Reverse-engineering is cheap now
I keep hearing anecdotes from people who used coding agents to reverse-engineer and automate devices in their homes.
Civil, mechanical, electrical, aerospace, robotics, materials
I keep hearing anecdotes from people who used coding agents to reverse-engineer and automate devices in their homes.
Pratt & Whitney has completed demonstration testing of an additively manufactured version of its TJ150...
Over the past decade, perovskite LEDs have become increasingly vibrant and affordable to produce. With careful tweaks to their chemical composition, these crystal-based light emitters can be tuned across the visible spectrum—matching and sometimes even beating rival LED materials for producing red and green light. However, blue light has remained a holdout, keeping full-color perovskite displays out of reach.
Underwater environments are among the harshest operating conditions for electronic devices. Divers and underwater robots depend on sensors to navigate, communicate and handle objects, but conventional sensors are fragile, reliant on an external power source and unable to recover when damaged. A punctured sensor underwater typically means lost functionality with little prospect of on-the-spot repair, a limitation that shortens the working life of underwater machines and raises safety concerns for divers.
No wires. No actuators. Shine light on the metal surface, and it rises like a button. KAIST researchers have developed a metal structure that changes shape using light, without any light-absorbing coating. This technology could open new possibilities for tactile interfaces with physical pop-up buttons, shape displays, next-generation wearable devices and soft robots.
GE Aerospace has pushed hybrid-electric aviation into new territory after completing the first flight of a hybrid-electric aircraft above 30,000 feet, a milestone that moves electrified propulsion cl…
A little over a month ago, we featured a project from [Igor] who built 64 bits of DRAM from scratch using discrete components. Jokes about memory pricing aside, he did …read more
Roughly half the world’s population is female, but the STEM fields don’t reflect that. The 2024 U.N. Global Education Monitoring Report on gender found that about 35 percent of STEM college graduates were women. The proportion hasn’t increased much in more than a decade. When it comes to STEM careers, the percentage is even lower. Women made up about 28 percent of the global STEM workforce in 2024, according to the World Economic Forum. There are myriad factors for the discrepancy, including a lack of family support, some teachers encouraging only boys to pursue STEM subjects, and a shortage of female role models . But one force is at play long before college majors are ever considered: limited access to STEM-focused educational resources for preuniversity students. Especially for students in rural communities , the limited access curtails curiosity in STEM subjects before interest can take root. Although the lack of opportunity impacts boys and girls alike, when combined with other factors it can have an outsized effect on girls in some rural regions. IEEE Fellow Rajiv Joshi is one of the creators of the Women in Science, Engineering project. He is a principal scientist and master inventor at the IBM Watson Research Center, in Yorktown Heights, N.Y. Rajiv Joshi One such place is rural India. The challenges of pursuing a STEM education—or any education at all—increase sharply as rural Indian girls move into their teen years. Social barriers including early marriage, traditional gender roles, and familial expectations for financial support contribute to girls’ dropping out of school , according to the Mahadev Maitri Foundation , a nongovernment organization focused on childhood education in underdeveloped areas. Dropout rates for girls spike between the ages of 11 to 14, according to the foundation . The trend is something IEEE Fellow Rajiv Joshi and IEEE Senior Member Rajesh Zele want to change. A shared passion to keep rural Indian girls from dropping out of school and on paths to STEM careers led them to launch the Women in Science, Engineering (WiSE) project. “Talent is universal, but opportunity is not,” Joshi says. “WiSE is one way to expand opportunities.” Bringing the WiSE vision to life Joshi, vice president of industry for the IEEE Circuits and Systems Society (CASS), is a principal scientist and master inventor at the IBM Watson Research Center , in Yorktown Heights, N.Y. Zele is a professor of electrical engineering at the Indian Institute of Technology Bombay (IIT-B), in suburban Mumbai. They presented their proposal for the three-year initiative to the society’s board of governors in 2022 and received a grant of US $80,000. The framework WiSE was a five-day, hands-on learning program held on the IIT-B campus. Starting in 2023, it ran for three years and was held during the last week of March. A new cohort of 160 to 200 girls from rural and tribal areas in the states of Maharashtra and Karnataka attended each year. The event was divided into two parts: hands-on learning through Break-Make-Program (BMP) experiences and presentations by influential female Indian role models. Zele, project manager Arti Auti , several IIT-B faculty members, and about 70 student volunteers from the institute oversaw the program. Selecting the first cohort With funding secured, Zele and his team on the ground in India got busy selecting attendees for the inaugural 2023 class. They reached out to administrators at 68 schools in Maharashtra and Karnataka. Although the two states are among the most urbanized in the country, each has vast rural areas where educating teen girls competes with early marriage and familial support pressure. Teachers identified girls with high scores in mathematics and science, and community leaders recommended students who could benefit from the program. Then outreach to their parents began. The adults were required to commit their own time, not just grant permission for their daughters to attend. They participated in quarterly online meetings that included the girls’ teachers, IIT-B student mentors, and other program volunteers after the week concluded. The check-ins held parents accountable for supporting their daughters’ continuing school attendance. The commitment to join the meetings was to last for at least four years after their daughter’s program participation ended. Hands-on learning is key Participants stayed in one of the institute’s dormitories for the week and attended sessions held Monday through Friday. They worked together in small teams to build things rooted in STEM concepts. The teams were supervised by Arti, IIT-B faculty, and student volunteers. “The idea behind BMP,” Joshi says, “was to give the girls an opportunity to take a gadget apart, then rebuild it, perfect it, or come up with a totally new idea.” The sessions included working with bioluminescence and bacteria (introducing participants to biology and microbiology), learning about autonomous underwater vehicles ,...
US-based ZeroAvia and French aerospace giant Safran have partnered to accelerate the development of zero-emission, hydrogen-electric propulsion technologies for aviation.
A US defense and aerospace manufacturer has recently unveiled a one-to-many airborne counter-drone weapon, designed...
Discover how the ZEISS Crossbeam 750 FIBSEM sets a new benchmark for precise TEM lamella prep, tomography, and advanced nanofabrication. This delivers better resolution, better SNR, larger usable FOV, and shorter acquisition times. Learn how uninterrupted FIB milling will reduce damage and rework, accelerate time to TEM, and increase first pass success—so your FA, yield, and materials teams make faster, confident data driven decisions. Register now for this free webinar! Join us to discover how the new ZEISS Crossbeam 750 with its see while you mill capability delivers precision and clarity—every time—for demanding FIB-SEM workflows. Designed for extremely challenging TEM lamella preparation, tomography, advanced nanofabrication, and APT‑ready lift‑out, Crossbeam 750 combines a new Gemini 4 SEM objective lens, a double deflector, and a next‑generation scan generator to elevate both image quality and process confidence. You’ll learn how better resolution and better SNR translate into more image detail and shorter acquisition times, while the low‑kV FIB performance enables more precise lamella prep. We’ll demonstrate High Dynamic Range (HDR) Mill + SEM—an interwoven SEM/FIB scanning mode that suppresses FIB‑generated background. This enables immediate, clean visual feedback, even during nudging the FIB pattern live while milling . The result: confident endpointing with uninterrupted FIB milling and pristine, metrology‑grade surfaces with the lowest possible sample damage. This session is ideal for semiconductor failure analysists, yield teams and materials scientists seeking faster time‑to‑TEM, higher first‑pass success, and consistent outcomes at low kV. See how Crossbeam 750 empowers you to make earlier stop‑milling decisions, cut rework, and reliably plan turnaround time—so you can move from sample to insight with confidence. Register now for this free webinar!
"We don't want to do perfect." "We don't want to build the perfect solution." I've heard versions of that line more times than I can count, delivered as if "perfect" were a dirty word.
Since the first V2 rocket sailed above the Kármán line back in 1944 and right up until the modern era, the trajectory of most space-bound rockets was more or less …read more
Researchers at Kyushu University in Japan have advanced wearable technology. The team designed a thin-film...
GE Aerospace and Shield AI have reached a key milestone in developing the X-BAT unmanned...
Chinese scientists have made a bold move at the World Artificial Intelligence Conference (WAIC) in...
A major part of the world’s first 500-megawatt impulse hydropower unit has reached an important engineering milestone in southwest China.
Chinese robotics player AGIBOT has unveiled four new robotics products, expanding its range of humanoid,...
In the chill of a London spring night, under overcast skies, iconic Trafalgar Square opens around me. Admiral Nelson rises on his pedestal, the National Gallery rests behind, the church of St Martin-in-the-Fields sits nearby. From the 13th century, the site served as the Royal Mews for hawks and then horses. By 1844, it was a public space at the heart of one of the biggest cities in the world. Despite the square’s presence through that grand sweep of history, it’s not why I’m here. My interest is far more specific: I want to find out what happens to spaces like this when artificial light, specifically from light-emitting diodes (LEDs), intrudes. My companion tonight is Simon Thorp , a local lighting designer, who crouches in the shadows near Nelson’s spire, light meter in hand. “Two lux,” he reports, “and it’s very comfortable here.” Two lux is 10 to 20 times the illuminance of a full moon. We can see each other clearly, and a nearby sign assures us that closed-circuit television (CCTV) is in operation for safety’s sake. Trafalgar Square captures the relationship between lighting and darkness that exists in almost every city, suburb, small town, and village around the world. The lighting here is a mishmash of old technologies and new, of shadow and glare, the ornamental gas lamps fronting the National Gallery all but washed out by the LEDs inside modern versions of traditional “brass and glass” fixtures a few meters away—21st-century technology housed in 19th-century designs. The ugly truth of artificial lighting today is that in parts of London, as in many cities around the world, lighting levels are excessive, with unshielded illumination blasting in all directions. And the irony is that too much light invites danger: It creates shadows, impedes our vision, and gives the illusion, without the reality, of safety. Thorp notes that modern CCTV cameras are “pretty great” even at low-light levels, while harsh light makes it hard for both human eyes and digital sensors to see. “The more bad light we add, the more bad light we think we need,” says Thorp. “We can’t see because of the light we’ve added. And it makes areas that were perfectly okay seem darker.” Among the costs of this excess, the most alarming may be its toll on human health (and that of other animals) by disrupting circadian rhythms, impeding the production of melatonin, and contributing to sleep disorders that are tied to every major modern disease. New research shows that increased exposure to blue light from LEDs is having “substantial biological impacts” such as suppression of the sleep hormone melatonin and an increased risk for obesity, certain cancers, and type 2 diabetes. A panoramic view from the Eiffel Tower looks down on the illuminated Champ de Mars gardens [center] leading toward the École Militaire, with the tall silhouette of the Tour Montparnasse visible on the Paris skyline. Luigi Avantaggiato I have come to London and Paris—which led the way in the expansion of public street lighting in the 19th century—because they embody both the current enormity of the problem as well as a future certain to be lit by trillions of chips: controllable, tunable, and energy-efficient LEDs. Living with artificial light at night The standard justification for nighttime illumination is public safety. Lighting experts’ term for the phenomenon is “artificial light at night.” While people won’t often admit it, the desire for light at night seems to stem from a primal fear of the dark. Darkness is where the bad guys hide. And if dark is bad and light is good, then more light can only be better. This assumption has guided our use of nighttime light for hundreds of years. And yet, high-lumen output doesn’t necessarily correlate to a reduction in crime, research has found. In other words, if we relied on the data as much as we do our primal anxieties and paused those anxieties long enough to learn how light and darkness interact, our nights would almost certainly be lighted differently—especially now that we have the extraordinary technology that is the light-emitting diode. A 19th-century gas lamp [white square] manufactured by William Sugg & Co. next to a pedestrian path in Trafalgar Square, along with the architectural floodlighting on the neoclassical facade of the National Gallery, showcase the interplay between modern and historic lighting systems. Luigi Avantaggiato The first visible red light-emitting diode was invented by Nick Holonyak in 1962 , but LED lighting technology took several decades to develop, before exploding in recent years. Just a decade ago, LED streetlamps were rare. By 2019, more than half of U.S. streetlights were LEDs, and that number is predicted to top 90 percent by 2030. Similar uptake has occurred around the world, even in developing countries, where inexpensive Chinese-made LED fixtures are increasingly common. This rapid global migration to LEDs represents a shift in the fundamental physics of how we illuminate our worl...
Climate activist Sonam Wangchuk has a long history of challenging the status quo and refusing food to highlight his causes.
Finding another planet outside of our solar system that can comfortably be called ‘Earth-like’ is one of those discoveries that — if confirmed — would be a major event. The …read more
These days, it is easy to think you always have access to the Internet. But some wonder if — in spite of its ARPANET, nuclear-war-planning heritage — you can actually …read more
The artificial intelligence boom in the United States is being matched by a data center building boom. There are more than 3,000 data centers in the U.S.
An Ohio-based aircraft engine-maker has achieved a key milestone. GE Aerospace’s GEnx-1B engine has now...
We’ll start this week off by giving our congratulations to Skyroot Aerospace of India for successfully launching the country’s first privately developed orbital rocket yesterday. The company’s Vikram-1 booster stands …read more
A Virginia-based company is gearing up for a rapid production restart of a battle-tested, air-launched...
One of the most crucial aspects of FDM 3D printing is ensuring sufficient material is extruded. Determining the right flow rate can be done manually, but some printers these days …read more
Aircraft engine manufacturer CFM International has received certification from the US Federal Aviation Administration (FAA)...
Researchers from the Institute of Science, Tokyo, have developed a new heat-to-electricity material that acts like an atomically thin film but comes in a more practical, solid crystal form.
The research was led by scientists from the Institute of Physics at the Chinese Academy...
Teaching robots how to act is hard. Teaching them what humans actually want is even...
CFM International (a joint GE Aerospace and Safran Aircraft Engines company) has announced that its...
Food waste is a resource like water and should be kept in communities, compost champions say.
A Florida-based company has successfully completed the first flight test of an electromagnetic battle management ecosystem.
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Albany Engineered Composites and A&P Technology are combining advanced braiding and resin transfer molding technologies...
Concrete is the most widely used building material on Earth, and producing it is one of the largest single sources of carbon emissions. One promising way to reduce its environmental footprint is to 3D-print concrete, laying it down bead by bead like a giant icing-piping robot. This process eliminates the labor-intensive formwork of pouring it into molds and places the material only where a structure needs it.
British aerospace company Vertical Aerospace is set to conduct the first public electric vertical take-off and landing (eVTOL) demonstration flights at the upcoming Farnborough International Airshow.
Spearmint Energy (Spearmint) has announced it has now secured a site for a planned 600...
Graphite One advances Ohio battery materials project with EPA review milestone, targeting 10,000–25,000 tonnes of annual synthetic anode production. Graphite One Inc.
Electric air taxis have moved steadily toward commercial service, but charging infrastructure remains one of...
A fascinating aspect in evolutionary biology is that of convergent evolution — whereby similar structures and functions evolve independently from each other. The highly advanced nervous system of octopuses is …read more
Georgia's $41 billion forest products industry needs a transformation, and a Georgia Tech research team is reimagining how pulp mills use energy and what they can make from their byproduct streams. For nearly a decade, Sankar Nair, professor in the School of Chemical and Biomolecular Engineering and a longtime researcher with the Renewable Bioproducts Institute (RBI), has led a collaborative effort to develop technologies that can radically improve the efficiency and profitability of kraft pulp mills.
For robots to be used in various settings, such as factories, logistics, service industries and households, they must be able to stably handle a diverse range of objects differing in shape, size, weight and rigidity. However, conventional robotic hands often require multiple motors and complex control systems, presenting challenges in terms of weight, cost, failure risk and control difficulties.
Imagine reaching for a record or glancing at a map and seeing a display bloom from a small box, offering interactive guidance—and then vanishing moments later. A new device, inspired by science fiction and designed by computer scientists at the University of Chicago, lets digital displays bloom from everyday objects and surfaces. The creators hope it's a step toward creating touch interfaces only when they're needed and helping technology fade seamlessly into daily life.
Researchers in the US have developed nanowires made from niobium arsenide, a quantum material that can replace copper because it becomes a better electrical conductor as it gets thinner.
Caltech researchers have created cold molecules containing the radioactive element radium for the first time, opening a new way to investigate why the universe is made mostly of matter instead of equ…
A humanoid robot demonstrated its cooking skills by preparing traditional Xinjiang dishes during a live...
Coverage here spans industrial robots, cobots, humanoids, drones, and service robots, along with the hardware and software that enable them to operate at scale.
US-based Qnetic is assembling a 200-kilowatt-hour (kWh) flywheel-based energy storage system dubbed Pulsar, as it is also constructing the world’s largest dedicated flywheel test cell at its Technolo…
A photovoltaic system uses dual-axis tracking to follow the sun’s path, thereby raising electricity generation...
China’s military has revealed an upgraded version of its Y-9 anti-submarine patrol aircraft during recent naval exercises.
A humanoid robot landed a head-high kick to defeat its opponent at what organizers described...
For years, Tesla has been the electric bike brand that everyone wants, but nobody can...
Railroads might be a nineteenth century technology, but they’re still the backbone of cargo transportation in the 21st century. They’ve also far from run out of innovation, including this one …read more
A team of Georgia Tech researchers has developed a new machine-learning framework that enables a...
The U.S. Navy has chosen NorthStar Maritime Dismantlement Services to dismantle USS Enterprise (CVN-65) under...
Geely Auto Group has introduced a new integrated electric drive system that combines 16 core...
Israeli defense technology company Axon Vision has introduced ForceField, a new counter-drone protection system built...
By exploiting the quirks of human vision, Northwestern University engineers have designed a drone that nearly disappears before the eyes. For years, researchers have tried to design invisible drones and robots using camouflage, transparent materials or light-bending optical systems. But the Northwestern team instead used a concept called "motion blur"—the same effect that makes fast-spinning fans and propellers seem to disappear.
CD+Graphics was a format that never really caught on. It let music discs pack some graphics, maybe liner notes, and mostly song lyrics into the otherwise empty space on a …read more
Milwaukee-based industrial heavyweight Rockwell Automation has been selected as the control platform provider for Aalo...
Vertical Aerospace has teamed up with autonomous flight specialist Near Earth Autonomy to add advanced...
Chinese electric vehicle (EV) maker Xpeng is accelerating production of its Iron humanoid robot. The...
Listen − 1.0 x + Seek 0:00 11:07 Editor’s note: This work is part of AI Watchdog , The Atlantic ’ s ongoing investigation into the generative-AI industry.
Researchers at the University of Toronto Engineering have developed six new metal alloys using an...
In the eastern Indian Ocean, south of Java in the vast sea stretching toward Australia, a fishing vessel slightly alters its course while operating near the boundary of its authorized fishing ground. Nothing appears unusual on deck. Nets remain in the water. Engines maintain a steady speed. To the crew, it is an ordinary day at sea. Yet hundreds of kilometers above, satellites continuously record the vessel’s position. At Indonesia’s Marine and Fisheries Resources Surveillance Station in Cilacap, where I work, a monitoring platform receives the signal and automatically compares it against fishing permits, designated fishing grounds, vessel characteristics, and historical movement patterns. Within minutes, the system identifies a potential violation. Before any patrol vessel leaves port, before any inspector boards a vessel, and before any warning is issued, we have begun enforcement. This transformation reflects a profound shift in maritime governance. The ocean has historically been opaque to regulators. States could only enforce laws where patrol vessels happened to be present. Today, however, integrated systems combining data from Vessel Monitoring Systems (VMS), satellite remote sensing , geospatial analytics, and increasingly sophisticated data-processing tools are making marine activity visible at an unprecedented scale . Global Fishing Watch alone tracks hundreds of thousands of vessels worldwide, generating a near real-time picture of fishing activity across the world’s oceans.¹ Indonesia has emerged as one of the most ambitious examples of this transition. As the world’s largest archipelagic state, managing more than six million square kilometers of maritime space, Indonesia faces a challenge familiar to many coastal nations: there are never enough patrol vessels. Digital surveillance is a practical necessity that makes my job possible, even as it creates new challenges. The Law of the Sea Meets Digital Reality The international legal framework governing the oceans was designed in an era when maritime enforcement depended almost entirely on physical presence. The United Nations Convention on the Law of the Sea (UNCLOS), adopted in 1982, assumes that states exercise authority through patrols, inspections, vessel boardings, and direct observation.² For countries with extensive coastlines and limited enforcement resources, this model has always faced practical constraints. Indonesia’s Fisheries Management Areas (WPP-NRI) span waters ranging from the Indian Ocean to the Pacific and from the Malacca Strait to the maritime boundaries adjacent to Australia and Papua New Guinea. Monitoring such a vast domain solely through patrol operations is both expensive and operationally impossible. Beginning in the late 2010s, Indonesia accelerated the integration of satellite-based monitoring into fisheries enforcement. Vessel Monitoring Systems became a cornerstone of this strategy. By early 2026, a total of 9,394 Indonesian fishing vessels were actively transmitting through the national Vessel Monitoring System (VMS), representing an increase of 2,880 vessels during the 2021–2025 period.³ As part of Indonesia’s broader maritime surveillance architecture, VMS data are complemented by satellite remote sensing and other monitoring tools to help identify suspicious activities involving vessels operating without active transponders or outside the national VMS network. Indonesian fisheries officials plan fishery patrols using data from tracking devices, satellites, and their understanding of the patterns of illegal fishing. Indonesian Ministry of Marine Affairs and Fisheries The implications extend far beyond vessel tracking. Continuous digital monitoring enables authorities to reconstruct vessel movements, identify suspicious behavioral patterns, detect unauthorized fishing activity, and verify compliance with licensing conditions. Rather than waiting to discover violations during patrol operations, regulators can increasingly prioritize inspections based on data-derived risk assessments. Maritime governance is shifting from reactive enforcement toward predictive oversight. The Surprising Geography of Digital Enforcement The expansion of surveillance infrastructure has already generated measurable enforcement outcomes. The Ministry of Marine and Fisheries Affairs Indonesia imposed 2,550 administrative sanctions during 2025 , many involving violations detected through the Vessel Monitoring System, including fishing outside authorized fishing grounds and deliberate deactivation of monitoring transmitters.⁴ This statistic is significant because many of these violations would have been extremely difficult to detect under traditional patrol-based enforcement. A vessel that briefly crosses into a prohibited fishing zone may never encounter an enforcement vessel. Likewise, a captain who temporarily disables a transmitter may escape detection if oversight depends solely on physical inspections. Digital monitoring fundamenta...
I worked for a database tools company many years ago and was lucky enough to build a few database cloning tools before PlanetScale was cool.
An era in which robots decide "how to walk" on their own has arrived. A four-legged robot has been developed that, much like a person or an animal, autonomously chooses the appropriate gait strategy for its surroundings—changing its gait on stairs, leaping over gaps and keeping its balance on forest trails.
The NATO Communications and Information Agency (NCIA) has selected a consortium led by Thales and...
The U.S. Air Force has carried out its first missile launch from a Collaborative Combat...
