectivity model—strong signals near towers and weak signals far away—future 6G networks will let homes connect to multiple towers simultaneously, using a technology called distributed MIMO (multiple-input, multiple-output).

Where 5G’s version of MIMO (a.k.a. massive MIMO) concentrates user communication with dozens of antennas at a single tower, distributed MIMO uses antennas across multiple base stations and coordinates them to deliver signals to your home from multiple directions simultaneously.

The practical result: Because no single tower is responsible for any given connection, the “edge” of a cell network—that outer boundary where signal strength falls off and service degrades—no longer represents a hard limit on who gets well served. A home that would once have been too distant from a tower, or blocked by terrain, could now be within reach of several base stations working together.

6G may eventually adopt distributed MIMO technology for mobile users, when synchronization challenges and other signal engineering hurdles are solved and deployed for real-world cellular networks. The jury, as of 2026, is still out on whether the full distributed MIMO problem will be solved once the 6G standards start to be set in place, within three years.

As demand for FWA grows, carriers will also deploy increasingly capable millimeter-wave infrastructure for fixed customers first—the stationary CPE use case that millimeter wave best suits. The dense millimeter-wave antenna infrastructure that FWA requires is the same infrastructure that future mobile applications will eventually inherit. AR glasses, AI-powered wearables, and other bandwidth-hungry applications originally promised for 5G are not canceled—they are waiting for the infrastructure to arrive.

The pathway to FWA is being prepared at lower frequencies, too. There is growing interest today in the largely unoccupied FR3 band, which spans roughly 7 to 24 gigahertz, situated between crowded low/mid-bands and the much higher millimeter-wave frequencies.

Recent field trials by Nokia have demonstrated FR3’s viability for both cellular and FWA applications. FR3 is emerging as one of the more promising near-term frontiers for extending FWA coverage beyond its current footprint.

None of this was the plan. No carrier executive in 2020 stood on a stage and announced that 5G’s defining achievement would be delivering living room broadband to rural homes and suburban subdivisions underserved by cable.

FWA became 5G’s killer app because the engineering economics made it happen. Surplus wireless capacity met unmet consumer broadband demand, with the physics of a stationary receiver doing the rest.

That is not a criticism of the engineers or the carriers. It is simply how technology sometimes advances—sideways, through gaps nobody was trying to fill.

But FWA’s model of prioritizing unconnected users may in the end prove to be telecom’s on-ramp to everything else. Fix the digital divide first. Tomorrow’s sci-fi future appears set to follow close behind.

By most accounts, the United States appears poised to fall woefully short of meeting new electricity demand over the next five years as data centers and domestic manufacturing proliferate.

Ian Magruder

Ian Magruder is the founder of Utilize Coalition and previously served as director of market mobilization at Rewiring America, an affordable electrification advocacy group.

Building new power plants and transmission lines may seem like the obvious solution, but there are other options, says Ian Magruder, founder of Utilize Coalition, a nonprofit based in Washington, D.C. The U.S. uses only about half of its grid capacity, and a lot more power could be tapped by deploying a spate of newly available technologies.

Backed by Google, Tesla, HVAC systems manufacturer Carrier, and several other companies, Utilize Coalition advocates for more thorough use of grid capacity through policy change and new technologies. Magruder spoke with IEEE Spectrum about those efforts.

Why does the United States use only half of its grid?

Ian Magruder: Most studies have found that average utilization rates are between 40 and 55 percent across different geographies. And the reason is that we’ve built our grid to meet peak demand. We have to ensure that on the hottest summer day or the coldest winter morning we have enough power. But in many parts of the country, we really only hit peak a few days a year, and it’s really only a few specific hours within those days.

It didn’t used to be this way. What’s changed?

Magruder: Over the last 20 years we’ve seen the gap between average use and peak use grow wider. There are a variety of reasons for that. Grid operators have become more conservative following major blackouts and reliability events. And with more variable-generation sources such as wind and solar, grid operators are building in more capacity. But this also presents us with an incredible opportunity to get more out of the grid using new technologies.

What technologies are being deployed to address the problem?

Magruder: Pairing battery storage with energy generation is a key part of this, as are other kinds of distributed energy resources, like managed [electric vehicle] charging and smart thermostats. I would also say that transmission technologies that safely maximize the current in power lines, increase conductivity, and optimize power routes all play a critical role here. And then there’s demand flexibility, which is when utility customers adapt their power use to accommodate the grid during peak hours. Some really good work is being done around flexible data centers.

Is grid underutilization also happening elsewhere in the world?

Magruder: It’s a global phenomenon, but it varies widely by country. European grids face similar dynamics as [those in] the U.S., and in some places utilization is even lower. But Australia and the United Kingdom are further ahead in measuring and managing utilization with new technologies.

What’s the downside to overbuilding our grids?

Magruder: Mainly cost. Electricity rates have gone up, and we [at Utilize Coalition] think it’s because utilization has gone down. A report that we released earlier this year shows that a 10 percent increase in grid utilization could save Americans over US $100 billion over the next decade.

Imagine sitting down at your desk and logging in for a performance review, with an AI system analyzing the conversation. You’ve been working long hours, balancing deadlines, and your manager asks how you’re doing. You say you’re fine, and maybe even smile, but there’s a hint of hesitation and your voice wavers. As you shift your posture, your shoulders slump.

These are subtle cues that to the human eye might hint at underlying stress. But to an AI model that’s been trained only to categorize emotions as “happy” or “sad,” such nuances are likely lost. It logs the words and a smile and moves on—and unless your human manager intervenes, the fact that you’re tired, unfocused, and maybe a couple of days from burnout never enters the equation.

“Emotion AI,” which estimates how people feel based on facial expressions, voice tone, and behavior, seems to be suddenly everywhere; it’s being used in employee well-being and recruitment interviews, education platforms, and driver-monitoring systems. Technology call-center platforms such as NiCE and Genesys use AI to detect when a customer sounds frustrated and prompt agents in real time to slow down or respond with more empathy. Giant companies like Meta and startups such as Hume AI are developing more-expressive voice AI systems that can detect emotional cues in the person they’re “talking” to and adjust how they communicate.

What’s more, hundreds of companies already offer virtual AI companionship apps, a fast-growing market that may be worth an estimated US $555 billion by 2035—and robot buddies have also entered the picture. Intuition Robotics’s ElliQ, for example, is a small device vaguely resembling a white desk lamp that’s now being used to engage older adults in conversation in hopes of reducing loneliness.

But while the field of emotion AI is advancing at a rapid clip, most existing systems are focused on detecting a limited number of signals to label one specific emotion at a time—which is insufficient if you’re trying to understand the human condition. In the real world, human signals and emotions are contextual, overlapping, and constantly changing. A laugh can signal joy, nervousness, or both; a raised voice might signal enthusiasm just as easily as frustration. To make the job of emotion detection even more difficult, reactions differ greatly from one individual to the next, depending on demographics, cultural background, and countless other variables.

In other words, there’s a gap between what we’re expecting AI to pick up on and what AI can actually deliver. That’s the gap a new field of research—what we call human-context AI—is working to close. Instead of looking at just one input and labeling it, human-context AI increasingly has the capacity to take stock of an individual’s personality and character, and to track emotions in real time while combining multiple inputs, including facial dynamics, voice, tone, language, and behavior. Crucially, responses are also evaluated in the context of a specific environment, such as a performance review or professional coaching session. The result? Computers are learning to read the scene, rather than just the screen.

The Origins of Emotion AI

The story of emotion-sensing AI began almost three decades ago in the MIT Media Lab, where the American electrical engineer and computer scientist Rosalind Picard coined the term “affective computing.” Her work introduced the radical idea that computers could be taught to recognize and respond to human emotions.

Picard’s early experiments focused on single modalities: facial expressions, tone of voice, and physiological signals, such as skin conductance or heart rate. The goal was to give machines a window into human feeling, helping them become more empathetic. It was an exciting vision, but back then the science and hardware weren’t ready. Computing power was limited, sensors were crude, and datasets were narrow and biased.

Josie Norton

Over the next decades, researchers and companies got better at measuring the many ways in which humans express themselves. In the 2010s, sentiment analysis—the processing of large volumes of text to suss out emotional undertones—began to reach the mainstream. At the same time, marketing firms, including my company, Neurologyca, began using video and webcams to measure and catalogue customer reactions. Biometric devices and activity trackers, such as Fitbits and Apple watches, also became ubiquitous, generating new streams of data about people’s sleep, step counts, stress levels, and more.

Unsurprisingly, scientists soon confirmed that larger volumes of personalized data led to greater accuracy in reading human emotions. In 2019, researchers at Cornell demonstrated that combining multiple types of signals improves emotion sensing. Their system joined physiological data, such as brain activity measured by electroencephalography (EEG) and heart rate, with visual cues like facial expression, outperforming systems that relied on just one input. Around the same time, Picard and her team at MIT found that humanoid robots trained on data unique to a specific person were substantially better at reading that person’s reactions and feelings than robots acting without personalized data.

More recent studies align with these findings. In 2024, scientists in South Korea showed that fusing physiological, environmental, and personal data to recognize emotion resulted in a 32 percent error reduction. Another paper, published in 2025, demonstrated that user-specific information significantly enhances emotion recognition performance.

Today, our devices know who we are; our habits and tendencies, likes and dislikes. They’ve also gotten smaller and more efficient. Tiny, low-power cameras and microphones embedded in phones, laptops, and virtual-reality and augmented-reality devices can detect dozens of human signals simultaneously, from eye movements and micro-expressions to breathing rhythms, voice modulation, and posture. Advances in computing have also made it possible to integrate audio, video, biometric, and text data, often without even transmitting raw data to the cloud. And researchers at Stanford, Cambridge and MIT, and Kyoto University, in Japan, as well as the Software College of Northeastern University in Shenyang, China, are exploring how fusing such inputs can refine the sensitivity and accuracy of human-machine interactions.

And yet, despite so many breakthroughs, machines still can’t reliably interpret emotion or even physical stress. Just last year, a survey published in the Journal of Psychopathology and Clinical Science revealed that stress scores on smartwatches rarely, if ever, matched the level of stress that users were experiencing. In fact, a quarter of those surveyed reported feeling the direct opposite of what their smartwatches were reporting.

Why the disconnect? We’ve gotten very good at capturing signals, but not at interpreting them. A fitness tracker might infer from your heart rate that you’re stressed and recommend easing off training, but it doesn’t know if your increased heart rate is due to excitement, tiredness, or an extra cup of coffee. Gauging emotions in real-world settings is even more difficult. To solve this complex problem, machines need context.

From Neuromarketing to Emotion-Sensing AI

My company, Neurologyca, was founded in Spain in 2015, and started out in neuromarketing. Working with major European brands and conglomerates, our cofounder, Juan Graña, had realized that companies lacked solid data on consumers. At the time, most customer feedback came through surveys, which posed questions such as, “On a scale of 1 to 10, how joyful does this car advertisement make you feel?” or “Which emoji best describes your mood?” Naturally, these overly simplistic tools led to high levels of self-reporting bias, as people often misjudge or misstate their own reactions.

To get around this problem, Neurologyca set up labs, using neuroscience and cognitive science to more accurately capture human responses to products, logos, advertisements, and experiences. In addition to using biometric tools such as heart monitors, eye trackers, and EEG, we recorded millions of video frames of human reactions, logging each specific context and the resulting facial and bodily movements. To do this, we mapped over 790 points of reference, including corners of the mouth, size of the eyes and pupils, blink rate, and angling of the head. All of this data was collected and stored anonymously under strict European privacy standards.

Next, we paired this information with findings from decades of neuroscience and behavioral science studies on how biometrics, speech patterns, and human movement are related to emotion—research we continue to gather from academic institutions across Europe. We also created a database of situational contexts—for example, “watching a dog food commercial” or “hearing a new song”—and the human feelings they engendered.

