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<img src="https://spectrum.ieee.org/media-library/cute-wheeled-home-robot-with-a-tablet-face-set-against-a-blue-heart-patterned-background.jpg?id=66906422&width=1245&height=700&coordinates=0%2C187%2C0%2C188"/> In 2018, Amazon brought me in as the lead UX Sound Designer for <a href="https://spectrum.ieee.org/amazon-astro-robot" target="_blank">Astro, their first consumer home robot</a>. Astro used cameras and other sensors to map and navigate your <a href="https://spectrum.ieee.org/ai-robots" target="_blank">home and workplace</a>, 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 <a href="https://www.amazon.com/Introducing-Amazon-Astro/dp/B078NSDFSB" target="_blank">Astro</a> 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. I argued for Astro to not focus on Alexa, along with the majority of the UX team. 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 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, <a href="https://www.aboutamazon.com/news/devices/meet-astro-a-home-robot-unlike-any-other" target="_blank">Astro became Astro rather than Alexa</a>, 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.<h2>Character Is a Design System</h2>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. <iframe frameborder="0" height="auto" lazy-loadable="true" scrolling="no" src="https://www.youtube.com/embed/r1eS3TitrHc?rel=0" style="position:absolute;top:0;left:0;width:100%;height:100%;" width="100%"></iframe>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: <iframe frameborder="0" height="auto" lazy-loadable="true" scrolling="no" src="https://www.youtube.com/embed/HtePtQyiTDs?rel=0" style="position:absolute;top:0;left:0;width:100%;height:100%;" width="100%"></iframe>Astro goes from nothing, into the emotional moment, and then lands back on nothing. No build up, no cool down, 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.<h2>Story and Sound at the Beginning</h2>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. <iframe frameborder="0" height="auto" lazy-loadable="true" scrolling="no" src="https://www.youtube.com/embed/coPva7ltAgM?rel=0&start=261" style="position:absolute;top:0;left:0;width:100%;height:100%;" width="100%"></iframe>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, animation 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.<h2>Context Is Where Character Becomes Real</h2>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.<h2>What Industry Is Missing</h2>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 roadmap 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.<h2>My Asks to Product Leaders</h2>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.<div class="horizontal-rule"></div>An unabridged version of this story can be read on <a href="https://medium.com/@mikeforstmusic/what-amazons-astro-taught-me-about-giving-ai-a-soul-989fcd9c45f4" target="_blank">Medium</a>.

Jun 17, 2026

IEEE’s 2026 Education Week Events Emphasized Lifelong Learning

<img src="https://spectrum.ieee.org/media-library/a-black-woman-speaking-into-a-microphone-in-front-of-a-presentation-screen.jpg?id=66951490&width=1245&height=700&coordinates=0%2C197%2C0%2C197"/> The rapid evolution of the global engineering landscape requires continuous education. For one week in April, the IEEE community focuses on its educational frameworks. <a href="https://educationweek.ieee.org" rel="noopener noreferrer" target="_blank">IEEE Education Week</a>, 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 <a href="https://educationweek.ieee.org/events/" rel="noopener noreferrer" target="_blank">live and virtual events</a>, <a href="https://educationweek.ieee.org/resources/" rel="noopener noreferrer" target="_blank">online resources</a>, and <a href="https://educationweek.ieee.org/special-offers/" rel="noopener noreferrer" target="_blank">promotions</a> that champion the cycle of lifelong learning.<a href="https://spectrum.ieee.org/u/maryellen-randall" target="_self">IEEE President Mary Ellen Randall</a> 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 <a href="https://www.ieee.org/jamie-moesch" rel="noopener noreferrer" target="_blank">Jamie Moesch</a>, managing director of <a href="https://ea.ieee.org" rel="noopener noreferrer" target="_blank">IEEE Educational Activities</a>.<h2>Coordination across the organization</h2>The event relied on the cooperation of 120 IEEE partners. Involved organizational units included the <a href="https://www.comsoc.org/" rel="noopener noreferrer" target="_blank">IEEE Communications Society</a>, the <a href="https://ieee-edusociety.org/home" rel="noopener noreferrer" target="_blank">IEEE Education Society</a>, and chapters and sections from around the world, including in <a href="https://educationweek.ieee.org/event/epics-in-ieee/" rel="noopener noreferrer" target="_blank">Brazil</a>, <a href="https://events.vtools.ieee.org/m/549648" rel="noopener noreferrer" target="_blank">Colombia</a>, and <a href="https://gnsu.ac.in/ieee" rel="noopener noreferrer" target="_blank">India</a>. They produced 114 events, 23 resources, and 11 special offers.“These collaborations help members remain current in a changing technological environment,” says <a href="https://www.ieee.org/about/assembly/vp-of-ea" rel="noopener noreferrer" target="_blank">Timothy Kurzweg</a>, vice president of <a href="https://ea.ieee.org" rel="noopener noreferrer" target="_blank">IEEE Educational Activities</a>. “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 ActivitiesThe 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.<h2>Available educational tools</h2>Here are a few of the <a href="https://educationweek.ieee.org/events/" target="_blank">virtual events</a> held during Education Week—most of which are available on demand:<ul><li><a href="https://www.youtube.com/watch?v=jIJICVfsk8A&t=55s" rel="noopener noreferrer" target="_blank">Celebrating Excellence: The EPICS in IEEE Contributor Awards and Service Learning Showcase.</a></li><li><a href="https://www.airmeet.com/e/0dea9e90-279b-11f1-ac08-e5de564d93ce" rel="noopener noreferrer" target="_blank">Classroom to Startup: Uniting Academia and Industry.</a></li><li><a href="https://ieee-edusociety.org/post/announcement/ieee-education-week-2026" rel="noopener noreferrer" target="_blank">IEEE’s Role in Shaping AI-Ready Engineering Education Globally.</a></li><li><a href="https://www.youtube.com/watch?v=AE1mOgejM9M&t=167s" rel="noopener noreferrer" target="_blank">Leveraging IEEE Standards to Enhance Engineering Service Learning Projects (EPICS in IEEE).</a></li><li><a href="https://www.youtube.com/watch?v=G4Ac2ugTEJo&t=1s" rel="noopener noreferrer" target="_blank">Mastering the Modern Job Market: The Power of IEEE Microcredentials.</a></li><li><a href="https://www.airmeet.com/e/8da7cd00-0da7-11f1-8218-ef26d078c8ee" rel="noopener noreferrer" target="_blank">TryEngineering Volunteers Making an Impact in STEM.</a></li></ul>The Education Week website highlights <a href="https://educationweek.ieee.org/resources/" rel="noopener noreferrer" target="_blank">resources</a> and <a href="https://educationweek.ieee.org/special-offers/" rel="noopener noreferrer" target="_blank">offers</a> shared by IEEE organizational units, including:<ul><li><a href="https://www.ieee.org/education/mud.html" rel="noopener noreferrer" target="_blank">A half-off discount for members on IEEE e-learning courses.</a> The catalog covers such topics as computing, power and energy, and telecommunications. </li><li><a href="https://www.comsoc.org/education-training/demand-training" rel="noopener noreferrer" target="_blank">IEEE Communications Society on-demand webinars.</a> Learn the latest trends and innovations.</li><li><a href="https://ieeetv.ieee.org/channels/wie" rel="noopener noreferrer" target="_blank">IEEE Women in Engineering career-focused, upskill, and reskill webinars.</a> The presentations cover a variety of topics including agentic AI, leadership, and robots.</li><li><a href="https://innovationatwork.ieee.org" rel="noopener noreferrer" target="_blank">IEEE Innovation at Work.</a> The e-newsletter covers emerging technologies, education, and training for technical professionals.</li><li><a href="https://iln.ieee.org" rel="noopener noreferrer" target="_blank">IEEE Learning Network.</a> Hundreds of <a href="https://spectrum.ieee.org/ieee-professional-development-suite" target="_self">continuing education courses</a>, all in one place.</li><li><a href="https://tryengineering.org/teachers/lesson-plans/" rel="noopener noreferrer" target="_blank">IEEE TryEngineering lesson plans.</a> The easy-to-use, <a href="https://spectrum.ieee.org/tryengineering-oncampus-expansion" target="_self">engaging activities</a> and plans help teach engineering concepts to preuniversity students.</li><li><a href="https://tryengineering.org/explore-resources/collections/" rel="noopener noreferrer" target="_blank">IEEE TryEngineering collections.</a> The lesson plans and multimedia resources, developed with partners and IEEE technical societies, are designed to introduce technical topics and deepen student understanding.</li></ul>Individuals who were unable to attend the live sessions can find the archived content on the IEEE Education Week website.The website also accepts <a href="https://secure.ieeefoundation.org/site/Donation2;jsessionid=00000000.app30118b?mfc_pref=T&1980.donation=form1&idb=615936264&df_id=1980&NONCE_TOKEN=0B9ED08DC05E53935E33CB9C4B08F5C2&mfc_pref=T" rel="noopener noreferrer" target="_blank">donations for education-related funds</a> managed by the <a href="https://www.ieeefoundation.org/" rel="noopener noreferrer" target="_blank">IEEE Foundation</a>.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.

Jun 17, 2026

Behind the Scenes of a Technical Interview

<img src="https://spectrum.ieee.org/media-library/an-illustration-of-stylized-people-wearing-business-casual-clothing.webp?id=65257424&width=1245&height=700&coordinates=0%2C112%2C0%2C113"/> This article is crossposted from IEEE Spectrum’s careers newsletter. <a href="https://engage.ieee.org/Career-Alert-Sign-Up.html" rel="noopener noreferrer" target="_blank">Sign up now</a> to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company <a href="https://www.parsity.io/" rel="noopener noreferrer" target="_blank">Parsity</a> 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.<h2>Most teams have no standard prep</h2>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.<h2>“Scoring” that isn’t really scoring</h2>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.<h2>Prestige bias and politics</h2>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.<h2>What you can actually control</h2>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<h2><a href="https://spectrum.ieee.org/nsf-x-labs" target="_self">NSF Experiments With New Kind of Science Funding</a></h2>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. <a href="https://spectrum.ieee.org/nsf-x-labs" target="_blank">Read more here. </a><h2><a href="https://spectrum.ieee.org/7-ways-engineers-flourish-ai" target="_self">7 Ways New Engineers Can Flourish in the Age of AI</a></h2>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. <a href="https://spectrum.ieee.org/7-ways-engineers-flourish-ai" target="_blank">Read more here. </a><h2><a href="https://spectrum.ieee.org/collections/career-advice/" target="_self">Collection: Career Advice for Engineers, From Engineers</a></h2>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. <a href="https://spectrum.ieee.org/collections/career-advice/" target="_blank">Read more here. </a>

Jun 17, 2026

How Musicians Can Get Paid for Training AI

<img src="https://spectrum.ieee.org/media-library/conceptual-illustration-of-two-quarter-note-stems-going-through-an-s-resembling-a-dollar-sign.jpg?id=66750724&width=1245&height=700&coordinates=0%2C187%2C0%2C188"/> 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 <a href="https://spectrum.ieee.org/ai-art-generator" target="_blank">complicated the definition of use</a>. 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.<h2>Music Royalties for the AI era </h2><a href="https://www.sureel.ai/" target="_blank">Sureel</a>, a startup Warner Music Group just <a href="https://www.musicbusinessworldwide.com/warner-music-group-acquires-sureel-ai-the-attribution-startup-that-traces-how-ai-models-use-artists-work/" target="_blank">acquired</a>, has partnered with the Swedish copyright agency <a href="https://www.stim.se/" rel="noopener noreferrer" target="_blank">STIM</a> to explore the potential for<a href="https://www.stim.se/en/news/stim-launches-the-worlds-first-ai-license-for-music" rel="noopener noreferrer" target="_blank"> music creators to get paid when their music is used to train generative AI tools</a>. 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 <a href="https://www.soundverse.ai/whitepaper.pdf" rel="noopener noreferrer" target="_blank">2025 white paper</a>. 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 Co-President 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: <a href="https://spectrum.ieee.org/midjourney-copyright" target="_self">Generative AI Has a Visual Plagiarism Problem</a>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 modelling 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 towards 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 <a href="https://www.theverge.com/news/790405/warner-universal-music-ai-deals" rel="noopener noreferrer" target="_blank">Universal, Warner, and major AI companies</a> to work together on training models with copyright consent. Although <a href="https://www.musicbusinessworldwide.com/sunos-licensing-talks-with-major-labels-in-limbo-with-no-path-forward-report/" rel="noopener noreferrer" target="_blank">little is certain</a>, 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. <h2>Making AI training pay off </h2> 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 <a href="https://forum.ircam.fr/projects/detail/rave/" rel="noopener noreferrer" target="_blank">IRCAM’s RAVE</a> model or <a href="https://www.jenmusic.ai/stylefilters" rel="noopener noreferrer" target="_blank">Jen’s Style Filters</a>. Meanwhile, more mainstream “end user” creative applications may be shifting towards a focus on fan engagement. <a href="https://www.nytimes.com/2026/03/24/technology/openai-shutting-down-sora.html" rel="noopener noreferrer" target="_blank">OpenAI’s sudden dropping of Sora</a>, despite being in negotiations with Disney and <a href="https://www.youtube.com/watch?v=-XZQx4PFqvs" rel="noopener noreferrer" target="_blank">Suno’s recent emphasis on building fan engagement experiences that draw directly on the work of artists</a>, 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.”

Jun 17, 2026

The Secret to Marathon-Winning Humanoid Robots

<img src="https://spectrum.ieee.org/media-library/a-red-and-black-humanoid-runs-alone-through-a-marathon-course.jpg?id=66940897&width=1245&height=700&coordinates=0%2C66%2C0%2C66"/> On April 19, 2026, the <a href="https://www.cnn.com/2026/04/19/china/china-robot-half-marathon-intl-hnk" rel="noopener noreferrer" target="_blank">Honor Lightning humanoid robot ran a half-marathon in 50 minutes and 26 seconds</a>, beating the human world record by 7 minutes and the best robot time from 2025 by almost two hours.How did they do it? Is there some magical technology or technique that unlocked this performance? How did they beat the significantly better-known Unitree (who reportedly had to supply an ice backpack to try and complete the race without overheating)? My doctoral thesis involved <a href="https://www.avikde.me/p/phd-defense" rel="noopener noreferrer" target="_blank">building and controlling hopping and running robots</a>, and <a href="https://www.avikde.me/p/ghost-robotics-minitaur" rel="noopener noreferrer" target="_blank">since then I’ve tried to design and build efficient commercial legged robots</a>, 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.<hr/><h3>The Physics of Running</h3><a href="https://spectrum.ieee.org/ai-institute" target="_blank">Running</a> 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.<a href="https://spectrum.ieee.org/ev-motor" target="_blank">Electric motors</a> use energy to produce torque- the higher the torque, the more energy lost as heat. Adding a geartrain 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: <img alt="A graph showing the relationship between gearing and motor efficiency, with an optimal gearing ratio in the relationship between stance and swing." class="rm-shortcode" data-rm-shortcode-id="4c2224acc293d6b3ce8b8b6553aa30f5" data-rm-shortcode-name="rebelmouse-image" id="10bd7" loading="lazy" src="https://spectrum.ieee.org/media-library/a-graph-showing-the-relationship-between-gearing-and-motor-efficiency-with-an-optimal-gearing-ratio-in-the-relationship-between.jpg?id=66940901&width=980"/> The power consumed by a robot leg is minimized at an optimal gear ratio (30:1 in this example).Avik De/Datawrapper<h3>How Honor Did It</h3>While the Lightning’s motor specifications are not published, the hip and knee motors roughly have a 110-150mm outer diameter. For an approximate set of motor parameters, I looked to the <a href="https://www.tq-group.com/en/products/tq-robodrive/servo-kits/ilm115x25/" target="_blank">ILM115x25 motor</a> due to its relevant size and detailed specifications.We can use a simple physics model to estimate the power consumption for running at 7 m/s (the Lightning’s average half marathon speed) as gear ratio varies: <img alt="A graph showing that optimal gearing for a robot\u2019s motor dissipates the amount of heat that the motor generates." class="rm-shortcode" data-rm-shortcode-id="e04f969907417a25696dd3127e090008" data-rm-shortcode-name="rebelmouse-image" id="185f3" loading="lazy" src="https://spectrum.ieee.org/media-library/a-graph-showing-that-optimal-gearing-for-a-robot-u2019s-motor-dissipates-the-amount-of-heat-that-the-motor-generates.jpg?id=66940912&width=980"/> 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/DatawrapperWe 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 400W.However, the dissipated knee power ( typically the main thermal limiting factor) is ~150W. This is almost an unavoidable consequence — running at human speeds with a humanoid-sized 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 <a href="https://eu.36kr.com/en/p/3775418378027520" target="_blank">trick up its sleeve</a>:<blockquote>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.</blockquote>Liquid cooling is not new, but it’s definitely not a commodity. It has shown up in research periodically, and on the commercial side <a href="https://apptronik.com/news-collection/apptronik-readies-its-humanoid-robot-for-a-summer-unveil" rel="noopener noreferrer" target="_blank">Apptronik tried it for a few of their prototypes</a> but (to my knowledge) does not use it on their main <a href="https://apptronik.com/apollo" target="_blank">Apollo</a> platform. Basic air convection-based cooling would not continuously be able to extract 150W out of the knee motor, and so the cooling technology is a key enabler of this type of performance.<h3>Why Others Couldn’t Compete</h3>Why did Honor’s competitors, including more <a href="https://www.forbes.com/sites/johnkoetsier/2026/01/09/top-10-humanoid-robot-companies-by-shipments-revealed/" rel="noopener noreferrer" target="_blank">established and widely-shipped humanoids</a> such as from <a href="https://www.unitree.com/g1" target="_blank">Unitree</a> or <a href="https://www.agibot.com/" target="_blank">Agibot</a>, 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: <img alt="A graph showing that robots with gear ratios optimized for running or walking are inefficient when walking or running respectively." class="rm-shortcode" data-rm-shortcode-id="5bbe64af17f8581b4106547f468728a4" data-rm-shortcode-name="rebelmouse-image" id="616f5" loading="lazy" src="https://spectrum.ieee.org/media-library/a-graph-showing-that-robots-with-gear-ratios-optimized-for-running-or-walking-are-inefficient-when-walking-or-running-respective.jpg?id=66940939&width=980"/> 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/DatawrapperThe 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 300W (red arrow), more than 2x 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.<h3>Closing Thoughts</h3>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. <img alt="A comparison showing two similar humanoid robots, but one has significantly smaller motors on its hips." class="rm-shortcode" data-rm-shortcode-id="3ef7dc89b86a70493190325135f1f20f" data-rm-shortcode-name="rebelmouse-image" id="19121" loading="lazy" src="https://spectrum.ieee.org/media-library/a-comparison-showing-two-similar-humanoid-robots-but-one-has-significantly-smaller-motors-on-its-hips.jpg?id=66941011&width=980"/> The Honor Lighting robot [right] has much larger motors driving its legs than the Unitree H1 robot [left], making it a more efficient runner but a less efficient walker.Left: Wei Zhiyang/Zhejiang Daily Press Group/VCG/Getty Images; Right: VCG/Getty ImagesHowever, the Lightning is not as well-suited to other tasks as a robot designed for greater versatility. Engineering is always characterized by tradeoffs, 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 <a href="https://en.wikipedia.org/wiki/Deep_Blue_versus_Garry_Kasparov" rel="noopener noreferrer" target="_blank">Deep Blue’s 1997 defeat of Garry Kasparov in chess</a>, 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, and only risks diminishing Honor’s engineering achievement on one hand, and human athletic achievement on the other.

Jun 15, 2026

Engineering Is Critical to Boosting Food Security

<img src="https://spectrum.ieee.org/media-library/illustration-of-a-drone-being-used-to-collect-crop-data-on-a-wheat-farm.jpg?id=66888131&width=1245&height=700&coordinates=0%2C156%2C0%2C157"/> Nearly 750 million people face hunger today, according to the <a href="https://www.wfp.org/" rel="noopener noreferrer" target="_blank">U.N. World Food Program</a>. And by 2050, global demand for food is expected to <a href="https://research.wri.org/wrr-food" rel="noopener noreferrer" target="_blank">increase by 50 percent from 2010 levels</a>, the <a href="https://www.wri.org/" rel="noopener noreferrer" target="_blank">World Resources Institute</a> says.A <a href="https://spectrum.ieee.org/precision-agriculture" target="_self">smart agriculture</a> special-issue report recently released by the IEEE <a href="https://smartag.ieee.org/about/" rel="noopener noreferrer" target="_blank">Smart Agri-Food Initiative</a> 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 <a href="https://engineering.msu.edu/directory/faculty/johnv" rel="noopener noreferrer" target="_blank">John Verboncoeur</a>, chair of the smart-food program and professor of electrical and computer engineering at <a href="https://msu.edu/" rel="noopener noreferrer" target="_blank">Michigan State University</a>, 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 <a href="https://www.ufl.edu/" rel="noopener noreferrer" target="_blank">University of Florida</a> in 1985-86, where he helped develop an SmartAg aeroponics system for <a href="https://www.nasa.gov/" rel="noopener noreferrer" target="_blank">NASA</a> for the <a href="https://www.space.com/space-exploration/missions/international-space-station" rel="noopener noreferrer" target="_blank">International Space Station</a>. 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 <a href="https://engineering.msu.edu/news/smartag-initiative" rel="noopener noreferrer" target="_blank">SmartAg Initiative</a> 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 <a href="https://smartvillage.ieee.org/" rel="noopener noreferrer" target="_blank">the IEEE Smart Village</a> 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.”<h2>From digital twins to autonomous harvesting</h2>A central theme across the Smart Agri-Food Systems report is the convergence of <a href="https://spectrum.ieee.org/tag/automation" target="_self">automation</a>, <a href="https://spectrum.ieee.org/tag/data-analytics" target="_self">data analytics</a>, and <a href="https://spectrum.ieee.org/tag/sustainability" target="_self">sustainability</a>.One paper, “<a href="https://ieeexplore.ieee.org/document/10757158" rel="noopener noreferrer" target="_blank">Smart Agriculture, Precision Agriculture, Digital Twins in Agriculture: Similarities and Differences</a>,” addresses the confusion regarding how researchers and practitioners define and apply the technologies to farming.The paper was written by <a href="https://scholar.google.com/citations?user=g4uefZ8AAAAJ&hl=tr" rel="noopener noreferrer" target="_blank">Dilan Onat Alakuş</a>, a research assistant in the software engineering department at <a href="https://www.klu.edu.tr/dil/en" rel="noopener noreferrer" target="_blank">Kırklareli University</a>, in Türkiye, and <a href="https://abs.firat.edu.tr/en/iturkoglu" rel="noopener noreferrer" target="_blank">Ibrahim Türkoğlu</a>, a software engineering professor at <a href="https://www.firat.edu.tr/en" rel="noopener noreferrer" target="_blank">Fırat University</a>, 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:• <a href="https://www.ibm.com/think/topics/smart-farming" rel="noopener noreferrer" target="_blank">Smart agriculture</a> systems integrate sensors, artificial intelligence, robotics, and analytics to improve efficiency and sustainability at scale.• <a href="https://www.nifa.usda.gov/grants/programs/precision-geospatial-sensor-technologies-programs/precision-agriculture-crop-production" rel="noopener noreferrer" target="_blank">Precision agriculture</a> 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.• <a href="https://stories.tamu.edu/stories/revolutionizing-farming-with-digital-twin-technology/" rel="noopener noreferrer" target="_blank">Digital twins</a> 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.<h2>Smart farming in practice</h2>The report shifts from theory to application in a paper describing bustani, which means my garden in Arabic. The <a href="https://www.siemens.com/en-us/company/insights/bustanica-smart-sustainable-food-production/" rel="noopener noreferrer" target="_blank">Bustanica</a> project in Saudi Arabia is an automated <a href="https://naes.unr.edu/publication.aspx?PubID=2756" rel="noopener noreferrer" target="_blank">hydroponic</a> vertical farming system developed by researchers at the <a href="https://www.pmu.edu.sa/" rel="noopener noreferrer" target="_blank">Prince Mohammad Bin Fahd University</a>, in Al-Khobar, Saudi Arabia. The “<a href="https://ieeexplore.ieee.org/document/10262605" rel="noopener noreferrer" target="_blank">Bustani: A Microcontroller-Based Automated Hydroponic Vertical Farming Solution</a>” paper was written by Hussah Alotaibi, a computer engineer at <a href="https://www.aramco.com/" rel="noopener noreferrer" target="_blank">Saudi Aramco</a>, the country’s national oil company; <a href="https://faculty.pmu.edu.sa/PMUFaculties/Details/abashar" rel="noopener noreferrer" target="_blank">Abul Bashar</a>, Widad Karsou, and Shehvar Khan, researchers in the university’s computer engineering and computer science department; and <a href="https://www.linkedin.com/in/salahudeantohmeh/" rel="noopener noreferrer" target="_blank">Salahudean Tohmeh</a> from the university’s robotics laboratory.The Bustanica system combines hydroponics with <a href="https://modernfarmer.com/2018/07/how-does-aeroponics-work/" rel="noopener noreferrer" target="_blank">aeroponics</a>, 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 <a href="https://store-usa.arduino.cc/products/arduino-mega-2560-rev3?srsltid=AfmBOoo0R26HAmA6wzpWcLox4xblaJMN5pJd3LrQ9-WxRSNeOFexbpg_" rel="noopener noreferrer" target="_blank">Arduino Mega</a> processes the sensor data. A <a href="https://store-usa.arduino.cc/products/nodemcu-esp8266?srsltid=AfmBOooGec0X-8y74JWHtORpxFCN-kITJ_YiiUZfFC8_GcmiBYh0RlwV" rel="noopener noreferrer" target="_blank">NodeMCU</a> <a href="https://store-usa.arduino.cc/products/nodemcu-esp8266?srsltid=AfmBOooGec0X-8y74JWHtORpxFCN-kITJ_YiiUZfFC8_GcmiBYh0RlwV" rel="noopener noreferrer" target="_blank">ESP8266</a>—a low-cost, open-source IoT platform—handles Wi-Fi communication and cloud connectivity.The system sends the data through Google’s <a href="https://firebase.google.com/firebase-and-gcp" rel="noopener noreferrer" target="_blank">Firebase cloud platform</a>, 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.” —<a href="https://engineering.msu.edu/directory/faculty/johnv" target="_blank">John Verboncoeur</a>, chair of the IEEE Smart Agri-Food initiativeThe 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 <a href="https://developer.android.com/" rel="noopener noreferrer" target="_blank">Android application</a> 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 <a href="https://apps.apple.com/us/app/amazon-alexa/id944011620" rel="noopener noreferrer" target="_blank">Amazon Alexa</a> virtual assistant and machine learning tools for plant disease detection and growth analysis.<h2>Robotics and labor challenges</h2>The “<a href="https://ieeexplore.ieee.org/document/9328092" rel="noopener noreferrer" target="_blank">Toward an Efficient Tomato Harvesting Robot</a>” 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 <a href="https://www.researchgate.net/profile/Hyoung-Son" rel="noopener noreferrer" target="_blank">Hyoung Il Son</a>—a professor of biosystems engineering and robotics at <a href="https://global.jnu.ac.kr/jnumain_en.aspx" rel="noopener noreferrer" target="_blank">Chonnam National University</a> 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 <a href="https://spectrum.ieee.org/tag/machine-vision" target="_self">3D machine vision</a>,<a href="https://spectrum.ieee.org/robots-getting-a-grip-on-general-manipulation" target="_self"> </a><a href="https://spectrum.ieee.org/tag/robotic-arm" target="_self">robotic arms</a>, <a href="https://spectrum.ieee.org/robots-getting-a-grip-on-general-manipulation" target="_self">suction-based grippers</a>, 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.<h2>Agriculture as a systems problem</h2>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, “<a href="https://ieeexplore.ieee.org/document/9823634" rel="noopener noreferrer" target="_blank">Sustainable and Smart Agriculture: A Holistic Approach</a>,” 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 <a href="https://www.cuchd.in/" rel="noopener noreferrer" target="_blank">Chandigarh University</a>, 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.<a href="https://spectrum.ieee.org/tag/wireless-networks" target="_self">Wireless sensor networks</a> 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 <a href="https://www.sciencedirect.com/science/article/pii/S2667318521000106" rel="noopener noreferrer" target="_blank">machine learning models</a> 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 <a href="https://www.cat.com/en_US/by-industry/agriculture.html" rel="noopener noreferrer" target="_blank">Caterpillar</a>, <a href="https://www.cnh.com/" rel="noopener noreferrer" target="_blank">CNH</a>, <a href="https://www.deere.com/en/attachments-accessories-and-implements/riding-mower-attachments/?CID=PPC_MDS_RLE_enUS_r00203_6750007&gclsrc=aw.ds&gad_source=1&gad_campaignid=23567875588&gbraid=0AAAAADJlG2AVOkwf8jCPTL3Is7RpWpuxP&gclid=CjwKCAjwwpDQBhAuEiwAa-4WowUzQ4o3w2BdVyCxuJfxtXaK9rQw8pBa5ZteOqvaNPIr9M_v55wKNxoCqmAQAvD_BwE" rel="noopener noreferrer" target="_blank">John Deere</a>, and <a href="https://www.kubota.com/" rel="noopener noreferrer" target="_blank">Kubota</a> and technology companies including <a href="https://www.bosch.com/" rel="noopener noreferrer" target="_blank">Bosch</a>, <a href="https://www.google.com/" rel="noopener noreferrer" target="_blank">Google</a>, <a href="https://www.intel.com/content/www/us/en/homepage.html" rel="noopener noreferrer" target="_blank">Intel</a>, and <a href="https://www.microsoft.com/" rel="noopener noreferrer" target="_blank">Microsoft</a>. Challenges remain, however, in communication reliability, sensor cost, and scalable data infrastructure, the authors say.<h2>SmartAg beyond the farm</h2>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 <a href="https://spectrum.ieee.org/topic/transportation/" target="_self">transportation</a>, <a href="https://spectrum.ieee.org/topic/energy/" target="_self">energy</a>, and industrial systems.The convergence explains IEEE’s growing involvement. Modern agriculture now combines electronics, <a href="https://spectrum.ieee.org/tag/communications" target="_self">communications</a>, <a href="https://spectrum.ieee.org/topic/computing/" target="_self">computing</a>, and <a href="https://spectrum.ieee.org/tag/control-systems" target="_self">control systems</a>.Agriculture requires that integration, Verboncoeur says: “The challenge isn’t just inventing technology. It’s making systems practical, affordable, and deployable.”<h2>What’s next for smart agriculture?</h2>The special issue marks an early stage for the IEEE Smart Agri-Food initiative, which plans to develop <a href="https://www.osha.gov/agricultural-operations/standards" rel="noopener noreferrer" target="_blank">standards</a>; 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.”