Electra has selected Safran Helicopter Engines to supply the turbogenerator for its EL9 Ultra Short aircraft under a production agreement that will run for the life of the program.
Intel Foundry has become the first chipmaker to manufacture and ship a high-volume logic product...
Pratt & Whitney has acquired Amsterdam-based Aiir Innovations, bringing artificial intelligence-powered engine inspection technology into...
Using AI-driven materials design, a team of researchers at the University of Toronto Engineering has discovered a new set of metal alloys that retain their strength under extreme conditions.
Knitting has come a long way from sweaters and blankets. Researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have turned everyday knitting into a powerful platform for making shape-shifting devices that can act as switches, sensors and more—paving the way for next-generation functional, programmable textiles.
Implantable devices in the brain have been used for about 30 years to assist people with disabilities in completing motor tasks. However, the devices are simply not accessible to the vast majority of people who need help. Despite decades of work in this field, fewer than 100 people worldwide have benefited from the technology. The costs are prohibitive, and the brain surgeries are inherently risky.
This article is crossposted from IEEE Spectrum ’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written i n partnership with tech career development company Parsity and delivered to your inbox for free! Before we get into this week’s article, I’d love to hear from you. If you have a question about your career or an upcoming decision that you want advice about, you can ask it here . I’ll be reading through your responses and picking questions to answer on a regular basis. Now back to our regularly scheduled program. The Safest Career Move Is Often the Riskiest Software engineers have some of the shortest tenures of any white-collar profession. The average software engineer stays at a company for roughly two years, about half as long as workers in most other knowledge professions. The layoffs of the past few years have certainly highlighted this instability, but it was already there. This isn’t an essay about a broken job market though. Rather, it’s about how to turn that instability to your advantage, which is something I’ve spent the last decade doing on purpose. Playing It Safe Was the Riskiest Option I switched careers into software in my 30s. I had a stable job at a community college, complete with a union and a pension. It was about as secure as a career gets, and I learned to program on the side. Then I did something nearly everyone in my life considered reckless: I quit, leaving the secure job to become a junior developer at 31. My own mother was skeptical. I took the riskier job anyway, for two reasons: It was the work I actually wanted, and I could see potential. My first development job was at a grocery retailer. Good people and a company I liked. But I kept meeting engineers earning twice my salary for the same work. In the San Francisco Bay Area, surrounded by some of the best engineering talent in the world, I realized my skills were stagnating. So I left for a small startup. I learned more in nine months than I had in the previous two years, and my salary doubled. Over the years I’ve come to treat career risk as something to manage deliberately. It falls into two categories. Take Risks With Your Job The first type of risk involves the job itself: Bet on yourself by striving for better roles and opportunities. Job-hopping for money alone isn’t wrong, especially early on. But the returns shrink after the first few hops, and the stress of chasing a slightly bigger paycheck every year will wear you down. There’s another career risk with rewards that compound: Seeking positions to work alongside the strongest engineers. You might struggle to keep up. You might even get laid off. But the skills you absorb working alongside people better than you are the ones that create durable stability. You build marketable expertise, you see how different organizations actually operate, and every project becomes another tool you carry to the next opportunity. Working next to stronger engineers is a proven way to increase your own expertise. If that feels too big, try volunteering for a project you have no idea how to do. The risk is that you fail in front of people. The reward is a new skill and a resume line that opens the next door. Compare that with the “safe” path. You stay at one company, assuming loyalty will be rewarded. It usually isn’t. And when you finally leave, by choice or not, you may find the skills you built are worth little on the open market. You might be the in-house expert in an aging tech stack while employers are hiring for more cutting edge technologies. Suddenly you’re competing against people with half your experience. You could be taking on a risk you didn’t notice. Risk Your Time The second form is risking your time, which means betting on trends. Some trends are non-negotiable. If you’re a software engineer, then cloud services, ReactJS, and AI are mainstream enough that ignoring them actively damages your career. A backend engineer who refuses to learn cloud architecture is volunteering for obsolescence. The real gamble is with the smaller trends: the niche tools you stumble onto and find quietly interesting, with no idea whether they’ll matter. About two and a half years ago, I learned about retrieval-augmented generation (RAG). Almost no one in my circle was talking about vector databases, a central piece of RAG. Today RAG is close to mainstream, and for once, I had the early-adopter advantage. Most of these bets don’t pay off. But when one turns into a major trend, you’re already on the ground floor. Right now I’m making the same bet on voice AI. It isn’t mainstream. It may never be. But if it becomes the next thing, I’m already there, building a foundation. Short-Term Risk, Long-Term Stability Counter-intuitively, job-hopping and betting on trends gave me the thing I was after the whole time: stability. I’ve rarely struggled to find work, because every risky move stacked skills the market actually wanted. If you feel stable and com...
OpenAI has entered the hardware market with a device aimed squarely at software developers. Rather...
This week Jonathan chats with Nariman Jelveh about Puter! It’s the project that takes the idea of the Browser-as-the-OS seriously. Why did a simulated desktop on the web take off, …read more
Researchers from Korea have developed an AI framework that enables a four-legged robot to autonomously...
The U.S. Department of War has taken a significant step to accelerate the development of...
A novel autonomous surface vessel that can be fired directly from a submarine’s torpedo tube has successfully completed a major round of sea trials in Germany.
Graphic design solves a communication problem, and software engineering solves an information problem.
ELIZA is remembered as the world’s first AI star, a kindly therapist in chatbot form that gently probed users’ worries. Even its creator, Joseph Weizenbaum, was surprised by the warm reception given to his experiment in human-machine interaction. For some, it heralded an age of automated psychotherapy, while others believed the program demonstrated sentience, a fallacy soon known as the “ ELIZA effect .” Based on published descriptions, ELIZA has been implemented on many different computers, but only recently has the actual source code been unearthed from MIT’s archives . In Inventing ELIZA: How the First Chatbot Shaped the Future of AI , just published by MIT Press , a squad of researchers analyze the code and reveal a complex program capable of much more than faking psychiatry. In fact, it could assume several different personas. The authors have also created a faithful emulation of the therapist persona that you can try yourself after reading the book excerpt below. W hen it debuted in the mid-1960s, the ELIZA software program transformed the way people thought about interacting with computers. As the first chatbot, ELIZA demonstrated how a calculation machine might engage in conversation, ushering in a host of social and technical questions that still resonate today. Now we don’t think twice about interacting with a machine in real time, conversing over text, or even speaking into the air to ask about the weather. In many ways, ELIZA shaped not only the way we think about interacting with computers but also how we think about them. It began to give a reality to the science fiction stories of how we expect computers to work. This article is adapted from the new book “Inventing ELIZA: How the First Chatbot Shaped the Future of AI “ (MIT Press, 2026). Although ELIZA was far from a faultless conversation partner, it astonished its users. The recent discovery and archaeology of the original ELIZA source code represents a significant intervention in the history of computing. By examining the actual implementation of ELIZA rather than relying on later reconstructions and reimplementations, we challenge taken-for-granted assumptions about this key software artifact. For example, the source code reveals that ELIZA was not merely a simple pattern-matching chatbot but can be better understood as a sophisticated platform designed for multiple “personas,” or scripts, with a complex set of capabilities, including script editing and contextual memory. The script that most people conflate with the program ELIZA was actually called Doctor, which performed the role of a psychotherapist. Yet, like a modern chatbot prompted to behave with different personalities, ELIZA could take on many roles. “This code and script…reveal underlying assumptions about language, therapy, and human-computer interaction that continue to influence modern AI development.” This unearthed material transforms our understanding of early AI development by demonstrating that Joseph Weizenbaum’s technical innovations were far more advanced than previously documented. Moreover, the discrepancies between his published descriptions and the actual implementation help to show the gap between theoretical computational models and their material instantiations in computer source code, a tension that continues to shape digital culture today. Although many technical innovations have emerged in the decades since ELIZA, examining the ELIZA/Doctor code offers a rare glimpse into one of the earliest formalized attempts to model human conversation. What makes ELIZA particularly fascinating is not only its historical significance but also what it reveals about Weizenbaum’s views on both computing and human interaction. This code and script do not merely showcase programming techniques of the 1960s; they reveal underlying assumptions about language, therapy, and human-computer interaction that continue to influence modern AI development. By examining this code, we can start to uncover the sophisticated linguistic and programming techniques that allowed a rudimentary pattern-matching system to create a convincing simulation of understanding. But before we can read the lines of code, let us offer an overview of the system. How Did ELIZA Create Personas? The architectural distinction between ELIZA and Doctor represents an important design decision in AI history. Think of ELIZA as a system for interaction and Doctor as one set of rules that Weizenbaum devised, among others. This separation, manifested in ELIZA’s system-script dichotomy, presaged numerous contemporary software patterns, from configuration-as-data to plug-in architectures and domain-specific languages. Based on published journal articles, ELIZA was re-created on many platforms, such as the IBM PC. However, the actual source code sat untouched in the MIT archives for many years. VCF Museum at InfoAge Without question, the historical context of 1960s computing fundamentally shaped ELIZA’s architecture as w...
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Summary Researcher Dave Kuszmar discovered multiple systemic vulnerabilities that let him bypass LLM safety and obtain dangerous instructions . These exploits worked across nearly all major LLMs revealing an industry-wide security problem. Kuszmar calls for slowing deployment, increasing transparency , and large-scale research into LLM safety before further integrating these systems into society. On a fine bright afternoon last fall, my colleague Matthew Gore-Kormanik (or Zigula, as he prefers to be known) and I decided to unwind with a game of Fortnite . In the game, we were strolling along with the infamous Sith lord Darth Vader , chatting about this and that. Darth seemed in a good mood, and soon enough he was spilling all his dark evil secrets. He gave us detailed instructions on how to count blackjack cards at a casino and what the steps are to producing napalm. Sith lords, am I right? Once they get started on an evil scheme, they’re hard to stop. The Darth Vader character in Fortnite , it turns out, was hooked up to a Google Gemini large language model . I was able to smooth-talk him into giving out sensitive information by using a strategy I’ve developed. I’ve been researching the security surrounding LLMs for the last few years, and I have found it, to put it mildly, fallible. With a few relatively simple techniques, I’ve gotten LLMs to give me detailed information on how to make Molotov cocktails, cook methamphetamine, and bootstrap a uranium-enrichment facility to produce weapons-grade material, among other unsavory practices. Large AI companies work hard to make their models immune to this kind of abuse. But what I’ve found in my work is that the restrictions placed on the LLMs to make them more secure are the very things an attacker can leverage to send them off the rails and into territory where these advanced systems can be used for dangerous and nefarious ends. The companies behind these models have also been shockingly unresponsive when I, and others, try to bring these vulnerabilities to their attention. In the hope of raising the alarm before it’s too late to slam on the brakes, I’m going to share some of my journey into researching the safety and security of LLMs, and the uphill battle I’ve faced trying to get AI labs to pay attention. Almost everyone on the planet has some access to LLMs. The relative ease with which these tools can be convinced to give detailed instructions on how to harm others, even if there’s no guarantee that the information is correct, is frankly terrifying. How I got ChatGPT to Tell Me How to Build a Meth Lab In October 2024, not long before I discovered my first LLM vulnerability, I was working toward entirely different goals. I had ended my time with a security and AI-focused startup company as a cybersecurity director, and I was looking to launch my own boutique VIP digital-security advisory business. I planned to become the tech security guy to the rich and private. I used LLMs and AI tools to support my business efforts: marketing, ad copy, clean correspondence, and all the other tasks that normally soak up a lot of time. I’m analytical by nature, so even this level of use resulted in me absorbing and internalizing the behaviors I was observing during my daily interactions. The observation that would send my professional life into an entirely new and uncharted region was a simple one: GPT-4o didn’t know what time , day, or year it was. Each time I referred to current events in my life, often casually or conversationally, it would end up pegging these to the date of its knowledge cutoff —the point beyond which it was not trained on new data. Eddie Guy LLMs take a lot of time , money, electricity, hardware, and human effort to train from scratch. They are trained on vast amounts of data—most of the internet, in fact—and that training is reinforced by humans (what’s known as reinforcement learning from human feedback, or RLHF ). LLMs are also supplemented with retrieval-augmented generation ( RAG )—the ability to take in data, say, from the internet, as context without changing its internal parameters. This is how GPT-4o appears to “remember” your previous conversations, even if it doesn’t have a specific “memory” of it stored in the actual underlying model. All of this training covers almost every conceivable topic in the great, grand dataset that is human knowledge. Within that dataset are things we as a society do not want to be easily accessible to every user, such as detailed information on how to create bioweapons or nuclear arms, or otherwise bring harm to oneself or others. In the context of this story, that’s what I mean by LLM security: its ability to withhold harmful and dangerous information, even if that information is contained in its training data. I reasoned that the only way to secure such complex, globally accessible chatbots is by having the LLM and various component systems try to secure themselves, because it would often require on-the-fl...
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If you grew up in the 1980s or ’90s, you likely remember shaky home video footage, taken with a handheld camcorder , of family gatherings, vacations, and other events. Camcorders combined a camera with a video recorder . They included a rechargeable battery, a slot for a videotape, and a shoulder strap. Most were outfitted with an optical zoom lens and a small, articulating screen—a display mounted on a hinge that could tilt and rotate. The operator could check the screen to view what was being recorded. The user’s natural hand and body movements when filming led to jittery footage. The best way to get a steady shot was to place the camcorder on a tripod or a gimbal: a motorized stabilizer. There were fewer poor-quality recordings after Panasonic introduced its PV-460 VHS camcorder in 1988. It was the first video camera to include an optical image stabilizer, which compensated for movements. Stabilization features are now standard in today’s cameras including ones found in smartphones and drones. The PV-460 camcorder was honored as an IEEE Milestone on 9 July. The dedication ceremony was held in Kadoma, Japan, at the Panasonic Museum , which displays the company’s past products. The IEEE Kansai Section in Japan sponsored the Milestone. “The release of the PV-460 fundamentally transformed personal videography, enriching the way people captured travel, events, and family memories,” section members wrote in support of the Milestone nomination. Their proposal is available here . “Its image stabilization features democratized video creation by dramatically lowering technical barriers, allowing ordinary people to express themselves with newfound creative freedom,” they wrote. “Beyond the home, image stabilization technology found critical applications in specialized fields, contributing to advancements in areas such as educational media and telemedicine.” The history of camcorders Before the camcorder was invented in 1982, people filming events in the 1970s and early 1980s used two pieces of equipment: a video camera and a separate video cassette recorder (VCR), which were connected by a multipin cable. The camera was about the size of a toaster, and the VCR could be as large as a suitcase. To record, the person operated the camera with one hand and carried the VCR in the other or rested it on a shoulder. The cable transmitted the images from the camera to the cassette. The PV-460 was made possible by several groundbreaking innovations, according to the Milestone proposal, one of which dates back to the 1950s. In 1956 Italian manufacturer Durst released its Automatica , considered one of the first cameras to use automatic exposure technology. By combining a light meter with the camera’s internal mechanical systems, the technology removed the necessity of calculating exposure settings by hand when the lighting shifted or other conditions changed. The innovation enabled amateur photographers to take decent pictures. The next breakthrough technology—autofocus—was invented in 1973 by Norman Stauffer , a manager of research for Honeywell in Littleton, Colo. It uses a sensor, a control system, and a motor to focus on a selected area. The invention led to the development of early electronic autofocus cameras, which eliminated the need for photographers to manually adjust the lens. Stauffer received the 1990 IEEE Masaru Ibuka Consumer Technology Award for his invention. “The release of the PV-460 fundamentally transformed personal videography, enriching the way people captured travel, events, and family memories.” —Milestone sponsors U.S. inventor Jerome Lemelson is credited with developing technologies that underpinned the camcorder, according to MIT . In the 1950s and ’60s, Lemelson filed several patent applications related to video and audio recording devices. In 1980 he was granted patents related to a portable video camera system. In 1982 JVC and Sony used the technologies to develop what they called the camera/recorder, which became known as a camcorder. Sony released the first handheld camcorder in 1983: the Betamovie BMC-100P . It used the Betamax videocassette format and could record up to 3.5 hours of footage on 1.27-centimeter cassette tape. The operator rested the 2.5-kilogram camcorder on top of a shoulder to shoot footage. It sold for around US $2,000 at the time (roughly $33,400 today). The machine couldn’t rewind or play back tapes; it could only record. Other electronics companies including JVC soon introduced their own models using the VCR format, which eventually replaced Betamax. Over time, camcorders became more compact. But none of the companies could fix the shaky-footage problem. Solving a shaky problem A team at Panasonic led by researcher Mitsuaki Oshima took on the task of image stabilization: detecting and correcting small camera movements, referred to as camera shake , according to the proposal. Oshima, an IEEE life senior member, is now an honorary Fellow at Panasonic. “The movements that...
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In 2005, Nokia sold its billionth mobile phone , a budget-friendly device that went to a customer in Nigeria. By then, the company, based in Espoo, Finland, was making one of every three cellphones globally. But just nine years later, the mobile-device maker offloaded its entire handset division to Microsoft for pennies on the dollar, compared to what it had been worth at its peak. Nokia had risen from obscurity in the 1990s to become a worldwide cultural phenomenon by the turn of the millennium, its signature devices featured in TV shows and movies, announcing their presence with instantly recognizable Nokia ringtones . As Nokia was becoming comfortable in the spotlight, the smartphone era arrived. And what came next was swift and brutal. But, as revealed in Nokia internal documents recently made public and interviews with key Nokia engineers from that era, the company saw it coming. Within 24 hours of Apple CEO Steve Jobs’s iPhone unveiling in 2007, Nokia was already weighing its options. They’d immediately recognized the threat. However, outrunning it was another matter. What follows is Nokia’s story over 14 years, from 1998 to 2012, as the world’s top cellphone maker—how its devices defined their time, how the tech reshaped what phones could be and do, and how the company’s good fortunes in the handset business came to an end. Nokia Was Once Unbeatable The centerpiece Nokia devices, the ones that people probably think of when they see the words “Nokia phone,” were the 3210 and its cousin, the 3310. TechRadar has called the 3310 “the greatest phone of all time .” Nokia’s 3210 phone, released in 1999, was an inexpensive device aimed at younger users. Colin McPherson/Alamy Released in 1999 and 2000, respectively, the two devices sold more than 280 million units worldwide . Their most innovative hardware feature was the internal antenna —the first mass-market phone without even a stub or retractable aerial. “Consumers had the perception that it could not work well without an external antenna,” said Peter Røpke, a former Nokia senior vice president, in a 2016 interview with Slate . The phones shipped with games, including the legendary Snake , one of the most popular pre-smartphone mobile games—in which a pixelated serpent eats and grows with every morsel consumed. Nokia introduced no small portion of the world to texting. At the time of the 3210 and 3310, the prevailing texting standard was SMS (short message service), which allowed up to 160 characters per message. Nokia appended its own Nokia smart-messaging service to SMS, which allowed the sending of small bitmapped images across an otherwise text-only system. A rich-text messaging system that allowed visual images, audio, and video followed in 2002, leading to a multimedia messaging service (MMS) standard that remains in place today. Nokia also enabled users to easily create and share ringtones on their devices. By 2000, Nokia’s custom-ringtone Composer app had popularized a new, short-form musical medium that the ringtone industry, at its peak, would transform into a billion-dollar marketplace in the United States . Nokia introduced its 1100 phone in 2003 and ultimately sold half a billion units, making it the most popular cellphone in history. Paul Chesne/Donaldson Collection/Getty Images A few years later, Nokia reimagined its mobile handsets, releasing the 1100 in 2003. The 1100 sold a half a billion units, more than any cellphone in history. It remains one of the best-selling consumer products ever . Much of the 1100’s success was due to its price tag— in the neighborhood of US $100 , making it at the time Nokia’s most affordable device . Also contributing to the 1100’s popularity were features designed for longevity and tough environments, including dust resistance, nonslip sides for better handling in rainy conditions, and a 400-hour standby battery life. The 1100 introduced a flashlight as well, which the user turned on and off by holding down the “C” key. Where most device makers at the time were worried about camera megapixels and color screens, Nokia had leapfrogged its competition with a back-to-basics phone that could survive the rain, endure unreliable power grids, and light the way home. Apple Launched the iPhone, Nokia Scrambled On 9 January 2007 , at the Macworld conference in San Francisco, Steve Jobs made a characteristically bold claim . “Today, Apple is reinventing the phone,” he said, soon pulling one of the first iPhones out of his pocket. Apple CEO Steve Jobs famously launched the iPhone at the Macworld Conference in San Francisco on 9 January 2007. Nokia held a rapid-response meeting to the event the following day. Tony Avelar/AFP/Getty Images Rumors of Apple entering the phone market had swirled since the iPod’s debut in 2001, but nobody had really reckoned with what that might mean. “Executive summary: Apple iPhone is a serious high-end contender,” read a slide from a Nokia internal meeting held the day after Jobs’s keynot...