In our work with companies, not only did this approach allow us to recognize nuanced emotions, it also let us identify which reactions indicated positive or negative outcomes. Take, for example, the context of horror-film trailers: Our research helped us figure out that the most successful elicit a very specific mix of emotions, namely a little bit of fear, a little bit of anxiety, but also some joy. With this knowledge, we could quickly rate viewer reactions to help a film company figure out how to tweak its trailer for the desired impact.

Neurologyca

Within a few years, we discovered that a model trained on our database could accurately evaluate emotion using just a webcam. We stopped needing to host focus groups in rooms full of equipment. Instead, we were able to do such things as sending out a new perfume sample to paid participants around the world along with a link. When people opened the link, it turned on their cameras, allowing us to record their faces as they sniffed the perfume for the first time. Suddenly, we had expanded our reach: Rather than using small focus groups in one or two countries, we could quickly assess 1,000 people across the planet, comparing how someone in Japan, India, or Germany might feel about a certain product.

About four years ago, as AI was becoming pervasive, we realized that our models had applications well beyond neuromarketing. Importantly, these models are grounded in directly observed human behavior rather than inferred patterns or loosely labeled open datasets. Looking beyond brands and companies, we established that our model could be integrated into AI systems to help them understand human emotion at a much more granular level. In other words, we could provide a layer of context.

For Empathetic AI, Context Is Key

When we talk about “a layer of context,” we mean three different types of context. The first is situational or environmental context; for example, a performance review, a telemedicine session, or a horror-film viewing. The second is personal context, which includes an individual’s specific history, goals, and baseline state. The third is behavioral context, which covers the individual’s reaction over the course of the event or interaction by evaluating real-time changes in attention, confidence, engagement, and cognitive load.

Most systems today focus on only situational context, although some are starting to include personal context. Very few include behavioral context or combine all three in a meaningful way. What we’ve built at Neurologyca is a logic layer that fuses the three and translates them into structured, machine-readable information that allows AI systems and agents to respond more effectively. Our technology is being used to enhance systems in development, as well as some that have already been deployed, including driver-safety apps like Netradyne, home assistants like Amazon Alexa, and health-care AI platforms like Sully.ai.

It works as follows: Situational context is determined by the platform or application, be it a professional coaching session, a meditation app, or a driver’s safety monitor. Personal context already lives within each respective platform—or if not, it can be created through sharing of personal data or monitoring via camera. (Most wellness and professional-development apps, for example, contain each user’s profile, history, and prior sessions.) Last but not least, behavioral context is collected and analyzed in real time using our models. In the end, our logic layer fuses these three streams of information.

Our system doesn’t assign fixed weights to the three contexts. Instead, it provides a continuous calibration, with the balance shifting depending on the specific situation. For example, a pause in speech might signal uncertainty in a performance review, but something entirely different in a relaxation setting. If signals are ambiguous or overlapping, our system reflects that uncertainty through lower confidence scores rather than forcing a definitive interpretation.

What’s more, our system can work without ever sending raw data to the cloud, thereby easing privacy concerns. In many cases, video, audio, and biometric signals never leave the device. Instead, our lightweight models extract information locally and share only what’s necessary. Cloud systems, meanwhile, are used for training, pattern analysis, and model improvement. The result is a hybrid architecture: edge-based processing for speed and privacy combined with cloud-based learning for continuous improvement.

The result? By incorporating context, AI systems are beginning to interpret aspects of the human state as interactions unfold, dynamically adapting to emotions rather than reacting after the fact. The range of potential applications is broad and still evolving. Picture a professional-development platform that uses a human avatar to perform a mock interview and then provide feedback and tips on how to appear more confident, likeable, and well-informed. Or a meditation app that knows exactly how well you slept and how anxious you’re feeling, and can recommend an appropriate breathing meditation. Or a humanoid robot teacher that can tell when a student is confused or bored and step in to get them back on track.

Avoiding Potential Dangers on the Road Ahead

There have long been debates about the ethics of emotion-sensing AI. Some critics question whether systems should attempt to infer human feelings from external signals at all. They argue that reducing people to measurable outputs risks oversimplifying human experience while opening the door to manipulation, surveillance, and unfair judgments in workplaces, schools, and public spaces.

We take those risks extremely seriously. In fact, our technology aims to reduce the dangers of oversimplifying human emotion. Human-context AI is not based on the assumption that a machine can definitively know what someone is feeling. Rather, it is an attempt to move beyond simplistic labels by incorporating situational, personal, and behavioral context, while explicitly representing uncertainty when signals are ambiguous or incomplete.

That said, ethical concerns regarding implementation are real and have shaped the kinds of projects we pursue. We would never, for example, accept military engagements to help with interrogations. Not only for ethical reasons: Emotion AI cannot reliably detect deception, and claiming otherwise would be overstating what the technology can actually do. And while our technology can be used to gauge crowd behavior and predict things like when a football stadium is at risk of becoming destructively rowdy, we don’t want our technology deployed for surveillance. In short, we believe that using our logic layer on anyone who hasn’t opted in would be intrusive and ethically problematic.

In Europe, our systems are designed to comply with the EU AI Act’s restrictions on emotion recognition in workplaces and schools; as we expand into the United States, we apply jurisdiction-specific guidelines while maintaining the same core ethical commitments.

We also don’t advise companies to become overly reliant on our technology. Hiring and firing decisions should not be based on our outputs alone. Instead, our logic layer is designed to support human understanding and surface emotions that might otherwise go unnoticed.

Let’s return to the scenario of the performance review. Never mind basic AI—all humans, and even great managers, miss things during conversations. There’s a lot happening at once, as people process what’s being said, how to respond, and the greater context of the situation. These days, many exchanges also occur virtually or via video, adding more distractions while shared context is stripped away.

While we would never claim that our models understand humans better than their fellow humans, we believe we can offer an added layer to help managers capture and interpret behavioral signals that might otherwise get lost, providing greater visibility into how a conversation is unfolding.

Our model can track patterns moment to moment, picking up, for example, a shift in engagement, an instance when something didn’t land, or a change in how someone is behaving. The model won’t tell the manager what these moments mean or what to do about them; it simply makes them easier to see and follow up.

Human-context AI is at an early stage. The use cases, the adoption patterns, and the actual impact are all still evolving. At the same time, emotion-sensing systems are quickly being incorporated into real products and platforms. And without context—without knowing why people feel the way they do—AI risks misunderstanding us in critical moments.

Artificial intelligence is the transformative, strategic technology of the early 21st century. It is significantly reshaping practically every aspect of our lives, including in ways that probably no one anticipated. Its rate of adoption and impact have been unprecedented when compared with other technologies.

AI as a distinct field was formally established in 1956 at the Dartmouth Summer Research Project on Artificial Intelligence, proposed by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. In their August 1955 proposal for the research project, the scientists introduced the term artificial intelligence and envisioned machines capable of simulating human intelligence.

AI is the “science of making machines do things that would require intelligence if done by men,” as defined by Minsky. The professor received the ACM Turing Award, which is often called the “Nobel Prize in computing.”

Since AI’s humble beginnings 70 years ago, it has evolved significantly in its capabilities, gained prominence, and earned widespread adoption across many areas including business, education, finance, health care, industry, and the military.

IEEE’s contributions to the progress and adoption of AI throughout its journey are substantial and multifaceted.

As we celebrate AI’s 70th birthday, understanding its history, current status, limitations, and concerns is key to harnessing it for good.

The technology’s roller-coaster evolution

Although AI emerged as a distinct field in 1956, its intellectual roots extend back further. The ideas and theories that underpin AI predate modern computers such as the ENIAC, unveiled in 1946.

In 1943 Warren Sturgis McCulloch, a neurophysiologist and cybernetician, and Walter Pitts, a logician working in computational neuroscience, were inspired by the human brain. The two devised mathematical models of artificial neurons, demonstrating that artificial neural networks could perform logical computation.

Frank Rosenblatt, a Cornell psychologist, later advanced those ideas by developing the perceptron, an early neural network that laid the foundation for modern machine learning and deep learning.

A major milestone came in 1950, when celebrated computer scientist Alan Turing posed the question, “Can machines think?” In his 1950 landmark paper “Computing Machinery and Intelligence,” published in Mind, he explored the nature of machine intelligence. He introduced the “imitation game,” later known as the Turing test, as a practical means of evaluating it. The test remains an influential concept in AI and the philosophy of intelligence, as I discussed in my article “The Turing Test at 75: Its Legacy and Future Prospects,” published in IEEE Intelligent Systems.

Claude Shannon, recognized as the father of information theory, explored the potential of machines for complex reasoning tasks in his 1950 article “Programming a Computer for Playing Chess,” published in Philosophical Magazine.

In 1956 AI became a formal discipline, inspiring scientists to explore and advance it further. John McCarthy developed Lisp in 1958, and it became the dominant programming language for AI research and development. In 1959 Arthur Lee Samuel, a computer science professor at Stanford, introduced the term machine learning to describe programs that could improve their performance through experience.

In the early 1980s, renewed enthusiasm and government funding fueled the development of symbolic AI, a rule-based expert system (also known as a knowledge-based system) that encodes domain-specific knowledge as sets of rules. A notable example was MYCIN, designed to diagnose infectious diseases.

Although successful in limited domains, expert systems’ inherent limitations have restricted their broader adoption. Expert refers to a computer system that mimics human experts in a specific domain. It was popular in the early days of AI, and subsequently disappeared with advances in AI such as neural networks and machine learning.

AI’s journey was marked by periods of soaring expectations and disappointing progress, known as “AI winters,” during which funding, interest, and confidence declined. Analyses of the episodes revealed recurring causes and insightful lessons for the field.

A new phase of growth—often described as “AI spring”—emerged in the 2010s with advances in deep learning, the rise of large language models, the transformer architecture, and generative AI (GenAI).

“The imperative before us today is not only to advance AI’s capabilities but also to ensure that it remains human-centered, trustworthy, ethical, and dedicated to enhancing human well-being and societal progress.”

Unlike earlier approaches that processed information sequentially, a transformer model analyzes an entire sequence of text or audio, assessing the importance of each word or component relative to others, enabling dramatic advancements in GenAI and its applications.

Ashish Vaswani, a former computer scientist at Google, and his colleagues at Google Brain introduced the transformer architecture that underpins today’s generative AI systems in their influential 2017 paper “Attention Is All You Need.” Vaswani and Sam Altman—chief executive of OpenAI, which offers ChatGPT—are widely regarded as the masterminds behind the GenAI revolution.

AI reached new heights with the public release of ChatGPT in 2022, followed quickly by a wave of chatbots and generative AI tools that accelerated global interest.

More recently, the rise of agentic AI systems capable of increasingly autonomous operation has expanded AI’s capabilities and impact.

AI’s 70-year journey reflects an extraordinary interplay of vision, experimentation, setbacks, innovation, and impact.

For further information and diverse perspectives on AI history, check out my curated collection of articles.

Strengths and promises

AI’s pragmatic strength lies in its ability to process information, recognize patterns, and perform cognitive tasks at an unprecedented speed and scale. It can analyze vast amounts of data, extract insights, and identify trends or anomalies that are difficult for humans to detect. The programs can automate routine tasks and repetitive knowledge work, improve productivity, and reduce costs.

Chatbots and other forms of GenAI can answer queries and rapidly create text, images, videos, music, software code, educational materials, and other content on the fly in response to a user’s prompts, accelerating information-gathering, innovation, and decision-making. AI summarizes, translates, and rephrases text effectively and can assist in idea generation. It also facilitates natural-language interactions, making technology more accessible to nonexperts and the diverse global community. Its multimodal capabilities enhance its usefulness across diverse domains. Additionally, it can serve as a powerful collaborator, augmenting creativity and problem-solving capacity rather than replacing human intelligence.