Jun 15, 2026

This 1976 University Experiment Spun Up the U.S. Wind Industry

<img src="https://spectrum.ieee.org/media-library/a-man-and-a-woman-wearing-dressy-winter-coats-watch-a-crew-of-informally-dressed-men-working-on-the-construction-of-a-wind-turbi.jpg?id=66894045&width=1245&height=700&coordinates=0%2C62%2C0%2C63"/> 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.<div class="rm-embed embed-media"><iframe height="110px" id="noa-web-audio-player" src="https://embed-player.newsoveraudio.com/v4?key=q5m19e&id=https://spectrum.ieee.org/william-heronemus-wind-energy&bgColor=F5F5F5&color=1b1b1c&playColor=1b1b1c&progressBgColor=F5F5F5&progressBorderColor=bdbbbb&titleColor=1b1b1c&timeColor=1b1b1c&speedColor=1b1b1c&noaLinkColor=556B7D&noaLinkHighlightColor=FF4B00&feedbackButton=false" style="border: none" width="100%"></iframe></div>The team of <a href="https://www.umass.edu/" target="_blank">UMass</a> 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. <img alt="Nine men standing and sitting on scaffolding that holds up the rotor and blades of a wind turbine" class="rm-shortcode" data-rm-shortcode-id="2fe8307b7317d6799f5adc56fd1fa009" data-rm-shortcode-name="rebelmouse-image" id="e44af" loading="lazy" src="https://spectrum.ieee.org/media-library/nine-men-standing-and-sitting-on-scaffolding-that-holds-up-the-rotor-and-blades-of-a-wind-turbine.jpg?id=66893951&width=980"/> 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 <a href="https://www.linkedin.com/in/medds/" target="_blank">Michael Edds</a>, 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. <img alt="A man in a suit jacket leaning over a map that\u2019s rolled out on a table " class="rm-shortcode" data-rm-shortcode-id="ac598e732203be24bce9d209cc12f7e3" data-rm-shortcode-name="rebelmouse-image" id="6061c" loading="lazy" src="https://spectrum.ieee.org/media-library/a-man-in-a-suit-jacket-leaning-over-a-map-that-u2019s-rolled-out-on-a-table.jpg?id=66894051&width=980"/> 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 <a href="https://www.peterasmus.com/" target="_blank">Peter Asmus</a> put it in his 2000 book, Reaping the Wind: “<a href="https://www.umass.edu/windenergy/about/history/heronemus/index.html" target="_blank">William Heronemus</a> was a dangerous man suggesting an audacious departure from the status quo.” <img alt="Modular home and wind turbine on a grassy hill on a sunny day " class="rm-shortcode" data-rm-shortcode-id="361cf08fb708d083a8bb3d373f3ccf4a" data-rm-shortcode-name="rebelmouse-image" id="0c4bb" loading="lazy" src="https://spectrum.ieee.org/media-library/modular-home-and-wind-turbine-on-a-grassy-hill-on-a-sunny-day.jpg?id=66894076&width=980"/> 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 LibrariesWhat 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 <a href="https://ember-energy.org/" target="_blank">Ember Energy</a>, 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 <a href="https://research-hub.nlr.gov/en/persons/robert-thresher/" target="_blank">Robert Thresher</a>, 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 <a href="https://research-hub.nlr.gov/en/persons/robert-thresher/" target="_blank">National Laboratory of the Rockies</a>). “In my mind he was the father of the people that went out and really made the industry what it is today,” he says.<h2>William Heronemus and the History of Wind Power</h2>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. <img alt="Man sits in a chair at his desk, leaning back and holding his eye glasses " class="rm-shortcode" data-rm-shortcode-id="29d1d2c5d9c9df57024f6f25ff3ca227" data-rm-shortcode-name="rebelmouse-image" id="af5ec" loading="lazy" src="https://spectrum.ieee.org/media-library/man-sits-in-a-chair-at-his-desk-leaning-back-and-holding-his-eye-glasses.jpg?id=66894082&width=980"/> 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. <img alt="Hand-drawn sketch of three wind turbine rotors mounted on a single freestanding pole" class="rm-shortcode" data-rm-shortcode-id="b15b340ec25c8a3cf286b93fe970327d" data-rm-shortcode-name="rebelmouse-image" id="13605" loading="lazy" src="https://spectrum.ieee.org/media-library/hand-drawn-sketch-of-three-wind-turbine-rotors-mounted-on-a-single-freestanding-pole.jpg?id=66894094&width=980"/> 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 <a href="https://en.wikipedia.org/wiki/Shoreham_Nuclear_Power_Plant" target="_blank">Shoreham Nuclear Power Plant</a> 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, <a href="https://spectrum.ieee.org/floating-offshore-wind-turbine" target="_self">as necessity pushes the industry to build in deeper waters</a>. Testing began on <a href="https://spectrum.ieee.org/green-hydrogen-offshore-wind" target="_self">the first floating electrolysis platforms</a> in 2023, and multirotor turbine prototypes are in development in China, Norway and Scotland.<h2>The UMass Amherst Wind Turbine Legacy</h2>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. <img alt="A man in a suit standing very straight outsider a modular home" class="rm-shortcode" data-rm-shortcode-id="96d2b39c565092306041f3fd581d2638" data-rm-shortcode-name="rebelmouse-image" id="fd9ad" loading="lazy" src="https://spectrum.ieee.org/media-library/a-man-in-a-suit-standing-very-straight-outsider-a-modular-home.jpg?id=66894100&width=980"/> 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 <a href="https://ieee-pes.org/" target="_blank">IEEE Power and Energy Society</a>’s 1974 winter meeting, where Heronemus suggested scrapping the utilities’ then nuclear-focused research arm, the <a href="https://www.epri.com/" target="_blank">Electric Power Research Institute</a>. 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 href="https://spectrum.ieee.org/three-mile-island" target="_self">a reactor at Three Mile Island</a> 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 <a href="https://www.hks.harvard.edu/faculty/john-holdren" target="_blank">John Holdren</a> opined in a 1971 <a href="https://www.sierraclub.org/" target="_blank">Sierra Club</a> 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.” <img alt="Hand-drawn sketch of a bridge-like structure across a highway containing five wind turbines that resemble giant fans" class="rm-shortcode" data-rm-shortcode-id="115d1e5e5724981c6df541b570415e05" data-rm-shortcode-name="rebelmouse-image" id="0ea43" loading="lazy" src="https://spectrum.ieee.org/media-library/hand-drawn-sketch-of-a-bridge-like-structure-across-a-highway-containing-five-wind-turbines-that-resemble-giant-fans.jpg?id=66894107&width=980"/> 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 <a href="https://ntrs.nasa.gov/api/citations/19730018091/downloads/19730018091.pdf" target="_blank">1972 federal panel on renewable energy</a>. 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.) <img alt="Hand-drawn sketch of a massive structure built over the length of a highway holding wind turbines that resemble giant fans " class="rm-shortcode" data-rm-shortcode-id="5dfe81607ae07e27818ac2c6cb26ddec" data-rm-shortcode-name="rebelmouse-image" id="9b105" loading="lazy" src="https://spectrum.ieee.org/media-library/hand-drawn-sketch-of-a-massive-structure-built-over-the-length-of-a-highway-holding-wind-turbines-that-resemble-giant-fans.jpg?id=66894112&width=980"/> 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 <a href="https://www.linkedin.com/in/sandy-butterfield-24b38513/" target="_blank">Sandy </a><a href="https://www.linkedin.com/in/sandy-butterfield-24b38513/" target="_blank">Butterfield</a>, who would later become chief engineer for wind-turbine testing at NREL. “They gave us just enough to fail,” says Butterfield. <img alt="A crane in the midst of vertically erecting a wind turbine on a single pole " class="rm-shortcode" data-rm-shortcode-id="30e3242484b0502fe0192acbf79d476e" data-rm-shortcode-name="rebelmouse-image" id="53850" loading="lazy" src="https://spectrum.ieee.org/media-library/a-crane-in-the-midst-of-vertically-erecting-a-wind-turbine-on-a-single-pole.jpg?id=66894118&width=980"/> 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.”<h2>From Helicopter Rotor to Wind Turbine</h2>Team WF-1 got a quick start, thanks to earlier, privately financed work by a couple of doctoral students, including <a href="https://scua.library.umass.edu/stoddard-forrest-s-1944/" target="_blank">Forrest “Woody” Stoddard</a>. 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. <img alt="Six men squat around a turbine blade that\u2019s wrapped in plastic" class="rm-shortcode" data-rm-shortcode-id="2e2f8a16e4c7c7e5b2dc572ecfa24680" data-rm-shortcode-name="rebelmouse-image" id="2001a" loading="lazy" src="https://spectrum.ieee.org/media-library/six-men-squat-around-a-turbine-blade-that-u2019s-wrapped-in-plastic.jpg?id=66894134&width=980"/> 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 <a href="https://www.mcgill.ca/" target="_blank">McGill University</a> in Montreal. Production of WF-1’s fiberglass blades got started at UMass in 1974 under the direction of doctoral student <a href="https://composite-eng.com/" target="_blank">Ted Van Dusen</a>. 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 <a href="https://prabook.com/web/duane_ellis.cromack/230343" target="_blank">Duane Cromack</a> and <a href="https://scholar.google.com/citations?user=NmB8VIwAAAAJ&hl=en&oi=sra" target="_blank">Jon McGowan</a>. 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. <a href="https://www.linkedin.com/in/louismanfredi" target="_blank">Louis Manfredi</a>, another ocean engineering student, teamed up with master’s student <a href="https://scholarworks.umass.edu/entities/publication/0fe58480-7291-449b-ad9e-9b04625a2132" target="_blank">Jim Sexton</a> on the nacelle housing the generator and drivetrain. <a href="https://scholarworks.umass.edu/entities/publication/40f08f39-f951-46ba-9d92-89865a0fe8bb" target="_blank">Fred Antoon</a> adapted the truck axle. <a href="https://www.linkedin.com/in/brian-kuhn-18616228/" target="_blank">Brian Kuhn</a> did drawings. <img alt="Chains and moving parts inside the rotor of a wind turbine" class="rm-shortcode" data-rm-shortcode-id="b4a8763fd385fece03dbb82995f21441" data-rm-shortcode-name="rebelmouse-image" id="ef40f" loading="lazy" src="https://spectrum.ieee.org/media-library/chains-and-moving-parts-inside-the-rotor-of-a-wind-turbine.jpg?id=66894144&width=980"/> 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, <a href="https://patents.justia.com/inventor/daniel-f-handman" target="_blank">Dan Handman</a>, 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. <img alt="A man in a harness standing at the top of a wind turbine on a single pole, high in the air" class="rm-shortcode" data-rm-shortcode-id="ba216463bf2eea813371abf85a3350bc" data-rm-shortcode-name="rebelmouse-image" id="a4a0a" loading="lazy" src="https://spectrum.ieee.org/media-library/a-man-in-a-harness-standing-at-the-top-of-a-wind-turbine-on-a-single-pole-high-in-the-air.jpg?id=66894172&width=980"/> 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.)<h2>The Great California Wind Rush</h2>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. <img alt="Five wind turbines mounted on freestanding poles on farmland" class="rm-shortcode" data-rm-shortcode-id="44de49883ce5d0d09dcde569e6a3bd24" data-rm-shortcode-name="rebelmouse-image" id="06407" loading="lazy" src="https://spectrum.ieee.org/media-library/five-wind-turbines-mounted-on-freestanding-poles-on-farmland.jpg?id=66894183&width=980"/> 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—<a href="https://granitegeek.concordmonitor.com/2017/11/29/nations-first-real-wind-farm-new-hampshire/" target="_blank">20 turbines on a mountain in New Hampshire</a>. California’s water authority placed an order for 100 MW of wind power, and in 1981 US Windpower began <a href="https://www.nytimes.com/1983/02/14/us/private-investors-selling-wind-power-to-utilities.html" target="_blank">installing hundreds of turbines in Altamont Pass</a>, 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.<h3></h3> “An entire generation of U.S. wind engineers got their graduate training, at least in part, using the Wind Furnace.”—Harold WallaceThere, 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 <a href="https://www.umass.edu/windenergy/home/index.html" target="_blank">UMass Wind Energy Center</a>, which has awarded over 300 graduate degrees.WF-1 now rests in the <a href="https://americanhistory.si.edu/collections/object/nmah_1389175" target="_blank">Smithsonian Institution’s collections</a> 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 <a href="https://profiles.si.edu/display/nwallaceh1102006" target="_blank">Harold Wallace</a>, 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 <a href="https://www.usatoday.com/story/news/nation/2026/01/09/trump-assails-windmills-and-wind-energy-as-junk-theyre-losers/88108694007/" target="_blank">Trump boasted to oil executives</a> 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 <a href="https://www.bostonglobe.com/2026/01/28/business/ge-vernova-offshore-wind-losses/" target="_blank">$600 million loss in 2025 for GE Vernova’s wind business</a>. GE Vernova is the only major wind turbine manufacturer remaining in the United States, and it too can be <a href="https://patents.google.com/patent/US5083039A/en" target="_blank">traced back to Heronemus via a US Windpower patent</a>.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 <a href="https://en.myse.com.cn/" target="_blank">Ming Yang Smart Energy Group</a> deployed a two-rotor offshore prototype. The company says <a href="https://www.rechargenews.com/technology/mingyang-building-50mw-offshore-wind-turbine/2-1-1888862" target="_blank">its next iteration will generate a whopping 50 MW</a>—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.

Jun 12, 2026

Award-Winning Researcher Trains Robots to Make Educated Guesses

<img src="https://spectrum.ieee.org/media-library/a-young-asian-professor-monitors-her-student-as-they-control-a-robotic-gripper.jpg?id=66879067&width=1245&height=700&coordinates=0%2C469%2C0%2C469"/> <a href="https://yenlingkuo.com" rel="noopener noreferrer" target="_blank">Yen-Ling Kuo</a> always wanted to understand how things worked. When she was growing up in Taiwan, reading the story of <a href="https://ethw.org/Michael_Faraday" rel="noopener noreferrer" target="_blank">Michael Faraday</a> in elementary school piqued her curiosity about the natural world. During that time, she was introduced to <a href="https://en.wikipedia.org/wiki/Logo_(programming_language)" rel="noopener noreferrer" target="_blank">Logo</a>, a computer program with a turtle cursor to help children learn basic coding through hands-on experimentation.It was Kuo’s introduction to programming logic.<h3>Yen-Ling Kuo</h3> EmployerUniversity of Virginia in CharlottesvilleTitleAssistant professor of computer science Member gradeMemberAlma matersNational Taiwan University; MITIn high school she learned the capacity computers held. She could write programs that completed tasks independently, she realized.“Once I discovered how powerful computers could be,” she says, “I knew I wanted to focus on using them to solve real-world problems.”Kuo, an IEEE member, never lost her interest in the “how” behind processes and tools. Her curiosity, combined with a stint working at a Silicon Valley company, led her to focus on innovations that live at the intersection of cognitive and computer sciences. Kuo, now an <a href="https://engineering.virginia.edu/faculty/yen-ling-kuo" rel="noopener noreferrer" target="_blank">assistant professor</a> of computer science at the <a href="https://www.virginia.edu/" rel="noopener noreferrer" target="_blank">University of Virginia</a> in Charlottesville, last year received the <a href="https://www.ieee-ras.org" rel="noopener noreferrer" target="_blank">IEEE Robotics and Automation Society</a>’s inaugural <a href="https://engineering.virginia.edu/news-events/news/more-honors-computer-scientist-wins-2025-wira-early-career-contribution-award" rel="noopener noreferrer" target="_blank">Outstanding Women in Robotics and Automation Early Career Contribution Award</a>. The award is part of the <a href="https://www.ieee-ras.org/wira-paper-awards-icra25/" rel="noopener noreferrer" target="_blank">IEEE-RAS Women in Engineering’s Outstanding Women in Robotics and Automation (WiRA) Paper Awards</a>, which promote excellence and recognize the impact that female researchers have on robotics and automation fields at different stages in their academic careers.Kuo’s winning paper, “<a href="https://diffdagger.github.io/" rel="noopener noreferrer" target="_blank">Diff-DAgger: Uncertainty Estimation with Diffusion Policy for Robotic Manipulation</a>,” demonstrates a novel method to help robots better identify and estimate uncertainty when faced with scenarios on which they’ve not been trained. The method reduces the amount of human supervision, improves a robot’s rate of successful task completion, and opens up a path to introduce more complex models with bigger data demands into interactive robot learning.She says her research will help people working in the robotics and automation fields more efficiently collect the data needed for effective model training.<h2>Silicon Valley’s impact</h2>Kuo earned bachelor’s and master’s degrees in computer science at the <a href="https://www.ntu.edu.tw/english/" rel="noopener noreferrer" target="_blank">National Taiwan University</a>, in Taipei, in 2009 and 2012. As she was nearing completion of her master’s degree, she did what many computer science graduates do: She pursued a summer internship at a tech company.She spent the summer of 2011 at Google’s campus in Kirkland, Wash., working on the company’s <a href="https://adwords.googleblog.com/2011/05/comparison-ads-now-part-of-new-google.html" rel="noopener noreferrer" target="_blank">comparison ads project</a>.When her internship ended, she joined the <a href="https://www.media.mit.edu" rel="noopener noreferrer" target="_blank">MIT Media Lab</a> as a visiting student, working on the <a href="https://en.wikipedia.org/wiki/Open_Mind_Common_Sense" rel="noopener noreferrer" target="_blank">Open Mind Common Sense project</a> with <a href="https://web.media.mit.edu/~lieber/" rel="noopener noreferrer" target="_blank">Henry Lieberman</a>.As she was considering pursuing a Ph.D., a call from Google changed her plans. The company offered her a full-time role as a software engineer.“I viewed the job offer as a positive development,” she says. “I believe it can never hurt your future research career to get some real-world experience under your belt.”She was hired in 2012 and helped build techniques that incorporate computer vision and natural language processing to improve the customer shopping search experience. She led the company’s <a href="https://techcrunch.com/2016/09/06/google-is-launching-shop-the-look-to-let-you-search-and-shop-by-outfit/" rel="noopener noreferrer" target="_blank">Shop the Look initiative</a>, a predecessor to Google’s current <a href="https://blog.google/products-and-platforms/products/shopping/google-shopping-ai-mode-virtual-try-on-update/" rel="noopener noreferrer" target="_blank">AI-powered shopping experience</a>. The project connected social media content with search results, something the company had struggled to do in the past.Kuo and her team were tasked with building a connection between the natural language people use to describe an item and an image that matches the searcher’s intent. It was at a time when the <a href="https://spectrum.ieee.org/tag/neural-network" target="_self">neural network</a>—using deep learning models to power Google products—was gaining momentum at the company. Integrating neural network tools into her work was a requirement—which raised questions for Kuo.“I was applying the neural network tools,” she says. “But I didn’t have 100 percent certainty about how they actually worked.”She considered how she could become more knowledgeable about deep learning models. It was a full-circle moment. She decided that after nearly four years at Google, it was time to earn a Ph.D. in computer science. She returned to MIT in 2016.<h2>The question that changed everything</h2><a href="https://people.csail.mit.edu/boris/boris.html" rel="noopener noreferrer" target="_blank">Boris Katz</a>, one of Kuo’s Ph.D. advisors, is a principal research scientist and the head of the MIT <a href="https://www.csail.mit.edu" rel="noopener noreferrer" target="_blank">Computer Science and Artificial Intelligence Laboratory</a> (CSAIL)’s <a href="https://www.csail.mit.edu/research/infolab" rel="noopener noreferrer" target="_blank">InfoLab</a>. He also led the creation of the <a href="https://start.csail.mit.edu/index.php" rel="noopener noreferrer" target="_blank">START Natural Language System</a>, the world’s first Web-based question-answering system.When the two met, Katz asked Kuo why she wanted to pursue a doctorate degree. She explained her interest in understanding how neural networks work and in using that knowledge to connect the physical world with human language.He suggested she attend a <a href="https://bmm.mit.edu/" rel="noopener noreferrer" target="_blank">summer course</a> at MIT’s <a href="https://cbmm.mit.edu" rel="noopener noreferrer" target="_blank">Center for Brains, Minds, and Machines</a>, a research initiative that <a href="https://sqi.mit.edu/research/cbmm" rel="noopener noreferrer" target="_blank">ran from 2013 through 2025</a>. CBMM’s objective was to bring together computer scientists, cognitive scientists, and neuroscientists to understand how human intelligence works. The goal was to use the resulting insights to establish an engineering practice to build artificial intelligence systems.For Kuo, it was a chance to better understand human intelligence and identify ways it could be replicated in machines.“It was an opportunity for me to interact with other scientists and gain insight into how people learn, understand, and figure things out in the world,” she says. “I saw it as a very useful and inspiring way to incorporate those ideas into my own research work.”During her Ph.D. studies, she was a research assistant at CSAIL. The experience helped shape her doctoral research, which focused on building AI systems that apply past learning to new situations. She developed machine learning models to support the efforts, including language understanding and social interactions.She completed her Ph.D. in computer science in 2022 with a minor in cognitive science.After graduation, she continued her work and collaboration at CSAIL, particularly on projects that involved the “theory of mind” concept.<h2>Theory of mind spurs innovation</h2>Theory of mind isn’t new, having originated with <a href="https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/does-the-chimpanzee-have-a-theory-of-mind/1E96B02CD9850016B7C93BC6D2FEF1D0" rel="noopener noreferrer" target="_blank">primatologists studying chimpanzees</a> in the late 1970s. The theory recognizes that others have their own thoughts, beliefs, and perspectives. It’s a skill that allows humans to infer someone’s mental state and predict their behavior without verbal communication.“It’s like when college roommates are moving into their dorm. They may not talk too much, but they work together naturally to coordinate their activities and accomplish goals,” Kuo says. “They can infer and mentally interpret each other’s behaviors and signals to make decisions and complete tasks without words.”She brought her theory of mind research to the University of Virginia when she joined as an assistant professor in 2023.Kuo conducts her research in UVA Engineering’s multidisciplinary cyberphysical <a href="https://engineering.virginia.edu/labs-groups/link-lab" rel="noopener noreferrer" target="_blank">Link Lab</a>. Her broad focus is on developing computational models that help robots interpret both direct data and silent signals, from language and movements to a person’s gaze. If successful, it could give robots the same sort of physical and theory of mind reasoning capabilities that power physical and social interactions among humans.“There are no computational frameworks yet available that will translate this kind of understanding into a robot efficiently,” she says.She adds that the process to get there begins with improving how robots learn to perform tasks.<h2>The evolution of robot learning</h2>Historically, one way robots learned was to mimic humans. A researcher would manually guide a robot through a task, like cutting an apple, and it would repeat the movements. The robot was successful until the environment changed, such as when its hand was in a different position or the apple was at a different angle. The robot was then faced with a situation for which it hadn’t been trained. Without any data available to help it correct course, the robot would start making small errors that eventually led to a full system crash. <img alt="Diagram of a robotic gripper delicately holding a potato chip. Labels describe how the gripper\u2019s visual perception and tactile sensing prevent the chip from breaking." class="rm-shortcode" data-rm-shortcode-id="76442a7dd57b85e82dfbaee6fcbcee1b" data-rm-shortcode-name="rebelmouse-image" id="bfe1e" loading="lazy" src="https://spectrum.ieee.org/media-library/diagram-of-a-robotic-gripper-delicately-holding-a-potato-chip-labels-describe-how-the-gripper-u2019s-visual-perception-and-tact.jpg?id=66879111&width=980"/> This diagram describes how the robotic gripper’s visual perception and tactile sensing prevents a potato chip from breaking.<a href="https://force-gripper.github.io/" target="_blank">Xuhui Kang, Yen-Ling Kuo, et al.</a>To solve the problem, researchers developed the dataset aggregation (DAgger) method. As a robot performed a task, a researcher was on standby to provide real-time corrections during unexpected scenarios. The correction data was continuously added to the robot’s model, teaching it how to recover from mistakes.To reduce the human monitoring effort, robot-gated DAgger was created to enable bots to query humans when the machines became uncertain.The most popular approach to make the query decision is to train multiple models to consider when determining a course of action. If the models all agree, the robot proceeds. If they don’t agree, the robot is likely to get stuck and ask for help.Although the multiple model approach was widely adopted, it has limitations. Practically speaking, as models become more complex, it is hard or impossible to train multiple copies. A more fundamental issue is that disagreement among models doesn’t always imply uncertainty; it could just mean there are different ways to accomplish a task.<h2>The Diff-DAgger solution</h2>That is the gap Kuo’s research team closed with the novel Diff-DAgger research. The approach builds on diffusion policy, a technique that helps robots account for different ways a task can be performed.The new method repurposes diffusion loss, the signal a robot uses to improve its model during training, as a real-time confidence check. During task execution, the robot computes the signal and compares it against values from its training data using a statistical test. The signal spikes when the robot faces an unfamiliar situation and is uncertain how to proceed. The signal stays silent when the robot’s current action is close to what it learned before.The spike represents the robot’s ability to self-diagnose and predict an imminent failure. Human intervention is triggered only when the signal spikes. No spike means the robot can be left to complete its decision-making process on its own.Kuo’s team achieved <a href="https://diffdagger.github.io" target="_blank">significant results</a>: Failure prediction rates were improved by 39 percent. Task completion rates were increased by 20 percent, and tasks were completed nearly eight times faster.Her research at UVA gained attention from the <a href="https://www.nsf.gov" rel="noopener noreferrer" target="_blank">National Science Foundation</a>, which honored her last year with a <a href="https://www.nsf.gov/funding/opportunities/career-faculty-early-career-development-program" rel="noopener noreferrer" target="_blank">Career Award</a>, the foundation’s flagship grant for early-career researchers. The five-year US $665,000 grant supports her research that builds computational models for human-robot interactions through theory of mind reasoning.She also received the Toyota Research Institute’s <a href="https://engineering.virginia.edu/news-events/news/uva-and-toyota-research-institute-aim-give-your-car-power-reason" rel="noopener noreferrer" target="_blank">Young Faculty Researcher Award</a> to teach cars to reason about interactions on the road and with the driver.As service robots and self-driving vehicles become more available, such works are likely to make interactions between humans and robots more intuitive and useful.Kuo ultimately wants to build more robust robots that are able to integrate into a social space with humans by engaging with us through grounded interactions, she says.<h2>The impact of IEEE</h2>Like many IEEE members, Kuo was introduced to the organization as a student. In 2018 she submitted her first paper, “<a href="https://arxiv.org/abs/1810.00804" rel="noopener noreferrer" target="_blank">Deep Sequential Models for Sampling-Based Planning</a>,” to the <a href="https://www.ieee-ras.org/conferences-workshops/financially-co-sponsored/iros/" rel="noopener noreferrer" target="_blank">IEEE/Robotics Society of Japan International Conference on Intelligent Robots and Systems</a> while pursuing her Ph.D. at MIT. Her IEEE involvement grew alongside her professional career.“It was a natural segue to transition from student to a full IEEE member,” she says. Today she is an active volunteer with the IEEE Robotics and Automation Society, a reviewer for submitted papers, and a presenter and panelist at conferences.She says one of the best parts of attending conferences is having the opportunity to engage with students. She also enjoys participating as a panelist at luncheons, she says, because it gives her one-on-one time with student attendees. She can share her knowledge and offer insights as they prepare to embark on their career.Her goal in the coming years, she says, is to broaden her involvement with IEEE initiatives and branch out to other technical committees. Sharing knowledge and learning from others is essential to anyone’s <a data-linked-post="2670807151" href="https://spectrum.ieee.org/influence-your-career" target="_blank">career growth</a>, she says, and “IEEE offers a great opportunity for both.”