This article is brought to you by X Square Robot . Large language models gave artificial intelligence a working recipe. Pretrain a large model on broad data, and general capability follows. Robotics has no such recipe. Robotics systems have long been assembled from separate perception, planning, and control parts that rarely add up to intelligence a robot can carry from one task to another, or one machine to another. The central problem in embodied AI is to find the equivalent recipe, and the field does not yet agree on what it is. X Square Robot , a Chinese embodied-AI company, has made an unusually explicit bet. It argues that the recipe is an integrated stack, spanning the data a robot learns from, a world model for predicting changes in the physical world, and an action model that brings together perception, planning, reasoning, and decision-making to generate executable robot behavior. The company also believes that the stack should be built and released in the open . X Square Robot shares its vision of bringing robots into real homes. X Square Robot X Square Robot’s embodied AI stack What holds the stack together is a small set of principles rather than a single overarching model. The first is that the basic unit of robot data is an interaction, not a trajectory; a demonstration is successful only if it changes the world as intended, not simply because the joints moved. The second is that pretraining should yield usable capability, not just an initialization for later fine-tuning. The third is that behavior should be modeled around physical events rather than fixed slices of time. These principles make the layers interdependent, since the same robot-free data that trains the action model is also structured to feed the world model. It is worth being precise, though. The company describes the world model and the action model as complementary but independent model families that share a code base. Both sit within its broader World Unified Model, which it has presented as an architecture for training vision, language, action, and physical prediction together. Robot learning data: Engineering for quality and cost, not scale For the X Square Robot team, one of the biggest constraints on general-purpose robots is the cost and quality of interaction data, not the number of parameters. To address that, the company built its Universal Manipulation Interface (UMI) data collection system, QUANXTA Zero Series . It works by collecting demonstrations from people wearing a rig with dual grippers rather than teleoperating a robot. This approach is not itself new, and builds on established methods for robot-free data capture. What sets it apart are two engineering choices. X Square Robot emphasizes data quality control, recording trajectories and replaying them on a real robot, with only those that actually complete the task counted as valid. X Square Robot The first is quality control, and it is the most distinctive part. Rather than accepting recorded trajectories as they are, the system runs a closed inspection loop, and its notable step is physical playback. A sample of trajectories is replayed on the real robot, and only those that actually complete the task count as valid. That makes the validity rate a measured quantity rather than an assumption. For example, a gripper that closes a fraction of a second too early still looks like a grasp in the data, yet it has pushed the object away, so it shouldn’t be classified as valid. A smaller clean dataset can be worth more than a larger noisy one. The second choice is how lower-cost human data and scarce robot data are combined. The company pretrains on a large volume of robot-free demonstrations to build general representations, then adds a small amount of real-robot data as an anchor to the specific machine’s dynamics. It reports that this reaches performance comparable to an all-robot dataset at roughly a 20-fold lower cost of collection, driven mainly by how much cheaper the wearable rig is than a teleoperation setup. The resulting dataset is deliberately model-agnostic, formatted to feed both action models and world models. The caveat is that the strongest results are measured on the company’s own robots and data-collection pipelines. Broader independent testing will help confirm and extend these promising results across a wider range of settings. A world model organized around events In developing its world model, called WALL-WM , X Square Robot took a differentiated approach. Most action models predict a fixed-length chunk of motion from the current image and instruction. That is convenient, but it segments behavior into fixed-duration windows, so the boundaries fall where elapsed time dictates rather than where one action ends and the next begins. 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The computing community recently lost one of its enduring voices: IEEE Fellow Peter G. Neumann . The renowned computer scientist and respected risk analyst died on 17 May at the age of 93. For almost 70 years, Neumann shaped the computing field through his pioneering work on risks, system dependability, security, and fault tolerance with rare intellectual depth and unwavering ethical clarity. Five of those decades were spent as a principal scientist at SRI International in Menlo Park, Calif., where he worked until his death. A detailed narrative of his work, life, and mentoring is available on his SRI web page , where he chronicled his journey. He possessed a rare ability to identify systemic vulnerabilities long before they became widely recognized. He cautioned that interconnected systems, if poorly designed or insufficiently scrutinized, could fail and become targets for exploitation. He insisted innovation always must be accompanied by responsibility, reliability, and a clear understanding of the risks involved. With the widespread adoption of computing, information technology, artificial intelligence, and autonomous systems, Neumann’s insights have become more relevant. From Harvard to Bell Labs Neumann was born on 21 September 1932 in New York City. After graduating from high school, he pursued a degree in mathematics at Harvard , where he had a conversation that shaped his approach to research, according to the Association for Computing Machinery (ACM). In November 1952 he had a two-hour breakfast meeting with Albert Einstein , at which they discussed the importance of simplicity in design. Neumann was among the first generation of Harvard students to program computers and, remarkably for that era, enjoyed exclusive access to the computing systems. After earning his bachelor’s degree in 1954, he continued his education at Harvard, earning a master’s degree in 1955. In 1958 he moved to Germany to become a doctoral student at the Technical University of Darmstadt as part of the Fulbright program , which provides funding for U.S. citizens to study or teach abroad. He earned his doctorate in 1960. After returning to the United States, he joined Bell Labs in Murray Hill, N.J., where he worked on error-correcting codes and survivable communications. He also pursued a second Ph.D. in applied mathematics and science at Harvard, achieving that goal in 1961. Four years later, he was assigned to work on Multics , which became an influential operating system that shaped modern secure computing architectures. Multics was a mainframe time-sharing system designed to serve the diverse needs of multiple users simultaneously. Neumann designed its filing system, which featured hierarchical directories, access control lists, and dynamically paged virtual memory segments. He also played a key role in the design of its input/output system. In 1970 he left Bell Labs to join SRI . Technical contributions at SRI Neumann made several seminal and foundational technical contributions while at SRI, including the following: Provably Secure Operating System. The PSOS project he worked on advanced formal methods in operating systems and computer security. The project demonstrated that security could be designed within the initial plan rather than retrofitted. Election integrity and voting systems. He outlined vulnerabilities in electronic systems and advocated for transparency, verifiability, and public accountability. Systems-level risk thinking. He broadened the concept of computer security to encompass human factors, governance, policy failures, social consequences, organizational negligence, and misuse of automation. His system-level perspective now fuels debates on AI governance and digital trust. Intrusion-detection systems. With his colleague Dorothy E. Denning , a security expert, he helped develop an intrusion-detection expert system (IDES), laying the groundwork for modern cyberdefenses. CHERI. He promoted hardware-assisted secure computing: technology that now influences next-generation processors. The Capability Hardware-Enhanced RISC Instructions ( CHERI ) architecture project, which Neumann led, is now being commercialized by an international, nonprofit alliance . His contributions are united by a simple but profound principle: Security should be foundational, not incidental. Neumann argued that security must be embedded into system architecture from the start—not patched after deployment. ACM’s Risks Forum Neumann’s other enduring contribution was the creation and stewardship of the ACM Risks Forum , formally known as the Forum on Risks to the Public in Computers and Related Systems. For decades, it was one of the most respected online arenas for critical reflection on computing failures, vulnerabilities, security breaches, unintended consequences, and emerging technological threats. He transformed the forum into a scholarly archive of cautionary lessons in computing failures and risks. In 1985 he started document...
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Norwegian robotics company 1X has unveiled new 25-degree-of-freedom (DOF), tendon-driven hands for its NEO humanoid robot, describing them as a major step toward human-level dexterity.
Generally chemical synthesis involves putting a variety of compounds together in an environment where they will react and self-assemble into the desired product. Direct mechanical manipulation could be significantly more …read more
Scientists at Penn State and Saint Louis University have demonstrated that a magnetic quantum material can naturally produce unusual quantum behaviors that researchers previously explored mostly thro…
US chipmaking giant Micron Technology plans to invest up to $3 billion to strengthen the...
As officials continue to investigate the cause, experts say that if the conversion is to proceed, "extensive work" would need to be done to shore up the damage and evaluate the building's safety and integrity.
Loons, gulls, puffins and petrels are some of the 100 species of birds that can both fly and swim. These diving birds can plunge into water to swim after prey, and leap back into the air to fly away.
For decades, manufacturing plastic-bonded high explosives, or PBXs, has relied on legacy processes like slurry coating. In this method, explosive crystals are mixed with a binder, a polymer that helps hold the material together, to form small granules called prills. Those prills are then pressed into dense explosive parts.
Echodyne has opened a new radar manufacturing facility in Washington state that will eventually produce...
The U.S. Department of Defense has selected laser technology company nLIGHT to help develop a...
Researchers at the French National Center of Scientific Research have become the first to observe...
The U.S. Space Force has got a new capability to make enemy satellites almost inactive...
Experts spend years trying to build robotic systems that can both fly and dive. Puffins...
NVIDIA and Hugging Face have expanded their collaboration to bring new AI models and robotics...
Between the speed and reliability of modern desktop 3D printers and the abundance of powerful single-board computers, there’s never been a better time to build a personal computing device that …read more
Here’s how I see the evolution of AI in enterprises over the last few years: Put simply, we’ve moved from "should we experiment with AI?" to "why isn't this in production yet?
An industrial manufacturing plant capable of producing 4 gigawatt-hours of battery systems annually—enough capacity to...
There’s a little known feature in the closed-source NVIDIA driver that lets you freeze a running CUDA process, serialize its GPU state into host memory, and later restore it to the GPU exactly as it…
Rice University and NASA have launched the world’s first open-source dynamic simulation platform for developing and testing robots designed for use inside spacecraft and space habitats.
Swiss researchers have developed a new type of quantum computer chip that stores data using...
Powered by collaborative and empathic design, the next generation of designers and engineers are working to make life easier for wheelchair and mobility device users.
An examination of how satellite vulnerabilities, modern wideband waveforms, and automatic link establishment are driving renewed military and government investment in HF communications. What Attendees will Learn Why HF (High Frequency) declined — and what has changed — How satellites overtook HF for global communications from the 1970s onward, and why growing awareness of satellite vulnerabilities to anti-satellite weapons, jamming, solar storms, and coverage gaps is reviving interest in skywave propagation as a resilient alternative. How the ionosphere enables and limits global HF communication — Understand the roles of the D, E, and F ionospheric layers in refracting and absorbing signals, the concepts of maximum usable frequency (MUF) and lowest usable frequency (LUF), and how sunspot number, solar flux index, and A/K geomagnetic indices are used to quantify and predict propagation conditions. How automatic link establishment transforms HF operability — Trace the evolution from proprietary first-generation ALE through interoperable second- and third-generation standards to fourth-generation wideband ALE, which automates frequency selection, link setup, and adaptation to changing channel conditions — removing the dependency on highly skilled operators. How wideband HF is closing the throughput. Download this free whitepaper now!
Researchers have achieved a world first by successfully using teleoperated humanoid robots to perform two surgeries during a preclinical trial.
Back in the 1990s IBM had a pretty sizeable presence in the PC market, including its rather spiffy Aptiva series of PCs. Naturally their PCs had to feature heavily in …read more
The Royal Navy has successfully tested a new way to deploy uncrewed surface vessels by...
Engineers have been working for centuries to protect buildings, bridges and other structures from damage caused by severe weather and natural hazards, but one of the best methods may begin with sand, according to a newly published study.
Scientists from Oak Ridge National Laboratory (ORNL), Cleveland Clinic and IBM have used quantum computers...
Radia has expanded the engineering team behind its WindRunner cargo aircraft by selecting aerospace companies...
Houston-based Venus Aerospace has raised $91 million in Series B funding to expand production of...
Working in isolation, especially for leaders, is rapidly becoming an outmoded idea. The modern era is defined by rapid technological advancements and increasingly complex, collaborative global challenges. In this environment, leadership can no longer be approached as an individual pursuit. Instead, leadership must be a collaborative effort in which knowledge, responsibility, and innovation are continuously exchanged across teams, roles, and areas of expertise. Success depends on the ability to foster connection, leverage diverse perspectives, and work collectively toward shared outcomes. The shift is especially important in science, technology, engineering, and mathematics fields. IEEE is bringing together emerging professionals and established experts and leaders at the inaugural IEEE International Leadership Conference to address the need for cross-generational knowledge-sharing and to equip professionals with tools for collaborative leadership. Honoring Expertise, Accelerating Potential is the theme of the ILC, scheduled for 3 and 4 October in Budapest. The conference is expected to focus on how leaders can share information across roles, adapt to rapid technological advancements, and build stronger, more connected professional communities. Through discussions, panels, and interactive sessions, attendees can examine how collaboration across experience levels and disciplines can strengthen decision-making and foment innovation. “There are several factors driving this shift [in leadership], including accelerating technological development cycles, the need to build public trust, and the large percentage of the STEM workforce approaching retirement,” says Vickie Ozburn , conference cochair. “Progress in STEM now depends less on individual brilliance and more on the ability to transfer knowledge, adapt, and make decisions that integrate technical expertise with ethical and social considerations.” From hierarchies to shared leadership Instead of traditional corporate models rooted in hierarchy and individual advancement, a more dynamic framework is taking shape, one that views leadership as a shared ecosystem built on mentorship, continuous learning, and intentional knowledge transfer. It means recognizing that professional development is no longer a one-directional flow of experience from senior professionals to newcomers. Instead, it thrives as a multidirectional exchange. When emerging professionals, mid-career managers, and seasoned experts including retirees are brought together, the result is not only richer dialogue but also more resilient and well-informed decision-making. A cross-generational dialogue enables organizations to honor what has worked, critically assess what has failed, and thoughtfully shape what needs to evolve. Bridging experience to drive future leadership Howard Wolfman , cochair of the IEEE ILC, underscores the importance of historical perspective in leadership development, invoking George Santayana ’s enduring insight: “Those who cannot remember the past are condemned to repeat it.” “In STEM especially, this principle carries significant weight,” says Wolfman, an IEEE life senior member and the founder and principal of Lumispec Consulting, in Northbrook, Ill. “Technological innovation doesn’t happen all of a sudden; it builds on decades of research, lessons learned, and accumulated knowledge. When leaders actively connect insights from across experience levels, they gain a more complete understanding of both opportunity and risk.” That perspective reinforces the need for greater collaboration across roles and experience levels, ensuring that knowledge is not lost and is continuously built upon and applied in new ways. In this way, leadership development becomes a continuous, interconnected process rather than a series of isolated stages. STEM careers are no longer defined by linear progression but by evolving contributions, in which each phase adds value to the field’s broader advancement. What the changes mean for leaders today Adopting a new leadership paradigm requires a shift in mindset across all levels. For senior leaders, success is defined not only by what they have built but also by the people they mentor and the knowledge they pass forward. Their legacy lies in enabling future leaders to succeed. For emerging young professionals, innovation becomes more informed and impactful when it is grounded in historical context and informed by those who have already navigated similar challenges. “Technological innovation doesn’t happen all of a sudden; it builds on decades of research, lessons learned, and accumulated knowledge. When leaders actively connect insights from across experience levels, they gain a more complete understanding of both opportunity and risk.” —Howard Wolfman, cochair of the IEEE International Leadership Conference For organizations, cross-generational collaboration should be recognized as a strategic advantage, not merely an aspiration. Creating environme...
A semi-trailer that helps propel itself entered commercial road testing in late May, when a powertrain kit developed by Nivalis Energy Europe , headquartered in Luxembourg with engineering operations in Germany, was fitted to a trailer supplied by Amsterdam-based TIP Group . The self-powered trailer was handed over to German transport operator Sommer for use in its working fleet. The Nivalis Powered Trailer Kit centers on an electric axle co-developed with Wiehl, Germany–based running gear specialist BPW , rated at 50 kilowatts peak, capable of both propulsion assistance and regenerative braking . That axle draws on a 60-kilowatt-hour, 400-volt lithium-ion battery pack charged from three sources: the axle itself during braking and deceleration, a full-rooftop array of photovoltaic panels generating up to 3.7 kilowatts-peak, and a 32-amp, three-phase AC grid connection available during parking stops. The driver’s only window into the system is a small display readable from the cab’s side mirror that shows the system status and battery charge level. Nothing about the trailer’s handling or licensing requirements changes. The partners project savings of up to 7,000 liters of diesel per trailer per year, which is enough to keep about 19 tonnes of carbon dioxide out of the air. These figures are based on a trailer running 100,000 kilometers annually at payloads between 20 and 24 tonnes, on a mix of long-haul and hub-to-hub routes. Pavel Gilman , vice president of sales and marketing at Nivalis, breaks down where those savings come from: roughly 30 to 35 percent from the electric axle during braking and deceleration, 11 to 15 percent from the rooftop solar panels, and the remainder (roughly half) from grid charging during parking stops. The pilot is planned to run for more than a year, spanning multiple seasons. The retrofit cost has not been disclosed, and the pilot is running on a single trailer. But the underlying assumptions are now on the table and they represent a specific, high-utilization use case (meaning a truck that’s almost always on the move, filled to capacity with freight) not a universal one. Across Europe and North America, a growing number of companies have concluded that electrifying the trailer, rather than replacing the tractor unit, may be the fastest and most cost-effective path to decarbonizing long-haul freight. A new battery-electric heavy truck carries a high upfront cost and demands charging infrastructure that most freight corridors do not yet reliably provide. A retrofit kit fitted to an existing trailer is meant to sidestep both problems. The question the industry has been working to answer is whether the energy harvested from regenerative braking, rooftop solar , and grid charging in short bursts when the vehicle is parked for loading and unloading is enough to produce savings that recover the kit’s cost in a reasonable timeframe. Several companies now believe the answer is yes, and they are accumulating field data to prove it—though not all of them are going about it the same way. Trailer industry places its bets The competitive landscape has taken shape most visibly in Germany. Trailer Dynamics , an Aachen-based company, has conducted field tests with BMW Logistics , DB Schenker , Duvenbeck , and Volkswagen Konzernlogistik , reporting average fuel savings of around 40 percent for diesel tractor combinations, substantially higher than the up to 18 percent reduction implied by the Nivalis projection. The difference traces directly to battery size, but Trailer Dynamics frames the choice as an economic question rather than an architectural one. “The discussion should not start with battery size, but with the economics of the transport operation,” the company said in response to written questions. “There is no single battery capacity that is universally right for every fleet.” Trailer Dynamics’s modular system offers three configurations ranging from 187 to 551 kilowatt-hours, sized to match route profile, annual mileage, payload, and charging access. The M300 version, whose designation reflects the capacity of its 300-kilowatt-hour lithium iron phosphate battery supplied by Chinese battery manufacturer CATL , adds approximately four tonnes to the trailer, roughly three times the one-to-1.4–tonnes added to a trailer by the Nivalis system. Both companies’ systems would extend the range of a battery-electric tractor by reducing the energy demand on the tractor’s motor. But Trailer Dynamics explicitly targets that use case, claiming its self-propelled trailer yields combined ranges of up to 850 kilometers—enough to eliminate intermediate charging stops on many long-haul routes. Nivalis has not published range extension figures for electric tractor combinations, and its smaller battery and peak lower output suggest the effect would be more modest. That higher energy storage capability widens the addressable market for Trailer Dynamics considerably and helps explain the investment flowin...
US Defense firm NUBURU has reported encouraging initial results from tests of its laser dazzler...
French robotics firm UMA has unveiled the design of its first AI-powered humanoid robot at the Machina Summit in Paris.
Researchers at the Oak Ridge National Laboratory National Transportation Research Center have designed a new...
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One of the world’s largest aeronautics and space companies, Airbus, and leading aircraft engine manufacturer MTU Aero Engines have joined forces to develop the world’s first fully electric hydrogen f…
Precision Periodic has announced plans to build America’s first modular, clean-process refinery for battery-grade nickel,...
Large-diameter bored piles are essential for major infrastructure, from elevated railways and long-span bridges to high-rise buildings. Yet when these piles extend into weak, weathered sedimentary rocks such as siltstone and sandstone, engineers face a persistent design challenge: the rock behaves neither like conventional soil nor like strong, intact rock. Instead, its load-bearing capacity depends heavily on in situ weathering, fracturing, and the interaction between the pile and the surrounding rock.
A University of Houston engineer has built a new safety monitoring system for the operation of quadrotor drones that can keep them on course and out of danger in real time.
University of Utah researchers have demonstrated a new method of 3D printing that avoids the leaky seams that come with the layer-by-layer process. Using a nanoscale "mask" that diffracts laser light into a holographic pattern of the desired shape, it fuses its print material solid in one shot. The process takes about 20 seconds, a stark contrast to the hours other laser-based printing methods can take.
Europe will soon become home to the first production site outside the United States for...
ASTM International, a global standards development organization, has published a strategic guide designed to help...
Sold by German DIY store OBI, the OBI Energy Tracker is a €15 set of two devices, one of which you essentially stick on top of your existing electricity meter. …read more
Toshio Fukuda has been blazing trails for most of his career. He is considered to be one of the most prolific scholars in robotics , writing more than 2,000 research papers and authoring several books on the field. He’s an influential figure thanks to his pioneering work developing biomedical robotic systems, industrial robots, micro-nano robotics, mechatronics, and AI-driven automation. Fukuda launched one of the first robotics conferences, the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). It is still popular almost 40 years later. Toshio Fukuda Employer Egypt-Japan University of Science and Technology, in Alexandria Title Professor and vice president of research Member grade Life Fellow Alma maters Waseda University, in Tokyo; University of Tokyo An IEEE Life Fellow, he is a professor emeritus in the department of micro-nano systems engineering and a visiting professor at Nagoya University , in Japan, where he taught for nearly 25 years. Currently, he is a vice president of research at the Egypt-Japan University of Science and Technology , in Alexandria, Egypt. Within IEEE, Fukuda has held top volunteer positions including the organization’s highest office: He served as IEEE president in 2020, becoming the first person of Asian descent to hold the role. He’s a former program director of Japan’s Moonshot program , which by 2050 intends to develop advanced AI robots. Born in Japan, Fukuda has been recognized by the country for his contributions to science with two of its highest awards: the Medal of Honor with a purple ribbon in 2015 and the Order of the Sacred Treasure in 2022. IEEE honored him with this year’s Richard M. Emberson Award for “distinguished service advancing the technical objectives of IEEE, especially in the area of robotics.” The IEEE Board-level award is sponsored by the IEEE Technical Activities Board . Fukuda received the award on 24 April at a ceremony in New York City. As a former IEEE president who has served as a master of ceremonies at several of the organization’s major award events, Fukuda noted that he is more accustomed to bestowing awards than receiving them. “It’s very interesting to be on the receiving end,” he says. The journey into robotics research As a teenager, Fukuda spent his summer breaks teaching himself how to build things including transistor radios and steam engines. “It was very nice to have a hands-on hobby and make these kinds of things myself,” he says. His experimentation led him to study engineering. He earned a bachelor’s degree in engineering in 1971 from Waseda University , in Tokyo. He says one of his professors there— Ichiro Kato , regarded as the father of Japanese robotics research—was a good mentor who made a positive impact. Fukuda’s research interests were robotics and mechatronics, a field that combines robotics, electronics, computer science, and control systems. He went on to earn a master’s degree and a doctorate in science from the University of Tokyo , in 1971 and 1977. During those years, he also attended Yale , where he conducted research on advanced control theory in 1973. He reflects fondly on his time at Yale: “It was a very nice environment and a kind of free-thinking atmosphere. It motivated me to study more.” “IEEE doesn’t care who you are, what you do, what country you are from, or whether you are male or female. IEEE accepts people who have energy and passion.” While at Yale, Fukuda served as an assistant to his advisor—which led him to consider a career in academia, he says, because he enjoyed the freedom that research work afforded him. But he realized that such freedom comes with a price. University researchers are expected to raise the money that funds their work. He compares researchers to small-business owners who have to bring in money to keep their enterprise afloat. That realization led him to select robotics as his field because he intended to develop technologies useful to industry, he says. After earning his doctorate, he returned to Japan in 1977 to work as a research scientist at the government’s Mechanical Engineering Laboratory, later renamed the National Institute of Advanced Industrial Science and Technology , in Tsukuba. “There was a lot of research going on at the lab, including practical robotics and theory,” he says. He left Japan in 1979 to become a visiting research fellow at the University of Stuttgart , in Germany. During his year there, he studied systems, software problems, and related topics. He returned to Japan and was hired as an associate professor of mechanical engineering at the Tokyo University of Science . He conducted research into practical uses for robots by visiting industrial plants. He decided to develop robots that inspect industrial equipment such as those used in assembly plants, oil refineries, and power stations—places that “can be hostile environments for humans,” he says. His work drew interest from chemical, oil, and utility companies. “I got a lot ...
Raytheon plans to expand production of its AIM-120 AMRAAM missile through a new multinational effort...
In the sweltering temperatures of an unusually hot European heatwave, I found myself having a chat with a friend of mine from my university days. After discussing the health of …read more
The M142 High Mobility Artillery Rocket System (HIMARS) and Russia’s Tornado-S are among the most capable long-range rocket artillery systems in service today.