AI is transitioning from standalone tools to autonomous, goal-driven systems. Agentic AI systems that can plan, act, and adapt with minimal human oversight are on the rise, enabling large-scale impact.

The 400-page AI Index 2026, published by the Stanford Institute for Human-Centered AI, reveals the technology’s enhanced capabilities and unprecedented adoption rates, outpacing those of the telephone, the television, the personal computer, and the Internet.

For a deep exposition on the current state of AI, read this analysis from IEEE Spectrum, which also published the “Great AI Reckoning” special report.

Weaknesses and concerns

Along with its benefits, AI presents significant risks and concerns. They include biased, discriminatory, and harmful responses; a lack of transparency and explainability in decision-making; privacy violations from data collected for AI training; and cybersecurity vulnerabilities including AI-powered attacks.

AI systems can hallucinate, generating confident but incorrect or fabricated information. Moreover, AI can facilitate and amplify the spread of misinformation, deepfakes, and manipulated content, undermining public trust and driving the algorithmic manipulation of public opinion. The flattering, people-pleasing, or affirming behavior known as AI sycophancy can be harmful as well.

Overreliance on AI could erode human judgment, critical thinking, and decision-making skills. And autonomous systems can make errors with serious consequences in critical domains including defense, health care, and transportation.

The technology’s development and deployment, therefore, must be guided by informed understanding, sound judgment, and responsible governance. In assessing AI’s suitability for any application, its capabilities, advantages, limitations, and risks must be carefully and holistically considered.

IEEE’s contributions

IEEE has not merely documented and disseminated AI’s progress. It has actively fostered, standardized, and guided it toward further advances and responsible use for the benefit of humanity. IEEE maintains a hub for information on its AI activities that is a valuable resource for researchers, developers, regulators, and users.

IEEE publishes 11 AI-focused journals that advance the frontiers of knowledge, including IEEE Intelligent Systems. In its AI at 70 commemorative issue, Intelligent Systems identified the 10 most influential AI articles published since 2000. The magazine, produced by the IEEE Computer Society, has inducted 10 pioneers into its AI Hall of Fame, honoring their contributions and impact on technology and society.

To foster AI research and development, since 2006, the magazine has recognized the field’s rising stars through its AI’s 10 to Watch awards. The biennial awards spotlight outstanding contributions of young researchers and professionals. Nominations for this year’s awards are open until 1 July.

Since the early days of AI, the IEEE Computer, Computational Intelligence, and Systems, Man, and Cybernetics societies have been among those that have fostered AI research and practice. The Computer Society offers a guide to becoming an AI developer.

IEEE and its societies sponsor more than 100 AI conferences annually. The conference archives are available in the IEEE Xplore Digital Library.

The IEEE Learning Network offers more than 200 courses across AI-related areas.

The IEEE Standards Association has developed more than 100 AI-related standards. Its CertifAIEd program promotes ethical design and deployment of autonomous intelligent systems.

The Institute has featured several IEEE members who have developed AI-driven applications, such as Abhishek Appaji, who has created tools to help detect psychiatric disorders.

Shaping AI’s future

The history of AI helps us understand the motivations behind developments and inspires and guides us toward the next phase of the technology’s innovation and revolution. AI’s trajectory is bound to be shaped by the collective choices we make now and in the future.

As Turing wrote in his 1950 landmark article, “We can only see a short distance ahead, but we can see plenty there that needs to be done.”

The imperative before us today is not only to advance AI’s capabilities but also to ensure that it remains human-centered, trustworthy, ethical, and dedicated to enhancing human well-being and societal progress.

Salome Mikadze-Struk is no stranger to adversity. The daughter of refugees, she built a software-development business as an undergraduate at the height of the COVID-19 pandemic and kept it running despite the outbreak of war in her native Ukraine. Now, she’s drawing on her experiences to mentor tech-startup founders and speak publicly about the importance of resilience in entrepreneurship.

Mikadze-Struk was studying at Georgetown University, in Washington, D.C., when COVID-19 struck. Classes went online, and she moved back to Ukraine. In the midst of that disruption she saw an opportunity to develop her business idea, called Movadex, by tapping Ukraine’s pool of talented young engineers. Then Russia invaded in early 2022, during her final semester. Taking online classes from bomb shelters and helping employees evacuate to safer parts of the country was surreal, she says, but the team kept the company afloat and she graduated later that year.

In 2023, Mikadze-Struk took a hiatus from her business to pursue an MBA at Stanford University, which she completed this year. In her precious spare time she’s been advising startups and giving talks, using her unique perspective to promote the need for resilience in entrepreneurship—something she thinks is increasingly important in the software industry as AI coding tools upend old business models.

“You need to be okay with risk, you need to be resilient. You need to be okay with disruption and okay with uncertainty,” she says, “because this is inevitably going to be part of this industry for the foreseeable future.”

An Early Focus on Education

Mikadze-Struk’s parents had settled in Ukraine after fleeing conflict in the Abkhazia region of Georgia in the early 1990s. “They left everything behind,” she says. “You can look on Google Maps and zoom in on where their houses were and it’s all rubble.”

Despite this backstory, Mikadze-Struk says she and her sister had a conventional middle-class upbringing in Kyiv. Her father ran a small shop and her mother was a stay-at-home mom. Her parents placed an emphasis on education and encouraged her to study hard and take part in extracurricular programs such as Ukraine’s Junior Academy of Sciences, which introduces students to research.

“They weren’t rich, so they knew that our way to make it in life was not through investments, but through merit-based accomplishments,” she says.

When Mikadze-Struk was 14, her family discovered the newly launched Ukraine Global Scholars program, a nonprofit that helps talented students secure scholarships abroad. The program helped her win a full scholarship to the Emma Willard School, a private girl’s school in Troy, N.Y.

Discovering Tech

After graduating high school in 2018, Mikadze-Struk was accepted to Georgetown to study business administration. But it was outside the classroom that her career direction began to take shape. She won a startup competition with a medical device she had developed for a school project and, while the business idea didn’t go anywhere, it sparked an interest in entrepreneurship.

Ukraine’s software industry was booming, and she began attending startup events and competitions in her home country the summer before starting college. There she met her eventual cofounder Nor Newman.

Despite both being just 18, they saw a gap in the market. The pair noticed many founders had strong ideas but lacked the technical expertise to realize them, while talented engineering students often struggled to gain real-world experience. Newman had begun informally connecting startups with his college friends, but the pair soon saw commercial potential. “We realized we could actually create our own startup studio and help startups as a team, versus just connecting people,” says Mikadze-Struk.

Then, when the COVID-19 pandemic struck in early 2020, halfway through her sophomore year, it brought both disruption and opportunity for Newman and Mikadze-Struk. While travel restrictions and lockdowns made life complicated, there was also a surge of companies looking to move their business online. “COVID really skyrocketed everything we were doing,” she says.

Sensing an opportunity, Mikadze-Struk and Newman incorporated Movadex in Ukraine in early 2020. From the start, they decided to focus on not only providing engineering talent, but also helping startups with product development. Many times, says Mikadze-Struk, a founder’s vision for the software doesn’t line up with what users actually want. “What really helped us grow is not just the engineering or quality of code, but rather a holistic approach to creating a product and actually getting into the brain of the user,” she says.

Navigating Adversity

Back in Ukraine, Mikadze-Struk had to juggle this booming business with studying remotely—taking classes at night and working during the day. It was exhausting, she says, but it also allowed her to immediately apply what she learned in business classes to building her startup.

Having successfully navigated the pandemic, Mikadze-Struk was dealt another wild card. In early 2022, Russia invaded Ukraine and her life was again turned upside down. It was particularly traumatic for her family, having already been forced from their home in Georgia once by war.

In 2023, Mikadze-Struk took an extended leave from her company to pursue an MBA at Stanford.Christie Hemm Klok

“For my parents to experience their daughters going through all the same things they had gone through was really heartbreaking,” she says. “But at the same time, because I’d heard so much about their story of resilience I had power in me to not fully break down.”

On the day of the invasion the founders told employees to take the day off and emailed clients to warn of potential disruptions. The next couple of days were spent checking on staff and evacuating as many as possible to their headquarters in Lviv, in Western Ukraine.

By the following Monday the business was back up and running. Soon afterward, they partnered with the Lviv IT Cluster business association’s nonprofit arm to help resettle refugees from the eastern part of Ukraine, where strikes were focused, and offer job placements. Throughout this period, Mikadze-Struk was also completing her final year at Georgetown remotely. “Half of my senior year was actually spent in bomb shelters,” she says.

Promoting Resilience in Entrepreneurship

That summer, Mikadze-Struk graduated with a bachelor’s degree in business administration and learned she had been accepted onto Stanford University’s MBA program. In 2023, she took an extended leave from Movadex and moved to California. She also gave birth to her daughter in 2024.

Balancing studies and parenthood was already a full-time job, but she continued to engage with the startup ecosystem by volunteering as a startup mentor and public speaker. Now, after graduating from Stanford, she is stepping back into a more active leadership role at Movadex, where she hopes to drive the company’s expansion into the United States. She also wants to develop a stronger focus on helping customers understand and implement AI in their businesses.

While AI is undeniably disrupting the tech industry, Mikadze-Struk, now an IEEE Senior Member, is fundamentally optimistic about its impact. “The way AI democratized access to building software and to prototyping…is just mind blowing,” she says.

But it will require a significant shift in mind-set for engineers, especially junior developers hunting for jobs. They need to “fall in love with AI” and embrace it as a powerful copilot, she says. As these tools increasingly take over the nuts-and-bolts work of coding, engineers also need to nurture higher-level skills like systems thinking and architectural design.

Perhaps most importantly, given the rapid pace at which the technology is evolving, engineers need to nurture their adaptability and resilience. “It’s both exciting and scary, because you don’t know what tomorrow will bring.”

Large language models have moved out of the research lab and into engineers’ daily workflow. LLMs serve as reasoning engines that can orchestrate complex tasks including identifying vulnerabilities in source code and transforming fragmented project discussions into rigorous technical specifications.

While the general public uses AI tools to write email and plan vacations, technical professionals use LLMs as core architectural elements that are fundamentally changing how digital infrastructures are built and maintained. As the AI models move into mainstream engineering practice, the demand for technical expertise is rising.

The LLM technology market is expected to grow by about 33 percent every year through 2030, according to MarketsandMarkets. The rapid expansion suggests that proficiency in implementing and securing the models is transitioning from a niche into a core requirement for technologists.

More than just a better search engine

To use LLMs effectively, technical professionals must move beyond treating them as conversational robots. At a fundamental level, the AI systems are built on the transformer architecture, a framework that replaced the older method of processing data in a fixed, sequential order. Unlike earlier models that analyzed information one step at a time, transformers use self-attention mechanisms to ingest vast datasets simultaneously.

For technical professionals, LLMs are core architectural elements that are fundamentally changing how digital infrastructures are built and maintained.

Relying on such LLMs without understanding their internal logic creates a significant reliability risk. To build tools that work consistently, developers must understand the core principles that govern how the models process information and generate results. By mastering how a model processes information and how its internal settings influence the result, developers can move away from a trial-and-error approach toward a more precise one to ensure the AI tool handles complex data reliably.

Four ways LLMs are changing jobs

Here are areas that integrate large language models.

Moving past basic prompts. Developers are using application program interfaces (APIs) to connect LLMs directly to their databases and software tools. Employing the APIs allows AI to perform work such as executing code or searching through internal repositories.

Fixing the “hallucination” problem. LLMs are at risk of hallucinations, which are generated facts or code that looks correct but actually is wrong or broken. To fix the problem, retrieval-augmented generation (RAG) forces AI to look up information in a trusted source such as a company’s database.

Prioritizing data security. When using AI with proprietary code, security is a major concern. Engineers must learn how to set up “private” instances of the models to ensure that sensitive company data stays within a secure cloud environment and is not used to train public versions.