Jun 11, 2026

Why Orbital Data Centers Are Harder Than Silicon Valley Thinks

<img src="https://spectrum.ieee.org/media-library/globe-surrounded-by-zeroes-and-ones-on-a-blue-background.png?id=66895710&width=1245&height=700&coordinates=0%2C95%2C0%2C96"/> “Space computing, the final frontier, has arrived,” Nvidia CEO Jensen Huang <a href="https://nvidianews.nvidia.com/news/space-computing" rel="noopener noreferrer" target="_blank">declared</a> at the <a href="https://www.nvidia.com/gtc/" rel="noopener noreferrer" target="_blank">Nvidia GTC</a> conference in March.Indeed, the idea of data centers in orbit has gone from science fiction to a serious spending category. Elon Musk’s <a href="https://www.spacex.com/" rel="noopener noreferrer" target="_blank">SpaceX</a> has <a href="https://x.ai/news/xai-joins-spacex" rel="noopener noreferrer" target="_blank">acquired</a> <a href="https://x.ai/" rel="noopener noreferrer" target="_blank">xAI</a> (also Musk’s) and is <a href="https://spacenews.com/spacex-offers-details-on-orbital-data-center-satellites/" rel="noopener noreferrer" target="_blank">planning</a> a constellation of space-based data centers. <a href="https://research.google/" rel="noopener noreferrer" target="_blank">Google</a>, not to be outdone, announced <a href="https://research.google/blog/exploring-a-space-based-scalable-ai-infrastructure-system-design/" rel="noopener noreferrer" target="_blank">Project Suncatcher</a> in partnership with <a href="https://www.planet.com/" rel="noopener noreferrer" target="_blank">Planet</a>, planning to launch two satellites equipped with Google Tensor Processing Unit (TPU) AI chips by early 2027. Startup <a href="https://www.starcloud.com/" rel="noopener noreferrer" target="_blank">Starcloud</a> has already <a href="https://www.pcmag.com/news/data-center-space-race-heats-up-as-starcloud-startup-requests-88000-satellites?test_uuid=04IpBmWGZleS0I0J3epvMrC&test_variant=B" rel="noopener noreferrer" target="_blank">filed</a> a proposal with the Federal Communications Commission for an 88,000-satellite constellation for orbital data centers. As Starcloud’s filing suggests, these companies are all proposing fleets of satellites numbering in the thousands, each housing a rack or multiple racks of AI-grade GPUs, interconnected with each other through free-space optical links and communicating back to Earth via microwave links, either directly or through other satellites.<div class="rm-embed embed-media"><iframe height="110px" id="noa-web-audio-player" src="https://embed-player.newsoveraudio.com/v4?key=q5m19e&id=https://spectrum.ieee.org/orbital-data-centers-heat&bgColor=F5F5F5&color=1b1b1c&playColor=1b1b1c&progressBgColor=F5F5F5&progressBorderColor=bdbbbb&titleColor=1b1b1c&timeColor=1b1b1c&speedColor=1b1b1c&noaLinkColor=556B7D&noaLinkHighlightColor=FF4B00&feedbackButton=true" style="border: none" width="100%"></iframe></div>Proponents <a href="https://x.com/patrick_oshag/status/1998440819078898140" target="_blank">tout</a> the many wonders of computing in space: abundant solar energy, free cooling, and freedom from Earth-based disturbances like earthquakes, floods, and protesters. But a sober look at the physics of space-based computing paints a much more nuanced picture.Free cooling is perhaps the biggest misconception. Space is cold, but it also has no atmosphere. That means the best heat-removal mechanisms, conduction and convection, are off the table. The only option is radiation. To prevent a chip from overheating in space, a large, costly surface area is required to dissipate the energy and then radiate it.Solar energy is abundant, but collecting it with functional solar panels that maintain perfect alignment toward the sun is a complex task requiring extensive <a href="https://spectrum.ieee.org/satellite-refueling-heats-up" target="_self">attitude control systems</a>. On top of that, ionizing radiation in space from cosmic rays and other sources poses a unique challenge, degrading the solar panels, the radiative coolers, and the chips themselves. Because regular maintenance in space is difficult, redundancy has to be built in at launch, and cost estimates have to account for efficiency degradation over time.At <a href="https://www.abiresearch.com/" target="_blank">ABI Research</a>, where I work as an aerospace analyst, we did a rough total-cost-of-ownership comparison between a data center on Earth and one in space. It showed that the cost to launch and run a GPU in space for a year is at least an order of magnitude higher than the same feat in a terrestrial data center. Our model was simple, assuming an Nvidia H100 server rack launched with the requisite-size solar panel and radiator on a spacecraft akin to Starcloud’s <a href="https://spectrum.ieee.org/nvidia-h100-space" target="_self">pilot launch</a>. We assumed SpaceX’s Starship was used at a highly optimistic launch cost per kilogram of US $44, and a terrestrial energy cost of $0.20 per kilowatt hour. This is a simple back-of-the-envelope calculation, but it does signal something real.From our perspective, the cost of delivery and space hardening of the payload makes general-purpose space-based data centers difficult to justify economically today, despite the fact that data-center builders in many regions are scrambling for electric power. However, there are niche applications where the much higher costs of computing in space could be justified. Examples include preprocessing data from Earth-observation satellites, real-time detection and tracking of hypersonic missiles, and active collision avoidance in the increasingly crowded low Earth orbit. Even for these, though, contending with fundamental physics will still be a demanding challenge. And a technologically compelling one, too.<h2>The Cooling Challenge in Space</h2>Cooling is where physics separates the science from the fiction. The governing equation for radiative cooling, the only type of cooling available in space, is known as the Stefan-Boltzmann Law. It states that the amount of power you can radiate is proportional to the area of the radiator times its temperature to the fourth power. For a space systems architect, the implications of this law are brutal. In orbit, the only variable we can control is area. This restriction creates a geometric penalty, or a “physics tax,” for cooling in space: The more power you need to reject, the bigger the area of the radiator you need to bring along from Earth.<h3></h3> <div class="flourish-embed flourish-chart" data-src="visualisation/28633310?602891"><script src="https://public.flourish.studio/resources/embed.js"></script><noscript><img alt="chart visualization" src="https://public.flourish.studio/visualisation/28633310/thumbnail" width="100%"/></noscript></div>The only cooling method available in space is radiation, and the radiator area required is derived using the Stephan-Boltzmann law. For a single chip drawing 700 watts, like Nvidia’s popular H100 GPU, the area required to keep it at 20 °C is just under 3 square meters, and it goes down to 1 square meter for an operating temperature of 85 °C. However, as the radiator surface is exposed to ionizing radiation, its emissivity decreases, and after 5 years in space the required area increases by about 40 percent. <h3></h3> To understand how big this baseline area is in practice, I used the Stefan-Boltzmann law to model the heat-rejection area needed to keep a single chip that draws 700 watts of power—such as the H100 GPU chip, an AI stalwart—at a constant 60 °C, usually considered the sweet spot for GPU longevity and stability. I further assumed that the radiator is perfectly facing deep space, at a chilly background temperature of 3 kelvins. By this calculation, a single chip would require 1.4 square meters of radiator surface.To put this into perspective, consider that a common AI rack can hold approximately 32 GPUs (four H100 server boards). With CPUs, memory, and networking equipment, this rack would draw around 40 kilowatts of power. This single rack includes 2.5 terabytes of memory—enough capacity to serve over 20,000 concurrent users or run 16 simultaneous instances of Llama 3, an open-source AI model. But to cool this thermal load in a vacuum, that single rack would require an 80-square-meter radiator, roughly the size of a pickleball court. For an aggregate 100-megawatt data center, you’d need at least 2,500 of those radiators.And that’s the best-case scenario. Additional problems are hidden in the low Earth orbit environment itself. Space exposes radiators and their coatings to a chemically hostile brew of ultraviolet light and atomic oxygen, quite the opposite of a clean-room environment. Over a LEO satellite’s typical 5-year lifespan, these elements degrade the radiator’s surface properties and lower its ability to shed heat.Including this degradation in the model reveals that as the radiator degrades from a “fresh” state to an “end-of-life” state, the physics demands a further penalty. To maintain that same 60 °C operating temperature for the GPU chips, the required surface area jumps from about 1.4 square meters per chip to nearly 2.0 square meters. In other words, the physics tax rises by 40 percent. Therefore, you must launch at least 40 percent more radiator mass, endure higher atmospheric drag, and sacrifice valuable launch volume just to survive the degradation of the thermal coating. This increase adds significantly to the launch cost and further erodes the economics of a space-based data center.<h2>The Silicon Challenge in Space</h2>Solving the heat problem is only part of the battle. The other significant challenge in low Earth orbit is ionizing radiation, which affects the computing hardware itself. Today’s satellites typically use radiation-hardened processors, which are very reliable but also much more expensive, and they perform poorly compared to commercial off-the-shelf <a href="https://ieeexplore.ieee.org/document/11068401" target="_blank">processors</a>.A standard rad-hard chip doesn’t have the processing power to run a modern large language model (LLM). As a result, satellite operators aspiring to launch a data center have no choice but to make a risky compromise: to use hardware meant for terrestrial use. In order to achieve the necessary compute density, orbital data centers must use the same Nvidia H100s or Google TPUs found in terrestrial server farms. The problem is that these chips are “soft” targets in space. High-energy particles can flip bits in memory or cause “latch-ups” in logic that fry the circuit.<h3></h3> <table border="“0”" style="white-space: unset;" width="100%"><thead><tr><th style="background-color: #000000; color: #FFFFFF; width: 25%;"> </th><th style="background-color: #265892; color: #FFFFFF; width: 25%;">SpaceX/xAI</th><th style="background-color: #000000; color: #FFFFFF; width: 25%;">Starcloud</th><th style="background-color: #265892; color: #FFFFFF; width: 25%;">Google Project Suncatcher</th></tr></thead><tbody><tr><td style="background-color: #ecece9; width: 25%;">Status</td><td style="background-color: #d2ebfa; width: 25%;">FCC filing, January 2026; AI1 design, June 2026</td><td style="background-color: #ecece9; width: 25%;">First satellite (H100) launched late 2025; second due October 2026</td><td style="background-color: #d2ebfa; width: 25%;">Research phase; 2-satellite demo with Planet Labs planned early 2027</td></tr><tr><td style="background-color: #ecece9; width: 25%;">Proposed scale</td><td style="background-color: #d2ebfa; width: 25%;">Up to 1 million satellites</td><td style="background-color: #ecece9; width: 25%;">5 GW total across ~100 launches</td><td style="background-color: #d2ebfa; width: 25%;">81-satellite clusters</td></tr><tr><td style="background-color: #ecece9; width: 25%;">Per satellite power</td><td style="background-color: #d2ebfa; width: 25%;">Up to 150 kW</td><td style="background-color: #ecece9; width: 25%;">40 MW per launch container</td><td style="background-color: #d2ebfa; width: 25%;">Not specified</td></tr><tr><td style="background-color: #ecece9; width: 25%;">Chips</td><td style="background-color: #d2ebfa; width: 25%;">Custom D3 chip from Terafab consortium or other</td><td style="background-color: #ecece9; width: 25%;">Off-the-shelf GPUs (Nvidia H100/Blackwell)</td><td style="background-color: #d2ebfa; width: 25%;">Google Trillium TPU v6e</td></tr><tr><td style="background-color: #ecece9; width: 25%;">Orbit</td><td style="background-color: #d2ebfa; width: 25%;">500–2,000 km LEO; sun-synchronous shells at 50-km intervals</td><td style="background-color: #ecece9; width: 25%;">Dawn-dusk sun-synchronous LEO (>99% sunlight)</td><td style="background-color: #d2ebfa; width: 25%;">Dawn-dusk sun-synchronous LEO (~650 km)</td></tr><tr><td style="background-color: #ecece9; width: 25%;">Cooling</td><td style="background-color: #d2ebfa; width: 25%;">Liquid circulating through radiators</td><td style="background-color: #ecece9; width: 25%;">Liquid circulating through radiators about half the size of solar arrays</td><td style="background-color: #d2ebfa; width: 25%;">No design published</td></tr><tr><td style="background-color: #ecece9; width: 25%;">Connectivity</td><td style="background-color: #d2ebfa; width: 25%;">Optical links to Starlink mesh, then to ground</td><td style="background-color: #ecece9; width: 25%;">Laser links to Starlink/Kuiper/Kepler; physical “data shuttle” modules for bulk data</td><td style="background-color: #d2ebfa; width: 25%;">Free-space optical links; radio for pilot mission ground links</td></tr><tr><td style="background-color: #ecece9; width: 25%;">Cost parity expected</td><td style="background-color: #d2ebfa; width: 25%;">Musk says 2–3 years</td><td style="background-color: #ecece9; width: 25%;">~$8M per 40-MW cluster over 10 years vs. $167M terrestrial (modeled)</td><td style="background-color: #d2ebfa; width: 25%;">Mid-2030s when launch hits <$200/kg (per Google’s own paper)</td></tr><tr><td style="background-color: #ecece9; width: 25%;">Key dependency</td><td style="background-color: #d2ebfa; width: 25%;">Terafab chip fab; Starship reusability</td><td style="background-color: #ecece9; width: 25%;">Starship reusability; off-the-shelf GPU radiation tolerance at scale</td><td style="background-color: #d2ebfa; width: 25%;">Launch cost reduction; thermal management solutions</td></tr><tr><td colspan="4" style="background-color: #ecece9;">Sources: <a href="https://www.datacenterdynamics.com/en/news/spacex-files-for-million-satellite-orbital-ai-data-center-megaconstellation/" target="_blank">www.datacenterdynamics.com;</a> <a href="http://www.cnbc.com/" target="_blank">http://www.cnbc.com/</a>; <a href="https://www.infoq.com/news/2025/11/google-suncatcher-space/" target="_blank">www.infoq.com</a>; <a href="http://www.datacenterdynamics.com" target="_blank">www.datacenterdynamics.com</a>; <a href="https://services.google.com/fh/files/misc/suncatcher_paper.pdf" target="_blank">services.google.com</a>; <a href="https://starcloudinc.github.io/wp.pdf" target="_blank">starcloudinc.github.io</a>; <a href="https://research.google/blog/exploring-a-space-based-scalable-ai-infrastructure-system-design/" target="_blank">research.google/blog</a>; <a href="https://x.com/SawyerMerritt/status/2064108916611420273" target="_blank">x.com</a></td></tr></tbody></table>Three big proposals for orbital data centers vary in satellite size, number, and cooling plans.<h3></h3> One possible option is to shield the computers from radiation with thick, absorbent panels. However, the shielding would add significantly to the already heavy satellites. The other option is to compensate for the radiation damage with redundancy. Indeed, edge computing architects are moving toward software-defined resilience, where instead of one perfectly hardened computer, operators fly a cluster of imperfect, commercial ones whose total cost could be as low as one-tenth to one-hundredth that of the rad-hard model.This redundant approach is used in many spacecraft, including <a href="https://cacm.acm.org/news/how-nasa-built-artemis-iis-fault-tolerant-computer/" target="_blank">Artemis II</a>, which recently carried astronauts around the moon, as well as SpaceX’s flight computers and the Hewlett Packard Enterprise edge servers for the International Space Station. By running three (or more) instances of the same calculation on three different nodes and comparing the answers, the system can detect a corrupted processor. If a node fails, the “orchestrator” reboots it while the others continue the mission. While this ensures resiliency, it also means that some fraction of the compute capacity is dedicated to redundancy, further increasing the costs.<h2>The Energy Challenge in Space</h2>An often-touted advantage of space-based data centers is the seemingly unlimited supply of free, clean energy from the sun. Solar energy in orbit is indeed abundant, at 1,361 watts per square meter. Of course, capturing that free energy is made possible only by the very costly launching of large solar panels into orbit. And those solar panels also degrade over time due to radiation exposure, typically losing 1 to 3 percent efficiency per year.Let’s say a solar array collects 1 MW of power to run an AI cluster. The laws of physics demand that the satellite must eventually radiate 1 MW of waste heat. Because the square area needed to generate the solar power—<a href="https://www.energydawnice.com/solar-panel-output-per-square-meter/" rel="noopener noreferrer" target="_blank">around 400 W/m2</a>—and to reject the heat—around 450 W/m2—are nearly equivalent, every square meter of power generation now demands approximately another square meter of cooling. The radiator needs to be a structural equal, not merely a passive coating on a surface used for something else.As Elon Musk recently <a href="https://www.youtube.com/watch?v=IgifEgm1-e0" rel="noopener noreferrer" target="_blank">noted</a> in Davos, the most efficient radiator is one that never sees the sun. By orienting the spacecraft so the solar panels face the sun and the radiators face the deep vacuum of space, efficiency skyrockets for both. But there’s a catch: Maintaining this perfect three-way alignment—panels to sun, radiator to the void, antennas to Earth—requires complex, high-torque attitude control systems. So this configuration means more payload and more computing power. Plus, these control systems are complex components with many failure modes, which is not optimal in a situation where maintenance is difficult.<h2>The Killer Apps for Computing in Space</h2>Given all these challenges of deploying massive radiators for satellites in the hostile environment of space, why build data centers in space at all?While training or inference on LLMs in space doesn’t seem economical today, there are other, very compelling applications for computing in space. Here are two: solving the downlink bottleneck from Earth-observation satellites and enabling collision-preventing maneuvers in the increasingly crowded low Earth orbit.The latest Earth-observation satellites, equipped with hyperspectral and synthetic aperture radar sensors, are used for a range of important reconnaissance missions, such as battlefield intelligence, tracking the global shadow fleet of ships carrying contraband, and assessing earthquakes or infrastructure failures down to the millimeter. These systems can generate hundreds of terabytes of raw data per day that must be transmitted to Earth. However, the radio-frequency “pipes” used to downlink the data are congested, and the ground infrastructure cannot absorb the sheer volume of raw data.Another immediate, mission-critical application for in-space computation is protecting the orbital environment. With over 17,000 satellites in orbit, the overwhelming majority of which are in low Earth orbit, avoiding collisions between these satellites is crucial. As NASA astrophysicist <a href="https://en.wikipedia.org/wiki/Donald_J._Kessler" rel="noopener noreferrer" target="_blank">Donald Kessler</a> pointed out back in 1978, a single space collision could cause a cascading effect that renders the entirety of LEO unusable.RELATED: <a href="https://spectrum.ieee.org/kessler-syndrome-space-debris" target="_self">Have We Reached a Space-Junk Tipping Point?</a>According to SpaceX’s recent annual report, the Starlink constellation executes a collision avoidance maneuver every 2 minutes on average. Each maneuver already <a href="https://spacexstock.com/25000-collision-avoidance-maneuvers-lessons-from-starlink/" rel="noopener noreferrer" target="_blank">relies</a> on onboard AI systems but still requires most of the processing to happen on the ground.<h3></h3> <img alt="A rendering of the Starlink satellite system depicted as bright dots surrounding the Earth." class="rm-shortcode" data-rm-shortcode-id="413f7488561cf1957b75df3d60150db8" data-rm-shortcode-name="rebelmouse-image" id="ff99a" loading="lazy" src="https://spectrum.ieee.org/media-library/a-rendering-of-the-starlink-satellite-system-depicted-as-bright-dots-surrounding-the-earth.png?id=66879236&width=980"/><h3></h3> As low Earth orbit gets increasingly populated, collision avoidance will have to break the traditional ground-loop model. In the megaconstellation era of space, the OODA (observe, orient, decide, act) loop must happen onboard, thereby reducing the analysis turnaround from minutes to milliseconds.The problem is that the flight computers standard on satellites are not built for this level of processing. The complex probability models required for maneuvering cannot currently be implemented by onboard computers in conjunction with their navigation systems. Clearly, more powerful computers are needed.This is the true economic justification for moving compute to space: to move insight generation there. By placing high-performance computing adjacent to the sensors, we can process terabytes of data in orbit and downlink only the relevant data in real time, and we can do the computations necessary to avoid satellite collisions in real time.<h2>The Future of Computing in Space</h2>So, assuming that some form of computing is inevitable in low Earth orbit in the foreseeable future, how will the heat be handled? The industry is currently experimenting with two main classes of solutions to cope with the Stefan-Boltzmann law.One creative option is to use origami-inspired radiators, the kind used for the James Webb telescope. Companies are developing flexible, high-conductivity composite radiators that fold into a tight cube for launch and unfurl into enormous yet lightweight thermal wings in orbit.Another possibility is to use liquid-droplet radiators. This concept proposes removing the rigid radiator structure completely and instead spraying a stream of coolant oil directly into the vacuum of space. The fluid travels through an open loop, exposed to the near-absolute zero of the void, maximizing radiative surface area before being caught by a collector and pumped back into the ship. It sounds like science fiction, but as the heat loads climb into the megawatts, liquid-droplet cooling may be the only way to cheat the mass limits of this exponential reality.<h3>Options for Future Radiator Design</h3> <img alt="Diagram of droplet-based heat exchanger system with labeled components and web-like graphs." class="rm-shortcode" data-rm-shortcode-id="a07a5b1926272e2c747f1de69ea6eda8" data-rm-shortcode-name="rebelmouse-image" id="ca3f4" loading="lazy" src="https://spectrum.ieee.org/media-library/diagram-of-droplet-based-heat-exchanger-system-with-labeled-components-and-web-like-graphs.png?id=66895778&width=980"/> Our rough total-cost-of-ownership model uses optimistic versions of current numbers, such as launch cost, chip cost, and power use. A critic might point out that future technology will improve, both in efficiency, purpose-built designs, and costs. Sure, the technology is bound to improve. But the critical factor isn’t just launch cost; it’s the computing power per unit mass and electric-power economics. Radiators and solar arrays can consume 65 to 70 percent of total satellite mass, and space-grade photovoltaics run orders of magnitude more expensive than terrestrial equivalents. <img alt="Spiral polygonal grid resembling a twisted spiderweb on a light background" class="rm-shortcode" data-rm-shortcode-id="0809489b27553697e7814fdf4e3009ed" data-rm-shortcode-name="rebelmouse-image" id="e2ced" loading="lazy" src="https://spectrum.ieee.org/media-library/spiral-polygonal-grid-resembling-a-twisted-spiderweb-on-a-light-background.gif?id=66895750&width=980"/> Chris PhilpotEven as launch costs fall, the mass and cost burden of power generation and thermal management will remain a fundamental problem. Current space-grade solar panels rely on germanium substrates, whose supply is concentrated in China. It will be extremely difficult to scale up availability of these substrates. A transition to radiation-tolerant perovskite solar panels or a similar alternative could change the economics significantly, but that possibility is five years away or more. The technology will get cheaper, but the bottlenecks of power and thermal architecture will remain. Recognizing the thermal reality of cooling in space forces us to shift how we view satellite operations. We are moving away from the “launch and forget” era toward an era of “autonomous logistics.” As our thermal model demonstrated, the harsh environment of space steadily attacks the hardware. UV radiation degrades thermal coatings; cosmic rays degrade silicon. In a traditional satellite model, when the radiator degrades or the memory fails, the satellite becomes space junk. For a multimillion-dollar data center, that disposal model is potentially ruinous. To make the economics of orbital computation work, the infrastructure must be serviceable and the rockets to launch them reusable. The orbital domain will require automated servicing vehicles capable of swapping out degraded radiator panels and upgrading fried servers. In these ways, the future of the orbital data centers is dependent on the innovations of an emergent in-space economy. There’s a good argument to be made that the need for space-based computation is less of a hype cycle and more of an enabler for the new space economy. Look no further than SpaceX’s recent regulatory filings proposing a constellation of up to a million satellites in low Earth orbit. At such a scale, routing all raw data back to Earth is physically impossible; the network itself must become the data center. However, the winners in this sector will be determined by the systems architects who most cleverly accommodate the thermodynamics and the companies with sufficient vertical integration to take on the massive costs of operating data centers in orbit. Ultimately, the physics tax is universal. Whether managing heat rejection in the vacuum of low Earth orbit or managing power density in a hyperscale facility in Northern Virginia, the constraint is never the silicon. It’s the thermodynamics.