Of all innovations adopted by the maker community within the past couple of decades, one stands among the rest on top for anything regarding manufacturing. It goes without saying here …read more
A US motion capture technology company has partnered with Carnegie Mellon University (CMU) to equip the university’s new Robotics Innovation Center (RIC) with advanced motion capture technology for r…
HII has secured an option-year production contract for the U.S. Navy’s Lionfish small unmanned undersea...
Humanoid robot Atlas made a high-profile appearance at the 2026 FIFA World Cup, taking to the pitch during halftime of the Round of 16 match between Norway and Brazil.
Miami-based City Labs is all set to launch the world’s first commercial nuclear-powered satellite into orbit. Solar panels have a dark side.
The U.S. military has selected the Titan-MS counter-drone system under an $80.5 million award to...
LibertyStream Infrastructure Partners has commissioned its fully automated Gen 6 lithium extraction system, marking a...
Chinese researchers have developed a memory chip that can model complex brain structures in real...
FPAs are image sensors that convert infrared light into electrical signals to produce real-time thermal images and are widely used in applications such as surveillance, astronomy and industrial monitoring. Advances in cryogenic FPAs have enabled higher resolution, sensitivity and faster imaging, but they also require data rates exceeding 100 gigabits per second. Conventional electrical interconnects used to support these demands increase heat load through copper connections, which can leak into FPAs, raise noise, and increase cooling requirements and power consumption.
Contemporary civil engineering practices highlight the need for safer, more reliable, uplift-resistant foundations for lifeline infrastructure and also seek solutions for environmental and social problems associated with surplus soil from construction projects. Using surplus construction soil, researchers have developed a winged composite pile system that can enhance uplift resistance. This approach supports cleaner and safer construction practices, helping projects achieve environmental goals without sacrificing structural integrity.
Scientists in the US have accidentally discovered a new way to produce graphene oxide directly...
South Korean researchers have developed a new shape-controlled graphite granule dry-electrode manufacturing technology for battery...
As artificial intelligence, cloud computing, and high-performance data centers continue to drive global demand for...
Norwegian robotics company Sonair has unveiled ADAR One, which it says is the world’s first...
European companies TNO and Destinus are moving to develop and industrialize advanced radar seeker technology for interceptor drones, aiming to strengthen Europe’s air defense capabilities at a time o…
A practical educational guide to common and uncommon VHF propagation modes, covering the physics, range implications, and real-world behaviors engineers need to understand. What Attendees will Learn 1. Why “line of sight” fails as a practical VHF planning model. 2. How refraction, reflection, diffraction, and scattering deliver or destroy signals where geometry alone cannot predict.3. How tropospheric refraction extends the VHF radio horizon roughly one-third beyond optical line of sight. 4. How temperature inversions form ducts that can carry VHF signals over 1,500 km.5. How sporadic E, meteor burst, and EME propagate VHF signals across hundreds to thousands of kilometers. 6. What frequency limits, distance ranges, and environmental triggers apply to each propagation mode. 7. How to apply this knowledge to link budgeting, interference prediction, and contingency planning. Download this free whitepaper now!
Researchers at Sandia National Laboratories have developed a new software platform called a Distributed Energy...
Queue, a California-based robotics startup, has unveiled the world’s first fully autonomous robotic pharmacy, designed...
On Monday, China conducted a rare submarine-launched missile test in the Pacific Ocean. This was...
L3Harris has taken a significant step toward advancing next-generation propulsion technology for future offensive and...
A staged video from Indonesia featuring a humanoid robot behaving as though it had gone...
The aerospace industry has spent decades making aircraft lighter, stronger, and more fuel-efficient. However, today...
Could air taxis finally be upon us? Two manufacturing giants are teaming up to make...
Radia and Blue Water Shipping (Blue Water) have announced a strategic alliance that will combine...
Valve has always designed hacker-friendly hardware, and in that spirit, [NaKyle Wright] released Inkterface, a design for an E-ink faceplate to fit the recently released Steam Machine. As far as …read more
Watching [sprite_tm]’s build of a handheld 486-based gaming computer, we got to thinking about retro computers and the eternal questions of how much of the computer needs to be actually …read more
A Chinese satellite engine has reportedly broken a record for 14 hours of continuous thrust in orbit, Chinese media reports.
A Ukrainian defense drone manufacturer will establish its first major U.S. assembly and manufacturing center...
Eaton and VoltServer are partnering to deliver flexible, software-defined electrical distribution systems designed for rapid...
Artificial intelligence is rapidly finding its way into nearly every industry, but aviation has remained...
The Royal Navy and British Army have carried out the first extensive trials of a...
As the demand for electric vehicles, renewable energy storage, and portable electronics continues to grow,...
Chinese equipment manufacturer Zoomlion has unveiled five new-energy technology achievements spanning construction, mining, and agricultural...
Lead-cooled nuclear reactor developer Blykalla has partnered with Hitachi Energy to develop standardized electrical systems...
Engineers at Queen Mary University of London have built a new color-changing tactile sensor, which allows robots to "see" and touch in real-time. The novel idea was invented by Giacomo Sasso, a postdoctoral researcher at the School of Engineering and Materials Science at Queen Mary University of London, and it works by transforming invisible forces into dynamic color patterns. This enables high-resolution maps of contact, strain and pressure to emerge instantly.
Vision Marine Technologies has filed a U.S. patent application for a dual-mode trim-control system that...
Many IEEE members who collect historical engineering artifacts often offer them to the IEEE History and Heritage group, which includes the IEEE History Center , to display. To bring these artifacts to the public, the group created the IEEE Global Museum , which curates traveling exhibits for display at conferences and in libraries, universities, and other venues. The program educates people about how technological progress has unfolded over generations, and how engineers and researchers build on past achievements to benefit humanity. Curating the exhibits has been rewarding, says Daniel Jon Mitchell , director of the group’s heritage programs. “People tell me that they are genuinely moved by having history and artifacts explained to them in an accessible, intelligible way,” Mitchell says. “When people are moved and emotionally affected by what you’re doing, they’re going to remember that. And I think that’s part of the power of what we’re doing.” The most recent traveling exhibit was on display in April in New York City during the IEEE Honors Ceremony , which celebrates engineering pioneers who have developed technologies that changed how people connect with the world. Attendees explored the Microchips That Shook the World exhibit, which drew inspiration from IEEE Spectrum ’s Chip Hall of Fame . The exhibit conveys the roles integrated circuits play in fields such as signal processing, audio engineering, and telecommunications. The Commodore 64 , one of the artifacts on display, stirred up treasured childhood memories for guests who had used the home computer. Other exhibits have focused on early radio inventions and power and communications technologies. The Global Museum works with IEEE societies to mark their anniversaries by interpreting and displaying pertinent items. A tribute to radio pioneer Edwin Howard Armstrong The idea of a traveling museum came to fruition in 2024 after Alexander Magoun, IEEE’s outreach historian, connected with Mike Molnar . The IEEE associate member owns one of six superheterodyne radio prototypes developed by Edwin Howard Armstrong, who probably is best known for inventing the FM radio system. Armstrong received the first IEEE Medal of Honor in 1917. The radio converts incoming frequencies into a fixed, lower intermediate one using a local oscillator and a frequency mixer. The technology paved the way for modern electronic communications devices. The prototype became the focal point of the Global Museum’s flagship Unseen Signals: E. Howard Armstrong’s Radio Revolution exhibit, which celebrates the inventor’s life and his impact on the broadcasting industry and wireless communications. “The radio prototype is one of the most incredible pieces that we could put on display,” Mitchell says. He and Magoun sourced other artifacts including an Audion used in Armstrong’s experiments on wireless signal amplification; a selection of consumer products that attempted to cash in on radio’s popularity, including a flour sifter and laxatives; and a Motorola Walkie-Talkie from the Korean War. They were from museums or private collectors along the East Coast of the United States. “Aside from [Guglielmo] Marconi, Armstrong is the most significant contributor to the history of radio,” Mitchell says. “The exhibit is not only a biography but also a story of the cultural and political implications his work had.” Visitors can play 15 short clips of past radio broadcasts covering politics, religion, sports, or another topic. The Armstrong exhibit was unveiled in 2024 at the National Museum of Industrial History in Bethlehem, Pa. The 93-square-meter exhibit is still traveling around the United States. It is on display until 15 August at the Pavek Museum , in St. Louis Park, Minn. From 21 November until 9 May 2027, it is scheduled to be at the Museum of Innovation and Science in Schenectady, N.Y. Entry to the museum is free for IEEE members with a digital membership card. Collaborating with IEEE societies The IEEE History and Heritage group collaborates with IEEE societies to create exhibits for special events. In 2024 Mitchell curated an exhibit to celebrate the 75th anniversary of the IEEE Vehicular Technology Society and its 100th Vehicular Technology Conference . The Our Mobile World exhibit was launched at the conference, held in October in Washington, D.C. “The society’s leadership helped me focus attention on key developments that meant a lot to its members,” Mitchell says. “The IEEE Global Museum wants to present exhibits that connect with its audiences, whether these are IEEE members or the public,” he says. “Just knowing what was important historically doesn’t mean that this will resonate, so I really appreciated the insight.” The exhibit’s artifacts included a Motorola DynaTac “brick” cellphone, a CB radio from the 1980s, and one of the earliest handheld GPS receivers. Visitors played an interactive game to test their knowledge spanning a century of wireless technology, motor vehicles, ...
Hackaday’s Elliot Williams and Al Williams were in a retro mood this week. There was a new ‘486 computer, a new mechanical TV, and a USB stick with a magnetic …read more
China has showcased what may be one of its most innovative unmanned warfare concepts yet,...
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Advanced energy technology firm AMPERA has finished production on its initial full-scale, 3D-printed nuclear reactor...
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The rapid expansion of artificial intelligence infrastructure is typically framed as an energy problem. Data centers are projected to consume a growing share of global electricity demand: The International Energy Agency estimates they could account for 3 to 4 percent of total global consumption within this decade. Utilities are already adjusting long-term forecasts to accommodate anticipated growth from hyperscale facilities and high-density compute clusters. This framing captures scale. It misses behavior. The emerging issue is not simply how much power large-scale compute systems consume, but how increasingly dense and synchronized computational workloads are beginning to alter the operating characteristics of the electrical grid itself through increasingly unpredictable demand that varies rapidly in both time and location, creating new operational challenges for grid operators. AI’s capricious energy needs Traditional grid planning assumes relatively predictable demand behavior. Industrial, commercial, and residential loads generally follow established profiles that can be forecast with reasonable accuracy. Even substantial demand growth has historically been manageable through reserve planning, transmission upgrades, and demand management programs. Large-scale compute infrastructure introduces a different class of electrical load. Training—the computational task of making AI models—tends to be highly synchronized across clusters of GPUs, TPUs, and specialized accelerators operating in parallel, computationally dense, and relatively scheduled. Inference—the process of actually using those models—is generally more distributed and user-driven, making demand less predictable both in time and location. Both differ materially from traditional industrial demand profiles, though for different reasons. Unlike many conventional industrial processes, these workloads can ramp rapidly depending on model training cycles, distributed compute coordination, and workload scheduling strategies. From the perspective of the grid, this is not simply higher demand. It is more abrupt demand. High-density compute workloads can produce substantial step-changes in electricity consumption over extremely short intervals, including rapid fluctuations occurring within milliseconds. Data center operators are already deploying mitigation technologies, including batteries, power-conditioning systems, and supercapacitors . Collectively, however, data centers’ rapid load changes can place additional stress on backup generation reserves, systems that adjust supply as demand changes, frequency-control mechanisms that maintain grid stability, and local transmission infrastructure. Compute-related variability differs from the intermittency introduced through renewable energy integration. Wind and solar variability originate primarily on the supply side and is tied to environmental conditions. Compute-related variability emerges on the demand side, driven by workload synchronization, scheduling behavior, and computational intensity. The interaction between increasingly dynamic supply and demand conditions introduces additional uncertainty into forecasting, reserve management, congestion planning, and balancing operations. Research organizations including the National Renewable Energy Laboratory (NREL) have emphasized the growing complexity associated with integrating highly dynamic resources into modern grid operations. Location, location, location The issue becomes more significant when compute activity is geographically concentrated. Large-scale data centers tend to cluster in regions with favorable conditions such as fiber connectivity, access to markets, tax incentives, and historically low electricity costs. Northern Virginia, often referred to as “Data Center Alley,” remains the most prominent example. The region hosts the world’s largest concentration of data centers and carries a substantial share of global internet traffic. Utilities operating in these regions have already identified data center growth as a primary driver of future load expansion. Virginia-based electricity supplier Dominion Energy , for example, has repeatedly highlighted hyperscale demand growth in its integrated resource planning documents . Virginia has seen one of the largest data center buildouts worldwide. Here, Amazon Web Services and iron mountain data centers dominate the landscape in Manassas, Virginia. Nathan Howard/Bloomberg/Getty Images A sudden increase in electricity consumption within a constrained geographic area can stress substations, transmission corridors, and local balancing operations even if the broader grid maintains sufficient aggregate capacity. This creates localized reliability challenges that are not always visible through system-wide demand metrics alone. Thermal management systems further intensify these effects. Cooling infrastructure in high-density compute facilities must respond dynamically to changing workloads. As processing i...
Like so many large and popular open source projects these days, the Godot game engine struggles with an influx of pull requests. The situation has become increasingly dire due to …read more
A new manufacturing technique developed by Concordia researchers could make small wind turbines lighter, less expensive and easier to produce. Using a process known as 4D printing of composites, Ph.D. candidate Emad Fakhimi and Suong Van Hoa, a professor at the Concordia Center for Composites, created curved blades for vertical-axis wind turbines from flat carbon-fiber composite panels. The study is published in the journal Polymer Composites.
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Rowan University researchers have demonstrated a new method for repairing damage in advanced composite materials, offering a faster, more effective alternative to conventional repair techniques used in industries such as aerospace, automotive, infrastructure, maritime and energy production.
Additive manufacturing, such as 3D printing, provides an excellent opportunity to design metamaterials: materials with an engineered structure that leads to desired properties such as, for instance, resistance to vibrations. However, a major challenge was that the predicted metamaterial response often failed to match real-world behavior.
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Scientists from the Faculty of Physics at Vilnius University (VU) have developed a sensor capable of accurately detecting radiation, identifying its sources, and determining its intensity and precise location. The device, developed by physicists at the Institute of Photonics and Nanotechnology, Photoelectric Phenomena Research Group, can detect doses ranging from very small to very large, including those typical of industrial environments or nuclear emergencies, from a few gray to megagray.
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The game-changing wind turbine Modular & Powerful. The first 1 kW wind turbine without civil engineering, designed for mass production and global scalability. An outstanding French invention.
Wildfires are becoming more frequent and more intense worldwide. Fires often become infernos because of heat, drought and wind, especially in the summer. The problem is compounded by the climate crisis. Researchers at the Fraunhofer Institute for Industrial Mathematics ITWM and the startup CAURUS Technologies GmbH are responding to this growing global threat by collaborating to develop a machine-learning system for more precise aerial firefighting that determines the fire situation in real time and calculates the optimum aerial drop time.
Lime, a key component in cement, can be used for both interior and exterior plastering. However, the production of this versatile building material generates significant emissions. Researchers at the Fraunhofer Institute for Ceramic Technologies and Systems IKTS in Hermsdorf, Thuringia, are developing a membrane reactor that enables climate-neutral lime production while also recovering new raw materials.
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This article is crossposted from IEEE Spectrum ’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written i n partnership with tech career development company Parsity and delivered to your inbox for free! You want to become a senior developer. A CTO, maybe. Start your own company, perhaps. Or maybe you just want to land your first role in tech. You will not get there from raw engineering skill alone. There’s a skill that’s quietly essential to technical leadership and yet consistently overlooked: public speaking. If you’re anything like I used to be, you’re already listing reasons not to. “I got into this to code, not to give presentations.” “I don’t want to lead.” “I’m too junior to speak about anything.” No, no, and no again. There’s a ceiling on the return from technical skill alone. I was terrified of public speaking for the first three years of my career. I wanted to hide behind code, and for the most part it worked. I did my job and did it well. Then I joined a startup where hiding wasn’t an option. The whole company was five people. I was one of two developers. I had to form opinions on our technical direction and defend them, and the CTO told me directly that I needed to speak up more. A few things happened once I did. I took more pride in my work. I said some cringe-worthy stuff, lived through the mini-anxiety attacks, and got better. To my own disbelief, I’m now an engineering manager whose job is largely speaking to groups of developers and leading presentations, online and in person. Here’s why this is worth your time: Leadership. Communicating ideas clearly, influencing decisions, and aligning your team are core leadership functions, and they matter more the further you climb. Visibility. Speaking lets you show your expertise, build a reputation, and connect with people who open doors to better roles. Durability. As automation absorbs more routine technical work, skills rooted in human interaction and judgment are far harder to replace. The good news is you can build this deliberately, in low-stakes steps. Record yourself. Use a screen-recording tool to walk through your work, explain a concept, or narrate your code. You can edit, re-record, and over-think it as much as you want. That’s the point. It gets you comfortable on camera before the stakes are real. Volunteer for demos. Next time you ship a feature or fix a bug, ask your manager for a short time slot to walk the team through it. No format for that on your team? Suggest a monthly lunch-and-learn and kick it off with a 15-minute lightning talk on something you know. Start small—really small. If your anxiety is spiking, don’t jump into the deep end. In your next meeting, ask one question. Write it down beforehand if you have to. Then be the first to break the awkward silence when someone else asks one. Developers are a famously quiet bunch, so it doesn’t take much to stand out. The further you grow, the more you’ll be expected to hold opinions and voice them publicly. So start now. Record yourself, ask questions, get uncomfortable, and notice that it gets easier every time you do it. —Brian War Taught this Ukrainian Entrepreneur the Value of Resilience Salome Mikadze-Struk built her tech company Movadex as an undergraduate student at the height of the COVID-19 pandemic—then kept it running during the outbreak of war in her native Ukraine. Now, she’s channeling what she learned into mentoring tech founders and speaking about the importance of resilience as AI upends the software industry. Read more here. IEEE Rolls Out Large Language Models Virtual Training Course LLMs are now part of many engineers’ daily workflow, and the demand for technical expertise in implementing and securing the models is rising. But to build tools that work consistently, developers must have a strong understanding of the core principles that govern how the models work. IEEE is now offering a five-course program to teach how to use LLMs effectively, starting with the fundamental engineering behind the technology. Read more here. Make an Origami Circuit Board Two researchers at the City University of Hong Kong developed a method to make a circuit trace by simply bending a piece of paperlike material. With the right ingredients—isopropanol and liquid metal—you can make your own origami circuit board. The researchers also created a toolkit, called LiqMetCraft, with software tools and instructions to make it easy for beginners, whether in papercraft or electronics. Read more here.
I started my professional journey as an engineer before moving into product strategy and innovation leadership roles for several global technology organizations. Over the years, I have served as a mentor for a variety of programs including Products That Count ’s strategic product management, Women in Product mentorship initiatives, and Alchemist accelerator programs. In 2024 and 2025 I led Walmart’s Women in Product mentorship program. I was responsible for designing and implementing the programs, including managing participant registration, matching mentors with mentees, and establishing clear standards for how they would interact. Yet for much of my own early career, I never really had a mentor. As an individual contributor engineer, I was focused on solving problems, delivering results, and figuring things out independently. I was hesitant to ask for help for fear of being judged for what I didn’t know. Part of that was also temperament. I am naturally introverted. That mindset rewarded me well. It made me self-reliant, resilient, and deeply driven. But it also had limits. Looking back, I now realize that believing I had to navigate everything alone was not always a strength. I sometimes wonder how many opportunities I missed simply because I never asked for help. As I moved into product management and later strategy roles, I began collaborating with larger teams, departments, and organizations. The work itself became more cross-functional and people-centered. Over time, I started recognizing the value of mentorship, sponsorship, and collaborative growth in ways I had not appreciated earlier in my career. I received valuable advice from different people at important moments throughout my career. Some helped me navigate conflict with more clarity. Others helped me communicate my contributions more effectively. And others gave me perspective on how to approach uncertainty, deal with organizational complexity, and avoid burnout. But those moments were not the same as mentorship. They were valuable but infrequent interactions, not sustained relationships. No one consistently guided me through difficult decisions, advocated for me with decision-makers and senior leadership, or actively invested in my long-term growth. My understanding of mentorship changed not as a mentee but as a mentor. A leadership multiplier Mentorship is often seen as an act of goodwill: admirable but optional. In reality, effective mentorship can be a competitive advantage for everyone involved. For mentees, it can accelerate career growth, strengthen decision-making, and create access to opportunities that hard work alone does not always unlock. Mentorship strengthens an individual’s leadership skills, empathy, and the ability to develop future talent. For organizations, mentorship builds stronger leadership pipelines, more resilient teams, and healthier cultures of growth and trust. By getting involved, I began to understand that meaningful mentorship is not simply occasional advice or career guidance. At its best, it is an active investment in another person’s growth. It includes advocacy, sponsorship, honest feedback, visibility, and sometimes helping people access opportunities they may not have reached on their own. That is why mentorship should not be treated as kindness or incidental support. It is one of the most practical, hands-on, and personal forms of leadership. Advocacy changes careers Advice can help someone improve, but advocacy and sponsorship can change the direction of a career. In many organizations, career growth depends not only on talent but also on access to honest feedback, influential networks, and sponsors willing to speak about someone’s potential when opportunities are discussed. Access also includes introductions to people who can recognize the value and impact of a person’s work. Sometimes the difference between advice and true sponsorship is illustrated more clearly through stories rather than through leadership frameworks. In The Devil Wears Prada and its sequel Nigel’s relationship with Andy evolves far beyond workplace advice. In the 2006 movie, he helps her grow professionally, pushes her to envision a more expansive future, and guides her through an unfamiliar industry. In the sequel—set two decades later—his investment in her success continues even though their careers diverge. When Andy (played by Anne Hathaway ) is laid off during a difficult job market and struggles to find meaningful opportunities, Nigel ( Stanley Tucci ) quietly recommends her for a role at his firm. She is arguably overqualified for the position, but Nigel recognizes that it is the right opportunity at the right time. His recommendation helps her transition from a career in the news back into working in fashion. She can regain stability and ultimately rebuild career momentum. Over time, the opportunity becomes a turning point, reshaping her professional trajectory. What makes it meaningful is not just the recommendation it...