The future of collaboration. By automating repetitive coding tasks and summarizing thousands of pages of documentation, LLMs let engineers spend more time on high-level designs and solving important issues.

Online course program helps with mastering the tech

The gap between people who use AI and those who understand how to build with it is growing wider. To help technical professionals stay ahead, IEEE offers a five-course online program, Large Language Models Demystified, available through the IEEE Learning Network.

The program, developed by IEEE Educational Activities in partnership with the IEEE Computer Society, is built for people who want to understand the “how” and the “why” behind the technology. Rather than just teaching basic prompting, the curriculum dives into the engineering behind generative AI, including:

• Evolution, impact, and hands-on exercises: the shift from statistical methods to modern transformers, including hands-on model optimization.
• Understanding transformer architectures: the mathematical core of self-attention and positional encoding, implemented in NumPy and Python.
• Architectural analysis and implementation: advanced LLM design with practical model-building exercises.
• Training and modeling with PyTorch: end-to-end pipelines in PyTorch, leveraging parameter-efficient techniques such as low-rank adaptation and quantization.
• Optimization, alignment, and deployment: performance scaling, reinforcement learning from human feedback (RLHF), group-relative policy optimization, RAG, and agentic AI.

Upon completion of the program, participants earn professional development credits and a digital badge from IEEE to verify their expertise.

Enroll in the course program on the IEEE Learning Network.

Organizations looking to prepare their teams to work on LLMs can connect with an IEEE content specialist to discuss group enrollment and tailored training paths.

In 2018, Amazon brought me in as the lead UX Sound Designer for Astro, its first consumer home robot. Astro used cameras and other sensors to map and navigate your home and workplace, and could proactively patrol, check up on loved ones, and transport small items using its built-in cargo bin. While there was a well-defined feature set and form factor, initially there was no character direction. In fact, even before Astro had a name, there were two main questions—was it simply Alexa on wheels, or was it a robot with its own character?

The Astro team was divided. One option was to focus on Alexa, and treat the mobile robot simply as an added utility. Along with the majority of the UX team, I argued for Astro to not focus on Alexa. Our belief was that a thing that moves through your home and turns toward you with intent can never be just an appliance. People would ascribe character to it whether we wanted them to or not, and so the only question was whether we shaped that character or let it happen by accident.

Ultimately, Astro became Astro rather than Alexa, and user testing backed up our decision. People didn’t see the robot as Alexa. They saw it as its own character, and that’s what they wanted it to be. Alexa on the device felt somewhat strange and creepy, but building Astro its own voice was too slow and expensive in 2018. So, we settled on Alexa as a supporting character that handled any actual talking, while Astro was the main character, communicating as much as it could without words, through sound, motion, and facial expressions.

I had been brought on to the Astro team to define the robot’s sound design language and voice. But there was no one to flesh out the robot’s actual character. You cannot make a single real decision about a character without defining it first. Every choice about how Astro moved, sounded, paused, or reacted was a character choice, and those choices required all disciplines working together. As sound lead, I was weaving together sound, motion, and character, and how they played together inside each story moment. The animators, who programmed Astro’s motion and facial expressions, were extraordinary at what they did, but the emotional arc they were animating came from the sound (and therefore character) work first. So I stepped into that role, which is where my real work started. What I learned about building character for robots applies to nearly everything being built in embodied AI right now.

Character Is a Design System

Developing a character for Astro meant answering questions that had never been asked about a product at Amazon: What is the emotional range of this robot’s baseline state? How does this robot communicate uncertainty without eroding trust? Where is the line between being expressive and annoying? What are the vulnerabilities of this device’s character?

These are design questions. They have real answers, and every team working on the product has to build from them. For example, Astro’s emotional range was designed to be relatively small at first. We never wanted Astro to get too sad or too angry. It could play sad, but would snap out of it quickly and end the reaction on a high note to keep things positive.

Character leaks out of every seam and can create a disjointed experience if not defined correctly. Even if it’s just animation timing that’s slightly off, or a response that’s technically correct but contextually tone-deaf, users feel every one of these inconsistencies, even if they can’t name them. Watch what happens at the beginning and end of this Sing sequence:

Astro goes from nothing, into the emotional moment, and then lands back on nothing. No buildup, no cooldown, no sense that the feeling came from somewhere or had anywhere to go. I pushed hard for better character stitching, the transitions in and out of expressive moments that make a performance feel continuous rather than assembled, but it never got implemented. The moment itself works. But without the stitching, it reads as a clip playing on a robot rather than coming from within the robot character itself.

Story and Sound at the Beginning

We had decided that Astro would have no spoken dialogue, but it had something that functioned the same way: a vocabulary of sounds, tones, and rhythms that acted as its voice. This vocabulary became the leading output of the character’s personality. The robot’s motion and facial expressions were built around it.

Astro’s wake-up sequence is a great example. Waking wasn’t just a boot animation on the screen; it was an entire performance. Slow and humble at first, the robot oriented itself quietly, then stretched its screen, checked its wheels, and finally, with an upward gesture toward its telescoping mast, it popped it up slightly, and did a little dance of joy. Sound, motion, and eyes hit every beat together in full choreography.

The character’s output in that sequence was first written as a story. Astro is waking up in its new home for the first time. Its main aspiration is to be part of a family, so this is the moment it has been waiting for, this is its purpose. Being the responsible character that it is, it wants to make sure everything is good to go before it introduces itself and starts learning its new home.

This narrative came first because it drove every other decision that we made. After the story was written, sound gave that story a metaphorical voice: the excited tones, the pacing as it checked its wheels, and the bright melodic phrase as Astro looked up at its new family for the first time and introduced itself. Once the sound was laid down, the animation team did their thing with motion and facial expressions, taking cues from the emotional arc the sound had established. Motion didn’t lead—it followed the feeling of the story and the sounds, the same way an animator follows a recorded vocal take.

That wake-up sequence became one of the most-discussed moments in early user testing. People described it as “alive.” What they were responding to wasn’t any single element. It was all three channels (sound, motion, and facial expressions) expressing the same defined character in harmony.

Context Is Where Character Becomes Real

The most compelling characters are defined not by a fixed disposition but by how they respond to their environments and the people in them. They’re still recognizably themselves even as they adapt. This is what I call contextual character. A robot living in a home doesn’t occupy a single emotional state. It moves through rooms with different energy, encounters people in different moods, operates at different times of day, and responds to an endless range of social situations it was never explicitly designed for.

We got close to a contextual character output with Astro’s sound. When a specific piece of environmental context was fed in, the system adapted beautifully, and Astro felt completely alive. But every state like this was still a prediction we made by hand—a situation we had to imagine in advance and design a response for. A random home throws more situations at a robot than anyone can possibly predict, so there was always a longer tail of moments the system was never prepared for.

The difference between a product people describe as “smart” and one they describe as “aware” often comes down to this. Smartness is capability. Awareness is context. Presence is character. And character is always in reaction to the people around it, to its environment, to its own evolving state. That’s what makes it feel like something is emotionally present with you.

This is where AI changes the game for character design in ways that go well beyond what was possible with Astro. AI-driven adaptation doesn’t require the contextual predictions that we relied on. It learns the specific rhythms, preferences, and emotional context of the people it lives and works with. The character doesn’t just respond to context. It grows into it.

What Industry Is Missing

The character and soul of the impending wave of embodied AI products appears to almost always be an afterthought. And character defined late is character defined by default. It becomes the sum of a thousand small decisions made by different people thinking about anything but character. People project character onto devices whether you plan for it or not, especially if those devices move—a robot that moves is already a character. If nobody has designed this character, the result will be products that feel like nothing, or worse, feel confusing and not trustworthy. Technically impressive, but lifeless.

We did not get this fully right with Astro. So many things were moving in parallel that character was rarely treated as a utility, and it made sense why. When you are building a first-of-its-kind product, the things that are the loudest are the ones that break, the deadlines, the costs, the features a customer can point to on a box. Character is quieter than all of that. It’s easy to assume it can come later. On a team as large as the Amazon Astro team, it’s lucky to get any idea onto the road map when it is competing with a hundred others that all feel more urgent in the moment. None of this came from people not caring. It came from character being the kind of thing that is hard to prioritize until you see what its absence costs you.

My Asks to Product Leaders

If you are building a product that will share physical or conversational space with people, three things are worth considering:

Define character before you define interactions. You need a defensible character with enough emotional logic to answer hard questions consistently. Find answers to character questions early, and have every discipline build from the same foundation.

Build story and sound into the character pipeline, not the production pipeline. Story and sound developed alongside character definition has the chance to inform motion, expression, and interaction logic. This requires a different kind of collaboration, and a different kind of hire.

Design for adaptation, not just consistency. A consistent character is necessary, but the products that will matter most in people’s lives are the ones that deepen through use. The infrastructure to support that is more and more accessible, but the design thinking to take advantage of it is still rare.

An expanded version of this story is available on Medium.

The rapid evolution of the global engineering landscape requires continuous education. For one week in April, the IEEE community focuses on its educational frameworks. IEEE Education Week, which just concluded its fifth year, provided a comprehensive overview of the resources available to professionals and students.

From 11 to 19 April, the organization supplied a variety of live and virtual events, online resources, and promotions that champion the cycle of lifelong learning.

IEEE President Mary Ellen Randall kicked off the week with the keynote: “Inspiring Tomorrow’s Innovators: How IEEE Educational Resources Can Open Pathways Into STEM.” The event served as a central point for programs that run throughout the year.

“Education Week allows different units to share resources with members and the public, covering everything from preuniversity programs to advanced professional training,” says Jamie Moesch, managing director of IEEE Educational Activities.

Coordination across the organization

The event relied on the cooperation of 120 IEEE partners. Involved organizational units included the IEEE Communications Society, the IEEE Education Society, and chapters and sections from around the world, including in Brazil, Colombia, and India. They produced 114 events, 23 resources, and 11 special offers.

“These collaborations help members remain current in a changing technological environment,” says Timothy Kurzweg, vice president of IEEE Educational Activities. “The goal is to provide accessible tools that assist members in both their own professional development and their efforts to mentor new engineers.”

“The week allows different units to share resources with members and the public, covering everything from preuniversity programs to advanced professional training.” —Jamie Moesch, managing director of IEEE Educational Activities

The participation metrics reflect a broad geographic interest. The IEEE Education Week website recorded more than 4,770 visitors, with primary engagement coming from India, Nigeria, and the United States. Nearly 240 digital badges were issued to people who completed educational quizzes.

To encourage participation, organizers enlisted 72 volunteer ambassadors to promote the week’s activities across their local networks and share key resources on social media.

Available educational tools

Here are a few of the virtual events held during Education Week—most of which are available on demand:

• Celebrating Excellence: The EPICS in IEEE Contributor Awards and Service Learning Showcase.
• Classroom to Startup: Uniting Academia and Industry.
• IEEE’s Role in Shaping AI-Ready Engineering Education Globally.
• Leveraging IEEE Standards to Enhance Engineering Service Learning Projects (EPICS in IEEE).
• Mastering the Modern Job Market: The Power of IEEE Microcredentials.
• TryEngineering Volunteers Making an Impact in STEM.

The Education Week website highlights resources and offers shared by IEEE organizational units, including:

• A half-off discount for members on IEEE e-learning courses. The catalog covers such topics as computing, power and energy, and telecommunications.
• IEEE Communications Society on-demand webinars. Learn the latest trends and innovations.
• IEEE Women in Engineering career-focused, upskill, and reskill webinars. The presentations cover a variety of topics including agentic AI, leadership, and robots.
• IEEE Innovation at Work. The e-newsletter covers emerging technologies, education, and training for technical professionals.
• IEEE Learning Network. Hundreds of continuing education courses, all in one place.
• IEEE TryEngineering lesson plans. The easy-to-use, engaging activities and plans help teach engineering concepts to preuniversity students.
• IEEE TryEngineering collections. The lesson plans and multimedia resources, developed with partners and IEEE technical societies, are designed to introduce technical topics and deepen student understanding.