Jun 11, 2026

Defining Autonomy for Wellness Robots in Senior Care

Jun 10, 2026

EPICS in IEEE’s Awards Honor Outstanding Students and Faculty

<img src="https://spectrum.ieee.org/media-library/four-men-tinkering-with-iot-parts-on-an-outdoor-table.jpg?id=66858524&width=1245&height=700&coordinates=0%2C469%2C0%2C469"/> The <a href="https://epics.ieee.org/" rel="noopener noreferrer" target="_blank">EPICS (Engineering Projects in Community Service) in IEEE</a> program, administered by <a href="https://ea.ieee.org/ea-programs" rel="noopener noreferrer" target="_blank">IEEE Educational Activities</a>, has launched the <a href="https://epics.ieee.org/excellent-epics-in-ieee-contributor-awards/" rel="noopener noreferrer" target="_blank">Excellent EPICS in IEEE Contributor Awards</a>. The recognitions honor the program’s outstanding students and faculty volunteers in Excellent Team Leader and Excellent Faculty Advisor categories.The awards recognize individuals whose leadership, mentorship, and commitment have meaningfully advanced the impact of <a href="https://spectrum.ieee.org/epics-in-ieee-student-projects" target="_self">EPICS projects</a>. Candidates must demonstrate clear, measurable contributions that elevate both the student experience and the outcomes delivered to community partners. Reviewers also consider other awards, publications, presentations, and professional achievements that reinforce the nominee’s credibility and leadership.Recipients must demonstrate outstanding project management and documentation, strong mentoring and collaboration, and high-quality outcomes.Here are this year’s recipients.<h2>Team Leader Award</h2><a href="https://www.instagram.com/p/DXfFA5LEQQR/" rel="noopener noreferrer" target="_blank">Surattana Kakay</a> is a computer engineering student at <a href="https://www.eng.rmutt.ac.th/" rel="noopener noreferrer" target="_blank">Rajamangala University of Technology Thanyaburi (RMUTT)</a>, located in IEEE Region 10 (Asia Pacific). Kakay, an IEEE student member, was honored for guiding her team in the design, development, and implementation of the <a href="https://epics.ieee.org/24-environmental-project-stories/testing-the-waters/" rel="noopener noreferrer" target="_blank">Automatic Water Level Control System project</a>, which aids rice farmers in Thailand.As the team leader, Kakay played a pivotal role in transforming the student initiative into an operational, community‑centered solution. Her inspiration was purpose-driven, she says.“My motivation was to apply engineering to real agricultural challenges, like water scarcity and <a href="https://spectrum.ieee.org/topic/climate-tech/" target="_self">climate change</a>,” she says. “I wanted to bridge advanced technology with the tangible needs of local farmers.”She managed the project end to end—coordinating workflow, assigning tasks based on team members’ strengths, and ensuring each phase of development aligned with the technical road map she created. She served as the primary liaison between the student team, the <a href="https://ptt-rrc.ricethailand.go.th/" rel="noopener noreferrer" target="_blank">Pathum Thani Rice Research Center</a>, and farmers to make sure the system was practical and user‑friendly, and that it addressed community needs.“Watching students grow as they design solutions that improve lives has been both inspiring and deeply humbling.” —Elizabeth Vidal-DuarteUnder her leadership, the team developed a low‑cost IoT‑based alternate wetting and drying (AWD) system that lets farmers remotely monitor and control water levels in rice paddies using smartphones. Kakay oversaw the integration of noncontact laser time‑of‑flight sensors to withstand harsh field conditions, and she championed the use of long-range technology connected to a free community Wi‑Fi network to eliminate Internet service fees.The results were transformative, Kakay says.“Our AWD system reduces water consumption by 63 percent and methane emissions by 7 percent annually,” she says. “Turning an<a href="https://spectrum.ieee.org/hands-on-projects-career-advice" target="_self"> academic assignment into a real‑world solution</a> that delivers measurable, sustainable results has been incredibly meaningful.”Her achievements advanced sustainability for Thailand’s most water‑intensive crop while demonstrating the potential of accessible engineering solutions.Beyond technical innovation, Kakay cultivated a culture of learning, continuity, and empowerment within her team. She introduced a mentorship framework to support future student cohorts. She and her team produced academic papers, visual media, and presentations to communicate the project’s value to scientific audiences as well as the general public.“Surattana Kakay is a pivotal figure in turning innovation into reality and delivering tangible benefits to the community,” says IEEE Member <a href="https://www.linkedin.com/in/thanasin-bunnam/" target="_blank">Thanasin Bunnam</a>, her faculty advisor and an assistant professor at RMUTT.Kakay’s leadership journey became a personal milestone, she says: “Leading this project transformed me from a student into a team leader. As a female engineer, it empowered me to advocate for women in engineering and show that gender is no barrier to technical excellence.”Through her guidance, the AWD project evolved from a classroom assignment into a solution that illustrates IEEE’s mission of advancing technology for humanity.<h2>Faculty Advisor Awards</h2><a href="https://www.linkedin.com/in/nshaghaghi/" target="_blank">Navid Shaghaghi</a>, a lecturer and researcher at <a href="https://www.scu.edu/" rel="noopener noreferrer" target="_blank">Santa Clara University</a>, in California, was recognized for his dedication to integrating <a href="https://www.edutopia.org/blog/what-heck-service-learning-heather-wolpert-gawron" rel="noopener noreferrer" target="_blank">service learning</a> into engineering education and fostering student innovation that benefits underserved communities in <a href="https://ieee-region6.org/" rel="noopener noreferrer" target="_blank">IEEE Region 6</a> (Western USA).During his more than six years of engagement with EPICS in IEEE, Shaghaghi, an IEEE senior member, has demonstrated exceptional leadership in advancing sustainable, human‑centered engineering through the long‑running <a href="https://epics.ieee.org/project/hydration-automation-ha-us/" rel="noopener noreferrer" target="_blank">Hydration Automation (HA) project</a> and the <a href="https://www.scu.edu/engineering/faculty/shaghaghi-navid/epic-lab/hivespy/" rel="noopener noreferrer" target="_blank">HiveSpy initiative</a>. They are part of Santa Clara University’s <a href="https://www.scu.edu/engineering/labs--research/labs/frugal-innovation-hub/" rel="noopener noreferrer" target="_blank">Frugal Innovation Hub</a> and <a href="https://www.scu.edu/engineering/faculty/shaghaghi-navid/epic-lab/" rel="noopener noreferrer" target="_blank">EPIC Research Laboratory</a>. The HA project is funded by <a href="https://epics.ieee.org/fischer-mertel-community-of-projects/" target="_blank">EPICS in IEEE Fischer Mertel Community of Projects</a>.Since 2019, Shaghaghi has served as principal investigator for the HA project, guiding its evolution from prototype to a robust, field‑tested irrigation automation system that supports small ranches and community farms in California.The HA project is a low‑cost system that helps reduce water waste by monitoring soil moisture and automating watering. By combining ultrasonic tank sensing, soil sensors, and ongoing technical support, the project improves efficiency, lowers operational costs, and promotes more sustainable urban agriculture.Under Shaghaghi’s guidance, more than 30 undergraduate and graduate students have gained hands-on experience in IoT development, field deployment, testing, and client collaboration.His commitment to frugal innovation and human‑centric design has resulted in solutions that are minimalist, affordable, sustainable, portable, and rugged—often challenging conventional approaches to agricultural technology.“Turning an academic assignment into a real‑world solution that delivers measurable, sustainable results has been incredibly meaningful.” —Surattana KakayThe HA project has produced new research publications and earned recognition, including a third-place finish by Shaghaghi’s graduate students at this year’s <a href="https://ieee-risingstars.org/2026/project-showcase/" target="_blank">IEEE Rising Stars Project Showcase</a>. During the annual event, students and young professionals present their technical innovations to industry leaders and peers.The HiveSpy project is a low‑cost, frame‑level IoT monitoring system that helps beekeepers automate labor‑intensive tasks and prevent hive swarming by tracking production yield in real time. By collecting frame‑weight data and generating optimized harvest schedules, the system reduces manual workload while improving the hive’s health and boosting honey output.Shaghaghi says his mentorship has been shaped by the realities of student turnover, a challenge he embraces with optimism and adaptability.“The transient nature of student teams is a challenge but one you must embrace, bear‑hug style,” he says. “By energizing your student community and welcoming new contributors, you’ll be amazed by the brilliant solutions they bring.”His philosophy has allowed him to cultivate a thriving pipeline of student innovators, he says, and he has strengthened his own professional practice as well.“I’ve been mentoring EPICS in IEEE students since 2019,” he says. “It has taught me resilience and how to operate on a tight budget while still delivering real‑world results.”Beyond the technical achievements, Shaghaghi’s work reflects a commitment to humanitarian technology and service learning. As the founder and director of the EPIC (Ethical, Pragmatic, and Intelligent Computer) lab, he has built a diverse, interdisciplinary community dedicated to innovation for the benefit of humanity.For him, he says, the EPICS in IEEE award carries profound meaning: “Receiving this award validates my deepest conviction in humanitarian technology research and strengthens my commitment to service‑learning education.”His students echo those sentiments. One team member said “Professor Shaghaghi is an engine of progress who keeps forging ahead.”Through his leadership, Shaghaghi has created an enduring model of <a href="https://spectrum.ieee.org/advice-leading-mentoring-greater-innovation" target="_self">mentorship</a>, innovation, and community partnership that is helping to shape the next generation of socially responsible engineers.<a href="https://www.linkedin.com/in/elizabeth-vidal-duarte/" rel="noopener noreferrer" target="_blank">Elizabeth Vidal-Duarte</a> is celebrated for her impactful mentorship and leadership in expanding EPICS in IEEE engagement across Peru and IEEE Region 9 (Latin America and Caribbean). Vidal-Duarte, a research professor at <a href="https://www.unsa.edu.pe/en/" rel="noopener noreferrer" target="_blank">San Agustin National University Arequipa</a>, in Peru, is a faculty advisor and technical mentor for two EPICS in IEEE projects. She encouraged students to apply to the EPICS program, helped them identify community needs, and supported them in crafting proposals grounded in service‑learning principles.Under her leadership, the students developed a functional <a href="https://epics.ieee.org/project/soft-robotic-glove-for-fine-motor-rehabilitation-and-task-specific-training-peru/" rel="noopener noreferrer" target="_blank">soft robotic glove</a> used at <a href="https://clinicalima.sanjuandedios.pe/" rel="noopener noreferrer" target="_blank">Clínica San Juan de Dios</a> to help patients improve their fine-motor skills. The clinic’s therapists use the device to measure the range of motion of joints at the beginning and end of each patient’s therapy session to improve their assessments. Compared with traditional manual measurements using a <a href="https://en.wikipedia.org/wiki/en:protractor?mobile-app=true&theme=false%29" rel="noopener noreferrer" target="_blank">goniometer</a>, the glove significantly reduces evaluation time and enables digitally recorded data, improving clinical efficiency and decision-making.The second project is an <a href="https://epics.ieee.org/project/assistance-system-for-emotion-detection-for-visually-impaired-people/" rel="noopener noreferrer" target="_blank">emotion‑recognition system</a> for people with visual impairment. The AI‑powered wearable helps recognize a person’s emotions through real‑time facial‑expression detection and haptic feedback.The project has resulted in the “Emotion-Aware Assistive System With Wearable Haptic Feedback for Visual Impairment” research paper, which is to be presented at the <a href="https://2026.cbms-conference.org/" rel="noopener noreferrer" target="_blank">IEEE International Symposium on Computer-Based Medical Systems</a>, to be held from 3 to 5 June in Limassol, Cyprus.Vidal-Duarte’s mentorship extends beyond the classroom. She visits rehabilitation centers and clinics to find people with visual impairments to ensure that the technologies she is helping to develop meet their needs.“EPICS in IEEE has moved me beyond teaching concepts to truly living engineering as a tool for human impact,” Vidal-Duarte says. “Watching students grow as they design solutions that improve lives has been both inspiring and deeply humbling.”Throughout the development of both projects, Vidal-Duarte provided sustained technical and organizational guidance, helping students define requirements, structure work plans, and overcome challenges in prototyping, testing, and validation.Reflecting on the broader impact of EPICS, she says the program has given her “more than methodologies and tools—it has given me perspective, purpose, and a global community that constantly challenges me to grow as a mentor and as a human being.”Her mentorship fostered not only technical excellence but also empathy, ethical awareness, and professional maturity among her students, she says. She guided them in preparing articles for submission to IEEE conferences, interdisciplinary collaboration, and hands-on fieldwork that bridged theory and real‑world constraints.“Her constant support, her belief in each student’s potential, and her commitment to developing leaders who make a difference define [her] as a faculty advisor,” says Valentina Chabilla, an EPICS in IEEE student team member.The EPICS recognition reflects her passion for teaching, her dedication to the community, and her impact on projects and students. Her commitment to accessible, sustainable innovation strengthened partnerships between the university and community groups, benefiting underserved populations.“Receiving this award is both an honor and a responsibility,” she says. “It reminds me of the real impact engineering can have on people’s lives and strengthens my commitment to guiding students in creating meaningful change.”Her leadership continues to inspire students to view engineering not just as a discipline but also as a powerful force for inclusion, dignity, and social impact.<h2>Advancing the mission</h2>The Excellent Contributor Award recipients exemplify the best of EPICS in IEEE. Through their leadership, they have strengthened the bridge between engineering education and community service, inspiring students to use their skills to create sustainable, real‑world impacts.As EPICS continues to expand its global reach, the contributions of Kakay, Shaghaghi, and Vidal-Duarte serve as powerful reminders of what is possible when educators, volunteers, and students work together to improve the lives of others through engineering.

Jun 10, 2026

We Are Crowdsourcing the Panopticon

<img src="https://spectrum.ieee.org/media-library/an-illustration-of-a-phone-with-an-eye-on-it-and-several-rings-of-snake-ouroboros.jpg?id=66820296&width=1245&height=700&coordinates=0%2C313%2C0%2C313"/> A man raises his phone as police move into a crowd. The video is shaky, loud, immediate. Within minutes, it is online. Within hours, it is everywhere. This is how accountability works now. Something happens, someone records it, and that footage can show what really happened, sometimes contradicting official accounts. It can empower citizens and create consequences for officials.But the footage’s life cycle does not end there.In recent months, civil liberties groups have <a href="https://www.aclu.org/press-releases/aclu-and-75-organizations-sound-alarm-on-metas-plans-to-add-facial-recognition-technology-to-ray-ban-and-oakley-eyeglasses" rel="noopener noreferrer" target="_blank">warned</a> that adding facial recognition to consumer smart glasses could turn everyday recording into something more troubling: real-time <a href="https://spectrum.ieee.org/facial-recognition-gone-wrong" target="_self">facial identification</a>. It reflects a broader shift already underway, where <a href="https://spectrum.ieee.org/capitol-riot-prosecutions-technology" target="_self">images and videos captured for one purpose can later be searched</a>, matched, and used for another.An ouroboros is an ancient Egyptian symbol, a snake or dragon eating its own tail. As I began to see patterns in my broader research on surveillance corporatism and governance lag, I began using the term “surveillance ouroboros” to describe this recursive pattern of observations intended to hold power accountable becoming new input for the same surveillance infrastructure.<h2>Facial recognition changes accountability</h2>During the George Floyd protests in 2020, people filmed police in real time. Phones were pointed at officers, not at each other. The goal was simple: to show what the state was doing. That footage spread quickly and became part of a much larger pool of public data.At the same time, reporting from outlets including <a href="https://www.nytimes.com/2020/01/18/technology/clearview-privacy-facial-recognition.html" rel="noopener noreferrer" target="_blank">The New York Times</a> and <a href="https://www.buzzfeednews.com/article/ryanmac/clearview-ai-local-police-facial-recognition" rel="noopener noreferrer" target="_blank">BuzzFeed News</a> showed that law enforcement agencies were using facial-recognition tools, including systems built by Clearview AI. Those systems were built from billions of images scraped from across the internet, including publicly available photos and videos. The basic approach is now routine: People record the state, or anything else (as in <a href="https://spectrum.ieee.org/capitol-riot-prosecutions-technology" target="_self">the January 6 attack</a> on the U.S. Capitol), and the state compiles that footage and data into a searchable environment, which may later be used to identify some of the same people who made the footage.Facial-recognition systems used by law enforcement are increasingly outpacing the legal safeguards.A 2023 Government Accountability Office <a href="https://www.gao.gov/products/gao-24-107372" rel="noopener noreferrer" target="_blank">review</a> found that federal law enforcement agencies continued to expand their use of facial-recognition systems for criminal investigations despite ongoing concerns around training, privacy protections, civil-liberties safeguards, and oversight. Earlier GAO findings showed that agencies had conducted roughly 60,000 facial-recognition searches before formal training requirements were put in place for personnel using the systems. The American Civil Liberties Union and other groups have <a href="https://www.aclu.org/press-releases/aclu-and-75-organizations-sound-alarm-on-metas-plans-to-add-facial-recognition-technology-to-ray-ban-and-oakley-eyeglasses" rel="noopener noreferrer" target="_blank">warned</a> that these tools could be used to identify people from images shared online, including protest-related footage. Concerns about facial recognition led some <a href="https://stateofsurveillance.org/articles/government/facial-recognition-bans-us/" rel="noopener noreferrer" target="_blank">U.S. states and cities</a>, including San Francisco and Boston, to restrict or ban government use of the technology, while federal agencies have continued to face <a href="https://www.gao.gov/products/gao-25-107302" rel="noopener noreferrer" target="_blank">scrutiny</a> over how such systems are tested, deployed, and audited. A 2024 analysis published in <a href="https://policyreview.info/articles/analysis/data-governance-risks-facial-recognition" rel="noopener noreferrer" target="_blank">Internet Policy Review</a> warned that facial-recognition systems used by law enforcement are increasingly outpacing the legal safeguards meant to govern them, creating growing tensions around data protection, oversight, and proportional use.<h2>The spy network that built itself</h2>Surveillance used to require infrastructure. Cameras had to be installed, and data had to be collected deliberately. That is no longer the case. People carry cameras everywhere. They record constantly and upload in real time. Events are documented from multiple angles without planning or coordination. The cumulative result is a continuous stream of usable data: faces, locations, timestamps, and interactions. The Internet of Things (IoT) also waits all around us, gathering information and releasing it when people least expect it, as <a href="https://www.law.gwu.edu/andrew-guthrie-ferguson" rel="noopener noreferrer" target="_blank">Andrew Guthrie Ferguson</a> describes in a recent <a href="https://spectrum.ieee.org/digital-surveillance" target="_self">excerpt</a> of his book Your Data Will Be Used Against You.RELATED: <a href="https://spectrum.ieee.org/digital-surveillance" target="_blank">“Sensorveillance” Turns Ordinary Life Into Evidence</a>Similar dynamics are emerging globally. A recent analysis in the <a href="https://academic.oup.com/ijlit/article/doi/10.1093/ijlit/eaaf022/8460644" rel="noopener noreferrer" target="_blank">International Journal of Law and Information Technology</a> examined how facial-recognition systems in China and Japan are expanding faster than the legal frameworks governing them. Reporting by <a href="https://www.theguardian.com/global-development/2026/mar/12/invasive-ai-led-mass-surveillance-in-africa-violating-freedoms-warn-experts" rel="noopener noreferrer" target="_blank">The Guardian</a> described the limited legal protections around the rapid deployment of AI-assisted surveillance infrastructure across parts of Africa.There used to be a clear distinction between surveillance and accountability. Surveillance meant the powerful watching the people; authorities tended not to share their imagery except under <a href="https://www.wired.com/2015/05/the-body-cam-hacker-who-schooled-the-police/" target="_blank">duress or a court order</a> and usually after a long delay. Accountability meant the people watching the powerful and often publishing imagery immediately to head off or counteract official mischief. That distinction <a href="https://journals.sagepub.com/doi/abs/10.1177/0539018419884410" target="_blank">no longer holds</a>. The same footage can serve both roles. A recording meant to expose misconduct can later be used to identify someone else entirely.Surveillance ouroboros is not a future risk. It is already here.This dynamic persists because people still need to record. In many places, it is one of the only tools available when formal accountability breaks down. When oversight institutions weaken or fail, public documentation becomes a substitute. In that environment, people turn to visibility. But that visibility comes with a cost. The more people that document, the more data that exists. The more data that exists, the easier it is to search, match, and store. Every video feeds the ouroboros. People are not feeding the system because they trust it. They are feeding it because the alternative is silence.Most of the people in these videos are not the focus. They are in the background, passing by or standing nearby. But that distinction does not matter once the footage enters a system. Today’s facial recognition can identify even a face that passed through the corner of a frame. Someone who did nothing can still become part of a dataset without ever knowing it. As recognition systems improve, older footage becomes more useful—and invasive. No single decision created this outcome. It emerged gradually through more cameras, better recognition, larger datasets, and easier integration. Each step made sense on its own. Together, they changed what recording means.Public recording is still necessary. Without it, many forms of abuse would remain hidden. But recording is no longer just exposure. It is also contribution. If you published imagery or video last year, you may already have contributed to a system you have never seen but the ouroboros has.Surveillance ouroboros is not a future risk. It is already here. Every time someone presses publish, they are doing two things at once. They are exposing power, and they are helping build the system that the powerful will later use to track the less powerful.

Jun 9, 2026

What Size Company Is Right for You?