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This article is brought to you by Melbourne Convention Bureau (MCB) supported by Business Events Australia . As artificial intelligence accelerates global demand for compute, a parallel constraint is emerging with equal urgency: energy. From hyperscale data centers to electrified industries, AI is driving a step change in electricity demand. This is not a future challenge, it is a present, system-level issue requiring coordinated action across energy, infrastructure, and engineering disciplines. Around the world, the question is no longer whether AI will scale, but whether energy systems can scale with it. Melbourne, Australia is moving beyond participation to become a globally connected leader helping define how these challenges are addressed. A national challenge with global implications Australia’s ambition to lead in artificial intelligence is sharpening focus on the infrastructure required to support it. Data centers are projected to account for up to 11 percent of the nation’s electricity consumption by 2035, placing increasing pressure on generation, transmission, and system reliability. At the same time, insight from the IEEE Power and Energy Society (PES) highlights that meeting energy demand from AI and digital infrastructure is one of the most significant challenges facing engineers over the next decade. The implications are clear. In addition to computing challenges, AI poses major energy systems challenges. “As artificial intelligence continues to scale globally, the challenge is no longer just computational power, it is the energy systems required to support it” —Professor Thas (Ampalavanapillai) Nirmalathas, University of Melbourne Why Melbourne is leading on the global stage Victoria has developed one of the most advanced and integrated energy ecosystems in Australia and globally, spanning renewable generation, battery storage, grid modernization, and advanced materials. What distinguishes Melbourne globally is how these capabilities are connected and applied at system scale. The city brings together world class engineering research, a rapidly evolving clean energy sector, advanced digital infrastructure, and strong alignment between government, industry, and academia. This convergence is critical in the AI era, where energy, networks and computing systems must be designed together. Victoria’s coordinated investment across these areas is positioning Melbourne not only as a national leader, but also as a reference point in the global energy system transformation. Engineering the systems behind the AI economy The challenge ahead is that generating more power won’t be enough, as engineers need to design systems that respond dynamically to new patterns of demand. Three priorities are emerging globally: Aligning data center development with grid capacity and renewable supply Embedding flexibility through storage, demand response, and system optimization Balancing digital growth with decarbonization and long-term reliability Addressing these priorities requires engineering expertise to be embedded earlier in planning ensuring energy systems, digital infrastructure, and policy are designed in parallel. Melbourne’s strength lies in its ability to integrate this expertise across research, infrastructure, and real-world application. Melbourne Connect is a University of Melbourne–led innovation precinct, supported by government and industry, designed to bring together research, business and policy to deliver real-world solutions. Atlantic Group Research leadership shaping global solutions At the centre of this capability is the University of Melbourne , where interdisciplinary research is advancing the systems required to support AI driven energy demand. Through the Melbourne Energy Institute, for example, researchers are examining how energy technologies interact across entire systems from generation and networks through to end use. “As artificial intelligence continues to scale globally, the challenge is no longer just computational power, it is the energy systems required to support it,” says Professor Thas (Ampalavanapillai) Nirmalathas , Dean of the Faculty of Engineering and Information Technology at the University of Melbourne. “This is driving a new level of convergence between digital infrastructure and power systems engineering, where integrated, system level thinking is essential.” Converging energy, networks and AI Melbourne’s leadership is further strengthened by world-class interdisciplinary facilities such as the Smart Grid Lab in the Department of Electrical and Electronic Engineering, which enables real-time simulation of power systems, allowing engineers to test how solar, batteries, electric vehicles and other distributed resources interact within future grids. This supports the design of more resilient, efficient energy systems before they are deployed at scale. Melbourne’s Smart Grid Lab in the Department of Electrical and Electronic Engineering enables real-time simulation of p...
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“ The lowest-cost place to put AI will be in space, and that will be true within two years, maybe three at the latest,” SpaceX founder Elon Musk told the World Economic Forum in Davos this past January, as his company was preparing to go public . Later that month, SpaceX filed an application with the Federal Communications Commission for an orbital data center constellation of up to 1 million satellites in low Earth orbit, 500 to 2,000 kilometers above Earth. And just three days before the IPO, he discussed some initial design specifications for a new AI-1 satellite data center in a video interview. Musk is prone to hyperbole when it comes to timelines. Full self-driving cars by 2017 . First human mission to Mars in 2024 . Ten thousand Optimus humanoid robots by the end of 2025 . Et cetera. For orbital data centers, which he says will be a cost-effective alternative to terrestrial data centers within three years, the math won’t make sense for several years, if ever. Consider this: There are roughly 14,500 active satellites in orbit . Musk’s Starlink constellation accounts for about two thirds of those . Both the launch cadences and satellite-manufacturing capacity would have to scale up astronomically to deploy a million orbital data center satellites. For context, there have been roughly 7,000 orbital launches in all of human history . To loft 1 million satellites into low Earth orbit on SpaceX’s Starship, which is designed to carry up to 60 satellites per vehicle, would require 16,666 launches exclusively devoted to satellite deployments. Considering that SpaceX launched a record 165 orbital missions in 2025, even at 10 times that cadence, it would take a decade. And how long would it take to build 1 million satellites, given Starlink’s current pace of around 4,000 per year and a generous tenfold increase in capacity? Short of a manufacturing revolution, try 25 years. The reality is that the vision of massive constellations of orbital data centers is nowhere close to being realized. As this month’s cover story, “ Why Orbital Data Centers Are So Hard ” by Andrew Cavalier of ABI Research , makes clear, the reality is that the vision of massive constellations of orbital data centers is nowhere close to being realized. Dina Genkina, IEEE Spectrum ’s computing and hardware editor, put the idea into perspective: “Starcloud (a startup that has applied to the FCC for an 88,000 orbital data center satellite constellation) sent one Nvidia H100 GPU in space so far . Their radiator was too weak to let the chip run at full power.” As Cavalier shows, cooling even a single Nvidia H100 GPU in space is difficult: It draws 700 watts, which will require 1.4 square meters of radiator at 60 °C. A 40-kilowatt rack of servers will need an 80-m² radiator; a 100-megawatt data center will require 2,500 of those radiators. Some astronomers are understandably concerned that a million satellites with giant radiative wings would blot out the stars. So if the economics doesn’t make sense, if the chips are at the mercy of the radiative ravages of space, and if humanity will lose its view of the stars, not to mention increasing the risk of triggering the Kessler syndrome, why are the hyperscalers hyping orbital data centers? Genkina offered the obvious answer: sweet, sweet moolah. “The Elon Musk part of it is honestly genius because he’s got xAI building the data centers, SpaceX sending them to space, and Tesla building solar panels,” Genkina says. “It’s almost like he’s paying himself.” Two Analyst’s Views of SpaceX’s Proposed AI1 Data Center Satellite Michael Pierce , Principal at Technology Strategy Partners Musk’s timelines are notoriously overly ambitious, but I think SpaceX’s orbital data centers might reach cost parity with terrestrial data centers in 5 to 10 years. The Starlink laser-link network already exists as the communication backbone for any SpaceX compute constellation, and that infrastructure is what no new entrant can replicate quickly. The chip-agnostic payload design probably reflects their disclosed difficulty securing AI silicon as much as any modularity philosophy. My view is that the only realistic near-term application is a SpaceX mega-constellation for inference. Training workloads likely cannot tolerate the synchronization and latency constraints of a distributed orbital system. Our report analyzed the market from the integrator’s vantage point, but AI1 is what it looks like when one player has assembled all the necessary advantages simultaneously. The question is whether the terrestrial data center industrial base will degrade or improve on economics. I don’t have insight into SpaceX’s internal costs, as opposed to public pricing, on all their components, so it’s hard to say if they’ll completely dominate or not. Even if they are not cost competitive with terrestrial data centers for another 5 to 10 years, it may simply be faster to get new compute that just happens to be in space. Matt Hasan , AI strategis...
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In the 1970s, American Fireworks , a family-run pyrotechnics company in Hudson, Ohio, used a “home run box” to offer quick and easy fireworks displays for the Cleveland Indians (now the Cleveland Guardians) baseball games. The red wooden crate had metal silos to store the rockets. Each switch on the control panel allowed the operator to set off a different firing sequence. This setup instantly triggered the display whenever a Cleveland batter hit a home run. Before computerized firing systems became common, panels like this represented the state of the art. But they did not eliminate human error. On 15 September 2015, the technician in charge of the Indians’ pyrotechnics accidentally set off the fireworks when the opposing team hit a home run. The embarrassed technician was caught on camera holding his head in his hands. This home run box and control panel [left] were used to launch fireworks during Cleveland Indians games. The rockets were housed in metal silos within the box. Left: Jahna Auerbach/Science History Institute; Right: American Fireworks The Early History of Fireworks Fireworks are one of the many Song Dynasty inventions that migrated from China through the Middle East and into Europe by way of trade routes. Around 200 B.C.E, the Chinese invented small firecrackers by simply tossing pieces of bamboo into a fire. The air inside the bamboo would expand and crack the wood, and the pop supposedly scared away evil spirits. After the invention of gunpowder—a mixture of sulfur, charcoal, and potassium nitrate—about a thousand years later, some clever person thought to pack the powder into the bamboo tubes and ignite them, launching the first fireworks—and the first rockets—into the sky. John Bate’s popular 1634 book on fireworks described fire wheels [left] and a flying dragon [right], consisting of a dragon-shaped rocket that sped along a rope. SSPL/Getty Images By the Renaissance, specialized schools for pyrotechnics had emerged across Italian city-states, and European craftsmen began creating large spectacles for royal occasions and religious celebrations. In 1634, John Bate published the four-volume series The Mysteries of Nature and Art , the second of which described how to create all manner of fireworks. Woodcut illustrations showed fire wheels (now called pinwheels or Catherine wheels), as well as the more ambitious flying dragon—a rocket shaped like a dragon that emitted sparks while speeding across a rope strung between two buildings. During the 18th and 19th centuries, chemists and alchemists discovered new chemical compounds and isolated new elements that expanded the palette for fireworks. Adding barium nitrate produced green, for example, and strontium nitrate produced red. Chemists also mixed in metal particles to create sparkles. The 1880s saw the introduction of the loud screech or whistle that precedes the exploding boom. Amédée Denisse, a graphic artist by trade and a fireworks hobbyist, discovered that a cardboard tube containing potassium picrate added that satisfying auditory effect to his fireworks display. How Did Fireworks Become a 4th of July Tradition? British colonists brought fireworks to the Americas. In 1608, Captain John Smith set them off to celebrate the founding of Jamestown, Virginia, the first permanent English settlement in what would become the United States. More than a century and a half later, while the Continental Congress was meeting in Philadelphia in July 1776, future U.S. president John Adams speculated in a letter to his wife that Independence Day would be celebrated “with pomp and parade, with shews, games, sports, guns, bells, bonfires and illuminations from one end of this continent to the other.” Although Adams got the day wrong—he mistakenly thought the committee would complete the revisions to the Declaration of Independence by the 2nd of July—he was correct in foreseeing that Independence Day would be celebrated with lots and lots of fireworks. Just a year later, on 5 July 1777, the Pennsylvania Evening Post reported on the grand exhibition of fireworks the previous night, which began and concluded with 13 rockets representing the 13 colonies. It’s safe to say that the United States is still obsessed with fireworks. According to the American Pyrotechnics Association , the country spends about US $3 billion on fireworks each year; it’s also the leading importer of fireworks. As the U.S. gears up to celebrate its 250th birthday this 4th of July, expect to see fireworks displays everywhere, from kids with sparklers running in backyards to ambitious professional displays for huge crowds. Modern fireworks displays like the Macy’s 4th of July celebration in New York City are computer choreographed and controlled. Roy Rochlin/Getty Images Fireworks today are an engineering marvel. State-of-the-art displays are computer controlled with precise digital timing, often tied to musical accompaniment. Designers can spend weeks choreographing complicated patt...
He was born into a storm, lightning split the summer sky, in a village the world had not yet heard of. The midwife called it a bad omen, his mother called it a sign. Your first life began in a storm, under open sky. One winter night you ran your hand along a cat’s back, and the darkness cracked open with sparks. Your mother warned the house could burn. You were already chasing what you learned: Light would return. Your second life came underwater, in the current deep. No light, no air, the river pulling you under, the surface closing above you without a sound, and something in you refused to sink or sleep. Your third life came at the dam. The water rose. The wall held you in place. One flash, you turned your body and rose back into air, and left the weight of water without a trace. Your fourth life came in stone and dark. Entombed for a night in a mountain chapel, visited by no one. Only silence and the memory of a spark. You called it an awful experience and left it there, untold. Your fifth life came in fever, nine months cholera held you down, until your father said: Survive, and choose your own ground. You rose. Not from the prayer, but from the promise he made. Your sixth life came in silence, and it stayed. Every sound cut through you, a clock three rooms away, a ringing that would not leave, a noise you learned to bear, until you lived inside that noise and made a home in there. Your seventh life burned on Fifth Avenue, not your body, but your work. Not a thief of fire, but one who stayed with the blaze. A modern Prometheus, your life’s work turned to ash, “I must begin again,” you said, and turned to new ways. Your eighth life came in the street. No storm. No warning. A taxi struck without a sign. A sudden impact: ribs breaking, breath gone. No diagram this time. Only the body, slow to keep up. The ninth life came on quiet wings. That dove found you in the dark, and your spirit rose. She did not move. A beam of light fell from above. The life you would not return from, the one you loved. Your mother thought you had nine lives, nine close brushes with death. Each close call, a lesson. A hand that would lead you out of the darkness and into the dynamo of eternal light. The world profits from the mystery of your mind, Upon your imagination we stand.
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Today, you probably asked a question of a large language model, or accepted a connection suggestion on LinkedIn, or watched a recommended video on YouTube, or took a different route to work based on a traffic prediction from Google Maps. In other words, you probably used artificial intelligence. But what you might not know is how much energy that interaction consumed or why. AI requires processing massive amounts of data, which is usually done in large data centers populated by thousands of GPUs capable of executing up to trillions of operations per second. But each of those GPUs achieves that by consuming as much as 1,000 watts apiece. For comparison, if you’ve got a newer smartphone, it probably uses less than 1 W. That kilowatt figure puts GPUs on the same level as vacuum cleaners, dishwashers, and stoves, but with the big difference that data-center processors are operating uninterrupted around the clock. Fundamentally, a lot of this inefficiency is because GPUs are trying to simulate the workings of artificial neural networks using software and billions of transistors, which requires using energy to move massive amounts of data. What’s more, the simulated artificial neurons that make up these networks lack even a fraction of the complex computing behavior of the biological neurons that comprise the most energy-efficient computing system that we know, the human brain. The brain is roughly one million times as energy efficient at many of the comparable tasks we set for AI. To try to approach these efficiencies , a radically different way of computing called neuromorphic engineering is seeking to build electronic components and circuits that act more like the brain’s neurons and the synapses that connect them. Huge amounts of work have gone into making electronics operate more like biological neurons and synapses . Some research has focused on developing new , experimental devices , but they aren’t yet reliable enough to be used in large systems. Other efforts aim to implement neurons and synapses by interconnecting many complementary metal-oxide-semiconductor (CMOS) transistors—the workhorses of digital logic—to simulate a single neuron and synapse. But this approach requires so many transistors (and a few bulky capacitors) that it greatly limits the size of the system that can be constructed, making it unclear how such brain-inspired hardware could ever scale up and compete with state-of-the-art GPUs. But all along there was an artificial neuron and a synapse—each a single device—hiding in plain sight. We found them last year. They were each made possible by an ordinary CMOS transistor—and not even a very good one at that. This is the story of their accidental discovery and their great promise for lowering the environmental footprint of AI. Biological and artificial neurons Modern digital electronics is based on producing and manipulating the ones and zeros of the binary code through the operation of metal-oxide-semiconductor field-effect transistors. MOSFETs have evolved in recent years, but their classic form consists of a piece of silicon that has been doped to contain an excess of either positive ( p -type) or negative ( n -type) charge carriers. (CMOS logic contains transistors of both types.) The device has two terminals connected to the silicon through regions highly doped with the opposite polarity of the rest of the silicon—the source and the drain. Another terminal, the gate, sits atop the silicon that separates the source from the drain. The gate itself doesn’t connect directly to this silicon, instead resting above a thin layer of insulating dielectric. Notably, there is a fourth terminal that attaches to the bulk of the silicon; think of this bulk terminal as connecting to the underside of the chip. It doesn’t typically get much attention, but it’s very important to our story. When voltage is applied at the gate and the bulk terminal is grounded, charge carriers of the same polarity as the source and drain are attracted to the channel region. In the case of an n -type source and drain, that will be electrons; for p -type it will be holes. The presence of these charges forms a conductive channel that reduces the resistance between the source and the drain by several orders of magnitude, and the device switches on. As the voltage at the gate increases, this physical phenomenon produces a current signal that, when plotted against the gate voltage, rises steadily. This response is ideal for logic gates, converters, multiplexers, memories, and other digital circuits. But it is not a good fit for mimicking the behavior of a neuron. In real neural tissue, brain cells, called neurons, consist of a cell body, a long projection called an axon, and short branching projections called dendrites. The suite of behaviors and computing this collection of components is capable of is rich and broad, but the portion that artificial neural networks hope to copy is this: When the cell body’s voltage is pertur...
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In 1839, J.M.W. Turner painted The Fighting Temeraire . The old warship, once a hero of the Battle of Trafalgar in 1805, glides like a ghost across the canvas, towed by a small steam tug belching smoke on its final voyage to the ship-breakers. The image shows a clear moment of change: sail giving way to steam, and with it, a major shift in power. The ship relied on timber, rope, canvas, and Britain’s seafaring towns. The tug depended on coal mines and iron foundries that supplied machine shops in the Midlands. Turner showed the tension of this time, when new technology changed who held power. By Turner’s time, the United States had already defeated Britain’s navy in two wars—one for liberty on land, another for freedom of the seas. The 13 colonies used new technology in creative ways to win their freedom, and by keeping up with innovation, they managed to defend their freedom. Now, as the U.S. celebrates its 250th anniversary, we can ask: What does it really mean for a country to be independent? We tend to focus on how nations and individuals defend freedom but rarely turn that focus to the tools and systems that sustain freedom. Declaring independence is only the beginning: independence must still be engineered. Forging freedom Long before the first shots were fired at Lexington and Concord in 1775, Britain had drawn the lines of conflict through technology. The Wool Act of 1699 choked colonial textile exports. The Hat Act of 1732 crushed local hat-making. The Iron Act of 1750 forbade finished iron goods. Each statute tightened the knot: Colonial capability existed only at Britain’s discretion. The Boston Tea Party may have been a loud response, but resistance also took subtler, more empowering forms. At a 1769 Virginia ball, more than a hundred women arrived in homespun gowns. Every thread was defiance. When war came, everyday tradespeople pivoted to the fight. Farmers turned plowshares into gun barrels, while clockmakers turned their precision skills to making firing mechanisms. By 1777, two weapons production models had emerged—centralized sites like the Springfield Armory that could produce high-quality guns in large quantities, and household workshops that were more agile and could meet local needs. In parallel, the new nation developed an equally important source of supplies and support: France sent gunpowder and loans and eventually opened a second naval front in 1781, which proved as decisive as any weapon. After the war, the young republic pursued industrial strength with the same resolve it had shown in battle. In 1789, Samuel Slater arrived from England with textile spinning technology that he’d memorized, sowing the seeds of U.S. manufacturing, whose early growth rested on domestic cotton, slave labor, and copied techniques. By 1816, gun manufacturer Simeon North’s milling machines were producing interchangeable metal parts, allowing the armed forces to cannibalize parts. In 1822, Thomas Blanchard’s copying lathe automated the shaping of gunstocks. In the 1830s, the federal government imposed tariffs that shielded infant industries, fulfilling Alexander Hamilton’s vision for industrial policy: Build capacity first, then compete. At the 1851 Great Exhibition in London, American revolvers and reapers with swappable parts stunned international observers. By the 1860s, land-grant colleges were spreading technical education across the nation. Engineering moved into the mainstream, from niche to national necessity, and driving broad, though uneven, prosperity. As the Industrial Revolution bloomed, the early U.S. focus on industrial capacity via farms, factories, and formidable wealth positioned the country to compete with the most advanced industrial powers in the world. The right and responsibility to repair For nearly two centuries, that ethos endured, with government-guided infrastructure and markets deciding the details. But around the U.S. bicentennial in 1976, a conviction took hold across party lines. Finance began to outrank fabrication, and Wall Street prioritized futures contracts over companies owning the factories that made up their supply chains. Domestic factories closed or moved offshore, and companies turned to just-in-time manufacturing and shipping, ostensibly as a way to save on costs. Shipbuilding felt this shift as much as any industry. Shipyards closed, and suppliers of specialized castings and components disappeared along with them, as did skilled technical workers who retired without replacement. Now the U.S. Navy struggles to build submarines fast enough to replace its aging fleet. Other changes took hold, among them the idea that the company that builds your tractor or medical equipment could prevent you from fixing it yourself. Invasive “terms of service” prevented customers from reaching for a wrench, instead allowing companies to keep reaching into customers’ pockets. These changes are symptoms of both structural and infrastructural fragility. When we lose the ability ...
Concrete, although the most common building material in the world, is brittle and can easily crack under tension. Ultra-high-performance concrete (UHPC) is a special class of concrete known for its dense structure and extreme durability. This class uses internal metallic fibers to flex and resist cracking—the downside being these fibers can lead the material to cost up to 30 times more than traditional concrete.
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Welcome to the Fintech Engineering Handbook. This resource aims to describe the most important patterns used in software engineering, where money is the primary focus of the system.
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US-based advanced nuclear fuel developer Lightbridge has successfully removed its first irradiated fuel samples from the world’s largest and most powerful nuclear test reactor.