Individuals who were unable to attend the live sessions can find the archived content on the IEEE Education Week website.

The website also accepts donations for education-related funds managed by the IEEE Foundation.

Updates and technical resources continue to be shared through the #EducationAtIEEE hashtag on social media channels.

Planning for IEEE Education Week 2027, scheduled for 3 to 11 April, is underway.

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

I’ve sat on both sides of the interview table several times over the past decade. You might be surprised to hear that I’ve often been just as nervous interviewing candidates as I was when being interviewed!

Nearly all the interview advice out there is about the candidate’s side, but understanding the other side can also help you prepare. Let me show you what I’ve seen firsthand, and what I’d bet is happening at the company you just interviewed with.

If you recently got rejected after an interview, this might explain what actually happened.

One caveat, because I’ve been on the receiving end of this: A couple of my recent interviews were run entirely by AI. These were screening rounds, but a growing share of job seekers now report being interviewed by a bot somewhere in the process. Everything below assumes you reached a person.

Most teams have no standard prep

You might assume companies train people to run interviews. Many don’t.

In practice, your interviewers may be much less prepared than it seems. Their prep might look like this: “Here’s a rubric from three years ago, figure it out.” Or: “Let’s grab a conference room between meetings and decide what to ask.”

The questions are often whatever the interviewer personally studied when they were job hunting. These days, they may be generated with an LLM the morning of.

Then the panel negotiates. One person wants to quiz candidates on data structures and algorithms for a role in which they design websites. Another insists system design is essential for a junior level position. People default to what was done to them and assume it’s normal because it was normal to them.

What’s normal to the spider is chaos to the fly.

“Scoring” that isn’t really scoring

After an interview, some processes I was part of had one simple scale to score candidates: yes, no, strong yes, strong no.

The result is predictable. Like the candidate? Strong yes. They rubbed you the wrong way but answered everything correctly? Somehow a soft yes at best.

Structured scoring with defined criteria measurably reduces this. The research backs it, and the rare times I saw it used well, it changed my own assessments. Yet many teams I worked on never used this approach.

Prestige bias and politics

Even with a strong scoring system, bias and office politics can change the outcome.

For instance, I once interviewed someone I was strongly against hiring. It was clear they didn’t know what they were doing, and they’d be running critical infrastructure. I gave a strong no with objective reasons, scoring notes, specific examples from the technical round.

Leadership pulled me into a meeting right after and asked why. I walked them through my notes.

What I didn’t know: Several of them already knew the candidate personally. They liked them. They wanted them hired. I said the decision was theirs, my assessment hadn’t changed, and wished them luck.

I’ve also watched a strong resume short-circuit an entire loop. The team saw a top-tier company name, skipped the standard technical rounds, lobbed a few softballs, and basically welcomed the candidate in.

But once this engineer got started, it turned out to be a poor fit. And it wasn’t the candidate’s fault. They were set up for failure, because nobody checked whether this person could do this job at this company.

In both cases, it didn’t work out.

What you can actually control

You could read all this and decide the system is broken or rigged.

The broken part is fair. The rigged part isn’t. People who are genuinely good at interviewing pass more often. It’s messy, but it’s not a lottery.

You can’t fight bias, politics, or a sloppy process. That’s like being mad at the weather. You can only play the two cards you’re dealt: your technical ability and your behavioral presence.

Most candidates obsess over the technical side and forget the behavioral rounds exist. But product managers, designers, and cross-functional leads—people with zero technical background—will judge you entirely on whether you can tell a clear story and seem like someone worth working with. If you’re unlikeable in the room, you’ve roughly halved your odds at every stage.

So here’s the unglamorous advice that actually works: put yourself on camera.

Talk through a project you led, a mistake you made, a hard problem you solved. Record it. Watch it back. Cringe. Do it again.

Think out loud, under pressure, with another human watching.

If you keep failing interviews, the fix isn’t always more technical prep. It’s getting better at being in a room with other people who are potentially more nervous, less prepared, and more biased than you ever imagined.

The process is broken. You can still win.

—Brian

NSF Experiments With New Kind of Science Funding

A new initiative from the U.S. National Science Foundation plans to distribute $1.5 billion of funding over 10 years to independent research organizations, which it calls “X-Labs.” The program is meant to support work being done outside of academic institutions, starting with two areas: scientific instruments for sensing and imaging, and interconnects and integrated photonics for quantum systems.

Read more here.

7 Ways New Engineers Can Flourish in the Age of AI

We’ve said it before, and we’ll say it again: AI is changing the engineering profession. So how can you stay in demand as the field’s tools evolve? A senior engineering manager at Walmart Global Tech offers seven quick tips.

Read more here.

Collection: Career Advice for Engineers, From Engineers

For even more expert tips, check out the new career advice collection from The Institute. These articles feature guidance written by working engineers, meant to help those in all stages of their careers stay at the forefront of their profession. Discover tips for technical presentations, dive into a specific career path like cybersecurity consulting, and more.

Read more here.

Musicians are accustomed to getting paid each time their creative work is used. Across vinyl/CD sales, streams, radio, cover versions, and those numerous niches like karaoke, there are agreements in place about what “use” means. Underlying this is a simple economic principle: The more something is used, the more money it makes.

Generative AI has complicated the definition of use. On the one hand, you could argue that the use of a piece of musical training data happens just once, at the point of training. On the other hand, creators would be right to complain that the creative essence of their work lives on in the structure of the model, used every time the model produces an output.

Now, companies like Sureel and SoundVerse are working to re-create the essential economic principle that motivates creativity in an era of AI. Such initiatives aim to turn the generative AI industry from one guilty of “the biggest act of copyright theft in history” into one that coexists harmoniously with hardworking artists.

Music Royalties for the AI era

Sureel, a startup Warner Music Group just acquired, has partnered with the Swedish copyright agency STIM to explore the potential for music creators to get paid when their music is used to train generative AI tools. Sureel’s software labels online media, such as a music file, with instructions determined by the owner. The instructions specify whether an AI company may use the media freely in training, limit its influence in any given training set, or avoid it altogether. The software then tracks how the AI company uses the media in training and sets licensing fees accordingly.

Meanwhile, the founders of the AI music company SoundVerse “[reject] one-time royalty buyouts as insufficient and [advocate] for ongoing participation of artists in the AI lifecycle,” they wrote in a 2025 white paper. They argue that each time a generative AI system produces an output, certain pieces of training data play a greater role than others. If the system outputs music resembling jazz, the jazz in the training set has arguably contributed more than, say, the folk music. You can therefore differentially reward each piece of training data for each output.

Sureel’s copresident Benji Rogers told me, “Attribution isn’t about re-creating the old economics. It’s about measuring, for the first time, the thing the old economics only approximated.”

Such influence attribution needs to do more than superficially measure how similar a training data point is to the AI output. The challenge is to attribute causality, or a relationship between the training data and the trained AI, Sureel CEO Tamay Aykut says.

Even if the AI industry achieved that, however, it might encourage people to create music designed to maximize training-data royalties. While all creative markets lead to new incentives (music streaming, for example, has driven songs to have shorter intros), the industry could do without another economic structure that is easily gamed, in which someone’s reverse-engineered pastiche diverts royalties away from original works of creative expression.

RELATED: Generative AI Has a Visual Plagiarism Problem

Inferring the influence of a particular piece of music on a generated piece of music, if a well-defined problem at all, may involve more advanced information theoretic principles, or modeling the actual historical role and impact of individual works. Aykut proposes that in carefully designed attribution systems, more unusual and unpolished musical works could even have more inherent value than radio standards.

Simon Gozzi, head of business development at STIM, says the company is in the process of seeing how Sureel’s attribution reports could underlie licensing agreements between musicians and AI companies. Could generative-AI attribution strategies not only sustain the economic logic that “popularity pays,” but also motivate musical experimentation and diversity? It’s a compelling concept when public sentiment rightly fears generative AI’s threat to cultural vibrancy, pushing power toward tech companies, deskilling creative workers, shrinking revenue in the creative sector, and filling the internet with slop. “Attribution is one of the few credible tools we have,” Rogers says.

There’s a window of opportunity to debate and establish approaches to paying for AI training data that serve a vibrant and sustainable creative sector.

The technical problem of training-data attribution is both complex and ill-defined. Just as a simplistic attribution strategy based on measuring similarity might motivate people to reverse-engineer the canonical works of a genre to capture royalties, a more complex attribution strategy based on some information theory of originality might be easily gamed or fail to reward human cultural production.

For creative workers, there’s good reason to fear that even with the best intentions, AI attribution will only compound the baroque and opaque arms races that they are already weary of navigating. Some voices within the music AI sector are also skeptical. Drew Silverstein, president of SourceAudio, says, “Attribution would seem to be the obvious answer, but it’s flawed in AI, so we have to look at other models.” He advocates simple negotiated agreements with an agreed or annually recurring price at the point of training.

Meanwhile, the copyright lawsuits that have dominated the generative AI revolution are beginning to give way to an increasing number of privately negotiated agreements, such as those between Universal, Warner, and major AI companies to work together on training models with copyright consent. Although little is certain, these agreements may have considerable influence over the industry norms that arise.

Right now, there’s a window of opportunity to debate and establish approaches that pay for AI training data while also sustaining a vibrant creative sector. Sophisticated engineering solutions will have a role to play, but they need to take into account the cultural complexity of the challenge, and enable fairness and transparency through good design.

Making AI Training Pay Off

It remains to be seen whether monolithic generative models such as Suno actually have as much credibility as first touted. In many creative applications of AI, there’s a renewed focus on smaller customized models that are tailored for specific human creative expressive needs such as IRCAM’s RAVE model or Jen’s Style Filters. Meanwhile, more mainstream “end user” creative applications may be shifting towards a focus on fan engagement. OpenAI’s sudden dropping of Sora, despite being in negotiations with Disney and Suno’s recent emphasis on building fan-engagement experiences that draw directly on the work of artists, following its deal with Universal, both point to teething troubles in the creative AI sector.

A move to smaller, more targeted models and applications would give more room for creator alliances. For example, collectives of musicians might band together to provide the training data for a smaller custom model, for which revenue splits might be egalitarian or based on other principles of fairness.

The same may possibly be true of hybrid model architectures and structured training regimes where different data sources are used at different points in the training process, as well as retrieval-augmented generation, which mixes context-specific information with training data to improve results. An approach that produces worse results but enables fairer or more transparent paths of attribution may be more successful if it brings creators on board with more lucrative royalty flows and even clear credits.

Also, no matter how sophisticated an attribution algorithm is, it will always be grounded in human decisions, ranging from the wise and the fair to the arbitrary and corrupt. Ask a music industry insider to explain how the percentage split between recording and songwriting royalties is determined, and you’re in for a long answer. At best, the machinery of training data attribution will enable open and informed discussion about what makes our creative and cultural sectors fair and vibrant. At worst, it will conceal already opaque private agreements in complex black boxes.

This is where national policies are vital. Attribution must be “multi-layered and auditable, open to expert and regulatory scrutiny,” Rogers says. Crafting such policies will take expertise from computer science, musicology, law, and economics. AI-competitive governments will be able to boost their cultural and creative sectors by supporting institutions that fulfil this purpose.

Even the most neoliberal economies look beyond markets to sustain cultural expression, whether through public arts funding or measures like local music quotas for radio. As the economic impact of generative AI in the creative sector takes form, taxation, redistribution, and active support of cultural infrastructures may still be the most effective way to support positive social outcomes. Taxing big AI and redistributing that revenue back to the creative workers that contributed to the industry’s wealth is, after all, another “AI attribution strategy.”

On 19 April 2026, the Honor Lightning humanoid robot ran a half-marathon in 50 minutes and 26 seconds, beating the human world record by 7 minutes and the best robot time from 2025 by almost 2 hours.