<img src="https://spectrum.ieee.org/media-library/an-illustration-of-stylized-people-wearing-business-casual-clothing.webp?id=65257424&width=1245&height=700&coordinates=0%2C112%2C0%2C113"/> This article is crossposted from IEEE Spectrum’s careers newsletter. <a href="https://engage.ieee.org/Career-Alert-Sign-Up.html" rel="noopener noreferrer" target="_blank">Sign up now</a> to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company <a href="https://www.parsity.io/" rel="noopener noreferrer" target="_blank">Parsity</a> and delivered to your inbox for free!<h2>Small Startup, Mid-Size Company, or Fortune 100? The Pros and Cons</h2>Early in my career, I walked into a shared office space on my first day as a full stack software developer and sat down between the CTO and the CEO to get onboarded. There were four of us in total. Before the day was over, I received my first assignment.This was one of the most formative—and most stressful—experiences of my professional life. In the decade since, I have worked at half a dozen companies including Fortune 100 firms, mid-size startups, and companies you’ve probably never heard of. I have also spoken with roughly a thousand developers at various stages of their careers.Most engineers entering the field are obsessed with landing at Google, Meta, or Amazon. But those roles represent approximately 0.6 percent of software engineering positions. For most of us, the real choice is between a small startup, a mid-size company, and a large enterprise. Each comes with tradeoffs, and your experience will differ from mine. What follows is an honest account of what you might reasonably expect.The Small StartupProsYour work actually matters. A feature you build might determine whether the company closes its next funding round. You gain exposure to the full spectrum of the business, from deployment pipelines to sales and operations and everything in between. You wear many hats out of necessity. For engineers who want to grow quickly and understand how a product is built end to end, few environments move faster.ConsEverything is on fire, always. Work-life balance is difficult to maintain when every release feels critical. Priorities shift without warning and culture tends to reflect the personality of whoever has the most influence in a small room. Startups optimize for speed over craft which means engineers learn to move fast but don’t always learn to build well, and that gap can follow you into your next role.The Mid-Size CompanyPros“So this is how a real business works.” There is process, documentation, a quality assurance function, and some form of career structure. The team is large enough to offer a diversity of experience and perspective. Stability is a myth, especially nowadays, but it is considerably more predictable than an early-stage startup.Cons“So this is how a real business works?” Processes that enable quality also produce friction. Access controls, approval workflows, and cross-team dependencies slow things down. The career ladder exists but it might stop at senior engineer. Without significant organizational growth, your salary and title can plateau early.The Large EnterpriseProsThat badge on your LinkedIn profile just bought you credibility for the next five years. Compensation at this level can be meaningfully higher, particularly when equity is included. The career ladder is long and clearly defined. Engineering practices at mature organizations tend to be more rigorous, and a well-known employer carries market value in future job searches.ConsIt’s slow. Technology stacks often lag industry trends by several years. Political dynamics shape advancement as much as technical ability does. Skill atrophy is a risk when you spend years on a narrow slice of a legacy system. You are now a small fish in a big pond and it will be harder to get noticed.The Roadmap I Would Take If I Could Start OverAccording to a recent Stack Overflow survey, 47 percent of professional developers work at companies with fewer than 100 employees. This may surprise you because social media is dominated by engineers who work at the most well known companies on the planet. The path most engineers imagine for themselves and the path most engineers actually walk are two very different things.If I could do it again, here’s the path I’d take: Start at a small company to build breadth and learn how a business works across functions. This also provides some room to experiment within different roles. Next, move to a mid-size organization with a clear goal of reaching a senior or leadership role. Making a lateral move is easier than trying to get up-leveled at the next company. Finally, target a more mature company where a leadership position opens the door to meaningful equity and long-term growth (aka stocks and bonuses).Each stop builds something the others cannot. The startup gives you range. The mid-size company gives you a taste of how larger orgs operate. The enterprise gives you leverage, credibility and maybe even some stability.Your path will not look like mine. At a five person startup, I had no idea what I was in for. Looking back, I would not trade it. Just know what you are signing up for before you sign.—Brian<h2><a href="https://spectrum.ieee.org/social-engineering-good" target="_self">Reclaiming Social Engineering for Good</a></h2>“Social engineering” is a concept that has become associated with phishing, in which scammers manipulate people into disclosing personal information. But shaping human behavior in this way doesn’t have to have such negative effects. Systems engineer Guru Madhavan argues that we need to reclaim the term and govern the practice to defend ourselves from bad actors and benefit from social engineering’s good side. <a href="https://spectrum.ieee.org/social-engineering-good" target="_blank">Read more here. </a><h2><a href="https://spectrum.ieee.org/medical-mobile-app-ieee-verified" target="_self">Get Your Medical Mobile App Verified by IEEE</a></h2>Smartphone apps are increasingly used to help manage medical conditions, but many of these have not been verified by any regulatory agencies. To help ensure these apps are credible, the IEEE Standards Association recently launched a directory listing apps that have been vetted by experts for technical soundness, ethical design, data security and privacy, and clinical efficacy. The registry will be publically available at no cost, and developers can now apply for approval. <a href="https://spectrum.ieee.org/medical-mobile-app-ieee-verified" target="_blank">Read more here. </a><h2><a href="https://spectrum.ieee.org/chip-design-academic-vs-industry" target="_self">Finding Success in Industry as a Chip Designer</a></h2>A veteran chip designer reflects on what he learned when moving from academia to industry, where the goal changes from proof of concept to ensuring a design works reliably at scale. Differences in risk tolerance, he discovered, lead to varying approaches in the rapidly growing semiconductor industry. <a href="https://spectrum.ieee.org/chip-design-academic-vs-industry" target="_blank">Read more here. </a>

Jun 9, 2026

The Pros and Cons of Job Hopping as an Engineer

<img src="https://spectrum.ieee.org/media-library/an-illustration-of-stylized-people-wearing-business-casual-clothing.webp?id=65257424&width=1245&height=700&coordinates=0%2C112%2C0%2C113"/> This article is crossposted from IEEE Spectrum’s careers newsletter. <a href="https://engage.ieee.org/Career-Alert-Sign-Up.html" rel="noopener noreferrer" target="_blank">Sign up now</a> to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company <a href="https://www.parsity.io/" rel="noopener noreferrer" target="_blank">Parsity</a> and delivered to your inbox for free!<h2>Job Hopping as an Engineer: The Pros and Cons</h2>I’ve changed jobs more times than I ever imagined I would. In the past 12 years, I’ve worked at seven different organizations. Some of those moves were forced by layoffs. Others were deliberate bets on my own trajectory. Job hopping, done strategically, is one of the fastest ways to accelerate your compensation and reinvent your professional identity. Engineers who understand when to move and when to stay tend to out-earn and out-rank their peers who simply wait for internal recognition.Unfortunately, most engineers either job hop too much or not enough, and both mistakes are expensive. Here are the pros and cons of job hopping as an engineer, and when to make a leap.Pro: It’s the fastest way to grow your salaryInternal raises and external offers operate on completely different logic, and most engineers don’t fully appreciate this until they make their first move.Within a company, compensation is anchored to your existing salary and capped by organizational pay bands. A strong performance review might get you 5 to 8 percent.An external offer is a clean slate. The company is bidding for your market value, not adjusting from your current baseline.My first deliberate job hop doubled my salary in a single year. A later move, at the same job title, pushed my compensation floor to a level that I never would have reached by staying put. Neither outcome was available internally. The math simply does not work in your favor when you stay.Pro: It lets you reinvent yourselfEvery new company is a chance to walk in as a slightly updated version of yourself: the version that learned something from the last place. The version that does not carry the baggage of whatever decision you made two years ago that all your coworkers still remember.Especially when you’re early in your career, this matters. You get to reframe your experience, take on a different scope, and establish a new reputation from scratch. That kind of reset is difficult to manufacture inside the same organization.Con: You don’t see the long-term outcome of your workThis is the part nobody talks about, and it took me years to fully appreciate it.When I joined one company, I built a component library for a website from scratch. Starting projects from scratch is exciting, and the initial implementation held up well for the early use cases. But as the organization scaled, the limitations of my original design became apparent.I stayed long enough to address them rather than handing that problem to someone else. That experience taught me more about software architecture than any new project ever had.Engineers who move every 18 months only ever experience the exciting part of building something. They never experience the part where their original decisions stop working. They just repeat the exciting part on a loop, never realizing the debt they are leaving behind.Con: You cannot job hop your way to a promotionAbove a certain level, things can change significantly.A new employer can evaluate your past performance through interviews, portfolios, and references. What they cannot do is evaluate your future potential the way a manager who has watched you grow over two or three years can. If you arrive as a senior engineer, you will almost certainly be hired as one.The promotions that actually changed my career trajectory—from senior to staff engineer, then engineering manager—all happened at one organization over four years. Those transitions required someone to observe my growth over time and make a bet on where I was headed next. That kind of credibility cannot be transferred on a resume.So when should you actually leave?The threshold I use is straightforward. If I have produced at least one measurable, clearly definable outcome at an organization, I have a reasonable basis for leaving. Impact, not tenure, is my unit of measure.I personally think that moving deliberately while early in your career will build a strong compensation baseline.Then become selective.Find an environment where real growth is available and stay long enough to build the credibility that job hopping cannot manufacture. Neither constant movement nor blind loyalty is the answer. The question worth asking at every stage is simple: Have I produced something meaningful here yet? If the answer is no, stay. If yes, it might be time to decide what’s next.—Brian<h2><a href="https://spectrum.ieee.org/socially-assistive-robotics" target="_self">The USC Professor Who Pioneered Socially Assistive Robotics</a></h2>What if robots didn’t just help us with physical tasks? USC Professor Maja Matarić helped define the era of socially assistive robotics, designed to provide personalized therapy and care through social interactions. Despite her influence in the field now, the award-winning roboticist didn’t see herself as an engineer at first.<a href="https://spectrum.ieee.org/socially-assistive-robotics" target="_blank">Read more here. </a><h2><a href="https://spectrum.ieee.org/steve-jobs-next-computer" target="_self">Steve Jobs’ Wilderness Years Shaped His Success as Apple CEO</a></h2>Steve Jobs is best known as the co-founder and CEO of Apple. But the 12 years he spent away from the company taught him the lessons necessary for his success. A new book tells the forgotten story of Jobs’ “wilderness” years and what he learned while at NeXT Computer. IEEE Spectrum spoke to the book’s author about Apple’s most iconic CEO and the company’s future as it prepares for new leadership under John Ternus. <a href="https://spectrum.ieee.org/steve-jobs-next-computer" target="_blank">Read more here. </a><h2><a href="https://spectrum.ieee.org/ieee-guide-cybersecurity-consultant" target="_self">Learn What It Takes to Become a Cybersecurity Consultant</a></h2>Cybersecurity consultants have never been more in demand, with data breaches and attacks costing organizations more than US $10 trillion annually to repair. To help you find the skills you need to stand out in the cybersecurity job market, the IEEE Computer Society offers a “What Makes a Great Cybersecurity Consultant” guide. It includes advice from experts, a list of certifications to pursue, and information on key cybersecurity conferences. <a href="https://spectrum.ieee.org/ieee-guide-cybersecurity-consultant" target="_blank">Read more here. </a>

Jun 9, 2026

The Computer Science Degree Isn’t Dead

<img src="https://spectrum.ieee.org/media-library/an-illustration-of-stylized-people-wearing-business-casual-clothing.webp?id=65257424&width=1245&height=700&coordinates=0%2C112%2C0%2C113"/> <h1></h1>This article is crossposted from IEEE Spectrum’s careers newsletter. <a href="https://engage.ieee.org/Career-Alert-Sign-Up.html" rel="noopener noreferrer" target="_blank">Sign up now</a> to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company <a href="https://www.parsity.io/" rel="noopener noreferrer" target="_blank">Parsity</a> and delivered to your inbox for free!<h2>The CS Degree Isn’t Dead. The Entry-Level Pipeline Is</h2>There is no shortage of people telling recent engineering graduates that their degree was a mistake and that AI is coming for their jobs before they even land one. I respectfully disagree.I have been a software engineer for 12 years, done well over 100 interviews on both sides of the table, and run Parsity, an AI engineering program. A few patterns emerge consistently in who actually breaks through in today’s job market. Here’s why I think the job market isn’t as dire as it looks, and what I would do if I were looking for my first tech job.<h2>The Numbers Need Context</h2>The Federal Reserve Bank of New York recently placed unemployment for recent CS graduates in the United States at 6.1 percent, with computer engineering graduates at 7.5 percent. Compared to philosophy majors at 3.2 percent and art history graduates at 3.0 percent, those figures look alarming. They require more context than most headlines provide.When researchers factor in underemployment (graduates working jobs that don’t require a college degree), then engineers are doing relatively well, coming in <a href="https://e.vnexpress.net/news/tech/tech-news/us-s-computer-science-grads-face-5th-highest-unemployment-rate-5048578.html" rel="noopener noreferrer" target="_blank">below 20 percent, against a 42 percent average</a> across all recent graduates. Many majors reporting lower unemployment are achieving that figure by accepting work entirely unrelated to their field. Scored across unemployment, underemployment, and early-career earnings together, CS and computer engineering <a href="https://www.encoura.org/resources/wake-up-call/the-labor-market-for-recent-college-graduates-part-2-labor-market-tradeoffs/" rel="noopener noreferrer" target="_blank">still rank among the top fields</a> for overall labor market outcomes.The degree is not the problem. The hiring pipeline is. Job postings labeled “entry-level software engineer” <a href="https://www.nucamp.co/blog/the-junior-developer-hiring-crisis-in-2026-how-to-get-your-first-full-stack-job" rel="noopener noreferrer" target="_blank">grew roughly 47 percent</a> between late 2023 and late 2024, while actual hiring into those roles <a href="https://ravio.com/blog/tech-hiring-trends" rel="noopener noreferrer" target="_blank">dropped approximately 73 percent</a> in the same window. So-called “ghost jobs,” used to create an illusion of company growth, are everywhere. This makes the front door harder to find, but it exists.<h2>Here Is What To Do About It</h2>Do a broad search of your (real-life) network. Roughly <a href="https://www.codesmith.io/blog/tech-hiring-trends-2025" rel="noopener noreferrer" target="_blank">26 percent of job offers</a> come through referrals. Look at your actual network—classmates, professors, past internship contacts, relatives—and identify people at companies that might be hiring. The goal is a warm introduction to someone who is or knows a decision maker. One introduction carries more weight than a hundred cold applications through a portal.Find symmetric risk. A junior engineer is a risky hire by definition. A startup carries a matching risk profile, meaning potentially lower compensation, no certainty of longevity, and higher performance expectations. But that shared risk creates mutual interest. The learning curve is steep, the exposure is broad, and the track record transfers directly. For engineers whose longer-term goal is a large organization, a startup is not a detour. It can be how you build the experience those organizations eventually want to see. The first job is for validation and learning. It is not a life sentence.Manufacture experience rather than waiting for it. Employers want experience but will not hire you to get it. The way through is to create it: a deployed project, an open-source contribution, building something real for a small business or family member. Recruiters are skeptical of toy projects. A deployed application solving a real problem, combined with the ability to talk clearly about the decisions you made and why, still moves the needle.Gain practical AI engineering skills, not just AI tool fluency. Using Cursor or Copilot is now a baseline expectation. What differentiates candidates is going one level deeper. Most working engineers, including senior ones, have not built a RAG pipeline or designed a multi-agent system. Understanding how to chunk documents, generate embeddings, store and query them from a vector database, and wire it into a production application puts a candidate ahead of a significant portion of the market on a skill in rapidly growing demand. AI and data science roles <a href="https://www.roberthalf.com/us/en/insights/research/data-reveals-which-technology-roles-are-in-highest-demand" rel="noopener noreferrer" target="_blank">grew 163 percent</a> in job postings in 2025. The engineers who understand how these systems actually work, not just how to prompt them, are in the shortest supply.Stop optimizing around conditions you cannot predict. Nobody anticipated the 2021 hiring boom. Nobody predicted this correction. Build durable skills. The demand for engineers who can reason clearly about systems is not going away. Where you start is not where you end.—Brian<h2><a href="https://theconversation.com/meta-and-microsoft-have-joined-the-tech-layoff-tsunami-is-ai-really-to-blame-281436" rel="noopener noreferrer" target="_blank">Meta and Microsoft have joined the layoff tsunami. Is AI really to blame?</a></h2>More major workforce reductions are on the horizon at Big Tech companies: Meta announced it will cut 10 percent of its workforce, or about 8,000 employees, and Microsoft plans to offer buyouts for 7 percent of its U.S. employees in a voluntary retirement program. The cuts are understood by many to be linked to AI. But is AI really to blame? For The Conversation, two academics at the University of Sydney give their two cents.<a href="https://theconversation.com/meta-and-microsoft-have-joined-the-tech-layoff-tsunami-is-ai-really-to-blame-281436" target="_blank">Read more here. </a><h2><a href="https://spectrum.ieee.org/roboticist-turned-teacher-eniac-replica" target="_self">This Roboticist-Turned-Teacher Built a Life-Size Replica of ENIAC</a></h2>Tom Burick got his start as a roboticist. But when a financial downturn forced him to close his robotics business, he thought of the effect teachers had on his life and decided to pay it forward. Burick now works as a technology instructor at a school for students with autism, where he recently led a project building a full-scale replica of ENIAC, an historic computer celebrating its 80th anniversary this year. <a href="https://spectrum.ieee.org/roboticist-turned-teacher-eniac-replica" target="_blank">Read more here. </a><h2><a href="https://spectrum.ieee.org/chinese-robots-us-ban" target="_self">Proposed Chinese Robot Ban is Latest U.S. Tech Sovereignty Move</a></h2>Across several industries, the United States has been moving toward limiting the use of sensitive technology made in China. Now, legislation has been introduced to extend the trend to ground robots, including humanoids, dogs, and crawlers. This could benefit some U.S.-based robotics firms—but many of these companies still rely on Chinese-made components. “The U.S. robotics industry is in a pickle,” writes Spectrum tech policy editor Lucas Laursen. <a href="https://spectrum.ieee.org/chinese-robots-us-ban" target="_blank">Read more here. </a>

Jun 9, 2026

Beyond Dexterity: Why Contact May Define the Next Era of Robotics

<img src="https://spectrum.ieee.org/media-library/humanoid-robot-with-four-arms-holding-a-red-balloon-sculpture-at-a-tech-expo.jpg?id=66870200&width=1245&height=700&coordinates=0%2C0%2C0%2C1"/> This article is brought to you by <a href="https://www.agilink-ai.com/" target="_blank">AGILINK</a>.Throughout the exhibition hall at the 2026 IEEE International Conference on Robotics (ICRA), in Vienna, one demonstration seemed to attract a disproportionate amount of attention.Two robotic hands were making a balloon dog. Slowly and deliberately, the robot twisted a long balloon into loops, bends, and joints without popping it. Visitors stopped, watched, and often returned with colleagues to watch again. <img alt="Crowd at a robotics expo watches a humanoid robot demonstrate its arm movements." class="rm-shortcode" data-rm-shortcode-id="29a8797093705fd5d7f3a0b18b28e8a0" data-rm-shortcode-name="rebelmouse-image" id="821bd" loading="lazy" src="https://spectrum.ieee.org/media-library/crowd-at-a-robotics-expo-watches-a-humanoid-robot-demonstrate-its-arm-movements.jpg?id=66870218&width=980"/> AGILINK’s balloon dog demonstration draws a crowd at ICRA 2026.AGILINKAt first glance, the demonstration appeared almost playful. Among roboticists, however, balloon twisting is widely recognized as an unusually difficult manipulation task.A balloon is lightweight, highly deformable, slippery, and extremely sensitive to force. Every twist changes its geometry and internal pressure, turning a seemingly simple activity into a continuously changing physical interaction problem.Humans navigate those changes almost intuitively. While making a balloon animal, people rarely think consciously about force regulation, slip prevention, or contact stability. They simply adjust.For robots, those adjustments remain remarkably difficult. The challenge is not merely moving fingers to the right positions. The harder part is maintaining stable interaction while the object itself is changing. <iframe frameborder="0" height="auto" lazy-loadable="true" scrolling="no" src="https://www.youtube.com/embed/eoGcFGwQNkw?rel=0" style="position:absolute;top:0;left:0;width:100%;height:100%;" width="100%"></iframe>Highlights from AGILINK’s ICRA 2026 demonstrations, including visuotactile sensing, in-hand manipulation, balloon-animal shaping, and other contact-rich tasks enabled by the company’s latest OmniHand platform.AGILINKThat distinction helps explain why the balloon dog drew so much attention in Vienna. What appeared to be a dexterity demonstration was, in many ways, a demonstration about contact itself.As robotic manipulation continues to advance, a growing number of researchers are arriving at a similar conclusion: many of the hardest problems in robotics begin only after contact occurs.<h2>Motion and Contact Intelligence for Robot Manipulation</h2>Balloon twisting combines two challenges that robotics has traditionally struggled to solve simultaneously: long-horizon task execution and contact-rich manipulation.The first concerns motion.A balloon dog is not created through a single grasp or twist. It emerges through a carefully ordered sequence of manipulations, each setting the conditions for what follows. A small rotational error introduced early may appear insignificant at first, yet several steps later it can prevent the final structure from forming altogether.In that sense, balloon twisting is a long-horizon task. Success depends not only on performing individual actions correctly, but also on preserving the future feasibility of the entire manipulation process.To address this challenge, <a href="https://www.agilink-ai.com/" target="_blank">AGILINK</a> began by collecting demonstrations from professional balloon artists. Human actions were mapped onto robotic hands to establish an initial manipulation policy. But successful demonstrations alone were insufficient.In practice, some of the most valuable learning occurred when execution began to drift toward failure. Whenever instability emerged, human operators intervened and corrected the manipulation in real time. Those interventions were recorded and incorporated into reinforcement-learning cycles, allowing the system to learn not only how successful demonstrations unfold, but also how experienced operators recover when things start to go wrong.Through this process, the robot gradually acquired the capabilities required for long-horizon task execution—a collection of abilities that AGILINK groups under the term motion intelligence: the ability to generate actions, coordinate bimanual behaviors, and execute extended manipulation sequences under real-world uncertainty. <img alt="Two robotic hands, one white open palm and one black forming an OK gesture, on display." class="rm-shortcode" data-rm-shortcode-id="7fb13b51d34cf6b0574f614644438b3b" data-rm-shortcode-name="rebelmouse-image" id="779ba" loading="lazy" src="https://spectrum.ieee.org/media-library/two-robotic-hands-one-white-open-palm-and-one-black-forming-an-ok-gesture-on-display.png?id=66870278&width=980"/> OmniHand 3 Ultra-M on display at ICRA 2026.AGILINKYet motion alone does not explain why balloon twisting remains difficult. The second challenge is contact.The robot must continuously regulate force, adjust contact locations, and respond to subtle changes in the object’s state. These decisions are difficult to encode through explicit rules. Even skilled human operators often rely on tactile intuition developed through experience rather than consciously articulated strategies.Analysis of those interventions revealed that many failures did not originate from incorrect action sequences, but from the breakdown of contact itself.To better capture those interaction dynamics, AGILINK collected contact-centric intervention data and incorporated those interactions into reinforcement-learning training. Rather than learning only which motions to perform, the system also learned how humans maintain stability when contact conditions begin to deteriorate.AGILINK describes this capability as contact intelligence: the ability to establish, maintain, and adapt physical interaction as force distribution, friction, deformation, and contact geometry continuously evolve.The distinction between the two capabilities is subtle but important. Motion intelligence determines what the robot intends to do. Contact intelligence determines whether it can continue doing it. For balloon twisting, both are necessary. One provides the sequence of actions. The other keeps those actions physically viable. <img alt="Robot makes balloon animal for visitor at tech expo booth." class="rm-shortcode" data-rm-shortcode-id="a214019840e864e15e6b91d8d70e6e74" data-rm-shortcode-name="rebelmouse-image" id="431a1" loading="lazy" src="https://spectrum.ieee.org/media-library/robot-makes-balloon-animal-for-visitor-at-tech-expo-booth.jpg?id=66870268&width=980"/> YouTuber KhanFlicks follows OmniHand’s motions while learning to fold a balloon dog at the AGILINK booth.AGILINKBetween a balloon slipping away and a balloon bursting lies a narrow region of stability. Successful manipulation depends on finding that region—and remaining within it throughout the task.<h2>Introducing the OmniHand 3 Ultra-M Dexterous Hand</h2>The balloon dog demonstration showcased a manipulation capability. It also revealed a broader question. How much contact intelligence can be achieved through learning alone? A robot can only regulate what it can perceive. It can only respond as quickly as its hardware allows.As manipulation tasks become increasingly complex, researchers are finding that progress depends not only on better policies, but also on richer sensing and faster physical response.That realization formed the backdrop for AGILINK’s second major announcement at ICRA 2026. Alongside the balloon dog demonstration, the company introduced the <a href="https://www.agilink-ai.com/ultra-m.html" target="_blank">OmniHand 3 Ultra-M</a>. <img alt="Two robotic hands beside a human hand, all raised open on a display table." class="rm-shortcode" data-rm-shortcode-id="8c59fb0ca42c4a24bb1b54d98d25513f" data-rm-shortcode-name="rebelmouse-image" id="e7eda" loading="lazy" src="https://spectrum.ieee.org/media-library/two-robotic-hands-beside-a-human-hand-all-raised-open-on-a-display-table.jpg?id=66870269&width=980"/> OmniHand 3 Ultra-M closely matches the size of an adult human hand.AGILINKThe two exhibits represented different stages of the same technological trajectory. If the balloon dog demonstrated what contact intelligence can already accomplish today, Ultra-M was designed to explore what contact intelligence may require next.<h2>Building Hardware for Contact Intelligence</h2>Roughly the size of an adult human hand, the <a href="https://www.agilink-ai.com/uploads/upload/files/20260530/a7b12675ce5e3b4e9b913801c0c6f659.pdf" target="_blank">OmniHand 3 Ultra-M integrates 20 active degrees of freedom</a> within a human-scale form factor.Its most distinctive feature is a fully direct-drive architecture. By adopting direct-drive actuation throughout the system, the hand is designed to enable faster and more transparent force regulation and higher force-control bandwidth, enabling faster response as contact conditions change. For contact-rich manipulation, responsiveness can be as important as sensing itself.By adopting direct-drive actuation throughout the system, the OmniHand 3 Ultra-M is designed to enable faster and more transparent force regulation and higher force-control bandwidth, enabling faster response as contact conditions change.The platform also incorporates tactile sensing across nearly the entire hand. Each fingertip contains a miniature vision-based tactile sensor, while more than 300 three-dimensional tactile sensing points are distributed throughout the palm. Together, they provide information not only about where contact occurs, but how contact is evolving.The system is designed to estimate pressure distribution, shear forces, local deformation, slip tendencies, and other interaction dynamics that often remain invisible to conventional position-based control systems.According to AGILINK’s tests, individual sensors achieve force resolution of approximately 0.005 N—roughly equivalent to detecting the weight of a sheet of paper resting on a fingertip. Spatial resolution reaches approximately 0.04 mm, while sensing density approaches 50,000 sensing points per square centimeter. <img alt="Robot arm delicately holds a feather, inset shows colorful dotted texture close-up." class="rm-shortcode" data-rm-shortcode-id="c9f9836a2a34c6020d974a51c0da7158" data-rm-shortcode-name="rebelmouse-image" id="8f1f1" loading="lazy" src="https://spectrum.ieee.org/media-library/robot-arm-delicately-holds-a-feather-inset-shows-colorful-dotted-texture-close-up.png?id=66870273&width=980"/> OmniHand 3 Ultra-M recognizes feather texture through vision-based tactile sensing.AGILINKFor dexterous robots, contact has traditionally been a largely hidden process. Ultra-M is designed to make that process more observable.Rather than simply detecting that contact has occurred, the system attempts to resolve where interaction is happening, how forces are distributed, whether instability is beginning to emerge, and how manipulation strategies should adapt in response.The balloon dog offered a glimpse of what contact intelligence can already accomplish. Ultra-M explores a different question: what capabilities may be required to push contact intelligence further?<h2>The Physical World Remains the Hardest Benchmark</h2>The significance of contact intelligence extends far beyond balloon animals. Many tasks that continue to resist automation involve unstable or deformable interaction: cable insertion, garment handling, flexible packaging, delicate assembly, connector mating, tool use, and household manipulation.These tasks are difficult not because robots cannot reach the correct location, but because maintaining stable interaction after contact begins remains extraordinarily hard.For decades, robotics achieved many of its successes by reducing uncertainty. Factories were engineered to make robotic motion predictable, repeatable, and highly structured. The physical world behaves differently.A growing share of robotics research is shifting toward interaction itself—understanding how robots can establish, maintain, and adapt physical contact within environments that remain fundamentally unpredictable.Objects shift. Materials deform. Friction changes. Contact evolves. Real environments rarely follow scripts. Seen through that lens, the balloon dog was never really about the balloon dog. What attracted attention at ICRA was not simply a visually impressive demonstration, but what it revealed: intelligence in the physical world is ultimately measured through interaction.As motion generation continues to mature, a growing share of robotics research is shifting toward interaction itself—understanding how robots can establish, maintain, and adapt physical contact within environments that remain fundamentally unpredictable.For robots moving beyond structured environments and into less predictable real-world settings, managing contact may become as important as motion itself.