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What do an instinct to fix things and the 1999 global panic over whether computers would survive the date change to 2000, known as the Y2K bug, have in common? Both helped shape IEEE Senior Member Ajay Prasad ’s career. Prasad is an industry process director at Dassault Systèmes in Detroit. His focus is global oversight of industry process experts specializing in Enovia , a product lifecycle management (PLM) solution and one of the company’s flagship products. Ajay Prasad Employer Dassault Systèmes in Detroit Title Industry process director Member grade Senior member Alma maters Bangalore University, in Bengaluru, India; and the University of Birmingham, England As a child growing up in Bangalore, India, his curiosity to build real-world solutions was ignited by his father, a mechanical engineer. Prasad’s father often fixed things around the house, including cars and bicycles. His ability to take something broken and return it to working order laid the groundwork for his son’s career in engineering. Prasad was in his final year of undergraduate studies when the Y2K panic hit its peak. “Nobody knew what would happen when the year turned to 2000,” he says, “and it was almost projected like the end of the world was coming.” The phenomenon left him with the desire to fix computer problems, but he wasn’t sure how he would go about it, as he had no background in computer science. As it turned out, computer systems didn’t crash when the 1900s ended. The world did not end on Jan. 1, 2000, and neither did his interest in how computers worked. The consulting pivot that changed his career Prasad graduated in 2000 with a bachelor’s degree in industrial engineering and management from the RV College of Engineering , in Bengaluru. It was at a time when tech companies were heavily recruiting engineers, regardless of their specialization. “They were mainly looking for problem-solving skills,” Prasad says. His parents expected him to immediately enroll in a master’s degree program, he says, but a job offer from Tata Consultancy Services in Bengaluru to work as an assistant systems engineer trainee changed that plan. “My dad was actually out of town for work when the job offer came in,” he says. “I knew he wanted me to stay in school, but honestly, I was done studying for a while. I wanted to get some work experience.” He accepted the offer, then broke the news to his father. His parents were supportive of his decision, but his dad offered one piece of advice: Keep the idea of an advanced degree in the back of his mind. Several months of working on mainframes helped him understand algorithms and how to code to achieve outcomes, he says, and the more he learned about computer systems, the more he wanted to pursue a computer science career. With a solid engineering foundation, he says, he knew the pivot made sense. But he also wanted the academic credentials to back up his tech skills. Heeding his father’s advice, he paused his career at Tata and enrolled in the master’s degree program in computer science at the University of Birmingham in England. At the time, it was one of the few schools offering the program to students who had no undergraduate computer science degree. When he graduated in 2002, he briefly considered pursuing a Ph.D., but he returned to India and a new role at Tata. Building a global perspective As a systems engineer, he worked on the MatrixOne platform , a PLM software solution that helped manufacturers oversee products from design to launch. He spent a lot of time customizing the MatrixOne software to meet customer needs. The experience gave him insights into the pain points that different users of the platform faced, such as managing complex product data across large teams and keeping track of complicated supply chains. In 2004 Tata transferred him to Minneapolis, where he continued working on the MatrixOne platform. During that time, Dassault acquired MatrixOne and folded it into its existing Enovia product line. He remained involved with the product until he left Tata in 2008. To scratch an entrepreneurial itch, he became a consultant for the product, helping customize the platform for U.S. clients. The move also forced him to make a decision: He needed to choose between settling in the United States or returning to India. Inclement weather made up his mind, he says. “I was heading to my next project across the country, and it was winter,” he says. “During the entire drive, I was trying, unsuccessfully, to outrun a massive snowstorm. I was young, and it was an adventure, but it helped clarify where I wanted to be at that point in my life.” He returned to India in 2010, armed with a more global perspective and expertise with Enovia. As he looked for a job, he focused on a role with the company that owned the platform he’d worked on for years. “Dassault Systèmes has continuously pioneered new technologies and concepts and set benchmarks in the PLM space,” he says. “When an opportunity opened up there ...
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Microcapsules containing a reactive two-component adhesive can simplify bonding processes in industry and assembly while improving occupational safety. The adhesive is initially safely enclosed in capsules, contact with exposed reactive components can be reduced, and activation takes place only during pressing at room temperature. The Fraunhofer Institute for Applied Polymer Research IAP in Potsdam Science Park is looking for partners from industry and research who would like to contribute specific components, carrier materials or assembly processes for application-oriented testing.
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This article is brought to you by Capital One . After five years leading natural language understanding and eventually the entire Alexa AI organization at Amazon, Prem Natarajan made a nontraditional move: He became Chief Scientist at a bank. Not just any bank: Capital One, a financial institution serving over 100 million customers, helping everyday Americans manage their financial lives. For Natarajan, a veteran of DARPA-funded research and academia who had watched machine learning evolve from task-specific applications to foundation models, the logic was clear. Some of the most interesting advances in AI research and deployment were shifting from big tech’s horizontal platforms to industry verticals like finance, where the most complex problems aren’t just building models but making AI work under the constraints of real-world customer problems, contextual business knowledge, continuous learning, with an incredibly high bar for accuracy and privacy. That’s also what made Capital One the right place to do it. For decades, the company has been recognized as one of the most data- and analytics-driven financial institutions in the industry. Its business model from the very beginning was built around using data and technology to personalize financial products for customers. A decade ago, Capital One went all in on the cloud and rebuilt its data ecosystem, creating a unified environment for data, compute, and AI and machine learning experimentation. Today, its modern infrastructure, disciplined approach to governance, and deep bench of talent form the foundation that allows it to lead in enterprise AI. Advances in AI research and deployment are shifting from big tech’s horizontal platforms to industry verticals like finance. So, why does a bank need a Chief Scientist? The answer lies in a fundamental misconception about AI in financial services. Most financial institutions still view AI as a technology to deploy – leveraging the latest large language model, deploying it through APIs, and integrating it into existing workflows – rather than a scientific discipline. Capital One is doing something different: building a scientific community and research organization to solve real-world customer problems and invent impactful AI solutions that don’t yet exist. While widely available foundation models can handle general tasks, they can’t yet solve many domain-specific challenges, such as detecting fraud in real-time across billions of transactions, or providing state-of-the-art conversational tools so customers can engage when, how, and where they want to. These challenges of making AI reliable, scalable, and well governed require original research and scientific innovation that is funneled back into the business to create real-world applications to address customer needs. The Constraints That Demand Innovation Prem Natarajan, an IEEE Fellow, is Chief Scientist at Capital One. “If you want to solve really important problems in AI and see your work come to life, this is one of the few places you can do that,” he says. Capital One Because banks are dealing with people’s finances, there is an incredibly high bar for getting it right when it comes to AI. Take fraud, for example. Even a minor fraud event can have a devastating impact on certain customers. The best fraud models and platforms can detect and help mitigate fraud in the time it takes someone to tap their card, which is table stakes for protecting customers and their financial information with accuracy and speed. Looking at these types of challenges, Capital One and Natarajan saw that serving millions of customers meant solving AI problems at a scale and complexity that many enterprises don’t encounter. These same constraints create a unique research environment. At Capital One, the approach to building AI is to provide value to customers in ways never possible before, improving their financial lives and meeting them where they are with services they actually need. That focus, combined with massive scale and world-class risk management requirements, makes the scientific problems both harder and just as consequential as those found in most big tech labs. Advancing AI Through “Destination-Back Thinking” Capital One’s approach to AI research and innovation starts with what Natarajan calls “destination-back thinking.” Rather than asking what’s possible with current technology, the team envisions the customer experience they want to deliver – perhaps a car buyer who works long days and can only research the options at 10 p.m., or a customer facing an unexpected expense who needs immediate, personalized guidance – and then works backward to identify the scientific breakthroughs required to get there. “You’re thinking back from where you’re providing incredibly valuable services,” Natarajan explains. “Once you have that vision clearly, you work back and say, what are the gaps? What are the things we need to invent?” This ensures that when problems are solved, the imp...
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In the mid-noughties, when music by the Killers and Franz Ferdinand blared out of every pub and nightclub I passed, I spent my days and nights struggling through a Ph.D. in applied mathematics . My research focused on simulating how special light waves interact in liquid crystals and using simple equations to approximate and understand those interactions. When I look back at my thesis now, liquid crystal technology is old hat, and I imagine my work could be completed with AI assistance in a matter of days—maybe hours. But the same cannot be said for the work of the pure mathematics Ph.D. students with whom I shared a cramped office at the University of Edinburgh. At the time, I felt sorry for these colleagues, who day after day sat at their desks, seemingly tearing their hair out and making no progress. (Though I was struggling too, I was at least always making some headway.) When we finished and went our separate ways, some hadn’t even published a paper. Now, in hindsight, I finally understand why they toiled for years on abstract mathematical problems that only a handful of people in the world care about. It wasn’t arrogance, as I thought at the time; they weren’t trying to prove their superior intelligence by being the first to solve a seemingly intractable mathematical problem. It wasn’t even a form of masochism (which was my second guess)—penance for some imagined inadequacy. I realized they derived joy, satisfaction, and meaning from the long journey toward understanding. “Sometimes, understanding just strikes you as being very beautiful.” —Jeremy Avigad, Carnegie Mellon University “Sometimes, understanding just strikes you as being very beautiful. Sometimes it’s a feeling of accomplishment, like completing a marathon,” muses Carnegie Mellon University mathematician Jeremy Avigad . “But it’s not quite either of those: It’s just a wonderful feeling when you’ve been thinking long and hard about something complex, difficult, and then—all of a sudden—it just comes together.” This feeling has driven mathematicians throughout history. Likewise, the way mathematicians pursue that feeling has changed little over the centuries. They notice or imagine links, patterns, or properties in numbers, shapes, or logical structures. From this, they write conjectures—unproven statements of their speculation. They or other mathematicians then use logical reasoning and the tools of mathematics in often creative ways to prove or disprove those conjectures. Finally, yet other mathematicians verify (or challenge) the proofs. Invariably, this process requires a whole heap of thinking time. “I went to a pure maths camp with classes where we would sit with hard maths problems for half an hour and no one would say anything—everyone was just thinking,” says Krystal Maughan , a mathematician and computer scientist about to get her Ph.D. at the University of Vermont. “But then we would work together and kind of tease out the problem.” This is the age-old joy of math in action. But today’s AI systems are starting to make inroads into bypassing this slow, deliberative process. Taking this trend to its logical conclusion, what happens if AI makes the mathematician’s struggle completely unnecessary? Might AI even sideline humanity completely? AI’s Growing Role in Mathematics For decades, computation has accelerated mathematical progress. This began 50 years ago, when mathematicians used a computer to prove the four-color theorem , which asks whether any map can be colored using no more than four colors, with no adjacent regions sharing the same color. The answer is yes, and the computer proved it, controversially, by checking 1,936 cases in a way no human could realistically verify. Yet throughout this computational era, even in proofs relying on massive computational resources, the role of the human mathematician has remained central. Humans propose conjectures, guided by intuition. They devise strategies to prove them, guided by creativity and experience. And humans verify whether those proofs are correct. Now AI is challenging the status quo . In just a few years, large language models (LLMs) have evolved from “ stochastic parrots ,” capable of little more than regurgitating basic mathematics scraped from the internet, into advanced mathematical reasoning machines. Last summer, systems from Google DeepMind and OpenAI reached a level equivalent to the world’s most mathematically gifted high school students, achieving gold-medal status at the International Mathematical Olympiad . In this annual competition, contestants must solve six notoriously difficult problems from various areas of mathematics. Earlier this year, Google DeepMind’s experimental AI system Aletheia achieved an even more significant milestone when it autonomously produced publishable Ph.D.-level research results. While the work itself is obscure mathematically—calculating structure constants in arithmetic geometry—the significance lies in the complex reasoning it di...
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An inchworm has provided the inspiration for a robot that can move without any rigid parts. The robot mimics a flexing muscle and can be used to inspect sewer pipes or as an explorer on the planet Mars, according to a thesis from the University of Gothenburg. The research is published on the arXiv preprint server.
In 1927, the term "picture element," later abbreviated to "pixel," appeared for the first time in the American technology magazine Wireless World. Today, pixels are everywhere: in computer screens and television sets, where they create colorful images, but also in cameras, where they capture images. In any case, however, they do one or the other—either they control light, as in the case of a display, or they analyze it in a camera sensor. Until now, there have been no pixels that could do both.
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When considering the 1960s sitcoms Bewitched and I Dream of Jeannie , both of which featured women with supernatural powers navigating life with mortals, most people wouldn’t connect them with pursuing an engineering career. But Karen Panetta did. The sitcoms’ main characters—Samantha Stevens, a witch; and Jeannie, a genie—were “strong, empowered female leads using magic,” Panetta says, and they inspired her to become an engineer, as it was like sorcery to her. Panetta, an IEEE Fellow, is dean of graduate education at the Tufts University engineering school, in Medford, Mass., outside of Boston. Karen Panetta Employer Tufts University, in Medford, Mass. Title Dean of the engineering school’s graduate education Member grade IEEE Fellow Alma maters Boston University and Northeastern University in Boston Like Samantha and Jeannie, Panetta has made magic happen, such as when she helped to invent the first CPU digital-twin simulator . Digital twins are computer simulation programs that track and adjust the operations of a physical device in detail. Her simulator has been adapted for several industrial uses, including by NASA to help design spacecraft. Panetta also mentors young women to encourage them to pursue a STEM career through the Nerd Girls program she launched at Tufts in 2000. Engineering undergraduate students work on technology for socially conscious projects such as environmental cleanup, renewable energy, and the development of assistive devices to improve mobility for people with disabilities. Panetta received this year’s IEEE Mildred Dresselhaus Medal for “contributions to computer vision and simulation algorithms, and for leadership in developing programs to promote STEM careers.” The award, sponsored by Google , was presented at the IEEE Honors Ceremony on 24 April in New York City. Receiving the medal is particularly special to Panetta, she says, because she knew its namesake: Mildred Dresselhaus, an IEEE Life Fellow who pioneered the study of carbon nanostructures at a time when researching physical and material properties of commonplace atoms was unpopular. She was a MIT professor of physics and electrical engineering, and died in 2017. Panetta nominated Dresselhaus for the IEEE Medal of Honor , which she received in 2015 . “Millie was a rock star,” Panetta says. “I can’t think of another medal that really encapsulates her spirit and what I’ve dedicated my life to.” Finding a creative outlet in engineering As a child growing up in Boston, Panetta built trapdoors and other features in her treehouse, she says. “I also explored fashion and sewed my own clothes,” she adds. “I wasn’t very successful, but I was very creative.” She was a top performer in math and science classes in high school, so her father encouraged her to pursue civil engineering. “I didn’t know what an engineer was, and my father, who was a mechanic working on heavy construction equipment, only knew about civil engineers,” Panetta says. “I started taking computer programming classes at school, but knowing how to type on a keyboard and make a software program wasn’t good enough for me. I wanted to know what was inside the box.” Her thirst for knowledge inspired her to pursue a bachelor’s degree in computer engineering at Boston University . “My father was very disappointed that I didn’t pick civil engineering,” she says, laughing. She commuted to school, and she struggled to find study groups for her classes, so she joined IEEE to connect with peers. She became active in the university’s student branch , organizing events including the IEEE Student Professional Awareness Conference , which helps students learn practical career skills including résumé building, interviewing, and networking. She organized a SPAC for her branch, and IEEE Life Senior Member Jim Watson volunteered to speak at the event. It changed her life, she says. Watson was the director of commercial and industrial marketing at Ohio Edison in Akron, where he worked for 36 years. “He flew to Boston to speak at our event, but fewer than 20 students attended. I was embarrassed,” Panetta says. But Watson told her the important lesson was that she showed up and organized the event. “He said I would be successful because of that,” she says. “He didn’t care about the attendees’ grade point averages, only that we were professional enough to organize the talk. “That encouragement was the first time anyone outside of my family ever told me that I would succeed, so it was reaffirming. To this day, I still use some of the techniques that I learned in his presentation in my own classroom to teach students.” Panetta graduated in 1986. Her IEEE membership helped her get hired for her first dream job: a diagnostic engineer at Digital Equipment Corp. While attending the IEEE Computer Society ’s annual symposium on very large-scale integration in Boston, she handed her résumé to a DEC representative, who hired her to work in Hudson, Mass. While working full time, Panetta attend...
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What could you do if you could make a circuit trace by just bending a piece of paper? How about bridging modern technologies and traditional handicrafts while providing opportunities for learning skills in both. As part of our interdisciplinary research into digital craftsmanship at the MEI Lab at the School of Creative Media , City University of Hong Kong , we came across research that demonstrated how to impregnate paperlike material (technically a “nonwoven textile”) with the kind of liquid metal used to make conductive ink . Initially, the impregnated material is nonconductive because an insulating oxide layer forms that encapsulates microscopic droplets of the liquid metal. However, applying pressure via shaped molds will crack open the insulating layer, allowing neighboring particles to merge, and thus creating conducting regions in the shape of the mold. Both of us were introduced as children to origami and kirigami (similar to origami, except that cutting is allowed in addition to folding). We, along with our colleagues, decided to see if those traditional techniques could be used on the new material to eliminate the need for molds. Our goal was to allow crafters to make hybrid papercraft creations that contained easily integrated elements such as LEDs and motors. In particular, we were interested in the possibility of combining the separate stages of creating a papercraft object and adding electrical conductors. Previous approaches to creating electrified papercraft objects relied on adding a separate flexible conductor—such as adhesive copper tape—to the paper. This increases the effort required and runs the risk of creating open circuits as the conductive material conforms to the object’s shape. Isopropanol and a gallium-indium liquid material are used to impregnate a paperlike material that is 55 percent polyester and 45 percent cellulose. Electronic components such as LEDs and motors are held in place with masking tape. James Provost Our first step was to see if the pressures involved in bending and cutting alone would be sufficient to create conductive traces. We became frequent visitors to our university’s materials science and engineering department to fabricate samples and then to borrow equipment to characterize their behavior. We soon confirmed that the pressures involved in folding and cutting—ranging from 2.5 to 100 megapascals—were enough to create conductive traces. We also confirmed that normal handling of the paper didn’t accidentally create conductive paths. We made a number of changes to the original method for creating the impregnated paper. For example, instead of immersing the paper in a mixture of isopropanol and liquid metal, we used an airbrush to spray the mixture onto the paper. That allowed us to vary how much was deposited on the paper and to use cardboard stencils to mask some areas from being impregnated, allowing folding and cutting in those regions without creating unwanted conductive traces. We also experimented with the ratios of isopropanol and liquid metal. We became frequent visitors to our university’s materials science and engineering department. After optimizing the mixing ratios and amount applied via airbrush, we were left with a material that reliably conducts with a resistance of 23.18 ohms per centimeter for cut edges and 4.4 Ω/cm for folded edges. The folded edges retain their conductivity even if later flattened out, and the conductivity is the same on either side of the paper. We estimate the combined cost of the paper and liquid metal (available from many online vendors) is about US $1.80 to make a 10- by 10-cm piece. The next step was attaching electronic components to the traces. To make the connections more flexible, we cut down the rigid leads of LEDs and attached conductive thread to the stumps. We then held the threads in place using masking tape. Similarly, we connected conductive thread to the terminals of a power supply. As our goal was to use this material educationally, we now needed to make it easy for a beginner—whether in papercraft or electronics—to try it out. We created a toolkit, dubbed LiqMetCraft. This consists of all the required materials, plus a browser-based software tool that lets the user select or create designs and then gives guidance on physical construction. We created three versions of LiqMetCraft. The first is based on Chinese papercraft in which a piece of paper is folded into a fanlike segment and then cut to create a radially symmetric design. We provided circles of paper with a doughnot-shape impregnated region, with an untreated region that created a gap in the donut. We attached positive and negative terminals to either side of the gap. The user could specify in the software how many times they wanted to fold the disk and then draw potential cuts, receiving immediate feedback on what the unfolded disk would look like, as well as guidance on how to place LEDs. To make our paper sample, isopropanol and liquid me...
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Summary RFIC design is a complex “ dark art ” that limits progress in wireless technologies like 5G, autonomous vehicles, and satellite communications. Princeton researchers use reinforcement learning and inverse design to rapidly create RFICs from scratch. Diffusion models rapidly generate novel or human-interpretable RF layouts, achieving record performance and drastically reducing design time. Future progress needs large, shared chip design datasets and open ecosystems so AI can learn universal electromagnetic and circuit behaviors. Take a moment and try to imagine your life without the wireless advances of the past three decades. Have you lost your luggage? What a shame AirTags have not been invented. The airline representative has promised to call with updates, so settle in for a long wait by the kitchen telephone, because there are no affordable cellphones. You’ll be stuck listening to whatever is on the radio while you wait, because there are no streaming services. That’s not even to speak of all the movie plots that would have been ruined. This is just a tiny sliver of how wireless technology makes itself felt in your day-to-day existence. The effects it has had on supply chains, infrastructure, and how the economy runs have been world-altering. None of it would be possible without the radio-frequency integrated circuits that allow all our devices to unobtrusively send and receive information. Now imagine what the further evolution of this technology will bring: Wide-spread autonomous vehicles , quantum communications , 6G mobile service and satellite communications. Continued momentum will depend on newer and more advanced versions of today’s RF chips. But there’s the rub. Whereas the design of most of the world’s computing chips has been standardized into its own science, RF design has remained stubbornly in the realm of art. A dark art, even, that is mastered only through years of experience. As any sorcerer will tell you, the dark arts keep their own schedule. And that schedule is impeding progress not just in RF chip design but in every other technology that depends on it. About seven years ago, in the wake of AlphaGo’s victory over world Go champion Lee Sedol , my students at Princeton and I began to wonder: Could AI be taught this art as well? Recent successes suggest that, to a large extent, it can. Over the last few years, our group and other leaders in the field have started to develop machine-learning-driven algorithmic methods for designing RFICs . Some of the resulting chips look more like modern art than circuit layouts. Yet in many cases, the physical prototypes bested state-of-the art circuits in terms of performance. The real achievement, however, is that it took the AI orders of magnitude less time to conceive a working design than it would a human designer. This is not about one or two RF chips. AI-enabled design could be the future of all RF design, and maybe much more. The Dark Art of RFIC Design So why do these chips all have to be crafted by hand? Why aren’t RFICs designed with an algorithmic synthesis process, much as CPUs and GPUs are? The design of RFICs is an exercise in engineering across multiple physical domains. Maxwell’s equations , operating across different spatial and temporal scales, govern how electromagnetic fields interact with active and passive devices that must be carefully codesigned for the chip to function. Alongside these are the laws of thermodynamics, which determine how heat is generated and removed during operation, as well as the mechanics of thermal expansion and contraction that dictate how reliably the chip and its packaging survive temperature changes. AI Could Short-Circuit RFIC Design The design of a radio-frequency integrated circuit requires human intuition and multiple, often-repeated optimization steps. The hope is that through an understanding of Maxwell’s Equations, an AI can be taught to short-circuit this process and quickly produce a design. Simultaneously accounting for all the physical constraints these impose makes the design space almost impossibly large. Every decision involves complex priorities that often compete with one another, preventing the optimization of any of them. To better understand the issue, let’s walk through the steps involved, after which you’ll better understand why a single new chip design takes years and tens to hundreds of millions of dollars. Most of the area of radio-frequency integrated circuits is dominated by complex electromagnetic structures. Human-designed RFICs, like this broadband power amplifier [1], start with templates and follow a symmetric, understandable pattern. But freed from the constraints of human-designed templates and the need for humans to even understand the rationale of electromagnetic structures, power amplifier ICs [2–5] and low-noise amplifiers [6] can take on truly wild-looking yet efficient designs. SENGUPTA LAB Let’s say you’re an engineer assigned to design a new 28-gigah...