How did Honor do it? Is there some magical technology or technique that unlocked this performance? How did the company beat the significantly better-known Unitree (which reportedly had to supply its robot with an ice backpack to try and complete the race without overheating)? My doctoral thesis involved building and controlling hopping and running robots, and since then I’ve tried to design and build efficient commercial legged robots, giving me a decent idea of the constraints involved. In this article, we take a look at the fundamental underlying constraints to try and answer these questions.

The Physics of Running

Running consists of alternating phases of a leg pushing against the ground (“stance phase”) and the body flying through the air (“aerial phase”). In the aerial phase, the body falls due to gravity, losing vertical momentum. The leg in stance phase pushes against the ground to redirect the vertical momentum upward, while the other leg swings forward to reposition for the next foothold.

Electric motors use energy to produce torque—the higher the torque, the more energy is lost as heat. Adding a gear train after the motor amplifies its torque and reduces its speed. A large reduction helps with torque production, but since the rotor of the motor itself has to spin faster, it becomes very sluggish at accelerating its output. This is obviously bad for the swing phase described above. These competing effects mean that for a particular motor, there is usually a sweet spot for the gear ratio:

The power consumed by a robot leg is minimized at an optimal gear ratio (30:1 in this example).Avik De/Datawrapper

How Honor Did It

While the Lightning’s motor specifications are not published, the hip and knee motors roughly have a 110-to-150-millimeter outer diameter. For an approximate set of motor parameters, I looked to the ILM115x25 motor due to its relevant size and detailed specifications.

We can use a simple physics model to estimate the power consumption for running at 7 meters per second (the Lightning’s average half-marathon speed) as gear ratio varies:

The light blue curve shows how to pick the optimal gearing (45:1). The dark blue curve shows how much heat will be produced in the knee motor, ~150W for the optimal gearing.Avik De/Datawrapper

We see that the drivetrain is not magical: with a gear ratio chosen for this task (we’ll return to this below), the approximate robot power consumption would be a very reasonable 400 watts.

However, the dissipated knee power ( typically the main thermal limiting factor) is approximately 150 W. This is almost an unavoidable consequence—running at human speeds with a humanoid-size robot will inevitably generate this amount of heat! Over a prolonged period, keeping the motor from overheating would be a challenge, but the Lightning has a trick up its sleeve:

According to Honor, the liquid-cooling pipes penetrate deep into the motors like capillaries. The high-power liquid pump has a heat-exchange flow rate of more than 4 liters per minute. Each of the four drive motors in the lower limbs is equipped with an independent liquid-cooling circuit.

Liquid cooling is not new, but it’s definitely not a commodity. It has shown up in research periodically, and on the commercial side Apptronik tried it for a few of its prototypes but (to my knowledge) does not use it on its main Apollo platform. Basic air-convection-based cooling would not continuously be able to extract 150 W out of the knee motor, and so the cooling technology is a key enabler of this type of performance.

Why Others Couldn’t Compete

Why did Honor’s competitors, including more established and widely shipped humanoids such as from Unitree or Agibot, not compete as well?

We can use the same model to generate an equivalent energetics plot for walking at 1.5 m/s, a much more modest but potentially more common activity for a commercial humanoid robot:

The solid and dashed light blue lines show a running-optimized design, while green lines show a walking-optimized design. The optimal ratio for walking is much lower (30:1 vs. 45:1). However, the power dissipated in the knee motor while running [dark blue] is much higher at 30:1 vs. 45:1—the price to pay for running with a walking-optimized design.Avik De/Datawrapper

The plot adds a new green curve for the walking power, and the optimal gearing is significantly different!

Let’s say you design your robot to excel at the normal walking task and choose the green design with 30:1 gearing. The knee motor power to run a half marathon is over 300 W (red arrow), more than two times what we had with the running-optimized design. It wouldn’t be so surprising to need ice packs!

Conversely, visually following the green curve shows that the running-optimized robot wastes more power for walking. Using larger motors sized for running increases the weight of the robot and wastes power when it is standing or walking. The larger motors also pose practical issues like bumping into objects while operating in homes or factories.

Closing Thoughts

Honor’s half-marathon performance was an impressive engineering effort and result. It didn’t need any magical leaps in technology, but the deployment of the capillary motor cooling solution is a notable advance without which this running pace would have been unsustainable. The cooling, weight optimization, and robustness advances may well be useful for more practical purposes like carrying heavy payloads down the line.

The Honor Lighting robot [right] has much larger motors driving its legs than the Unitree H1 robot, making it a more efficient runner but a less efficient walker.Left: Wei Zhiyang/Zhejiang Daily Press Group/VCG/Getty Images; Right: VCG/Getty Images

However, the Lightning is not as well-suited to other tasks as a robot designed for greater versatility. Engineering is always characterized by trade-offs, and making the correct ones separates good products from great ones. With consistently improving AI language models, this very human skill is becoming the most valuable one an engineer can have.

The news coverage seemed to overly focus on the fact that the human half-marathon record had been broken by a robot. Machines and humans have very different capabilities and constraints, so why should we ever have expected the half-marathon time for a robot and human to be related? As in Deep Blue’s 1997 defeat of Garry Kasparov in chess, where it couldn’t physically move the pieces, the Honor robot’s capabilities are much narrower than a human running elbow to elbow with other runners while visually navigating the course without GPS. Comparing the robot runner to a human runner is just an apples-to-oranges comparison, which only risks diminishing Honor’s engineering achievement on one hand and human athletic achievement on the other.

Nearly 750 million people face hunger today, according to the U.N. World Food Program. And by 2050, global demand for food is expected to increase by 50 percent from 2010 levels, the World Resources Institute says.

A smart agriculture special-issue report recently released by the IEEE Smart Agri-Food Initiative says meeting the demand will require technology to expand food production. The report highlights research, case studies, and new ways of applying technology to inform farmers, engineers, and policymakers.

Leading the initiative is IEEE Fellow John Verboncoeur, chair of the smart-food program and professor of electrical and computer engineering at Michigan State University, in East Lansing.

“Food security is becoming a systems-engineering problem,” Verboncoeur says. “We’re no longer talking only about tractors and irrigation. We’re talking about sensing, communications, computation, automation, and sustainability all working together.”

Although not formally trained as an agriculture scientist, Verboncoeur’s first involvement with smart agriculture was as an undergraduate at University of Florida in 1985-86, where he helped develop an SmartAg aeroponics system for NASA for the International Space Station. It used mist to spray the plants’ roots and lightweight pneumatic structures to hold the vegetation in place.

He has also chaired the executive committee of Michigan State’s SmartAg Initiative since it launched in 2017. He chaired the program’s leading interdisciplinary efforts to apply engineering and digital technologies to farming and food systems.

Verboncoeur connects the shift of using engineering as a force multiplier for farming to lessons learned from the IEEE Smart Village program, which supports projects and organizations bringing electricity and educational and employment opportunities to remote communities. Agriculture, he argues, requires the same systems-level mindset.

“The challenge isn’t just inventing technology,” he says. “It’s making systems practical, affordable, and deployable.”

From digital twins to autonomous harvesting

A central theme across the Smart Agri-Food Systems report is the convergence of automation, data analytics, and sustainability.

One paper, “Smart Agriculture, Precision Agriculture, Digital Twins in Agriculture: Similarities and Differences,” addresses the confusion regarding how researchers and practitioners define and apply the technologies to farming.

The paper was written by Dilan Onat Alakuş, a research assistant in the software engineering department at Kırklareli University, in Türkiye, and Ibrahim Türkoğlu, a software engineering professor at Fırat University, in Elazığ, Türkiye.

Unclear terminology can lead to inefficient investment and poor adoption of the technologies, the two authors say. They note that agricultural methods based on traditional practices and intuition lack a thorough analysis of their environmental and economic impacts.

They describe how three technologies can benefit farmers:

• Smart agriculture systems integrate sensors, artificial intelligence, robotics, and analytics to improve efficiency and sustainability at scale.

• Precision agriculture focuses on location-specific decisions. Farmers use GPS-guided equipment to map fields, deploy drones to monitor crop health, and install field sensors that track soil moisture and nutrient levels in targeted zones. The tools allow farmers to apply water, fertilizer, and pesticides only where needed—which can reduce waste and lessen environmental impact.

• Digital twins create virtual replicas of an agricultural area. The resulting models simulate the farmstead, crops, and irrigation systems, allowing growers to test scenarios and predict outcomes before implementing changes.

The authors emphasize that the categories overlap in practice. A digital twin might draw data from precision agriculture systems and feed recommendations into smart agriculture platforms.

Clearer distinctions help farmers select appropriate tools and avoid unnecessary complexity and costs, they say.

“This study contributed to conscious agricultural practices by differentiating agricultural technologies,” they wrote, adding that clearer definitions can increase productivity.

Smart farming in practice

The report shifts from theory to application in a paper describing bustani, which means my garden in Arabic. The Bustanica project in Saudi Arabia is an automated hydroponic vertical farming system developed by researchers at the Prince Mohammad Bin Fahd University, in Al-Khobar, Saudi Arabia. The “Bustani: A Microcontroller-Based Automated Hydroponic Vertical Farming Solution” paper was written by Hussah Alotaibi, a computer engineer at Saudi Aramco, the country’s national oil company; Abul Bashar, Widad Karsou, and Shehvar Khan, researchers in the university’s computer engineering and computer science department; and Salahudean Tohmeh from the university’s robotics laboratory.

The Bustanica system combines hydroponics with aeroponics, in which plant roots hang in the air and receive nutrients through a misting system. Together, the approaches allow crops to grow in compact indoor environments, using far less water than traditional methods.

The method integrates IoT sensors that continuously monitor water chemistry and reservoir conditions.

The system grows crops in controlled indoor environments. A closed-loop design recirculates water to reduce waste. Sensors measure pH levels, nutrient concentration, and water levels. An Arduino Mega processes the sensor data. A NodeMCU ESP8266—a low-cost, open-source IoT platform—handles Wi-Fi communication and cloud connectivity.

The system sends the data through Google’s Firebase cloud platform, which acts as a real-time bridge between sensors and control systems.

A mobile app lets users monitor and control the system remotely. It displays real-time data on lighting, nutrient levels, and water pump activity. When conditions move outside optimal ranges, automated dosing pumps adjust the levels as needed.

Engineering can’t solve all the world’s problems. But it absolutely has a role to play in helping the world feed itself.” —John Verboncoeur, chair of the IEEE Smart Agri-Food initiative

The system operates as a feedback loop, collecting data, transmitting it to the cloud, analyzing the conditions, and automatically triggering adjustments.

LEDs simulate sunlight. Ultrasonic sensors measure water levels. Electrical conductivity sensors track nutrient concentration. During testing, the system maintained stable environmental conditions and adjusted dosing dynamically as readings changed.

The authors describe the outcome as “a fully functional and automated vertical sustainable farm that creates desirable growing conditions, along with an Android application that provides real-time monitoring and notifications.”

Beyond automation, bustani reflects a broader shift toward merging agriculture with consumer technology and smart-home systems. Future plans include integrating the Amazon Alexa virtual assistant and machine learning tools for plant disease detection and growth analysis.

Robotics and labor challenges

The “Toward an Efficient Tomato Harvesting Robot” paper addresses autonomous harvesting, a long-standing challenge in agricultural robotics. Tomatoes in the field vary widely in size, shape, and ripeness, and they can bruise during handling. The paper was written by IEEE Senior Member Hyoung Il Son—a professor of biosystems engineering and robotics at Chonnam National University in Gwangju, South Korea—and his graduate students Jongpyo Jun, Jeongin Kim, and Jaehwi Seol.

The paper describes how robotics is increasingly being used to target crops once considered too delicate or variable for automation.