Jun 8, 2026

IEEE Celebrates Technology’s Brightest Minds at Annual Event

<img src="https://spectrum.ieee.org/media-library/a-dimly-lit-ballroom-filled-with-dinner-tables-during-an-awards-ceremony.jpg?id=66857734&width=1245&height=700&coordinates=0%2C187%2C0%2C188"/> New York City was the backdrop of this year’s IEEE <a href="https://spectrum.ieee.org/ieee-2026-honors" target="_self">Honors Ceremony</a>, held on 24 April.The event celebrates engineering pioneers who have developed technologies that have changed how people connect and learn about the world. This year’s celebrants included the engineers behind innovations such as text-to-donate technology, <a href="https://spectrum.ieee.org/abhishek-appaji-ai-diagnostic-tool" target="_self">AI-powered diagnostic tools</a>, and the graphics processing unit, among many others.Prior to the Honors Ceremony, IEEE hosted a forum on 23 April for a select group of early-career achievers to exchange ideas and experiences with laureates and awardees, speakers, and IEEE leaders. Attendees from around the world, working in a variety of technical areas, shared their journeys and explored the intersections of technologies, disciplines, and missions. The event culminated in Friday evening’s black tie Honors Ceremony, where IEEE celebrated medal laureates, including <a href="https://spectrum.ieee.org/2026-ieee-medal-of-honor" target="_self">Jensen Huang</a>, who received IEEE’s highest recognition, the <a href="https://spectrum.ieee.org/tag/ieee-medal-of-honor" target="_self">IEEE Medal of Honor</a>. Huang is a cofounder of <a href="https://www.nvidia.com/en-us/" rel="noopener noreferrer" target="_blank">Nvidia</a> and its chief executive. “IEEE has always been a home to those who see the future before others see it,” <a href="https://spectrum.ieee.org/ieee-presidents-note-engineering-renaissance" target="_self">Mary Ellen Randall</a>, IEEE president and CEO, said in her welcome speech. <a href="https://corporate-awards.ieee.org/ieee-awards-videos/" rel="noopener noreferrer" target="_blank">Video highlights</a> and <a href="https://corporate-awards.ieee.org/events/photo-and-video-gallery/" rel="noopener noreferrer" target="_blank">photos from the event</a> are available on the <a href="https://corporate-awards.ieee.org/" rel="noopener noreferrer" target="_blank">IEEE Awards website</a>.<h2>Exploring mission-driven tech and AI in art</h2>Friday morning began with a conversation between Randall and <a href="https://www.youtube.com/watch?v=OignKQOJT-U" rel="noopener noreferrer" target="_blank">Marian Croak</a>, the recipient of this year’s <a href="https://corporate-awards.ieee.org/award/ieee-founders-medal/" rel="noopener noreferrer" target="_blank">IEEE Founders Medal</a>. Croak was honored for “leadership in communication networks, including acceleration of digital equity, responsible artificial intelligence, and the promotion of diversity and inclusion.”Croak, who serves as vice president of engineering at <a href="https://about.google/" rel="noopener noreferrer" target="_blank">Google</a>, headquartered in Mountain View, Calif., pioneered Voice over Internet Protocol (VoIP) technologies. When a person speaks into a telephone, VoIP converts their voice into digital signals that are transmitted over the Internet rather than traditional phone lines. Her work enabled audio and video conferencing. She also developed text-to-donate technology to raise money for those affected by <a href="https://en.wikipedia.org/wiki/Hurricane_Katrina" rel="noopener noreferrer" target="_blank">Hurricane Katrina</a>, which devastated New Orleans in 2005. The technology enables customers to donate money to a charity via their mobile service provider, which then bills them. “Empathy has always been a driving force in the engineering that I’ve done,” she said.She shared advice on how to stay creative: “Get out of the office. Go to an art museum, exercise, or play with children.” Croak said her grandchildren inspire her.<h3>An inside look at microchips</h3> During Friday evening’s Honors Ceremony cocktail hour, attendees explored the history of microchips at the <a href="https://www.ieee.org/about/history-center/globalmuseum" target="_blank">IEEE Global Museum</a>’s Microchips That Shook the World exhibit. The Global Museum, an IEEE History and Heritage program, develops traveling and digital exhibits focused on the history of technology. The museum’s mission is to promote awareness of how technological progress unfolds over generations and how engineers and researchers build on past achievements to benefit humanity.Drawing from <a href="https://spectrum.ieee.org/" target="_self">IEEE Spectrum</a>’s <a href="https://spectrum.ieee.org/welcome-to-the-chip-hall-of-fame" target="_self">Chip Hall of Fame</a>, the Microchips That Shook the World exhibit conveys the roles integrated circuits play in fields such as signal processing, audio engineering, and <a href="https://spectrum.ieee.org/topic/telecommunications/" target="_self">telecommunications</a>.Co-curators <a href="https://spectrum.ieee.org/u/stephen-cass" target="_self">Stephen Cass</a>, Spectrum’s special projects editor, and <a href="https://www.linkedin.com/in/daniel-jon-mitchell-726b131b2" target="_blank">Daniel Mitchell</a>, the IEEE senior historian, served as onsite docents for guests. The <a href="https://spectrum.ieee.org/commodore-64" target="_self">Commodore 64</a>, one of the artifacts on display, brought up many treasured childhood memories for guests who used the home computer. The exhibit also featured a preview of IEEE’s immersive video project “Inside the Microchip,” which delves beneath the silicon surface of the Nvidia NV20 microchip thanks to forensic photography and sophisticated computer-generated renders. The video, which will be released later this year, aims to teach preuniversity students about the technology.Microchips that Shook the World is possible thanks to donations from semiconductor company <a href="https://www.asml.com/" rel="noopener noreferrer" target="_blank">ASML</a>, the <a href="https://themenschfoundation.org/" rel="noopener noreferrer" target="_blank">Bill and Dianne Mensch Foundation</a>, and the <a href="https://eds.ieee.org/" rel="noopener noreferrer" target="_blank">IEEE Electron Devices </a>and <a href="https://eps.ieee.org/" rel="noopener noreferrer" target="_blank">IEEE Electronics Packaging societies</a>The daytime program also spotlighted AI’s use in the visual arts. <a href="https://spectrum.ieee.org/u/kathleen-kramer1" target="_self">Kathleen Kramer</a>, the 2025 IEEE president, interviewed artist <a href="https://refikanadol.com/" rel="noopener noreferrer" target="_blank">Refik Anadol</a>, who is scheduled to open an AI art museum on 20 June in Los Angeles. <a href="https://dataland.art/" rel="noopener noreferrer" target="_blank">Dataland</a>’s exhibits are powered by an open-access model developed by Anadol’s studio.For the museum’s first exhibition, “Machine Dreams: Rainforest,” the model collected visual data about the natural world from the <a href="https://www.si.edu/museums/natural-history-museum" rel="noopener noreferrer" target="_blank">Smithsonian National Museum of Natural History</a>, London’s <a href="https://www.nhm.ac.uk/" rel="noopener noreferrer" target="_blank">Natural History Museum</a>, and the <a href="https://www.birds.cornell.edu/home/" rel="noopener noreferrer" target="_blank">Cornell Lab of Ornithology</a>, with their permission. The information, including up to a half billion images, will form the basis for a variety of AI-produced art, Anadol said.Anadol said he was inspired to mix AI with art by the movie <a href="https://en.wikipedia.org/wiki/Blade_Runner" rel="noopener noreferrer" target="_blank">Blade Runner</a>. He said he believes “machines can become collaborators,” as “data is a form of pigment.”Data also plays an important role in the work of artist and author <a href="https://giorgialupi.com/" rel="noopener noreferrer" target="_blank">Giorgia Lupi</a>. The artist is a partner at design firm <a href="https://www.pentagram.com/work/ieee-honors-ceremony-2026" rel="noopener noreferrer" target="_blank">Pentagram</a>.Lupi said she uses data to tell stories, including chronicling her struggles with a chronic illness.“Data is an abstraction of our reality,” she said.One of her recent projects, “<a href="https://www.mta.info/agency/arts-design/digital-art/data-love-letter" rel="noopener noreferrer" target="_blank">A Data Love Letter to the Subway</a>,” was shown last year in the <a href="https://en.wikipedia.org/wiki/Dey_Street_Passageway" rel="noopener noreferrer" target="_blank">Dey Street Passageway</a> in New York City. The video was made using data from the <a href="https://www.mta.info/" rel="noopener noreferrer" target="_blank">Metropolitan Transportation Authority</a> about each train line, including timetables, ridership, and people’s travel habits. Based on the information Lupi gathered, she documented how commuters traveling on different subway lines encountered one another without realizing it.By exploring data on this year’s IEEE award recipients, she collaborated with IEEE to create <a href="https://corporate-awards.ieee.org/intersections/" rel="noopener noreferrer" target="_blank">an animated video illustrating the shared pathways and collaborations among the honorees</a>. It debuted at the Honors Ceremony.<h2>Honoring engineering giants</h2>The Honors Ceremony, held at <a href="https://ciprianievents.com/venue/new-york-42nd-street/" rel="noopener noreferrer" target="_blank">Cipriani 42nd Street</a>, recognized more than 20 laureates and innovators.More than 92 million selfies are taken worldwide every day, <a href="https://photoaid.com/blog/mobile-photography-statistics/" rel="noopener noreferrer" target="_blank">PhotoAiD estimates</a>. A selfie wouldn’t be possible without <a href="https://ericfossum.com/" rel="noopener noreferrer" target="_blank">Eric Fossum</a>’s invention of the <a href="https://www.ansys.com/simulation-topics/what-is-cmos-image-sensor" rel="noopener noreferrer" target="_blank">CMOS image sensor</a>. Developed at <a href="https://www.nasa.gov/" rel="noopener noreferrer" target="_blank">NASA</a>’s <a href="https://www.jpl.nasa.gov/" rel="noopener noreferrer" target="_blank">Jet Propulsion Laboratory</a>, in Pasadena, Calif., the “camera on a chip” was intended for use in space, but it is now found in smartphones, medical devices, and vehicles. Fossum, an IEEE Life Fellow, received the <a href="https://corporate-awards.ieee.org/award/ieee-jun-ichi-nishizawa-medal/" rel="noopener noreferrer" target="_blank">IEEE Jun-ichi Nishizawa Medal</a>, which recognizes outstanding contributions to materials and device science and technology.“Engineering is a pursuit of what must be possible. [IEEE is] the spirit, the conscience, of our profession.” —Jensen Huang, founder and CEO of NvidiaThe medal, he said, “is at the top of the IEEE staircase of being recognized by your peers.”The <a href="https://corporate-awards.ieee.org/award/ieee-nick-holonyak-medal/" rel="noopener noreferrer" target="_blank">IEEE Holonyak Medal for Semiconductor Optoelectronic Technologies</a> went to <a href="https://www.materials.ucsb.edu/people/faculty/steven-p-denbaars" rel="noopener noreferrer" target="_blank">Steven P. DenBaars</a>, a professor of materials and electrical and computer engineering at the <a href="https://www.ucsb.edu/" rel="noopener noreferrer" target="_blank">University of California, Santa Barbara</a>. DenBaars was honored for his work in semiconductors, which laid the foundation for high-resolution LED and laser displays, modern solid-state lighting, and more.“This work has always been a team effort...I’m excited and curious about the role gallium nitride micro LEDs will play in optical communications,” he said in his acceptance speech.The ceremony ended with the Medal of Honor presentation to Huang, who received a standing ovation. He was recognized for his “leadership in the development of graphics processing units and their application to scientific computing and artificial intelligence.”The IEEE honorary member donated his cash prize to <a href="https://spectrum.ieee.org/ieee-tryengineering-20-years" target="_self">IEEE TryEngineering</a>, which provides teachers with a library of lesson plans and offers educational summer camps. The <a href="https://www.influencewatch.org/non-profit/jen-hsun-and-lori-huang-foundation/" rel="noopener noreferrer" target="_blank">Jen-Hsun and Lori Huang Foundation</a> matched his gift, and the additional donation is destined to fund scholarships for new graduates. “Engineering is a pursuit of what must be possible. [IEEE is] the spirit, the conscience, of our profession,” Huang said.

Jun 5, 2026

50 Years of The Institute

<img src="https://spectrum.ieee.org/media-library/portrait-of-a-smiling-white-woman-with-curly-hair.jpg?id=66860120&width=1245&height=700&coordinates=0%2C187%2C0%2C188"/> <a href="https://spectrum.ieee.org/the-institute/" target="_self">The Institute</a> is celebrating its 50th anniversary this year. Launched in 1976, the publication was designed to keep members informed about IEEE and what its constituents were doing, as well as to report on the organization’s initiatives, <a href="https://spectrum.ieee.org/collections/world-standards-day/" target="_self">technical standards</a>, products, and services.That directive expanded over the years to include our reporting on key historical technical achievements recognized as <a href="https://spectrum.ieee.org/tag/ieee-history" target="_blank">IEEE Milestones</a> and support for <a href="https://spectrum.ieee.org/collections/celebrating-young-professionals-and-students/" target="_self">young professionals</a> with <a href="https://spectrum.ieee.org/collections/tips-on-how-to-elevate-your-career/" target="_self">career-guidance</a> articles and information about <a href="https://spectrum.ieee.org/ieee-professional-development-suite" target="_self">educational resources</a>.The Institute has gone through many iterations in the past 50 years. What began as a monthly four-page insert in the print edition of <a data-linked-post="2650270368" href="https://spectrum.ieee.org/how-ieee-spectrum-was-born" target="_blank">IEEE Spectrum</a> became a separate newspaper published six times a year and mailed along with Spectrum in 1977, and then a monthly publication the following year.Today we publish all of The Institute’s articles online, with a curated selection appearing in our 16-page quarterly printed in the March, June, September, and December Spectrum issues.To provide members with a quick summary of the latest online news, in 2003 a bimonthly newsletter, The Institute Alert, began appearing in your inbox. You also can stay up to date by following our <a href="https://www.facebook.com/IEEETheInstitute" rel="noopener noreferrer" target="_blank">Facebook</a>, <a href="https://www.instagram.com/ieeetheinstitute/" rel="noopener noreferrer" target="_blank">Instagram</a>, and <a href="https://www.linkedin.com/in/ieeetheinstitute/" rel="noopener noreferrer" target="_blank">LinkedIn</a> pages.Although much has changed, an original subsection from 1976—“IEEE People”—has been maintained for the past five decades. We continue to celebrate IEEE members from around the world through our profiles, which are among our most popular articles.As the longest-serving editor in chief for The Institute, it is a privilege for me and my staff to chronicle the stories of remarkable IEEE individuals. They are often-unseen visionaries and problem-solvers who work tirelessly behind the scenes on technologies that are reshaping the world. By highlighting their careers and how IEEE has played a role in their professional growth, we hope to inspire the next generation of engineers and technologists to continue a legacy of innovation and service to humanity.

Jun 3, 2026

7 Ways New Engineers Can Flourish in the Age of AI

<img src="https://spectrum.ieee.org/media-library/collage-of-a-white-female-college-graduate-surrounded-by-technical-symbols.jpg?id=66852442&width=1245&height=700&coordinates=0%2C288%2C0%2C289"/> New graduates’ careers are unfolding in an era when AI is not optional. The most successful engineers treat artificial intelligence as leverage, not competition.Here are seven tips to help keep young professionals in demand no matter how quickly the field’s tools evolve.1. Master the fundamentals first. AI tools can help you code, but you still need strong fundamentals in:<ul><li>Data structures and algorithms for problem-solving.</li><li>Operating systems, databases, and networking for system-level understanding.</li><li><a href="https://spectrum.ieee.org/top-programming-languages-2025" target="_self">Core programming languages</a> such as <a href="https://www.w3schools.com/cpp/" target="_blank">C++</a>, <a href="https://www.java.com/" target="_blank">Java</a>, and <a href="https://www.python.org/" target="_blank">Python</a>.</li></ul>AI can autocomplete syntax, but if you don’t understand how things work under the hood, you’re likely to struggle to debug or optimize.2. Learn how to work with AI, not against it. The best engineers will not try to out-code AI. Instead, they will learn to: <ul><li>Write clear prompts to generate better code snippets.</li><li>Review and debug AI-generated code for accuracy, performance, and security.</li><li>Use AI for productivity boosts while still exercising judgment.</li></ul>Think of AI as a teammate. The real skill is knowing when to trust it and when not to.3. Build projects that showcase end-to-end thinking. Employers increasingly look for engineers who can design and build systems, not just solve problems. Create projects that show you can: <ul><li>Define requirements clearly.</li><li>Use AI tools responsibly within the workflow.</li><li>Deliver a product that scales and is maintainable.</li></ul>4. Sharpen your system design skills early. Even junior engineers are now asked questions about basic system design with AI. Expect to explain to prospective employers: <ul><li>How you would <a href="https://spectrum.ieee.org/ieee-online-mini-ai-mba" target="_self">responsibly integrate AI</a> into a system.</li><li>How to design fallbacks when AI fails.</li><li>How to ensure scalability and reliability.</li></ul>5. Develop strong communication skills. Today’s engineers don’t just code in isolation. You will be expected to: <ul><li>Explain <a href="https://spectrum.ieee.org/ieee-course-technical-writing" target="_self">design choices</a> to teammates and stakeholders.</li><li>Document decisions clearly.</li><li>Collaborate effectively in cross-functional teams.</li></ul>This is one area where AI cannot replace you. Clear communication is a career accelerant.6. Stay curious and keep learning. The tech industry moves fast, and AI is accelerating that pace. Cultivate habits such as:<ul><li>Following industry news, blogs, and open-source projects.</li><li>Experimenting with new AI tools, frameworks, and libraries.</li><li>Engaging in communities such as <a href="https://github.com/" target="_blank">GitHub</a>, <a href="https://ieee-collabratec.ieee.org/" target="_blank">IEEE Collabratec</a>, <a href="https://www.linkedin.com/" target="_blank">LinkedIn</a>, and <a href="https://medium.com/" target="_blank">Medium</a>.</li></ul> Employers value engineers who keep themselves sharp and relevant.7. Think beyond coding. AI will increasingly handle routine coding tasks. The differentiators for you will be: <ul><li>Problem-framing: Can you take a vague idea and turn it into a solution?</li><li>Architectural judgment: Can you design systems that scale and last?</li><li><a href="https://spectrum.ieee.org/two-new-ai-ethics-certifications" target="_self">Ethical awareness</a>: Can you spot risks in AI use and address them responsibly?</li></ul>For more career advice, subscribe to the <a href="https://spectrum.ieee.org/newsletters" target="_blank">IEEE Spectrum Career Alert Newsletter</a>. The biweekly newsletter features the latest information on jobs, education, management, and the engineering workplace.

Jun 3, 2026

What It Takes for Future-Ready Power Distribution

<img src="https://spectrum.ieee.org/media-library/utility-workers-inspect-electrical-equipment-beside-a-service-truck-on-a-grassy-site.jpg?id=66649065&width=1245&height=700&coordinates=0%2C104%2C0%2C105"/> This sponsored article is brought to you by <a href="https://www.bv.com/en-US/projects/georgia-power-grid-investment-plan?utm_campaign=portfolio_for_power_utilities-pp-grid_solutions-noia-26-100223&utm_id=26-100223&utm_source=publication&utm_medium=qr-code&utm_content=power-generation&utm_tactic=na&utm_term=brand-awareness_26-bolder-vision-spectrum-native-article" rel="noopener noreferrer" target="_blank">Black & Veatch</a>.The biggest challenge facing utilities today isn’t what it seems. It’s not demand, even as load growth accelerates. It’s not extreme weather, even as “major events” become routine. It’s not cybersecurity, even as connections expand across the grid.<h3></h3> <img alt="Man in gray blazer and blue shirt posed against a plain white background." class="rm-shortcode" data-rm-shortcode-id="65a417dd727734e41721a8a829df1ac9" data-rm-shortcode-name="rebelmouse-image" id="222cc" loading="lazy" src="https://spectrum.ieee.org/media-library/man-in-gray-blazer-and-blue-shirt-posed-against-a-plain-white-background.jpg?id=66649170&width=980"/>The real challenge is this: Distribution systems were designed for a different reality.Long gone are the days of predictable demand, one-way power flow and isolated disruptions. At Black & Veatch, we see that leading utilities are no longer debating whether to modernize. They’re deciding how quickly they can do it, and how to do it at scale.Across grid modernization programs globally, three truths consistently emerge. They define what it takes to prepare the distribution system for what’s next:<h2>1. Outage response is not a resilience strategy</h2>Resilience is being redefined in real time. A strategy centered on mobilizing crews and restoring service as quickly as possible is reactive, and increasingly insufficient.Resilience has to shift upstream into integrated system design. That starts with hardening. Stronger poles, undergrounding and structural upgrades all have a role, particularly in high-risk corridors. We’re also seeing meaningful gains from how the network is configured and how quickly it can respond without waiting on manual intervention.This is where distribution automation programs can change outcomes. Strategically placed reclosers, automated switches and fault indicators help contain disruptions before they spread. When combined with feeder reconfiguration and updated protection strategies, distribution automation investments allow utilities to set more aggressive recovery targets and achieve measurable reductions in outage duration and customer impact.<h2>2. Future-readiness depends on DERs at scale</h2>Forecasting is less and less reliable. Only 19 percent of utilities report strong confidence in their ability to predict future load growth, according to the <a href="https://www.bv.com/en-US/resources/2025-electric-report" target="_blank">Black & Veatch 2025 Electric Report</a>. Distributed Energy Resources (DERs) like solar, storage, EVs and behind-the-meter generation are exciting solutions; but they fundamentally change how the system operates. Power is no longer just delivered. It’s injected, stored and redirected in ways the system was never designed to manage.At scale, these challenges show up quickly — particularly on feeders where distributed generation is approaching or exceeding hosting capacity. Protection coordination becomes more difficult when fault current comes from multiple directions. Voltage becomes less predictable as generation fluctuates throughout the day. And planning models must now account for highly variable, location-specific behavior.Distribution modernization is fundamentally changing how the system is designed and operated so it can absorb disruption, manage bi-directional flows and respond in real time.Adapting to bi-directional power flow requires more than incremental updates. Leading utilities are responding by building flexibility into the system, moving beyond static assumptions toward dynamic hosting capacity and interconnection studies, planning that incorporates DER, EV adoption and localized load growth, and infrastructure aligned with the communications and control needed to manage it.<h2>3. The edge must be intelligent, visible and secure</h2>As system stress and complexity increase, utilities need far greater visibility and control over the network. Historically, utilities relied on customer calls, Supervisory Control and Data Acquisition (SCADA) at the substation level and field crews to understand what was happening on the system. That model doesn’t hold up. You can’t effectively manage a system you can’t see. Plus, the most critical events are increasingly happening beyond the substation — on feeders, laterals, and at the edge where DER and customer behavior are interacting with the grid.Grid-edge technologies have become essential. Sensors, Advanced Metering Infrastructure (AMI) and automated switching provide the raw data and control needed to move from reactive to proactive operations. In more advanced deployments, utilities are creating centralized control environments that allow operators to see and manage the distribution system in near real time. That capability is enabled by:<ul><li>Advanced communications networks to form the backbone of real-time grid visibility</li><li>Distribution Management System (DMS) and Outage Management System (OMS) to enable faster, more coordinated system response</li><li>Analytics, AI and machine learning to improve situational awareness, anticipate system conditions, and support operational decision-making</li></ul>The same connectivity enabling this real-time visibility and control also introduces new vulnerabilities, blurring the line between physical and cyber risk, yet many utilities manage them separately. Only 22 percent have unified teams in place, even as threats continue to rise, including a 50 percent increase in substation attacks and growing exposure to malware and ransomware, according to the <a href="https://www.bv.com/en-US/resources/2025-electric-report" target="_blank">Black & Veatch 2025 Electric Report</a>. Cybersecurity and resilient network design must be embedded into the architecture from the outset—not layered on after the fact.<h2>See what bolder vision looks like</h2>Distribution modernization is fundamentally changing how the system is designed and operated so it can absorb disruption, manage bi-directional flows and respond in real time.To learn about a successful program, check out <a href="https://www.bv.com/en-US/projects/georgia-power-grid-investment-plan?utm_campaign=portfolio_for_power_utilities-pp-grid_solutions-noia-26-100223&utm_id=26-100223&utm_source=publication&utm_medium=qr-code&utm_content=power-generation&utm_tactic=na&utm_term=brand-awareness_26-bolder-vision-spectrum-native-article" target="_blank">Georgia Power’s recent grid modernization program</a>. Black & Veatch partnered with the utility on large-scale infrastructure upgrades. The results? Outages are down 76 percent, restoration times have improved by more than 80 percent and communities across Georgia are powered by a grid built to meet the future head-on.When the state faced the most destructive storm in the company’s history, Hurricane Helene, Georgia Power deployed a rapid response team that utilized its “smart grid” and restored power to more than 1 million customers within days.A grid built to meet the future head-on—that’s the result of bolder vision.