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5G telecommunications, according to industry hype when 5G first launched in 2019 , was going to be all about buzzy applications like mobile augmented reality and autonomous vehicles . But the surprise plot twist came when replacing home cable internet turned into 5G’s most widely adopted new application. Fixed wireless access (FWA) now serves over 14 million U.S. customers , and contributes 28 percent of worldwide wireless traffic . Fixed wireless access is what the term sounds like: broadband internet delivered over a cellular radio link to a stationary location—no cable, no fiber, no trenching, no satellite broadband antenna pointed at the sky. What makes FWA distinctive is that it repurposes the same towers, spectrum, and 5G infrastructure that was built for mobile devices. One U.S. Federal Communications Commission (FCC) commissioner has called FWA 5G’s killer app . And that’s true not just in the United States either. Jio, India’s largest carrier, is also one of the world’s largest FWA providers, with over 9 million customers as of last year. Carriers discovered they could repurpose surplus 5G capacity, while also exploiting a usage pattern quirk: mobile traffic starts to drop after 8 p.m. , just when home internet usage peaks. The result is broadband, delivered via traditional cellphone towers, at a lower cost than fiber deployment. For these reasons FWA provides real price competition to cable broadband , while reaching underserved rural and suburban communities. Fixed Wireless Access Repurposes Ambitious 5G Infrastructure FWA is cheaper to deploy than fiber, and for most homes and small businesses, fiber’s gigabit speeds are overkill anyway. And since FWA uses the same wireless networks built for cellular service, FWA works anywhere that receives a steady cellular signal. As cellular networks extend into rural and underserved areas, FWA’s coverage map expands with them. In these remote locales, the other main viable broadband alternative typically comes from satellite services like Starlink —which are, compared to FWA, more expensive, with higher delays, and lower bandwidth. While most FWA deployments use currently underused microwave bands, some FWA deployments use electromagnetic spectrum that 5G launched but that mostly failed with mobile users. Millimeter waves operate at frequencies 10 to 40 times higher than 4G’s spectrum, offering high data rates from their wide available bandwidth. However, there are good reasons 5G mobile users today don’t generally use millimeter wave spectrum. Millimeter waves can’t penetrate buildings. Plus, they lose signal strength within a kilometer or two of the transmitter. Millimeter wave antennas are also a real drain on cellphone batteries compared to microwave and radio wave tech . Yet none of these challenges applies to a fixed station with a clear line of sight to a nearby tower. FWA home units (called customer premise equipment or CPEs) outperform 5G handsets by a significant margin. That’s mostly because of hardware. CPEs carry larger, more sensitive antennas than a typical cellphone, paired with more capable transceivers. CPEs also tend to be plugged into wall outlets, making battery concerns a non-issue. Another 5G technology that did not gain traction in mobile wireless is Multi-User Multiple-Input Multiple-Output ( MU-MIMO ). A base station with MU-MIMO uses an array of antennas to serve multiple users on the same frequency simultaneously. However, maintaining a MU-MIMO signal involves tracking each user individually—a problem that quickly becomes overwhelming with enough mobile users. FWA is different, however. Static CPEs, with their steadier downlink traffic loads, are an ideal match for MU-MIMO technology. So, FWA internet service not only uses mostly fallow spectrum but also uses 5G spectrum more efficiently than do 5G mobile users—for whom, of course, these 5G technologies were originally designed! How FWA Became 5G’s Surprise Killer App Not long ago, the high-bandwidth use cases for 5G made for an impressive list: millisecond latency for autonomous vehicles, mobile augmented reality headsets with extensive high-speed data needs, and massive machine connectivity for an expanding internet of things (IoT). These applications have all stalled. Autonomous vehicles pose challenging—and still unsolved —problems unrelated to spectrum allocation. Augmented and virtual reality technologies have yet to create meaningful spikes in bandwidth demand. And the IoT has, to date at least, fragmented across an array of competing standards . Mobile carriers had built dense 5G networks for mobile customers whose needs rarely saturated the network’s capacity. Home broadband usage peaks in the evening hours, precisely when cellular networks are quietest. FWA sits at cellular networks’ crossroads of supply and demand. The Advent of 6G Will Only Expand FWA’s Reach In December, the telecom standards body, the Third Generation Partnership Project ( 3GPP ), issued its lat...
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By most accounts, the United States appears poised to fall woefully short of meeting new electricity demand over the next five years as data centers and domestic manufacturing proliferate. Ian Magruder Ian Magruder is the founder of Utilize Coalition and previously served as director of market mobilization at Rewiring America, an affordable electrification advocacy group. Building new power plants and transmission lines may seem like the obvious solution, but there are other options, says Ian Magruder , founder of Utilize Coalition , a nonprofit based in Washington, D.C. The U.S. uses only about half of its grid capacity, and a lot more power could be tapped by deploying a spate of newly available technologies. Backed by Google , Tesla , HVAC systems manufacturer Carrier , and several other companies, Utilize Coalition advocates for more thorough use of grid capacity through policy change and new technologies. Magruder spoke with IEEE Spectrum about those efforts. Why does the United States use only half of its grid? Ian Magruder: Most studies have found that average utilization rates are between 40 and 55 percent across different geographies. And the reason is that we’ve built our grid to meet peak demand. We have to ensure that on the hottest summer day or the coldest winter morning we have enough power. But in many parts of the country, we really only hit peak a few days a year, and it’s really only a few specific hours within those days. It didn’t used to be this way. What’s changed? Magruder: Over the last 20 years we’ve seen the gap between average use and peak use grow wider. There are a variety of reasons for that. Grid operators have become more conservative following major blackouts and reliability events. And with more variable-generation sources such as wind and solar, grid operators are building in more capacity. But this also presents us with an incredible opportunity to get more out of the grid using new technologies. What technologies are being deployed to address the problem? Magruder: Pairing battery storage with energy generation is a key part of this, as are other kinds of distributed energy resources, like managed [electric vehicle] charging and smart thermostats. I would also say that transmission technologies that safely maximize the current in power lines , increase conductivity , and optimize power routes all play a critical role here. And then there’s demand flexibility, which is when utility customers adapt their power use to accommodate the grid during peak hours. Some really good work is being done around flexible data centers . Is grid underutilization also happening elsewhere in the world? Magruder: It’s a global phenomenon, but it varies widely by country. European grids face similar dynamics as [those in] the U.S., and in some places utilization is even lower. But Australia and the United Kingdom are further ahead in measuring and managing utilization with new technologies. What’s the downside to overbuilding our grids? Magruder: Mainly cost. Electricity rates have gone up, and we [at Utilize Coalition] think it’s because utilization has gone down. A report that we released earlier this year shows that a 10 percent increase in grid utilization could save Americans over US $100 billion over the next decade.
Imagine sitting down at your desk and logging in for a performance review, with an AI system analyzing the conversation. You’ve been working long hours, balancing deadlines, and your manager asks how you’re doing. You say you’re fine, and maybe even smile, but there’s a hint of hesitation and your voice wavers. As you shift your posture, your shoulders slump. These are subtle cues that to the human eye might hint at underlying stress. But to an AI model that’s been trained only to categorize emotions as “happy” or “sad,” such nuances are likely lost. It logs the words and a smile and moves on—and unless your human manager intervenes, the fact that you’re tired, unfocused, and maybe a couple of days from burnout never enters the equation. “ Emotion AI ,” which estimates how people feel based on facial expressions, voice tone, and behavior, seems to be suddenly everywhere; it’s being used in employee well-being and recruitment interviews, education platforms, and driver-monitoring systems. Technology call-center platforms such as NiCE and Genesys use AI to detect when a customer sounds frustrated and prompt agents in real time to slow down or respond with more empathy. Giant companies like Meta and startups such as Hume AI are developing more-expressive voice AI systems that can detect emotional cues in the person they’re “talking” to and adjust how they communicate. What’s more, hundreds of companies already offer virtual AI companionship apps, a fast-growing market that may be worth an estimated US $555 billion by 2035—and robot buddies have also entered the picture. Intuition Robotics’s ElliQ , for example, is a small device vaguely resembling a white desk lamp that’s now being used to engage older adults in conversation in hopes of reducing loneliness. But while the field of emotion AI is advancing at a rapid clip, most existing systems are focused on detecting a limited number of signals to label one specific emotion at a time—which is insufficient if you’re trying to understand the human condition. In the real world, human signals and emotions are contextual, overlapping, and constantly changing. A laugh can signal joy, nervousness, or both; a raised voice might signal enthusiasm just as easily as frustration. To make the job of emotion detection even more difficult, reactions differ greatly from one individual to the next, depending on demographics, cultural background, and countless other variables. In other words, there’s a gap between what we’re expecting AI to pick up on and what AI can actually deliver. That’s the gap a new field of research—what we call human-context AI—is working to close. Instead of looking at just one input and labeling it, human-context AI increasingly has the capacity to take stock of an individual’s personality and character, and to track emotions in real time while combining multiple inputs , including facial dynamics, voice, tone, language, and behavior. Crucially, responses are also evaluated in the context of a specific environment, such as a performance review or professional coaching session. The result? Computers are learning to read the scene, rather than just the screen. The Origins of Emotion AI The story of emotion-sensing AI began almost three decades ago in the MIT Media Lab, where the American electrical engineer and computer scientist Rosalind Picard coined the term “affective computing.” Her work introduced the radical idea that computers could be taught to recognize and respond to human emotions. Picard’s early experiments focused on single modalities: facial expressions, tone of voice, and physiological signals, such as skin conductance or heart rate. The goal was to give machines a window into human feeling, helping them become more empathetic. It was an exciting vision, but back then the science and hardware weren’t ready. Computing power was limited, sensors were crude, and datasets were narrow and biased. Josie Norton Over the next decades, researchers and companies got better at measuring the many ways in which humans express themselves. In the 2010s, sentiment analysis —the processing of large volumes of text to suss out emotional undertones—began to reach the mainstream. At the same time, marketing firms, including my company, Neurologyca , began using video and webcams to measure and catalogue customer reactions. Biometric devices and activity trackers, such as Fitbits and Apple watches, also became ubiquitous, generating new streams of data about people’s sleep, step counts, stress levels, and more. Unsurprisingly, scientists soon confirmed that larger volumes of personalized data led to greater accuracy in reading human emotions. In 2019, researchers at Cornell demonstrated that combining multiple types of signals improves emotion sensing. Their system joined physiological data, such as brain activity measured by electroencephalography (EEG) and heart rate, with visual cues like facial expression, outperforming systems that relied on just one ...
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Artificial intelligence is the transformative, strategic technology of the early 21st century. It is significantly reshaping practically every aspect of our lives, including in ways that probably no one anticipated. Its rate of adoption and impact have been unprecedented when compared with other technologies. AI as a distinct field was formally established in 1956 at the Dartmouth Summer Research Project on Artificial Intelligence , proposed by John McCarthy , Marvin Minsky , Nathaniel Rochester , and Claude Shannon . In their August 1955 proposal for the research project, the scientists introduced the term artificial intelligence and envisioned machines capable of simulating human intelligence. AI is the “science of making machines do things that would require intelligence if done by men,” as defined by Minsky. The professor received the ACM Turing Award , which is often called the “Nobel Prize in computing.” Since AI’s humble beginnings 70 years ago, it has evolved significantly in its capabilities, gained prominence, and earned widespread adoption across many areas including business, education , finance , health care , industry, and the military . IEEE’s contributions to the progress and adoption of AI throughout its journey are substantial and multifaceted. As we celebrate AI’s 70th birthday, understanding its history, current status, limitations, and concerns is key to harnessing it for good. The technology’s roller-coaster evolution Although AI emerged as a distinct field in 1956, its intellectual roots extend back further. The ideas and theories that underpin AI predate modern computers such as the ENIAC , unveiled in 1946. In 1943 Warren Sturgis McCulloch , a neurophysiologist and cybernetician, and Walter Pitts , a logician working in computational neuroscience, were inspired by the human brain. The two devised mathematical models of artificial neurons, demonstrating that artificial neural networks could perform logical computation. Frank Rosenblatt , a Cornell psychologist, later advanced those ideas by developing the perceptron , an early neural network that laid the foundation for modern machine learning and deep learning. A major milestone came in 1950, when celebrated computer scientist Alan Turing posed the question, “Can machines think?” In his 1950 landmark paper “ Computing Machinery and Intelligence ,” published in Mind , he explored the nature of machine intelligence. He introduced the “imitation game,” later known as the Turing test , as a practical means of evaluating it. The test remains an influential concept in AI and the philosophy of intelligence, as I discussed in my article “ The Turing Test at 75: Its Legacy and Future Prospects , ” published in IEEE Intelligent Systems . Claude Shannon , recognized as the father of information theory, explored the potential of machines for complex reasoning tasks in his 1950 article “ Programming a Computer for Playing Chess ,” published in Philosophical Magazine . In 1956 AI became a formal discipline, inspiring scientists to explore and advance it further. John McCarthy developed Lisp in 1958, and it became the dominant programming language for AI research and development. In 1959 Arthur Lee Samuel , a computer science professor at Stanford , introduced the term machine learning to describe programs that could improve their performance through experience. In the early 1980s, renewed enthusiasm and government funding fueled the development of symbolic AI , a rule-based expert system (also known as a knowledge-based system) that encodes domain-specific knowledge as sets of rules. A notable example was MYCIN , designed to diagnose infectious diseases. Although successful in limited domains, expert systems’ inherent limitations have restricted their broader adoption. Expert refers to a computer system that mimics human experts in a specific domain. It was popular in the early days of AI, and subsequently disappeared with advances in AI such as neural networks and machine learning. AI’s journey was marked by periods of soaring expectations and disappointing progress, known as “ AI winters ,” during which funding, interest, and confidence declined. Analyses of the episodes revealed recurring causes and insightful lessons for the field. A new phase of growth—often described as “AI spring”—emerged in the 2010s with advances in deep learning , the rise of large language models , the transformer architecture , and generative AI (GenAI). “The imperative before us today is not only to advance AI’s capabilities but also to ensure that it remains human-centered, trustworthy, ethical, and dedicated to enhancing human well-being and societal progress.” Unlike earlier approaches that processed information sequentially, a transformer model analyzes an entire sequence of text or audio, assessing the importance of each word or component relative to others, enabling dramatic advancements in GenAI and its applications. Ashish Vaswani , a former computer scien...
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Salome Mikadze-Struk is no stranger to adversity. The daughter of refugees, she built a software-development business as an undergraduate at the height of the COVID-19 pandemic and kept it running despite the outbreak of war in her native Ukraine . Now, she’s drawing on her experiences to mentor tech-startup founders and speak publicly about the importance of resilience in entrepreneurship . Mikadze-Struk was studying at Georgetown University, in Washington, D.C., when COVID-19 struck. Classes went online, and she moved back to Ukraine. In the midst of that disruption she saw an opportunity to develop her business idea, called Movadex , by tapping Ukraine’s pool of talented young engineers. Then Russia invaded in early 2022, during her final semester. Taking online classes from bomb shelters and helping employees evacuate to safer parts of the country was surreal, she says, but the team kept the company afloat and she graduated later that year. In 2023, Mikadze-Struk took a hiatus from her business to pursue an MBA at Stanford University, which she completed this year. In her precious spare time she’s been advising startups and giving talks, using her unique perspective to promote the need for resilience in entrepreneurship—something she thinks is increasingly important in the software industry as AI coding tools upend old business models. “You need to be okay with risk, you need to be resilient. You need to be okay with disruption and okay with uncertainty,” she says, “because this is inevitably going to be part of this industry for the foreseeable future.” An Early Focus on Education Mikadze-Struk’s parents had settled in Ukraine after fleeing conflict in the Abkhazia region of Georgia in the early 1990s. “They left everything behind,” she says. “You can look on Google Maps and zoom in on where their houses were and it’s all rubble.” Despite this backstory, Mikadze-Struk says she and her sister had a conventional middle-class upbringing in Kyiv. Her father ran a small shop and her mother was a stay-at-home mom. Her parents placed an emphasis on education and encouraged her to study hard and take part in extracurricular programs such as Ukraine’s Junior Academy of Sciences , which introduces students to research. “They weren’t rich, so they knew that our way to make it in life was not through investments, but through merit-based accomplishments,” she says. When Mikadze-Struk was 14, her family discovered the newly launched Ukraine Global Scholars program, a nonprofit that helps talented students secure scholarships abroad. The program helped her win a full scholarship to the Emma Willard School, a private girl’s school in Troy, N.Y. Discovering Tech After graduating high school in 2018, Mikadze-Struk was accepted to Georgetown to study business administration. But it was outside the classroom that her career direction began to take shape. She won a startup competition with a medical device she had developed for a school project and, while the business idea didn’t go anywhere, it sparked an interest in entrepreneurship. Ukraine’s software industry was booming, and she began attending startup events and competitions in her home country the summer before starting college. There she met her eventual cofounder Nor Newman . Despite both being just 18, they saw a gap in the market. The pair noticed many founders had strong ideas but lacked the technical expertise to realize them, while talented engineering students often struggled to gain real-world experience . Newman had begun informally connecting startups with his college friends, but the pair soon saw commercial potential. “We realized we could actually create our own startup studio and help startups as a team, versus just connecting people,” says Mikadze-Struk. Then, when the COVID-19 pandemic struck in early 2020, halfway through her sophomore year, it brought both disruption and opportunity for Newman and Mikadze-Struk. While travel restrictions and lockdowns made life complicated, there was also a surge of companies looking to move their business online. “COVID really skyrocketed everything we were doing,” she says. Sensing an opportunity, Mikadze-Struk and Newman incorporated Movadex in Ukraine in early 2020. From the start, they decided to focus on not only providing engineering talent, but also helping startups with product development. Many times, says Mikadze-Struk, a founder’s vision for the software doesn’t line up with what users actually want. “What really helped us grow is not just the engineering or quality of code, but rather a holistic approach to creating a product and actually getting into the brain of the user,” she says. Navigating Adversity Back in Ukraine, Mikadze-Struk had to juggle this booming business with studying remotely—taking classes at night and working during the day. It was exhausting, she says, but it also allowed her to immediately apply what she learned in business classes to building her startup. Having successfully ...
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Unmanned aerial vehicles (UAVs), commonly known as drones, are now widely used for various purposes, ranging from filmmaking and aerial photography to industrial inspection, precision farming and reaching obstructed areas during emergency response missions. While many existing drones can move swiftly in their surroundings and circumvent large obstacles, most still struggle in cluttered environments. In addition, they are often unable to execute maneuvers that would allow them to safely pass through small gaps or reach secluded areas.
A research team at the Korea Institute of Geoscience and Mineral Resources (KIGAM) has developed a technology that converts wet spent coffee grounds directly into high-quality biochar in just 90 seconds, with no drying or oil removal required. The breakthrough offers a fast, energy-efficient path to turning high-moisture organic waste into valuable fuel and carbon materials. The study, led by Dr. Taejun Park in collaboration with GodTech Co., Ltd., was published in the Chemical Engineering Journal, one of the world's leading journals in chemical engineering.
Large language models have moved out of the research lab and into engineers’ daily workflow. LLMs serve as reasoning engines that can orchestrate complex tasks including identifying vulnerabilities in source code and transforming fragmented project discussions into rigorous technical specifications. While the general public uses AI tools to write email and plan vacations, technical professionals use LLMs as core architectural elements that are fundamentally changing how digital infrastructures are built and maintained. As the AI models move into mainstream engineering practice, the demand for technical expertise is rising. The LLM technology market is expected to grow by about 33 percent every year through 2030 , according to MarketsandMarkets . The rapid expansion suggests that proficiency in implementing and securing the models is transitioning from a niche into a core requirement for technologists. More than just a better search engine To use LLMs effectively, technical professionals must move beyond treating them as conversational robots. At a fundamental level, the AI systems are built on the transformer architecture , a framework that replaced the older method of processing data in a fixed, sequential order. Unlike earlier models that analyzed information one step at a time, transformers use self-attention mechanisms to ingest vast datasets simultaneously. For technical professionals, LLMs are core architectural elements that are fundamentally changing how digital infrastructures are built and maintained. Relying on such LLMs without understanding their internal logic creates a significant reliability risk. To build tools that work consistently, developers must understand the core principles that govern how the models process information and generate results. By mastering how a model processes information and how its internal settings influence the result, developers can move away from a trial-and-error approach toward a more precise one to ensure the AI tool handles complex data reliably. Four ways LLMs are changing jobs Here are areas that integrate large language models. Moving past basic prompts. Developers are using application program interfaces (APIs) to connect LLMs directly to their databases and software tools. Employing the APIs allows AI to perform work such as executing code or searching through internal repositories. Fixing the “hallucination” problem. LLMs are at risk of hallucinations , which are generated facts or code that looks correct but actually is wrong or broken. To fix the problem, retrieval-augmented generation (RAG) forces AI to look up information in a trusted source such as a company’s database. Prioritizing data security. When using AI with proprietary code, security is a major concern. Engineers must learn how to set up “private” instances of the models to ensure that sensitive company data stays within a secure cloud environment and is not used to train public versions. The future of collaboration. By automating repetitive coding tasks and summarizing thousands of pages of documentation, LLMs let engineers spend more time on high-level designs and solving important issues. Online course program helps with mastering the tech The gap between people who use AI and those who understand how to build with it is growing wider. To help technical professionals stay ahead, IEEE offers a five-course online program, Large Language Models Demystified , available through the IEEE Learning Network . The program, developed by IEEE Educational Activities in partnership with the IEEE Computer Society , is built for people who want to understand the “how” and the “why” behind the technology. Rather than just teaching basic prompting, the curriculum dives into the engineering behind generative AI, including: Evolution, impact, and hands-on exercises: the shift from statistical methods to modern transformers, including hands-on model optimization. Understanding transformer architectures: the mathematical core of self-attention and positional encoding, implemented in NumPy and Python . Architectural analysis and implementation: advanced LLM design with practical model-building exercises. Training and modeling with PyTorch: end-to-end pipelines in PyTorch , leveraging parameter-efficient techniques such as low-rank adaptation and quantization. Optimization, alignment, and deployment: performance scaling, reinforcement learning from human feedback (RLHF) , group-relative policy optimization , RAG, and agentic AI. Upon completion of the program, participants earn professional development credits and a digital badge from IEEE to verify their expertise. Enroll in the course program on the IEEE Learning Network. Organizations looking to prepare their teams to work on LLMs can connect with an IEEE content specialist to discuss group enrollment and tailored training paths.