The researcher combined 3D machine vision, robotic arms, suction-based grippers, and rotating cutting tools to build a harvesting machine capable of operating in unstructured outdoor environments. The system aims to reduce reliance on manual labor while improving harvesting efficiency and consistency.

Agriculture as a systems problem

Verboncoeur says the developments highlighted in the papers reflect a broad transformation in how engineers view the agricultural industry.

“Agriculture used to be seen primarily as managing the challenges of planting, watering, and fertilizing plants, and using machines to make the process less labor-intensive,” he says. “Now it’s also a data problem, a communications problem, an energy problem, and a resilience problem.”

Another featured paper, “Sustainable and Smart Agriculture: A Holistic Approach,” examines how technology can address environmental and demographic pressures. The paper was written by Surender Singh and Sannihit , researchers at the computer science and engineering and the civil engineering departments at Chandigarh University, in Mohali, India.

Farmers must increase food production while reducing environmental damage from depleting water resources, overapplication of fertilizer, deforestation, and greenhouse gas emissions, the authors say. They describe smart farming as “a revolution in food production” that can allow farmers to generate higher yields from existing resources through connected technologies and data systems.

The authors highlighted the issue of rapid urbanization. By 2050, they report, nearly 70 percent of the global population will live in cities, increasing pressure on food supply chains and distribution systems.

Wireless sensor networks will play a central role in the transformation, the researchers say. The networks use small, connected devices to monitor soil moisture, temperature, humidity, light intensity, and crop conditions. The system transmits the data to cloud platforms, where machine learning models analyze trends and recommend actions.

The authors emphasize that decision support, not automation alone, drives the greatest value of crop harvest. Farmers can integrate the information into crop management strategies to improve productivity while reducing their environmental impact.

They also note increasing collaboration between industry leaders such as Caterpillar, CNH, John Deere, and Kubota and technology companies including Bosch, Google, Intel, and Microsoft. Challenges remain, however, in communication reliability, sensor cost, and scalable data infrastructure, the authors say.

SmartAg beyond the farm

The implications of the tech advances that make farming more efficient extend beyond agriculture. Many of the same technologies—remote sensing, wireless sensor networks, AI analytics, and cloud platforms—support transportation, energy, and industrial systems.

The convergence explains IEEE’s growing involvement. Modern agriculture now combines electronics, communications, computing, and control systems.

Agriculture requires that integration, Verboncoeur says: “The challenge isn’t just inventing technology. It’s making systems practical, affordable, and deployable.”

What’s next for smart agriculture?

The special issue marks an early stage for the IEEE Smart Agri-Food initiative, which plans to develop standards; create structured ways for farmers, researchers, governments, and agribusinesses to work together; and devise deployment strategies for smart systems.

Future research is likely to focus on interoperability between platforms, data sharing, and scalable deployment models. Digital twins are expected to play a larger role as computing power and sensor density increase. Simulating agricultural systems before applying changes in the field will become commonplace, experts predict.

Adoption depends on more than technical capability, though. The central tension moving forward lies between innovation and practicality.

“Farmers face challenges in adopting such technology due to cost, electricity availability, communication infrastructure, and vulnerability of connected devices,” Singh and Sannihit wrote.

Smart agriculture offers improved efficiency, in addition to reducing the inputs of water, fertilizer, and time that would otherwise be spent on tasks machines can handle autonomously. But the benefits matter only if systems function reliably across diverse environments—from industrial farms to small, family-run operations in food-insecure regions.

For IEEE, agriculture now sits within core engineering domains. The stakes extend beyond technology itself, Verboncoeur says.

He adds that: “Food insecurity affects stability, health, education, and economic development. Engineering can’t solve all the world’s problems, but it absolutely has a role to play in helping the world feed itself.”

A half century ago, a scrappy crew at the University of Massachusetts Amherst erected a wind turbine on Orchard Hill, the highest point on campus. It was a frugal production, cobbled together from the rear axle of a Ford truck, a donated generator and microcontroller, a steam pipe, and various handcrafted steel and fiberglass parts, including its 4.5-meter blades.

The team of UMass engineering grad students, faculty advisors, and one precocious undergrad built it to prove that wind energy could keep rural homes toasty in New England’s frigid winters, as a way of trimming U.S. oil dependence—a national imperative in the aftermath of the 1973–1974 energy crisis. To illustrate the point, they also assembled a modular home there on Orchard Hill, and outfitted it with heaters that would be powered by the turbine.

In 1975 and 1976, a crew from the University of Massachusetts Amherst designed and constructed the 25-kilowatt wind turbine that kick-started the U.S. wind industry. Sandy Butterfield

It worked—too well. “We had to open up the doors in the dead of winter. It was just too damn hot,” recalls Michael Edds, who designed the turbine’s electrical system and served as the project’s first resident engineer. Fittingly, they dubbed the turbine the “Wind Furnace.”

The turbine maxed out at 25 kilowatts—puny compared to modern machines that generate up to 26 megawatts, but more than most energy experts expected from wind technology in November 1976. Back then, wind power still conjured up images of quaint Dutch mills and creaky prairie water pumpers. Crafty engineers would soon show that wind power could be so much more. And it all began with the brilliant, commanding, and often polarizing UMass professor leading the Wind Furnace project: William Heronemus.

A retired U.S. Navy captain, Heronemus had joined the UMass faculty in 1967. He’d earned Bronze Stars for valor in World War II, designed and built nuclear submarines, and liaised with the British Royal Navy on the Polaris missile. UMass had recruited Heronemus to do ocean engineering, but the energy crisis and his growing misgivings about nuclear power shifted his attention to renewable energy.

Heronemus, photographed circa 1973, publicly advocated for the buildout of wind turbines, both onshore and off, at immense scale. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

By 1972, Heronemus was advancing detailed designs to deploy wind turbines at immense scale. That year, at the Marine Technology Society’s annual gathering in Washington, D.C., he presented schemes for building thousands of them across the Great Plains as well as a vast grid of massive floating turbines transecting New England’s continental shelf. Wind power, he contended, could generate nearly a fifth of U.S. electricity needs by the year 2000. Never mind that the technology for such an enormous buildout had yet to be commercialized. Espousing grand schemes made Heronemus a quixotic figure.

He also vigorously attacked the commercialization of nuclear power, creating enemies within electric utilities and U.S. government agencies that saw nuclear technology as the future. They didn’t appreciate his claims that a cleaner energy future via wind was ready to be tapped, and that the push for nuclear power and its radiological risks was unnecessary. As author and energy analyst Peter Asmus put it in his 2000 book, Reaping the Wind: “William Heronemus was a dangerous man suggesting an audacious departure from the status quo.”

The UMass Amherst wind turbine generated most of the energy to heat a modular home through the cold, windy winters on Orchard Hill. Solar thermal panels provided some heat during windless periods. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

What happened on Orchard Hill in 1976 marked Heronemus’s turn from provocateur to changemaker. The success of the experimental turbine set off waves of technological and industrial developments that forever changed the energy landscape. Within a few years, the students he trained and the entrepreneurs he inspired were building the world’s first modern wind farms and leading the Great California Wind Rush—the market that turned wind craft into an industry that’s still growing fast half a century later.

Globally, annual wind generation more than tripled between 2015 and 2025, according to data from Ember Energy, a think tank based in London. It will best nuclear’s global output by the end of this year, Ember predicts. And it all started with Heronemus, says Robert Thresher, longtime former director of wind research at the National Renewable Energy Laboratory (NREL) in Golden, Colo. (a U.S. Department of Energy lab rebranded late last year as the National Laboratory of the Rockies). “In my mind he was the father of the people that went out and really made the industry what it is today,” he says.

William Heronemus and the History of Wind Power

I got to know Captain Heronemus posthumously, interviewing his contemporaries and sifting through boxes delivered to the UMass Amherst archival research center’s 25th-floor reading room. During three visits there since 2023, I have discovered clues to his life, thinking, and research process amid the writings where he pitched his big ideas to the world. His papers include proposals to governments, utilities, and deep-pocketed philanthropists and investors, including Jane Fonda and Goldman-Sachs. Papers reveal the internationalism and commitment to service that took Heronemus on renewable-energy consulting trips to Pakistan, Cuba, Côte d’Ivoire, and beyond. Records show meetings with corporate powerhouses like Boeing and Grumman Aerospace and calls on politicians, including the senator and presidential hopeful Ted Kennedy. Postcards from former students exude gratitude.

Heronemus sits with a mock-up of a multirotor turbine in his cramped office in Marston Hall, UMass Amherst’s main engineering building. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

I learned that Heronemus turned his attention from ocean engineering to energy a few years after arriving at UMass, when he saw the growing string of nuclear power plants going up along the Connecticut River, which flows past Amherst en route to Long Island Sound. The U.S. government had picked nuclear power as an antidote to the 1970s oil crises, and Northeast utilities had jumped in big. But Heronemus and other UMass engineers worried that the riverside reactors’ waste heat would threaten the river’s ecosystem and bounty.

The advent of cooling towers to blow off heat into the air addressed the thermal pollution concern but created another: water depletion. (Nuclear plants consume about 60 million gallons of water per day, per reactor, on average.) And Heronemus perceived other nuclear power liabilities, stemming from his experience with nuclear propulsion on Navy ships. As a design engineer and head of construction and repair for a shipyard, he valued the military’s zero-accident standard for reactors but also knew the high cost of adhering to it. He argued that building expanded versions of the Navy’s pressurized water reactors to power cities and factories couldn’t be both safe and economical.

In 1971, Heronemus designed an offshore turbine with three rotors, but the first big multirotor prototype wouldn’t be built for another four decades. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

He predicted—accurately, as it turned out—that costs would rise sharply as the nuclear industry addressed safety and environmental concerns. “Each plant costs more than its predecessor. The shipyards involved with nuclear reactors came to that conclusion years ago,” he wrote in a 1973 research proposal. He also argued that the risks inherent in nuclear reactors and their radioactive waste were unnecessary given Earth’s abundant solar and wind energy resources. He broadcast those views wherever and whenever he could: before congressional committees, at U.S. Atomic Energy Commission hearings, at academic conferences, in media interviews, and even at Rotary Club luncheons.

At a 1973 licensing hearing for the proposed 820-MW Shoreham Nuclear Power Plant on Long Island, N.Y., for example, Heronemus called affordable nuclear energy a “myth.” He detailed, in its stead, a floating wind power system that could be moored off Long Island and sized to deliver more than four times as much electricity as the Shoreham plant. Each of the 640 floating platforms would carry six rotors and crank out up to 12 MW, some of which would power electrolyzers to generate hydrogen. The hydrogen would be fed to power plants or fuel cells to produce electricity when the wind wasn’t blowing. This seemingly futuristic idea drew on his Navy experience with water-splitting electrolyzers, which supplied the oxygen that enabled subs to remain submerged for months at a time, and NASA’s use of hydrogen fuel cells to power the Apollo missions.

More than five decades later, his vision for offshore wind power is big business. Floating platforms are now widely accepted as the future of offshore wind, as necessity pushes the industry to build in deeper waters. Testing began on the first floating electrolysis platforms in 2023, and multirotor turbine prototypes are in development in China, Norway and Scotland.

The UMass Amherst Wind Turbine Legacy

Photos in the UMass archives invariably capture Heronemus in jacket and tie, usually standing bolt straight. That commanding affect, plus his World War II veteran pedigree, Cold War engineering credentials, and his informed, pugnacious attacks made him a hard target for his adversaries in the nuclear establishment. He certainly wasn’t your typical antinuclear activist.

Wielding his Cold War engineering credentials and often dressed in a suit and tie, Heronemus fought hard against nuclear energy, arguing that wind was a far safer and cost-competitive resource.Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

But brutal candor in public settings probably won him as many enemies as friends. Consider his presentation at the IEEE Power and Energy Society’s 1974 winter meeting, where Heronemus suggested scrapping the utilities’ then nuclear-focused research arm, the Electric Power Research Institute. That stance no doubt created discomfort for the engineers in attendance who were involved in EPRI projects, or who aspired to be.