Jun 2, 2026

Direct-to-Cell Technology: Enabling Satellite Connectivity for Legacy Devices

Jun 1, 2026

IEEE President’s Note: Designing a Safer Digital World for Kids

<img src="https://spectrum.ieee.org/media-library/person-wearing-a-scarf-over-a-dark-sweater-with-a-blue-background.png?id=65004859&width=1245&height=700&coordinates=0%2C119%2C0%2C120"/> Children born after 2013 are the first generation to grow up fully immersed in digital systems, which weren’t designed with them in mind. One‑third of the world’s Internet users are younger than 18, according to <a href="https://www.unicef.org/northmacedonia/press-releases/more-175000-children-go-online-first-time-every-day-tapping-great-opportunities" rel="noopener noreferrer" target="_blank">UNICEF</a>, yet these systems shaping their daily lives were built for adults. They were optimized for engagement and designed long before people understood how profoundly digital environments influence children.For engineers and technical professionals, online safety is not an abstract policy debate. It is a design challenge that demands rigor, systems thinking, and ethical foresight.Governments around the world are also beginning to recognize the problem. Policymakers from across Australia, Brazil, the European Union, Indonesia, and the United States are responding to risks engineers have long understood: Addictive features, inappropriate content, opaque data practices, and algorithmic systems shape user behavior in ways that their creators did not fully predict. For years, technology moved faster than governance. Now governance is trying to catch up.<h2>Global Shift Toward Design Reform</h2><h3>Supporting National Digital Ambitions</h3> <img alt="Three men in suit jackets smiling and posing with a middle-aged woman." class="rm-shortcode" data-rm-shortcode-id="f7581428b6630311e33e124d134e7463" data-rm-shortcode-name="rebelmouse-image" id="cfc58" loading="lazy" src="https://spectrum.ieee.org/media-library/three-men-in-suit-jackets-smiling-and-posing-with-a-middle-aged-woman.png?id=66836634&width=980"/>In Athens this year I met with senior leaders of Greek government agencies and key national research institutions. Greece is moving quickly on <a href="https://digitalstrategy.gov.gr/en/" target="_blank">digital transformation</a> and responsible technology governance, and our discussions reinforced IEEE’s role as a trusted, neutral collaborator.We focused on supporting Greece’s ambitions in digital modernization and public‑sector innovation. We also discussed responsible AI and age-appropriate digital design in Europe and elsewhere. These engagements, grounded in shared values and long‑term commitment, strengthened IEEE’s presence within the European ecosystem and opened new pathways for collaboration on trustworthy AI and child‑focused digital well‑being.The European Union and the United Kingdom have been among the first to act, embedding age‑appropriate digital design into their broader children’s rights agenda. Drawing on IEEE expertise and global best practices, Indonesia is the first country in Asia, and Brazil is the first country in Latin America, to adopt age-appropriate design regulation. Australia is aiming to limit access to harmful content and addictive design features through age restrictions on certain platforms. And in the United States, in addition to federal efforts, states including California, New York, and Utah are enacting approaches including age-appropriate design principles.Across these efforts, a shared realization is emerging. Protecting children online is not simply about filtering content or adding parental controls. It requires rethinking the architecture of digital systems regarding how data is collected, how algorithms make decisions, how interfaces influence attention, and how AI interacts with the developing minds of young users.Engineers and technical professionals understand that design choices are never neutral. They encode values, incentives, and assumptions. When the user is a child, those choices carry greater weight.This is where IEEE’s work becomes more essential.<h2> Protecting Children Online</h2>For more than a decade, IEEE has been building technical and ethical foundations for safer digital experiences. The first IEEE standard on age-appropriate design in 2021 marked a turning point. It offers a structured, principled approach to designing with children’s rights in mind. <a href="https://spectrum.ieee.org/the-institute/" target="_self">The Institute</a>’s 2022 article “<a href="https://spectrum.ieee.org/ieee-standard-protecting-kids-online" target="_self">Use a New IEEE Standard to Design a Safer Digital World for Kids</a>” highlights how the standard helps translate those principles into engineering practice.Today the <a href="https://standards.ieee.org/" rel="noopener noreferrer" target="_blank">IEEE Standards Association</a>’s (SA) <a href="https://standards.ieee.org/initiatives/trustworthy-digital-experiences/" rel="noopener noreferrer" target="_blank">Trustworthy Digital Experiences</a> portfolio provides a practical, technically grounded framework for governments and industry. Spanning ethical design, data governance, algorithmic transparency, and child‑focused digital well‑being, it has already initiated discussions with government stakeholders around the world. This work helps bridge the gap between engineering realities and policy ambitions.No single country can solve these challenges alone. Many policymakers lack access to the combined expertise in technology, governance, and children’s rights needed to act quickly and effectively. This collaborative effort helps close that gap.The stakes are high. Without coordinated action, public policy will continue to lag behind technology, leaving children exposed to risks that could have been mitigated through thoughtful design. But with the right frameworks, governments can ensure digital systems respect children’s rights, support healthy development, and promote well‑being.IEEE’s emerging standards and collaborative technology policy work offer a path forward. By grounding national efforts in evidence‑based, rights-aligned design principles, IEEE is helping governments move from reactive regulation to proactive, coherent, and globally informed strategies for protecting children online.Safeguarding childhood in the digital age is both a moral imperative and an engineering challenge. And IEEE is helping to lead the way.—Mary Ellen RandallIEEE president and CEOPlease share your thoughts with me: <a href="mailto:[email protected]">[email protected]</a>.This article appears in the June 2026 print issue.

Jun 1, 2026

Why Sardinians Are Fighting the Renewable Energy Transition

<img src="https://spectrum.ieee.org/media-library/five-workers-in-reflective-safety-gear-and-helmets-standing-in-an-underground-tunnel.jpg?id=66832214&width=1245&height=700&coordinates=0%2C187%2C0%2C188"/> “Not in my backyard” is the rallying cry of citizens everywhere resisting projects proposed for their locality. Whether it’s affordable housing, a waste treatment plant, or a new data center, they may recognize the benefit of the activity. They just don’t want it near them. And the roots of that resistance differ from place to place. When it comes to the ongoing transition from fossil fuels to renewables, companies and policymakers need to know where, exactly, people are coming from.The Italian island of Sardinia is a textbook example. As IEEE Spectrum’s power and energy editor Emily Waltz discovered when she traveled there last October, Sardinian opposition to wind and solar projects runs deep. It spurred a quarter of the voting population to queue up in public squares in 2024 to sign a petition banning all construction of renewable energy. Waltz was surprised. She went there to see a promising new <a href="https://spectrum.ieee.org/co2-battery-energy-storage" target="_self">grid-scale energy storage system that uses domes</a> inflated with carbon dioxide. While reporting on that project, she interviewed residents, engineers, activists, and professors about their attitudes toward climate change and the Italian government’s grand plans for renewable energy on the island. And Waltz soon learned of Sardinians’ profound antipathy toward renewable energy and its deep ties to a history of invasion, occupation, and exploitation stretching back 2,700 years. It started with the <a href="https://en.wikipedia.org/wiki/Phoenician_and_Punic_Sardinia" target="_blank">Phoenicians</a> and then extended through the Romans, the Byzantines, and the Iberians. Sardinia was absorbed into a newly unified Italy in 1861, and it became an autonomous region of Italy in 1948. The island’s population is justifiably suspicious of outsiders, including the Italian government. “When you’re in Sardinia, the weight of history—you can feel it like in the air,” Waltz told me. “And it gets passed down from one generation to the next.”Now, Italy needs Sardinia to produce even more power to meet the country’s climate goals—something that Sardinians see as Rome’s problem, not theirs. “Sardinia already exports about 30 percent of its electricity. It’s not like they need more,” Waltz says. “So it’s hard to make the case to build, build, build.”The result of Waltz’s old-fashioned shoe leather reporting is this month’s <a href="https://spectrum.ieee.org/sardinia-renewable-energy-conflict" target="_blank">cover story</a>. She notes that the Sardinians she talked to aren’t climate-change deniers, and they don’t object to renewables per se. They just don’t like the way corporations and Italian policymakers are trying to plug into Sardinia like it’s one giant battery rather than the home of an ancient and proud people.“I think Sardinians would be more receptive to renewable projects if it was more of a ground-up, grassroots approach,” Waltz says. Indeed, this homegrown approach is already working in some places in Sardinia. She knows of more than 50 projects, called energy communities, where the residents are deploying renewables themselves. The idea also holds promise for other places struggling to get locals to buy into the renewable-energy transition. The Sardinian experience is both a cautionary tale and a blueprint. Ignore the weight of history that communities carry and your project risks failure. Meet the people where they are and you might just get somewhere. The same lesson applies whether you’re in <a href="https://unepccc.org/sulawesi-renewable-energy/" rel="noopener noreferrer" target="_blank">Sulawesi</a> or <a href="https://spectrum.ieee.org/broadband-internet-in-nigeria" target="_self">sub-Saharan Africa</a>. You just have to show up to learn it.

May 31, 2026

This DIY Bipedal Robot Used Pneumatic “Air-Muscles” Instead of Motors

<img src="https://spectrum.ieee.org/media-library/two-photos-of-a-prototype-humanoid-robot-with-a-wooden-frame-and-wires-and-other-components-strung-on-its-body.jpg?id=66825613&width=1245&height=700&coordinates=0%2C187%2C0%2C188"/> In 1987, <a href="https://shadowrobot.com/the-story-of-our-founder-richard-greenhill/" rel="noopener noreferrer" target="_blank">Richard Greenhill</a>, a British photographer who was fascinated by (but had no actual training in) robotics, decided he wanted to build a life-size humanoid that could do useful things, like carrying luggage. He was working at a startup called Intergalactic Robots, but he couldn’t convince anyone there to build such a machine, so he set about building one himself, in his attic.<div class="rm-embed embed-media"><iframe height="110px" id="noa-web-audio-player" src="https://embed-player.newsoveraudio.com/v4?key=q5m19e&id=https://spectrum.ieee.org/shadow-walker-biped-humanoid-robot&bgColor=F5F5F5&color=1b1b1c&playColor=1b1b1c&progressBgColor=F5F5F5&progressBorderColor=bdbbbb&titleColor=1b1b1c&timeColor=1b1b1c&speedColor=1b1b1c&noaLinkColor=556B7D&noaLinkHighlightColor=FF4B00&feedbackButton=true" style="border: none" width="100%"></iframe></div>To help with his project, he organized a weekly get-together of a dozen or so like-minded folks. Every Wednesday night, his wife, Sally, would make a big pot of spaghetti, and the group would tinker with components scavenged from old printers and picked up from junkyards. They called themselves the Shadow Group. They eventually constructed several different robots, but their main project was the two-legged Shadow Walker. <img alt="Two color photos of a casually dressed white man in a workroom posing with a partially assembled wooden robot." class="rm-shortcode" data-rm-shortcode-id="abd4fd0237110b3339a5f336e96006e6" data-rm-shortcode-name="rebelmouse-image" id="b189a" loading="lazy" src="https://spectrum.ieee.org/media-library/two-color-photos-of-a-casually-dressed-white-man-in-a-workroom-posing-with-a-partially-assembled-wooden-robot.jpg?id=66825888&width=980"/> In 1987, photographer Richard Greenhill organized a weekly gathering of DIY enthusiasts to work on projects in his attic, including the Shadow Walker. Richard Greenhill and David BuckleyGreenhill’s friend <a href="https://davidbuckley.net/DB/aboutme.htm" target="_blank">David Buckley</a>, a robotics and animatronics expert he’d met at Intergalactic, sketched out a rough design based on medical textbooks of human bone structure and muscle movement. The robot’s skeleton, made of maple, was greatly simplified—only one bone in the lower leg and a single wide toe on each foot. The ankle’s double-axis design allowed for two degrees of movement. The knee had no complicating kneecap.Greenhill didn’t want the robot to use motors, so its movement was controlled using compressed air to extend and contract 28 “air-muscles”—his version of a McKibben muscle, invented in the 1950s to mimic musculature with pneumatics. The muscles were connected to the bones across eight joints (hips, knees, ankles, toes), which provided 12 degrees of freedom.RELATED: <a href="https://spectrum.ieee.org/the-short-strange-life-of-the-first-friendly-robot" target="_self">The Short, Strange Life of the First Friendly Robot</a>The robot’s headless torso held the control valves, electronics, and computer interfaces. It stood 168 centimeters tall and 46 cm wide and weighed about 38 kilograms. The group managed to get the robot to stand up reliably and balance itself; it could even regain its center if pushed a little. But walking turned out to be more of a challenge.<a href="https://www.linkedin.com/in/walkerrich/" target="_blank">Rich Walker</a> joined the group as a teenager and began writing software to get the robot to stand. He was particularly interested in using neural networks to solve balancing problems, although he ran into a number of hardware obstacles, including the unreliability of the sensors and the valves, and the robot’s overall fragility. Over time, Walker and the team developed a standard library of routines to control the robot. Walker wrote a <a href="https://davidbuckley.net/DB/ShadowBiped/ShadBipedArchive/Shadow%20Robot%20Company%20Shadow%20Biped.htm" target="_blank">detailed description</a> of the Shadow Walker in 1999, which is available on David Buckley’s website.<h2>The 1st International Robot Olympics</h2>By the time the Shadow Group began developing Shadow Walker, engineers in academia and industry had been working on robotics for several decades. The world’s first industrial robot, the <a href="https://spectrum.ieee.org/unimation-robot" target="_self">Unimate</a>, debuted in 1961, and in 1967 Donald Michie and others began building a series of <a href="https://spectrum.ieee.org/freddy-robot-british-ai-winter" target="_self">Freddy </a>robots to investigate machine intelligence. The IEEE created its first dedicated robotics organization in 1984 when it established the IEEE Robotics and Automation Council, which became the <a href="https://www.ieee-ras.org/" target="_blank">IEEE Robotics and Automation Society</a> in 1987. Also in 1987, the nonprofit International Federation of Robotics was established to promote research, development, use, and cooperation in the field of robotics.As Shadow Walker pushed the limits for a DIY humanoid robot, industrial humanoids were also gaining ground. In 1986, Honda began working on its experimental (E-series) and later the prototype (P-series) humanoid robots, finally unveiling the P2 in 1996. The P2 stood 183 cm tall and weighed 210 kg. It was the first humanoid capable of stable, autonomous walking. This work eventually led to the development of the groundbreaking <a href="https://spectrum.ieee.org/honda-p2-robot-ieee-milestone" target="_self">ASIMO</a>. <img alt="Two color photos of a casually dressed bearded white man posing with a wooden robot leg and with a computer and other equipment." class="rm-shortcode" data-rm-shortcode-id="e7e2d6657e6037ef204eb6ab36e813d3" data-rm-shortcode-name="rebelmouse-image" id="45b6e" loading="lazy" src="https://spectrum.ieee.org/media-library/two-color-photos-of-a-casually-dressed-bearded-white-man-posing-with-a-wooden-robot-leg-and-with-a-computer-and-other-equipment.jpg?id=66826216&width=980"/> Greenhill’s friend, roboticist David Buckley, consulted medical textbooks to create Shadow Walker’s humanoid design.Richard Greenhill and David BuckleyIn the late 1980s, the public was both fascinated and horrified by the potential of robots. Businesses saw robots as a way to increase productivity, while workers worried they would take their jobs. Children viewed them as wondrous toys, while people with disabilities embraced them as tools of liberation. Military experts hoped robots would fight wars without endangering human soldiers, while politicians pondered if robots might eventually get to vote. Philosophers thought robots could challenge our notions of intelligence (and stupidity), while the religious struggled with concerns about the human race in a robot-dominated future. <img alt="Photo of two articulated feet made of pieces of wood strung with wires and other components." class="rm-shortcode" data-rm-shortcode-id="e8188dfa6302c3d8a0eaa3319645c146" data-rm-shortcode-name="rebelmouse-image" id="528f6" loading="lazy" src="https://spectrum.ieee.org/media-library/photo-of-two-articulated-feet-made-of-pieces-of-wood-strung-with-wires-and-other-components.jpg?id=66835726&width=980"/> Shadow Walker’s simplified anatomy included only one bone in the lower leg and a single wide toe on each foot.Science Museum GroupPeter Mowforth, cofounder of the <a href="https://en.wikipedia.org/wiki/Turing_Institute" target="_blank">Turing Institute</a> in Glasgow, noted these disparate visions for robots when he announced the 1st International Robot Olympics, to be held in 27 and 28 September 1990 and hosted by the Turing Institute and the University of Strathclyde. The Olympics would round up the world’s best robots and showcase them head-to-head.Mowforth himself thought all of the competing visions of robots were overblown. Steeped in machine learning research and robotics development, he knew firsthand the limitations of the state of the art: Robots rarely worked as intended, easily broke down, and glitched over seemingly trivial problems. He envisioned the Robot Olympics as a testbed to assess what the latest generation of robots could and could not do. <img alt="Photo of a headless and armless humanoid robot wearing red pants." class="rm-shortcode" data-rm-shortcode-id="6e64cccdcd490f06a27f02e2a64277b4" data-rm-shortcode-name="rebelmouse-image" id="3135a" loading="lazy" src="https://spectrum.ieee.org/media-library/photo-of-a-headless-and-armless-humanoid-robot-wearing-red-pants.jpg?id=66826230&width=980"/> At the 1990 Robot Olympics, held in Glasgow, Shadow Walker wore pants to conceal its pneumatic “air-muscles” from competitors.Adam Hart-Davis/Science SourceThe call for participation was wide open. Instead of having predetermined categories of competition, the organizers opted to see who applied to compete and then group them based on their claimed capabilities. In addition to picking the winners of individual events, the judges would select an overall Olympic champion based on the quality of the hardware, the sophistication of behavior, and novelty. Other prizes were given for young competitors, technologies that showed commercial potential, and design. In the end, more than 50 robots were entered, from a mix of universities, industry, and hobbyist groups from Canada, France, India, Japan, Mexico, the Soviet Union, the United States, the United Kingdom, and Yugoslavia.There were plenty of disappointments. Trolleyman, a golf-cart-like wheeled robot, suffered a power failure while carrying the opening Olympic torch through the streets of Glasgow. The pile rug in the arena tripped up many robots that had been trained only on flat, smooth floors. David Buckley later concluded that the events were too difficult, and that the Olympics didn’t push development forward. <iframe frameborder="0" height="auto" lazy-loadable="true" scrolling="no" src="https://www.youtube.com/embed/I37bUBVM854?rel=0&start=151" style="position:absolute;top:0;left:0;width:100%;height:100%;" width="100%"></iframe>Of course, there were winners. In a surprise triumph for vintage technology, the fully mechanical 19th-century Japanese Archer from the Museum of Automata in York, England, won gold in javelin, beating out competitors more than 100 years its junior. The overall Olympic Champion was Yamabico, Shoji Suzuki’s entry from the University of Tsukuba, in Japan, which won bronze in obstacle avoidance and gold in wall following, but was disqualified in the talking category for not speaking English.The Shadow Group had high hopes for Shadow Walker. Unfortunately, though, it failed to take a step, and the biped race was won by the Cardiff University Biped. Shadow Walker now resides in the <a href="https://collection.sciencemuseumgroup.org.uk/objects/co8366055/biped-robot" target="_blank">collections of the Science Museum</a> in London.<h2>The Legacy of Shadow Walker</h2>In 1997, a paying customer in search of a robotic leg compelled the Shadow Group to get serious and become a registered company. <a href="https://shadowrobot.com/" target="_blank">Shadow Robot</a> is now Britain’s oldest robotics company. Rich Walker, who had left the Shadow Group to earn a B.A. in mathematics and a diploma in computer science at the University of Cambridge, joined Shadow Robot in 1999 as technical director. Today he’s the director of the company.Shadow Robot specializes in durable <a href="https://robotsguide.com/robots/shadow" target="_blank">robot hands</a> rather than walking robots. But the focus on hands is also a legacy of the Shadow Group. Walker remembers that the Shadow Group’s first humanoid hand in the late 1990s was impressive simply for being able to pick up a pint of beer (a smooth-sided, thin-walled glass). Today, Shadow Robot’s hands are <a href="https://shadowrobot.com/dexterous-hand-series/" target="_blank">testbeds for dexterity</a>. Gone are the pneumatic muscles, replaced by actuators that move each finger with precision. The classic model contains 20 motors, allowing for <a href="https://www.drugs.com/medical-answers/abduction-adduction-mean-3562250/" target="_blank">abductive and adductive </a>movement with 24 degrees of freedom. <img alt="Black and white photo of a two-legged humanoid robot with its left leg raised, next to a man with his right leg raised while another man looks on." class="rm-shortcode" data-rm-shortcode-id="dd88249ff45a90cd091024b40970aeec" data-rm-shortcode-name="rebelmouse-image" id="8e4ba" loading="lazy" src="https://spectrum.ieee.org/media-library/black-and-white-photo-of-a-two-legged-humanoid-robot-with-its-left-leg-raised-next-to-a-man-with-his-right-leg-raised-while-ano.jpg?id=66826242&width=980"/> Shadow Walker’s operator wore a data suit that captured his movements and allowed the robot to copy them.Richard GreenhillIn a <a href="https://shadowrobot.com/why-your-industry-needs-dexterity-not-humanoids/" target="_blank">recent blog post</a>, Sejal Parsotomo, senior marketing executive at Shadow Robot, wrote that while humanoid robots are great for public relations, specialized dexterity is key for success: A robot that can walk into your factory may be impressive, but a robot that can <a href="https://spectrum.ieee.org/robot-hand-shadow-robot-company" target="_blank">reliably manipulate objects</a> is transformative.In its struggles to take more than a few steps, the Shadow Walker showed the inherent difficulty that robots had in mastering even low-level skills. In August 2025, Beijing hosted the <a href="https://www.whrgoc.com/about" target="_blank">World Humanoid Robot Games</a>. Competing in sports such as gymnastics, soccer, and track events, as well as more “useful” tasks like hotel cleaning and sorting medicine, these robots could literally have run circles around the competitors in the first Robot Olympics 35 years earlier. And yet, there is still so much work needed in order for robots to navigate the human-built environment. Despite the astonishing progress, we’re still not all that close to actually useful humanoid robots.Part of a <a href="https://spectrum.ieee.org/collections/past-forward/" target="_self">continuing series</a> looking at historical artifacts that embrace the boundless potential of technology.An abridged version of this article appears in the June 2026 print issue as “Learning to Walk.”<h3>References</h3> Richard Greenhill gives an <a href="https://shadowrobot.com/the-story-of-our-founder-richard-greenhill/" target="_blank">overview of his life</a> and the founding of the Shadow Group in a post on Shadow Robot’s corporate website.David Buckley has a compilation of resources on the <a href="https://davidbuckley.net/DB/ShadowBiped/ShadBiped.htm" target="_blank">Shadow Biped Walker</a>, including <a href="https://davidbuckley.net/DB/ShadowBiped/ShadBipedArchive/Shadow%20Robot%20Company%20Shadow%20Biped.htm" rel="noopener noreferrer" target="_blank">specifications</a> from the 1999 iteration and a <a href="https://davidbuckley.net/RS/History/Olympics90_files/Brochure.htm" rel="noopener noreferrer" target="_blank">brochure</a> from the 1st International Robot Olympics.There is coverage of the Robot Olympics worthy of a gossip sheet in <a href="https://ricerca.repubblica.it/repubblica/archivio/repubblica/1990/09/30/olimpiade-dei-robot.html" rel="noopener noreferrer" target="_blank">La Repubblica</a> and lovely footage of the competition in <a href="https://www.youtube.com/watch?v=I37bUBVM854" rel="noopener noreferrer" target="_blank">this TV-am interview</a> of Peter Mowforth by Lorraine Kelly.

May 30, 2026

Poetry for Engineers: Cyborg Laboratory

May 29, 2026

Make a Soft Digital Clock Tick With Millifluidics

<img src="https://spectrum.ieee.org/media-library/a-desktop-with-a-red-silicone-surface-where-a-typical-clock-would-have-7-segment-leds-numbers-are-formed-from-concave-depressio.png?id=66826918&width=1245&height=700&coordinates=38%2C0%2C38%2C0"/> Electrons are great. We use them to move vehicles, illuminate cities, and, of course, compute. But computation is not confined to the world of electronics. And shifting to alternative nonelectronic realms can unlock unique advantages: Photonic chips, for instance, process information with light while generating little heat. Another compelling alternative is <a href="https://en.wikipedia.org/wiki/Fluidics" rel="noopener noreferrer" target="_blank">fluidics</a>, which uses pressurized gases or liquids to build logic circuits. Pioneered in the 1960s but sidelined by microchips, the field reemerged in the 1990s as “<a href="https://en.wikipedia.org/wiki/Microfluidics" rel="noopener noreferrer" target="_blank">microfluidics</a>.” This approach aims to shrink laboratories onto a single chip by creating microscopic fluid channels with integrated micropneumatic control systems.Today, there is a second fluidic revival, this time in the domain of <a href="https://spectrum.ieee.org/tag/soft-robot" target="_self">soft robotics</a>. Scaling microfluidic designs up to the millimeter-scale range (millifluidics) enables the higher flow rates necessary to drive robotic actuators. These robots exploit the nonlinear behaviors of soft materials to create lifelike motion and safer interactions, often utilizing pressurized air.By building systems that “think” with the same air that powers them, we can drastically reduce the need for bulky electronic-to-pneumatic interfaces. This is the focus of my <a href="https://www.linkedin.com/company/soiboi/" rel="noopener noreferrer" target="_blank">Soiboi Studio</a> robotics lab. With millifluidic logic, I have steadily scaled the complexity of my designs. What began with a simple oscillator has most recently evolved into a clock featuring a soft, four-digit, seven-segment display.<h2>What Is Millifluidics?</h2>Building on <a href="https://doi.org/10.1109/JMEMS.2007.906080" rel="noopener noreferrer" target="_blank">microfluidics research from the early 2000s</a> and recent developments from the <a href="https://groverlab.org/" rel="noopener noreferrer" target="_blank">Grover Lab at the University of California, Riverside</a>, I’ve developed millifluidic devices using standard 3D printing and silicone casting. The basic architecture is simple: A flexible membrane is sandwiched between rigid layers embedded with networks of air channels.Just as electronics rely on differing voltage potentials, these fluidic circuits operate on the pressure difference between atmospheric pressure (logical 0) and a near-vacuum at around −60 kilopascals of relative pressure (logical 1). Using negative pressure means the membrane is pulled into openings. This creates robust seals that allow me to replicate electronic building blocks. <img alt="Major components of the soft clock. " class="rm-shortcode" data-rm-shortcode-id="4b17b5a0dc5c53a4d0f73fdfdd0db0d6" data-rm-shortcode-name="rebelmouse-image" id="cc1f8" loading="lazy" src="https://spectrum.ieee.org/media-library/major-components-of-the-soft-clock.png?id=66826949&width=980"/> A cast silicone membrane forms the face of the clock [top], while behind it sits 3D-printed millifluidic blocks [middle rows]. An Arduino Uno controls driver boards that operate solenoids, which are connected to valves that are attached to a vacuum pump [bottom row].James ProvostWhile fluidic resistors are easily realized by adjusting the channel geometry, the heart of the system is a valve that mimics a metal-oxide-semiconductor field-effect transistor, or <a href="https://doi.org/10.1109/JMEMS.2002.803414" target="_blank">MOSFET</a>. This vacuum “<a href="https://spectrum.ieee.org/transistor-timeline" target="_blank">transistor</a>” features a flow layer with two chambers (the source and drain) divided by a central valve seat and a control layer containing a cavity (the gate). A membrane runs between the control and flow layers and normally prevents airflow between the source and drain chambers. To switch the transistor on, a vacuum is applied to the gate chamber, sucking the membrane into the cavity and lifting it off the seat. This opens a path for airflow, equivalent to closing an electric circuit. By adding a <a href="https://doi.org/10.1038/nphys1637" rel="noopener noreferrer" target="_blank">small aperture to the membrane,</a> I created a check valve—the fluidic equivalent of a diode. By combining transistors and resistive “pull-down” channels, I can build a full suite of logic gates.The original microfluidic designs that inspired me were fabricated from <a href="https://doi.org/10.1016/S0925-4005(02)00468-9" rel="noopener noreferrer" target="_blank">etched glass</a> and milled acrylic. Adapting them for a standard 3D printer required reengineering the logic elements and mastering two critical fabrication techniques.First, I need airtight prints, yet printed plastic is notoriously porous. By printing at elevated temperatures, slow speeds, and slight overextrusion, I was able to fill microscopic gaps. When you’re using transparent filament, there’s a handy visual indicator: The more transparent the plastic appears, the lower its porosity.Second, I used glass for my print bed. By printing the upper and lower chambers directly against this bed, I got the interface surface to become mirror smooth. This finish is essential for creating reliable, airtight seals. A 0.3-millimeter silicone membrane is placed between the layers and secured with screws. <h2>How Does the Soft Clock Work?</h2>The clockface is a cast silicone membrane. Each digit segment is formed by a small underlying cavity. When air is evacuated from this cavity, the membrane is sucked inward to create a concave hollow; when atmospheric pressure is restored, the silicone pops back flush with the surface. The result is a mesmerizing, organic motion.The “brain” of the clock is an <a href="https://spectrum.ieee.org/the-making-of-arduino" target="_blank">Arduino Uno</a>, while the fluidics significantly reduce the hardware footprint. A four-digit, seven-segment display with two separator dots would require 29 solenoid valves to control directly. My clock needs just 11 valves. <img alt="An illustration of the three chambers of a pneumatic transistor, with two lower chambers separated by a wall overlaid by a membrane, with an upper chamber straddling the wall." class="rm-shortcode" data-rm-shortcode-id="6c1953705eb5c8b75c479ab1cef68107" data-rm-shortcode-name="rebelmouse-image" id="b7db3" loading="lazy" src="https://spectrum.ieee.org/media-library/an-illustration-of-the-three-chambers-of-a-pneumatic-transistor-with-two-lower-chambers-separated-by-a-wall-overlaid-by-a-membr.png?id=66826932&width=980"/> A pneumatic transistor is off when its upper control chamber is at atmospheric pressure [top]. When air is removed from the control chamber, it lifts a membrane, which allows air to flow between lower flow chambers and turns the transistor on [bottom]. James ProvostTo understand how it works, consider a standard electronic <a href="https://cdn.sparkfun.com/datasheets/Components/LED/1LEDREDCC.pdf" target="_blank">four-digit, seven-segment LED display</a>. This also uses 11 pins to drive its digits. (In clockface displays, an additional pin is required to drive the separator dots.) Every digit is connected to a shared data bus with seven lines, one per segment. The four control lines select individual digits. Only one digit is illuminated at time, and strobing the digits at least 50 times per second creates the illusion that all four are simultaneously illuminated.Such high-speed switching is not possible with air. Instead, I rely on memory. Each segment acts like a capacitor: By evacuating its cavity (logic 1), you “charge” the segment; by restoring atmospheric pressure (logic 0), you discharge it. Hence, each digit acts as an independent 7-bit memory. If the system is sufficiently airtight, the segments maintain their state for several seconds.Like the electronic display, the system utilizes a seven-line data bus. Each line connects to a solenoid valve that provides either vacuum or atmospheric pressure. To selectively address the individual digits, I placed a fluidic transistor between each segment and its data line. All the transistors’ control inputs for a given digit are combined into one “write enable” line connected to its own solenoid valve. Activating this valve allows me to write data into the corresponding digit’s memory.The clock updates one digit per second, meaning a full cycle across the face takes 4 seconds. This cycle also drives the separator dots: A set of fluidic diodes connects the enable lines to the dots’ cavities. Consequently, as each digit is addressed, the dots pulse automatically.This display is more than a clock; it is a soft robot that happens to tell time. By offloading computation to the same air that powers movement, the clock approaches a new class of machines that are simpler, lighter, and more integrated. I’m now developing a guide for getting started with vacuum-powered logic and may release a refined version of this clock in the future. <a href="https://www.youtube.com/watch?v=E1BLGpE5zH0" target="_blank">Watching the silicone skin morph</a> serves as a fascinating reminder that not all logic needs silicon; sometimes, all you need is flexible silicone and a flow of air. This article appears in the June 2026 print issue as “The Soft Clock.”

May 28, 2026

Finding Success in Industry as a Chip Designer

<img src="https://spectrum.ieee.org/media-library/engineer-testing-electronic-components-at-a-lab-bench-with-cables-and-equipment.png?id=66821207&width=1245&height=700&coordinates=0%2C97%2C0%2C97"/> I have been an application-specific IC (ASIC) designer for almost three decades. Over that time, I’ve moved through the full academic trajectory, from graduate student to full professor; later, I transitioned to industry after an unsuccessful stint at entrepreneurship. When I made the switch to the private sector in 2019, I began focusing on a critically important aspect of the electronic industry: silicon intellectual property. As much as 80 percent of the physical area in today’s most advanced chips is occupied by blocks that aren’t made for specific products or even designed by the consumer-facing companies that built them. Instead, chipmakers draw heavily on established silicon IP from companies like <a href="https://www.arm.com/" rel="noopener noreferrer" target="_blank">Arm</a>, <a href="https://www.cadence.com/en_US/home.html" rel="noopener noreferrer" target="_blank">Cadence</a>, <a href="https://www.rambus.com/" rel="noopener noreferrer" target="_blank">Rambus</a>, <a href="https://www.synopsys.com/" rel="noopener noreferrer" target="_blank">Synopsys</a>, and the company I work for, <a href="https://www.siliconcr.com/" rel="noopener noreferrer" target="_blank">Silicon Creations</a>. Throughout my career, I’ve designed chips for very different purposes, including enabling the research program in my academic lab and expanding the IP portfolio of my company. When I joined Silicon Creations, I had no idea how differently the industry approaches IC design and encountered a steep learning curve. Initially, it seemed that much of my two decades of academic research and training did not directly translate to the role. I had to learn new skills and adopt a new mindset.Today, demand for <a href="https://www.arm.com/glossary/asic" rel="noopener noreferrer" target="_blank">ASICs</a> is rapidly growing, driven by the need for specialized chips in the automotive sector, AI applications, and more. By <a href="https://www.coherentmarketinsights.com/industry-reports/asic-chip-market" rel="noopener noreferrer" target="_blank">one market estimate</a>, the ASIC market is expected to grow from US $23.4 billion to $38.8 billion by 2033, and the semiconductor industry as a whole is projected to <a href="https://www.mckinsey.com/industries/semiconductors/our-insights/hiding-in-plain-sight-the-underestimated-size-of-the-semiconductor-industry" rel="noopener noreferrer" target="_blank">hit $1 trillion by 2030</a>. The industry <a href="https://set.kellyservices.us/resource-center/business-resources/current-talent-trends-and-hiring-outlook-in-the-semiconductor-sector" rel="noopener noreferrer" target="_blank">needs more chip designers—</a>but if you’re coming from an academic background as I did, there are a few things you’ll need to know.<h2>Different goals lead to different strategies</h2>The differences between industry and academe begin with a divergence in purpose. In academia, my primary objective was to generate new knowledge: to propose a novel circuit technique, validate an unconventional architecture, or explore the limits of performance in a given domain. A successful chip is one that demonstrates a concept. In industry, it is not nearly enough to prove that something can work. The goal is to ensure that it works reliably, repeatedly, and at scale. Success is measured not by novelty but by whether the silicon meets specifications, yields as expected in production, and supports a competitive product delivered on schedule.This leads to a stark contrast in risk tolerance. Academic designs often deliberately push into unproven territory, where even partial success can yield valuable insight. In industry, however, we systematically minimize risk. The cost of failure makes first-time silicon success a central requirement—especially at advanced technology nodes, where the lithography masks used to transfer circuit designs onto silicon wafers alone can cost tens of millions of dollars. As a result, industry design flows are built around eliminating uncertainty through conservative margins, extensive validation, and careful reuse of proven solutions. “Academia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale.”This paradigm has existed since the 1970s, when application-specific chip design was established. However, the gulf between academia and industry has expanded since the mid-2010s, when <a href="https://spectrum.ieee.org/how-the-father-of-finfets-helped-save-moores-law" target="_self">FinFET technology</a>, a 3D architecture using vertical “fins” of silicon, was widely adopted in industry. System designs are also becoming increasingly modular with the <a href="https://spectrum.ieee.org/3-ways-chiplets-are-remaking-processors" target="_self">advent of chiplets</a>. This fundamentally altered the economics and complexity of ASIC development, with design costs rising by almost an order of magnitude. Initiatives like <a href="https://www.tsmc.com/english" target="_blank">Taiwan Semiconductor Manufacturing Co.</a>’s <a href="https://www.tsmc.com/english/dedicatedFoundry/services/university_program" target="_blank">University FinFET Program</a> and new government-funded <a href="https://pme.uchicago.edu/news/new-3m-us-national-science-foundation-grant-bolsters-american-chip-design" target="_blank">chip-design hubs</a> now let some well-resourced universities design for more advanced architectures, but the technology is still out of reach for many academics. <h2>What the industry-academia split means in practice</h2>Consider a startup developing an ASIC. Its engineering team may have deep expertise in a particular algorithm, sensor interface, or system architecture, the features that define its competitive advantage. But it is unlikely to possess world-class expertise in every supporting function. Developing each of these blocks internally would require significant time, capital, and specialized talent. Doing so could delay market entry beyond the startup’s viability.Even large semiconductor companies face similar constraints. Advanced-node development demands intense focus. Allocating a team to redesign a standard interface block that has already been implemented elsewhere may be difficult to justify when differentiation lies at the system level, such as an inference chip’s ability to speed up neural network computations. The time it takes to move a new chip from conception to market and risk mitigation, not self-sufficiency, govern most decisions about in-house development versus outsourcing.The economics of advanced IC manufacturing reinforce this reality. When the development cost of a leading-edge chip reaches hundreds of millions of dollars, minimizing risk becomes a central design imperative.In this context, silicon IP emerged as a practical solution. Similar to how software developers rely on preexisting libraries rather than writing every function from scratch, ASIC designers license predesigned, preverified silicon blocks—such as processor cores, memory interfaces, and security engines—from highly specialized IP vendors. These blocks can then be integrated into larger, increasingly complex systems. <h2>Design scope, verification, and time horizons</h2>With the use of silicon IP, industry is able to widen the scope of its designs. Academic efforts tend to focus on block-level innovation: a new analog-to-digital converter architecture or an ultralow-noise amplifier, for instance. These designs typically abstract away many of the complexities of bringing a chip to market, such as packaging constraints, long-term reliability, and manufacturing yield.In industry, the focus shifts to system-level integration. Modern systems on chips, or SoCs, incorporate dozens or even hundreds of functional blocks. Managing signal integrity, timing, firmware interaction, and system-level validation becomes as critical as the design of any individual block. Verification philosophy also diverges sharply. In academia, the goal of verification is to demonstrate that the concept works under nominal conditions, which may not always reflect how it would perform in real applications. Even if only a fraction of fabricated chips from a multiproject wafer operates correctly, the design may still be considered a success if it validates the underlying idea. At my academic lab for instance, we used to receive 40 chips from a <a href="https://www.tsmc.com/english/dedicatedFoundry/services/cyberShuttle" target="_blank">TSMC prototyping service</a> and started testing them in batches of five. If the first five or 10 chips proved functional, we had already collected more than enough data for a publication. If some of them failed, we weren’t required to mention this when publishing the results. In industry, verification is exhaustive, critical, and often dominates the development schedule. Failures are measured in parts per million, and even rare anomalies are carefully analyzed and documented to identify root causes and prevent recurrence. When I started at Silicon Creations, I was surprised by the level of detail and scrutiny designs face.Differences in time horizons and economic constraints reinforce each of these contrasts. Academic projects operate on flexible timelines aligned with research and funding cycles. If I missed a deadline, I just had to wait for the next cycle. Industry projects are driven by fixed product schedules and market windows, frequently targeting costly leading-edge nodes to achieve competitive performance, power, and area efficiency. Missing a deadline can negate the value of an entire design and may have major financial consequences along the entire supply chain.In essence, academia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale. Both are indispensable, but they operate under fundamentally different definitions of success. As ASIC complexity continues to grow, understanding both perspectives will be essential for the next generation of engineers navigating the evolving semiconductor landscape.This article appears in the June 2026 print issue.

Feb 9, 2022

Andrew Ng: Unbiggen AI

<img src="https://spectrum.ieee.org/media-library/andrew-ng-listens-during-the-power-of-data-sooner-than-you-think-global-technology-conference-in-brooklyn-new-york-on-wednes.jpg?id=29206806&width=1245&height=700&coordinates=0%2C0%2C0%2C474"/> <a href="https://en.wikipedia.org/wiki/Andrew_Ng" rel="noopener noreferrer" target="_blank">Andrew Ng</a> 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 <a href="https://stanfordmlgroup.github.io/" rel="noopener noreferrer" target="_blank">Stanford University</a>, cofounded <a href="https://research.google/teams/brain/" rel="noopener noreferrer" target="_blank">Google Brain</a> in 2011, and then served for three years as chief scientist for <a href="https://ir.baidu.com/" rel="noopener noreferrer" target="_blank">Baidu</a>, 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.<hr/> Ng’s current efforts are focused on his company <a href="https://landing.ai/about/" rel="noopener noreferrer" target="_blank">Landing AI</a>, 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 <a href="https://www.youtube.com/watch?v=06-AZXmwHjo" target="_blank">data-centric AI movement</a>, which he says can yield “small data” solutions to big issues in AI, including model efficiency, accuracy, and bias. Andrew Ng on... <ul> <li><a href="#big">What’s next for really big models</a></li> <li><a href="#career">The career advice he didn’t listen to</a></li> <li><a href="#defining">Defining the data-centric AI movement</a></li> <li><a href="#synthetic">Synthetic data</a></li> <li><a href="#work">Why Landing AI asks its customers to do the work</a></li> </ul> 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 <a href="https://spectrum.ieee.org/deep-learning-computational-cost" target="_self">unsustainable trajectory</a>. 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 <a href="https://cs.stanford.edu/~pliang/" rel="noopener noreferrer" target="_blank">Percy Liang</a> and <a href="https://crfm.stanford.edu/" rel="noopener noreferrer" target="_blank">some of my friends at Stanford</a> to refer to very large models, trained on very large data sets, that can be tuned for specific applications. For example, <a href="https://spectrum.ieee.org/open-ais-powerful-text-generating-tool-is-ready-for-business" target="_self">GPT-3</a> 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. <a href="#top">Back to top</a> 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 <a href="https://research.google/teams/brain/" rel="noopener noreferrer" target="_blank">Google Brain</a> 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 <a href="https://nips.cc/" rel="noopener noreferrer" target="_blank">NeurIPS</a> workshop paper advocating using <a href="https://developer.nvidia.com/cuda-zone" rel="noopener noreferrer" target="_blank">CUDA</a>, 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.” <a href="#top">Back to top</a> 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 <a href="https://neurips.cc/virtual/2021/workshop/21860" rel="noopener noreferrer" target="_blank">data-centric AI workshop at NeurIPS</a>, 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 <a href="https://developers.arcgis.com/python/guide/how-retinanet-works/" rel="noopener noreferrer" target="_blank">RetinaNet</a>. 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, <a href="https://www.cs.princeton.edu/~olgarus/" rel="noopener noreferrer" target="_blank">Olga Russakovsky</a> gave a really nice talk on this. At the main NeurIPS conference, I also really enjoyed <a href="https://neurips.cc/virtual/2021/invited-talk/22281" rel="noopener noreferrer" target="_blank">Mary Gray’s presentation,</a> which touched on how data-centric AI is one piece of the solution, but not the entire solution. New tools like <a href="https://www.microsoft.com/en-us/research/project/datasheets-for-datasets/" rel="noopener noreferrer" target="_blank">Datasheets for Datasets</a> 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 <a href="https://jupyter.org/" rel="noopener noreferrer" target="_blank">Jupyter notebook</a> 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. <a href="#top">Back to top</a> 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, <a href="https://tensorlab.cms.caltech.edu/users/anima/" rel="noopener noreferrer" target="_blank">Anima Anandkumar</a> 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. <a href="#top">Back to top</a> To make these issues more concrete, can you walk me through an example? When a company approaches <a href="https://landing.ai/" rel="noopener noreferrer" target="_blank">Landing AI</a> 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 <a href="https://landing.ai/platform/" rel="noopener noreferrer" target="_blank">LandingLens</a> 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. <a href="#top">Back to top</a> This article appears in the April 2022 print issue as “Andrew Ng, AI Minimalist.”

Feb 8, 2022

How AI Will Change Chip Design

<img src="https://spectrum.ieee.org/media-library/layered-rendering-of-colorful-semiconductor-wafers-with-a-bright-white-light-sitting-on-one.jpg?id=29285079&width=1245&height=700&coordinates=0%2C156%2C0%2C156"/> The end of <a href="https://spectrum.ieee.org/on-beyond-moores-law-4-new-laws-of-computing" target="_self">Moore’s Law</a> is looming. Engineers and designers can do only so much to <a href="https://spectrum.ieee.org/ibm-introduces-the-worlds-first-2nm-node-chip" target="_self">miniaturize transistors</a> and <a href="https://spectrum.ieee.org/cerebras-giant-ai-chip-now-has-a-trillions-more-transistors" target="_self">pack as many of them as possible into chips</a>. So they’re turning to other approaches to chip design, incorporating technologies like AI into the process.Samsung, for instance, is <a href="https://spectrum.ieee.org/processing-in-dram-accelerates-ai" target="_self">adding AI to its memory chips</a> to enable processing in memory, thereby saving energy and speeding up machine learning. Speaking of speed, Google’s TPU V4 AI chip has <a href="https://spectrum.ieee.org/heres-how-googles-tpu-v4-ai-chip-stacked-up-in-training-tests" target="_self">doubled its processing power</a> 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 <a href="https://www.linkedin.com/in/heather-gorr-phd" rel="noopener noreferrer" target="_blank">Heather Gorr</a>, senior product manager for <a href="https://www.mathworks.com/" rel="noopener noreferrer" target="_blank">MathWorks</a>’ 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. <img alt="Portrait of a woman with blonde-red hair smiling at the camera" class="rm-shortcode rm-resized-image" data-rm-shortcode-id="1f18a02ccaf51f5c766af2ebc4af18e1" data-rm-shortcode-name="rebelmouse-image" id="2dc00" loading="lazy" src="https://spectrum.ieee.org/media-library/portrait-of-a-woman-with-blonde-red-hair-smiling-at-the-camera.jpg?id=29288554&width=980" style="max-width: 100%"/> Heather GorrMathWorksThen, thinking about the logistical modeling that you see in any industry, there is always planned downtime that you want to mitigate; but you also end up having unplanned downtime. So, looking back at that historical data of when you’ve had those moments where maybe it took a bit longer than expected to manufacture something, you can take a look at all of that data and use AI to try to identify the proximate cause or to see something that might jump out even in the processing and design phases. We think of AI oftentimes as a predictive tool, or as a robot doing something, but a lot of times you get a lot of insight from the data through AI.What are the benefits of using AI for chip design?Gorr: Historically, we’ve seen a lot of physics-based modeling, which is a very intensive process. We want to do a <a href="https://en.wikipedia.org/wiki/Model_order_reduction" rel="noopener noreferrer" target="_blank">reduced order model</a>, 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 <a href="https://www.ibm.com/cloud/learn/monte-carlo-simulation" rel="noopener noreferrer" target="_blank">Monte Carlo simulations</a> 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 <a href="https://github.com/" rel="noopener noreferrer" target="_blank">GitHub</a> or <a href="https://www.mathworks.com/matlabcentral/" rel="noopener noreferrer" target="_blank">MATLAB Central</a>, 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.

Feb 7, 2022

Atomically Thin Materials Significantly Shrink Qubits

<img src="https://spectrum.ieee.org/media-library/a-golden-square-package-holds-a-small-processor-sitting-on-top-is-a-metal-square-with-mit-etched-into-it.jpg?id=29281587&width=1245&height=700&coordinates=0%2C156%2C0%2C156"/> 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 <a href="https://spectrum.ieee.org/ibms-envisons-the-road-to-quantum-computing-like-an-apollo-mission" target="_self">reaching a 1,121-qubit processor by 2023</a>, 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 <a href="https://www.nature.com/articles/s41563-021-01187-w" rel="noopener noreferrer" target="_blank">MIT have been able to both reduce the size of the qubits</a> 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 <a href="https://equs.mit.edu/william-d-oliver/" rel="noopener noreferrer" target="_blank">William Oliver</a>, the director for the <a href="https://cqe.mit.edu/" target="_blank">Center for Quantum Engineering</a> 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). <img alt="Golden dilution refrigerator hanging vertically" class="rm-shortcode rm-resized-image" data-rm-shortcode-id="694399af8a1c345e51a695ff73909eda" data-rm-shortcode-name="rebelmouse-image" id="6c615" loading="lazy" src="https://spectrum.ieee.org/media-library/golden-dilution-refrigerator-hanging-vertically.jpg?id=29281593&width=980" style="max-width: 100%"/> Superconducting qubits are measured at temperatures as low as 20 millikelvin in a dilution refrigerator.Nathan Fiske/MITIn that environment, insulating materials that are available for the job, such as PE-CVD silicon oxide or silicon nitride, have quite a few defects that are too lossy for quantum computing applications. To get around these material shortcomings, most superconducting circuits use what are called coplanar capacitors. In these capacitors, the plates are positioned laterally to one another, rather than on top of one another.As a result, the intrinsic silicon substrate below the plates and to a smaller degree the vacuum above the plates serve as the capacitor dielectric. Intrinsic silicon is chemically pure and therefore has few defects, and the large size dilutes the electric field at the plate interfaces, all of which leads to a low-loss capacitor. The lateral size of each plate in this open-face design ends up being quite large (typically 100 by 100 micrometers) in order to achieve the required capacitance.In an effort to move away from the large lateral configuration, the MIT researchers embarked on a search for an insulator that has very few defects and is compatible with superconducting capacitor plates.“We chose to study hBN because it is the most widely used insulator in 2D material research due to its cleanliness and chemical inertness,” said colead author <a href="https://equs.mit.edu/joel-wang/" rel="noopener noreferrer" target="_blank">Joel Wang</a>, 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.

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