It’s hard to say whether Heronemus’s campaign slowed nuclear development. The industry was already struggling with cost overruns when, in 1979, a reactor at Three Mile Island in Pennsylvania partially melted down and slammed the brakes on further expansion.

What is certain is that Heronemus spurred investment in wind power. When he started talking up wind in the early ’70s, even fellow travelers in the fledgling renewable energy movement were writing it off. As future White House science advisor John Holdren opined in a 1971 Sierra Club book: “There are few places in the world where the wind is strong enough and steady enough to make harnessing it for the large-scale production of power at all interesting.”

Heronemus dreamed up networks of wind turbines over and along highways after driving down the Garden State Parkway to a conference in Cape May, New Jersey. Ellen Heronemus

Heronemus countered the naysayers by quickly forging expert consensus around wind power’s immense potential, playing a key role as the sole wind expert on a 1972 federal panel on renewable energy. That joint National Science Foundation–NASA panel concluded that, in fact, wind could meet up to 19 percent of projected U.S. power demand by the year 2000.

Congress listened, sort of. After most Persian Gulf states restricted oil shipments to the United States in 1973, congressional appropriators dedicated US $1.8 million to wind-power research and development for 1974—up from zero—and by 1976 it had bumped that to $22 million. (For comparison, Congress gave nuclear power $714 million in 1976.)

Heronemus’s vision for a massive highway wind-power scheme was inspired in part by the wind-power advocate Percy Thomas, who in the 1940s and 1950s “talked a lot about how fresh New Jersey winds are,” he told the New York Times in 1974. “I got to thinking about what Thomas had said and how wind energy could be captured there.” Ellen Heronemus

The bulk of the funding for wind power flowed to big aerospace firms and to NASA, financing an ultimately fruitless attempt to leap straight to megawatt-scale wind turbines. UMass struggled to grab a slice of the leftovers to pursue Heronemus’s offshore wind system. Professors and students who worked with Heronemus told me they felt they’d been blackballed as payback for his activism and antagonism.

UMass finally caught a funding break when Heronemus dialed back his ambitions and proposed the 25-kW unit for Orchard Hill. A $130,000 federal grant landed in early 1975, and $150,000 more the following year. It was a “trivial” sum, according to team member Sandy Butterfield, who would later become chief engineer for wind-turbine testing at NREL. “They gave us just enough to fail,” says Butterfield.

A crane erects the “Wind Furnace” in November 1976. Sandy Butterfield

But the project triumphed, resulting in Wind Furnace 1, or WF-1 (pronounced “woof one”). The young engineers behind it credit their success to the confidence, sense of mission, and structure that Heronemus gave them. The self-described “hippies” called Heronemus “the Captain” out of both affection and respect.

As team member Edds puts it: “What showed in his demeanor and his actions was discipline, and it sort of rubbed off on us. We didn’t always dress like the Captain, but we knew we had to be disciplined, to be prepared, and just do the job.”

From Helicopter Rotor to Wind Turbine

Team WF-1 got a quick start, thanks to earlier, privately financed work by a couple of doctoral students, including Forrest “Woody” Stoddard. Stoddard had been designing helicopter rotors for the U.S. Air Force when Heronemus invited him to come work on wind power in 1972. Stoddard set about adapting helicopter-rotor theory to the closely related wind rotors, and his aerodynamics modeling proved essential to the engineering of the entire machine.

Woody Stoddard [far right, in hat] designed the fiberglass blades with Ted Van Dusen. The team assembled the blades in a campus shop, and when it was time to squeegee epoxy from the blades, it was all hands on deck. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

As WF-1’s de facto chief designer, Stoddard likely supported the team’s early choice to mimic a helicopter’s ability to “pitch” its blades. To fly forward, a helicopter continuously adjusts the lift created by each blade, turning the airfoil on its long axis to reduce lift as it swings past the front of the aircraft. Doing so tilts the nose down and moves the vehicle forward. In WF-1’s case, blades pitched to regulate torque, helping get the rotor spinning in low winds and then easing off to protect the machine in dangerously high winds.

Repurposing a truck axle to mechanically couple WF-1’s rotor and generator was one of several design elements borrowed from engineers at McGill University in Montreal. Production of WF-1’s fiberglass blades got started at UMass in 1974 under the direction of doctoral student Ted Van Dusen. A competitive rower, he had a side hustle making ultralight composite boats—a trade that had stalled his doctoral work at MIT but was an accelerant for WF-1.

The federal funds in 1975 allowed Heronemus to really spin up the project and recruit a squad of students to engineer the balance of WF-1’s components. They made good use of the UMass engineering machine shop and received guidance from faculty, including mechanical engineering professors Duane Cromack and Jon McGowan. But it was the dozen or so students who really cranked out the parts.

Most were master’s students, like Butterfield, who designed the blade-pitching mechanics. Edds, the team’s only electrical engineer, had come to UMass to learn ocean engineering, only to be diverted into handling WF-1’s generator. Louis Manfredi, another ocean engineering student, teamed up with master’s student Jim Sexton on the nacelle housing the generator and drivetrain. Fred Antoon adapted the truck axle. Brian Kuhn did drawings.

WF-1 contained a mechanism that pitched its blades to regulate torque in response to wind speed, a feature that became an industry standard.Sandy Butterfield

An 18-year-old freshman, Dan Handman, came aboard and soon made himself indispensable. When he approached Heronemus to introduce himself, Heronemus handed him three months’ worth of anemometer readings punched into recording paper, and told him to turn it into 15-minute averages. Figuring there had to be a more efficient method for analyzing wind speeds, Handman asked around and found a wind-averaging machine from an earlier student project. A month or so later, he’d installed it in a cabinet near Heronemus’s office and wired it to an anemometer on Orchard Hill.

Handman’s primary role on WF-1 was setting up its computerized control system, which tracked wind speed and sent commands to Butterfield’s pitch mechanism. The controls also tracked the generator’s speed and adjusted the current to its rotor windings, in accordance with calculations by Edds. Tweaking the current ensured that power demand from the electric heaters installed in the home below didn’t stop the rotor in weak winds.

Sandy Butterfield, part of the 1970s “UMass Mafia” team that built WF-1, became a wind-power entrepreneur and a top engineer at the National Renewable Energy Laboratory in Golden, Colo. Sandy Butterfield

The finished WF-1 really cranked up the heat, some of which was stored by heating water in tanks in the modular house’s basement, to be circulated through baseboards in windless periods. It turned out WF-1 was unusually efficient at capturing wind energy because its rotor could change speed with the wind, keeping the blades close to an aerodynamic optimum.

This varying rotor speed meant that the frequency of the electric power WF-1 produced also varied. Turbines linked to power lines must strive for the opposite—a steady output that synchronizes with the grid’s frequency—primarily 50 or 60 hertz. But it suited the home’s low-tech heating scheme just fine. (Electronic converters let today’s turbines have it all by ingesting a variable wave and outputting a new wave that’s synced to the grid.)

The Great California Wind Rush

In 1977, with WF-1’s success in hand, Heronemus projected that 3 million homes like the one on Orchard Hill could soon slash U.S. heating oil demand by 90 million barrels a year. That never happened, but an industry was born, starting with a Burlington, Mass. startup called US Windpower—the first “credible” U.S. turbine manufacturer, according to Thresher, who is now an emeritus researcher at the National Laboratory of the Rockies.

Belgian-made WindMaster turbines erected at Altamont Pass signaled the internationalism of the California wind rush. UMass team member Woody Stoddard conducted engineering analyses of many early designs deployed there.Bettman/Getty Images

Boston-area entrepreneurs Russell Wolfe and Stanley Charren launched US Windpower with Stoddard and Van Dusen after visiting Heronemus in 1974 and liking what they heard. They adapted WF-1’s design to make it suitable for grid-connected operation, building and breaking prototypes before erecting the world’s first grid-connected wind farm in 1980—20 turbines on a mountain in New Hampshire. California’s water authority placed an order for 100 MW of wind power, and in 1981 US Windpower began installing hundreds of turbines in Altamont Pass, east of San Francisco.

As more firms jumped to California, drawn by state government incentives, WF-1’s creators and the next cohort of UMass grads assumed important roles in the nascent market. Seven joined Energy Sciences, a startup cofounded by Butterfield. More joined U.S. Windpower. Stoddard left that company to start a consulting firm and ended up advising some of Denmark’s modern wind pioneers, which rapidly expanded thanks to the California market. Those early Danish firms made relatively simple, sturdy machines that subsequently scaled up and dominated globally for several decades — until China embraced wind power.

The California wind power boom peaked in 1986, after which energy prices collapsed and incentives faded. Most manufacturers were bankrupted by equipment failures and financial challenges, making the 1990s a tough time for wind power’s pioneers. Many UMass wind engineers, like Butterfield, joined Thresher’s operation at NREL, culling everything they could from the California experience.

“An entire generation of U.S. wind engineers got their graduate training, at least in part, using the Wind Furnace.”—Harold Wallace

There, Heronemus’s protégés became known as the “UMass Mafia.” Thresher says it attests to the crew’s impact: “There were others. But that UMass Mafia were really leaders in the field. I think that’s the heritage we got from Bill Heronemus. Those people were so impactful and the education they got [with Heronemus] was the key.” What Heronemus began at the university became the UMass Wind Energy Center, which has awarded over 300 graduate degrees.

WF-1 now rests in the Smithsonian Institution’s collections in Washington, D.C. It earned its place there, as Smithsonian’s only modern wind turbine, because it represents wind energy’s revival, according to Harold Wallace, Smithsonian’s curator for electricity collections. “An entire generation of U.S. wind engineers got their graduate training, at least in part, using the Wind Furnace,” he says.

Heronemus didn’t get to witness the production of the massive offshore machines that he foresaw. He lost his long fight with cancer in November 2002, at the age of 82, even as former students and family members were racing to patent his multirotor and floating turbine designs.

Had he lived longer, the Captain would almost certainly have railed against current U.S. energy policy. The U.S. government has never backed wind power as generously as he’d hoped. Wind supplied 10 percent of U.S. generation last year—that’s half the share in Europe—with offshore turbines providing only a tiny sliver. Federal support for wind power has been in a stop-go cycle since Ronald Reagan’s administration, and it’s hit a low again under President Donald Trump, who has vowed to stop wind power cold. As Trump boasted to oil executives in January: “We have not approved one windmill since I’ve been in office, and we’re going to keep it that way.”

Under Trump, stop-work orders have disrupted offshore projects from Massachusetts to Virginia, contributing to a nearly $600 million loss in 2025 for GE Vernova’s wind business. GE Vernova is the only major wind turbine manufacturer remaining in the United States, and it too can be traced back to Heronemus via a US Windpower patent.

In stark contrast, European and Asian countries have been going big on offshore wind and are now developing floating wind farms to push into deeper waters. China might be the one to finally conjure up Heronemus’s favored wind design: floating platforms bearing massive multirotor machines. In 2024, Zhongshan-based turbine maker Ming Yang Smart Energy Group deployed a two-rotor offshore prototype. The company says its next iteration will generate a whopping 50 MW—a twin-headed beast that would be the world’s most powerful wind machine.

That will be a bittersweet moment for the U.S. wind industry and Captain William Heronemus’s UMass Mafia, for whom such massive machines are a dream come true. Joanne Carroll, a retired member of the UMass Mafia, says she remembers the very moment, her freshman year, when Heronemus’s dream became hers. While he was lecturing in Introduction to Engineering about the hidden costs of coal-fired power, Heronemus walked to the window and said: “‘But out there there’s wind, and you can harvest that energy,’” Carroll recalled. “And I remember thinking: That’s what I want to do with my life.”

The author would like to give special thanks to UMass professor emeritus James Manwell for his assistance with this story.

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.