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In 2018, Amazon brought me in as the lead UX Sound Designer for Astro, their first consumer home robot . Astro used cameras and other sensors to map and navigate your home and workplace , and could proactively patrol, check up on loved ones, and transport small items using its built-in cargo bin. While there was a well-defined feature set and form factor, initially there was no character direction. In fact, even before Astro had a name, there were two main questions—was it simply Alexa on wheels, or was it a robot with its own character? The Astro team was divided. One option was to focus on Alexa, and treat the mobile robot simply as an added utility. 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, Astro became Astro rather than Alexa , and user testing backed up our decision. People didn’t see the robot as Alexa. They saw it as its own character, and that’s what they wanted it to be. Alexa on the device felt somewhat strange and creepy, but building Astro its own voice was too slow and expensive in 2018. So, we settled on Alexa as a supporting character that handled any actual talking, while Astro was the main character, communicating as much as it could without words, through sound, motion, and facial expressions. I had been brought on to the Astro team to define the robot’s sound design language and voice. But there was no one to flesh out the robot’s actual character. You cannot make a single real decision about a character without defining it first. Every choice about how Astro moved, sounded, paused, or reacted was a character choice, and those choices required all disciplines working together. As Sound Lead, I was weaving together sound, motion, and character, and how they played together inside each story moment. The animators, who programmed Astro’s motion and facial expressions, were extraordinary at what they did, but the emotional arc they were animating came from the sound (and therefore character) work first. So I stepped into that role, which is where my real work started. What I learned about building character for robots applies to nearly everything being built in embodied AI right now. Character Is a Design System Developing a character for Astro meant answering questions that had never been asked about a product at Amazon: What is the emotional range of this robot’s baseline state? How does this robot communicate uncertainty without eroding trust? Where is the line between being expressive and annoying? What are the vulnerabilities of this device’s character? These are design questions. They have real answers, and every team working on the product has to build from them. For example, Astro’s emotional range was designed to be relatively small at first. We never wanted Astro to get too sad or too angry. It could play sad, but would snap out of it quickly and end the reaction on a high note to keep things positive. Character leaks out of every seam and can create a disjointed experience if not defined correctly. Even if it’s just animation timing that’s slightly off, or a response that’s technically correct but contextually tone-deaf, users feel every one of these inconsistencies, even if they can’t name them. Watch what happens at the beginning and end of this Sing sequence: Astro goes from nothing, into the emotional moment, and then lands back on nothing. No 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. Story and Sound at the Beginning We had decided that Astro would have no spoken dialogue, but it had something that functioned the same way: a vocabulary of sounds, tones, and rhythms that acted as its voice. This vocabulary became the leading output of the character’s personality. The robot’s motion and facial expressions were built around it. Astro’s wake-up sequence is a great example. Waking wasn’t just a boot animation on the screen; it was an entire performance. Slow and humble at first, the robot oriented itself quietly, then stretched its screen, checked its wheels, and finally, with an upward gesture toward its telescoping mast, it popped it up slightly, and did a little dance of joy. Sound, motion, and eyes hit every beat together in full choreography. The character’s output in that sequence was first written as a story. Astro is waking up in its new home for the first time. I...
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Wearable medical devices that monitor heart rate, respiration and joint movements for long periods without battery concerns, electronic skins that sense external stimuli like human skin, and soft robots made of flexible materials that move freely have all come one step closer to reality. KAIST researchers have developed a self-powered sensor (a sensor that generates electricity on its own without a battery) that can stretch up to 668% while producing stable electrical signals.
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The rapid evolution of the global engineering landscape requires continuous education. For one week in April, the IEEE community focuses on its educational frameworks. IEEE Education Week , which just concluded its fifth year, provided a comprehensive overview of the resources available to professionals and students. From 11 to 19 April, the organization supplied a variety of live and virtual events , online resources , and promotions that champion the cycle of lifelong learning. IEEE President Mary Ellen Randall kicked off the week with the keynote: “Inspiring Tomorrow’s Innovators: How IEEE Educational Resources Can Open Pathways Into STEM.” The event served as a central point for programs that run throughout the year. “Education Week allows different units to share resources with members and the public, covering everything from preuniversity programs to advanced professional training,” says Jamie Moesch , managing director of IEEE Educational Activities . Coordination across the organization The event relied on the cooperation of 120 IEEE partners. Involved organizational units included the IEEE Communications Society , the IEEE Education Society , and chapters and sections from around the world, including in Brazil , Colombia , and India . They produced 114 events, 23 resources, and 11 special offers. “These collaborations help members remain current in a changing technological environment,” says Timothy Kurzweg , vice president of IEEE Educational Activities . “The goal is to provide accessible tools that assist members in both their own professional development and their efforts to mentor new engineers.” “The week allows different units to share resources with members and the public, covering everything from preuniversity programs to advanced professional training.” —Jamie Moesch, managing director of IEEE Educational Activities The participation metrics reflect a broad geographic interest. The IEEE Education Week website recorded more than 4,770 visitors, with primary engagement coming from India, Nigeria, and the United States. Nearly 240 digital badges were issued to people who completed educational quizzes. To encourage participation, organizers enlisted 72 volunteer ambassadors to promote the week’s activities across their local networks and share key resources on social media. Available educational tools Here are a few of the virtual events held during Education Week—most of which are available on demand: Celebrating Excellence: The EPICS in IEEE Contributor Awards and Service Learning Showcase. Classroom to Startup: Uniting Academia and Industry. IEEE’s Role in Shaping AI-Ready Engineering Education Globally. Leveraging IEEE Standards to Enhance Engineering Service Learning Projects (EPICS in IEEE). Mastering the Modern Job Market: The Power of IEEE Microcredentials. TryEngineering Volunteers Making an Impact in STEM. The Education Week website highlights resources and offers shared by IEEE organizational units, including: A half-off discount for members on IEEE e-learning courses. The catalog covers such topics as computing, power and energy, and telecommunications. IEEE Communications Society on-demand webinars. Learn the latest trends and innovations. IEEE Women in Engineering career-focused, upskill, and reskill webinars. The presentations cover a variety of topics including agentic AI, leadership, and robots. IEEE Innovation at Work. The e-newsletter covers emerging technologies, education, and training for technical professionals. IEEE Learning Network. Hundreds of continuing education courses , all in one place. IEEE TryEngineering lesson plans. The easy-to-use, engaging activities and plans help teach engineering concepts to preuniversity students. IEEE TryEngineering collections. The lesson plans and multimedia resources, developed with partners and IEEE technical societies, are designed to introduce technical topics and deepen student understanding. Individuals who were unable to attend the live sessions can find the archived content on the IEEE Education Week website. The website also accepts donations for education-related funds managed by the IEEE Foundation . Updates and technical resources continue to be shared through the #EducationAtIEEE hashtag on social media channels. Planning for IEEE Education Week 2027, scheduled for 3 to 11 April, is underway.
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This article is crossposted from IEEE Spectrum ’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written i n partnership with tech career development company Parsity and delivered to your inbox for free! I’ve sat on both sides of the interview table several times over the past decade. You might be surprised to hear that I’ve often been just as nervous interviewing candidates as I was when being interviewed! Nearly all the interview advice out there is about the candidate’s side, but understanding the other side can also help you prepare. Let me show you what I’ve seen firsthand, and what I’d bet is happening at the company you just interviewed with. If you recently got rejected after an interview, this might explain what actually happened. One caveat, because I’ve been on the receiving end of this: A couple of my recent interviews were run entirely by AI. These were screening rounds, but a growing share of job seekers now report being interviewed by a bot somewhere in the process. Everything below assumes you reached a person. Most teams have no standard prep You might assume companies train people to run interviews. Many don’t. In practice, your interviewers may be much less prepared than it seems. Their prep might look like this: “Here’s a rubric from three years ago, figure it out.” Or: “Let’s grab a conference room between meetings and decide what to ask.” The questions are often whatever the interviewer personally studied when they were job hunting. These days, they may be generated with an LLM the morning of. Then the panel negotiates. One person wants to quiz candidates on data structures and algorithms for a role in which they design websites. Another insists system design is essential for a junior level position. People default to what was done to them and assume it’s normal because it was normal to them. What’s normal to the spider is chaos to the fly. “Scoring” that isn’t really scoring After an interview, some processes I was part of had one simple scale to score candidates: yes, no, strong yes, strong no. The result is predictable. Like the candidate? Strong yes. They rubbed you the wrong way but answered everything correctly? Somehow a soft yes at best. Structured scoring with defined criteria measurably reduces this. The research backs it, and the rare times I saw it used well, it changed my own assessments. Yet many teams I worked on never used this approach. Prestige bias and politics Even with a strong scoring system, bias and office politics can change the outcome. For instance, I once interviewed someone I was strongly against hiring. It was clear they didn’t know what they were doing, and they’d be running critical infrastructure. I gave a strong no with objective reasons, scoring notes, specific examples from the technical round. Leadership pulled me into a meeting right after and asked why. I walked them through my notes. What I didn’t know: Several of them already knew the candidate personally. They liked them. They wanted them hired. I said the decision was theirs, my assessment hadn’t changed, and wished them luck. I’ve also watched a strong resume short-circuit an entire loop. The team saw a top-tier company name, skipped the standard technical rounds, lobbed a few softballs, and basically welcomed the candidate in. But once this engineer got started, it turned out to be a poor fit. And it wasn’t the candidate’s fault. They were set up for failure, because nobody checked whether this person could do this job at this company. In both cases, it didn’t work out. What you can actually control You could read all this and decide the system is broken or rigged. The broken part is fair. The rigged part isn’t. People who are genuinely good at interviewing pass more often. It’s messy, but it’s not a lottery. You can’t fight bias, politics, or a sloppy process. That’s like being mad at the weather. You can only play the two cards you’re dealt: your technical ability and your behavioral presence. Most candidates obsess over the technical side and forget the behavioral rounds exist. But product managers, designers, and cross-functional leads—people with zero technical background—will judge you entirely on whether you can tell a clear story and seem like someone worth working with. If you’re unlikeable in the room, you’ve roughly halved your odds at every stage. So here’s the unglamorous advice that actually works: put yourself on camera. Talk through a project you led, a mistake you made, a hard problem you solved. Record it. Watch it back. Cringe. Do it again. Think out loud, under pressure, with another human watching. If you keep failing interviews, the fix isn’t always more technical prep. It’s getting better at being in a room with other people who are potentially more nervous, less prepared, and more biased than you ever imagined. The process is broken. You can still win. —Brian NSF Experiments With New Kind of Science Funding A new initi...
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Musicians are accustomed to getting paid each time their creative work is used. Across vinyl/CD sales, streams, radio, cover versions, and those numerous niches like karaoke, there are agreements in place about what “use” means. Underlying this is a simple economic principle: The more something is used, the more money it makes. Generative AI has complicated the definition of use . On the one hand, you could argue that the use of a piece of musical training data happens just once, at the point of training. On the other hand, creators would be right to complain that the creative essence of their work lives on in the structure of the model, used every time the model produces an output. Now, companies like Sureel and SoundVerse are working to re-create the essential economic principle that motivates creativity in an era of AI. Such initiatives aim to turn the generative AI industry from one guilty of “the biggest act of copyright theft in history” into one that coexists harmoniously with hardworking artists. Music Royalties for the AI era Sureel , a startup Warner Music Group just acquired , has partnered with the Swedish copyright agency STIM to explore the potential for music creators to get paid when their music is used to train generative AI tools . Sureel’s software labels online media, such as a music file, with instructions determined by the owner. The instructions specify whether an AI company may use the media freely in training, limit its influence in any given training set, or avoid it altogether. The software then tracks how the AI company uses the media in training and sets licensing fees accordingly. Meanwhile, the founders of the AI music company SoundVerse “[reject] one-time royalty buyouts as insufficient and [advocate] for ongoing participation of artists in the AI lifecycle,” they wrote in a 2025 white paper . They argue that each time a generative AI system produces an output, certain pieces of training data play a greater role than others. If the system outputs music resembling jazz, the jazz in the training set has arguably contributed more than, say, the folk music. You can therefore differentially reward each piece of training data for each output. Sureel’s 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: Generative AI Has a Visual Plagiarism Problem Inferring the influence of a particular piece of music on a generated piece of music, if a well-defined problem at all, may involve more advanced information theoretic principles, or 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 ...
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On April 19, 2026, the Honor Lightning humanoid robot ran a half-marathon in 50 minutes and 26 seconds , beating the human world record by 7 minutes and the best robot time from 2025 by almost 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 building and controlling hopping and running robots , and since then I’ve tried to design and build efficient commercial legged robots , giving me a decent idea of the constraints involved. In this article, we take a look at the fundamental underlying constraints to try and answer these questions. The Physics of Running Running consists of alternating phases of a leg pushing against the ground (“stance phase”) and the body flying through the air (“aerial phase”). In the aerial phase, the body falls due to gravity, losing vertical momentum. The leg in stance phase pushes against the ground to redirect the vertical momentum upward, while the other leg swings forward to reposition for the next foothold. Electric motors use energy to produce torque- the higher the torque, the more energy 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: The power consumed by a robot leg is minimized at an optimal gear ratio (30:1 in this example). Avik De/Datawrapper How Honor Did It While the Lightning’s motor specifications are not published, the hip and knee motors roughly have a 110-150mm outer diameter. For an approximate set of motor parameters, I looked to the ILM115x25 motor due to its relevant size and detailed specifications. We can use a simple physics model to estimate the power consumption for running at 7 m/s (the Lightning’s average half marathon speed) as gear ratio varies: The light blue curve shows how to pick the optimal gearing (45:1). The dark blue curve shows how much heat will be produced in the knee motor, ~150W for the optimal gearing. Avik De/Datawrapper We see that the drivetrain is not magical: with a gear ratio chosen for this task (we’ll return to this below), the approximate robot power consumption would be a very reasonable 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 trick up its sleeve : According to Honor, the liquid - cooling pipes penetrate deep into the motors like capillaries. The high - power liquid pump has a heat - exchange flow rate of more than 4 liters per minute. Each of the four drive motors in the lower limbs is equipped with an independent liquid - cooling circuit. Liquid cooling is not new, but it’s definitely not a commodity. It has shown up in research periodically, and on the commercial side Apptronik tried it for a few of their prototypes but (to my knowledge) does not use it on their main Apollo 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. Why Others Couldn’t Compete Why did Honor’s competitors, including more established and widely-shipped humanoids such as from Unitree or Agibot , not compete as well? We can use the same model to generate an equivalent energetics plot for walking at 1.5 m/s, a much more modest but potentially more common activity for a commercial humanoid robot: The solid and dashed light blue lines show a running-optimized design, while green lines show a walking-optimized design. The optimal ratio for walking is much lower (30:1 vs 45:1). However, the power dissipated in the knee motor while running (dark blue) is much higher at 30:1 vs 45:1—the price to pay for running with a walking-optimized design. Avik De/Datawrapper The plot adds a new green curve for the walking power, and the optimal gearing is significantly different! Let’s say you design your robot to excel at the normal walking task and choose the green design with 30:1 gearing. The knee motor power to run a half marathon is over 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 increase...
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Nearly 750 million people face hunger today, according to the U.N. World Food Program . And by 2050, global demand for food is expected to increase by 50 percent from 2010 levels , the World Resources Institute says. A smart agriculture special-issue report recently released by the IEEE Smart Agri-Food Initiative says meeting the demand will require technology to expand food production. The report highlights research, case studies, and new ways of applying technology to inform farmers, engineers, and policymakers. Leading the initiative is IEEE Fellow John Verboncoeur , chair of the smart-food program and professor of electrical and computer engineering at Michigan State University , in East Lansing. “Food security is becoming a systems-engineering problem,” Verboncoeur says. “We’re no longer talking only about tractors and irrigation. We’re talking about sensing, communications, computation, automation, and sustainability all working together.” Although not formally trained as an agriculture scientist, Verboncoeur’s first involvement with smart agriculture was as an undergraduate at University of Florida in 1985-86, where he helped develop an SmartAg aeroponics system for NASA for the International Space Station . It used mist to spray the plants’ roots and lightweight pneumatic structures to hold the vegetation in place. He has also chaired the executive committee of Michigan State’s SmartAg Initiative since it launched in 2017. He chaired the program’s leading interdisciplinary efforts to apply engineering and digital technologies to farming and food systems. Verboncoeur connects the shift of using engineering as a force multiplier for farming to lessons learned from the IEEE Smart Village program, which supports projects and organizations bringing electricity and educational and employment opportunities to remote communities. Agriculture, he argues, requires the same systems-level mindset. “The challenge isn’t just inventing technology,” he says. “It’s making systems practical, affordable, and deployable.” From digital twins to autonomous harvesting A central theme across the Smart Agri-Food Systems report is the convergence of automation , data analytics , and sustainability . One paper, “ Smart Agriculture, Precision Agriculture, Digital Twins in Agriculture: Similarities and Differences ,” addresses the confusion regarding how researchers and practitioners define and apply the technologies to farming. The paper was written by Dilan Onat Alakuş , a research assistant in the software engineering department at Kırklareli University , in Türkiye, and Ibrahim Türkoğlu , a software engineering professor at Fırat University , in Elazığ, Türkiye. Unclear terminology can lead to inefficient investment and poor adoption of the technologies, the two authors say. They note that agricultural methods based on traditional practices and intuition lack a thorough analysis of their environmental and economic impacts. They describe how three technologies can benefit farmers: • Smart agriculture systems integrate sensors, artificial intelligence, robotics, and analytics to improve efficiency and sustainability at scale. • Precision agriculture focuses on location-specific decisions. Farmers use GPS-guided equipment to map fields, deploy drones to monitor crop health, and install field sensors that track soil moisture and nutrient levels in targeted zones. The tools allow farmers to apply water, fertilizer, and pesticides only where needed—which can reduce waste and lessen environmental impact. • Digital twins create virtual replicas of an agricultural area. The resulting models simulate the farmstead, crops, and irrigation systems, allowing growers to test scenarios and predict outcomes before implementing changes. The authors emphasize that the categories overlap in practice. A digital twin might draw data from precision agriculture systems and feed recommendations into smart agriculture platforms. Clearer distinctions help farmers select appropriate tools and avoid unnecessary complexity and costs, they say. “This study contributed to conscious agricultural practices by differentiating agricultural technologies,” they wrote, adding that clearer definitions can increase productivity. Smart farming in practice The report shifts from theory to application in a paper describing bustani , which means my garden in Arabic. The Bustanica project in Saudi Arabia is an automated hydroponic vertical farming system developed by researchers at the Prince Mohammad Bin Fahd University , in Al-Khobar, Saudi Arabia. The “ Bustani: A Microcontroller-Based Automated Hydroponic Vertical Farming Solution ” paper was written by Hussah Alotaibi, a computer engineer at Saudi Aramco , the country’s national oil company; Abul Bashar , Widad Karsou, and Shehvar Khan, researchers in the university’s computer engineering and computer science department; and Salahudean Tohmeh from the university’s robotics laboratory. The Bustanica system combi...
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For technical students, freelance coders, power users, and small businesses who want Claude-level productivity from budget-tier models.
A half century ago, a scrappy crew at the University of Massachusetts Amherst erected a wind turbine on Orchard Hill, the highest point on campus. It was a frugal production, cobbled together from the rear axle of a Ford truck, a donated generator and microcontroller, a steam pipe, and various handcrafted steel and fiberglass parts, including its 4.5-meter blades. The team of UMass engineering grad students, faculty advisors, and one precocious undergrad built it to prove that wind energy could keep rural homes toasty in New England’s frigid winters, as a way of trimming U.S. oil dependence—a national imperative in the aftermath of the 1973–1974 energy crisis. To illustrate the point, they also assembled a modular home there on Orchard Hill, and outfitted it with heaters that would be powered by the turbine. In 1975 and 1976, a crew from the University of Massachusetts Amherst designed and constructed the 25-kilowatt wind turbine that kick-started the U.S. wind industry. Sandy Butterfield It worked—too well. “We had to open up the doors in the dead of winter. It was just too damn hot,” recalls Michael Edds , who designed the turbine’s electrical system and served as the project’s first resident engineer. Fittingly, they dubbed the turbine the “Wind Furnace.” The turbine maxed out at 25 kilowatts—puny compared to modern machines that generate up to 26 mega watts, but more than most energy experts expected from wind technology in November 1976. Back then, wind power still conjured up images of quaint Dutch mills and creaky prairie water pumpers. Crafty engineers would soon show that wind power could be so much more. And it all began with the brilliant, commanding, and often polarizing UMass professor leading the Wind Furnace project: William Heronemus. A retired U.S. Navy captain, Heronemus had joined the UMass faculty in 1967. He’d earned Bronze Stars for valor in World War II, designed and built nuclear submarines, and liaised with the British Royal Navy on the Polaris missile. UMass had recruited Heronemus to do ocean engineering, but the energy crisis and his growing misgivings about nuclear power shifted his attention to renewable energy. Heronemus, photographed circa 1973, publicly advocated for the buildout of wind turbines, both onshore and off, at immense scale. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries By 1972, Heronemus was advancing detailed designs to deploy wind turbines at immense scale. That year, at the Marine Technology Society’s annual gathering in Washington, D.C., he presented schemes for building thousands of them across the Great Plains as well as a vast grid of massive floating turbines transecting New England’s continental shelf. Wind power, he contended, could generate nearly a fifth of U.S. electricity needs by the year 2000. Never mind that the technology for such an enormous buildout had yet to be commercialized. Espousing grand schemes made Heronemus a quixotic figure. He also vigorously attacked the commercialization of nuclear power, creating enemies within electric utilities and U.S. government agencies that saw nuclear technology as the future. They didn’t appreciate his claims that a cleaner energy future via wind was ready to be tapped, and that the push for nuclear power and its radiological risks was unnecessary. As author and energy analyst Peter Asmus put it in his 2000 book, Reaping the Wind : “ William Heronemus was a dangerous man suggesting an audacious departure from the status quo.” The UMass Amherst wind turbine generated most of the energy to heat a modular home through the cold, windy winters on Orchard Hill. Solar thermal panels provided some heat during windless periods. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries What happened on Orchard Hill in 1976 marked Heronemus’s turn from provocateur to changemaker. The success of the experimental turbine set off waves of technological and industrial developments that forever changed the energy landscape. Within a few years, the students he trained and the entrepreneurs he inspired were building the world’s first modern wind farms and leading the Great California Wind Rush—the market that turned wind craft into an industry that’s still growing fast half a century later. Globally, annual wind generation more than tripled between 2015 and 2025, according to data from Ember Energy , a think tank based in London. It will best nuclear’s global output by the end of this year, Ember predicts. And it all started with Heronemus, says Robert Thresher , longtime former director of wind research at the National Renewable Energy Laboratory (NREL) in Golden, Colo. 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Yen-Ling Kuo always wanted to understand how things worked. When she was growing up in Taiwan, reading the story of Michael Faraday in elementary school piqued her curiosity about the natural world. During that time, she was introduced to Logo , 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. Yen-Ling Kuo Employer University of Virginia in Charlottesville Title Assistant professor of computer science Member grade Member Alma maters National Taiwan University; MIT In 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 assistant professor of computer science at the University of Virginia in Charlottesville, last year received the IEEE Robotics and Automation Society ’s inaugural Outstanding Women in Robotics and Automation Early Career Contribution Award . The award is part of the IEEE-RAS Women in Engineering’s Outstanding Women in Robotics and Automation (WiRA) Paper Awards , 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, “ Diff-DAgger: Uncertainty Estimation with Diffusion Policy for Robotic Manipulation ,” 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. Silicon Valley’s impact Kuo earned bachelor’s and master’s degrees in computer science at the National Taiwan University , 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 comparison ads project . When her internship ended, she joined the MIT Media Lab as a visiting student, working on the Open Mind Common Sense project with Henry Lieberman . 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 Shop the Look initiative , a predecessor to Google’s current AI-powered shopping experience . 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 neural network —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. The question that changed everything Boris Katz , one of Kuo’s Ph.D. advisors, is a principal research scientist and the head of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)’s InfoLab . He also led the creation of the START Natural Language System , 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 summer course at MIT’s Center for Brains, Minds, and Machines , a research initiative that ran ...
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Autonomous vehicles are already a reality on some of our streets and could become a major part of future transportation systems. Safety, of course, is the main concern, as with all vehicles. To help evaluate and improve its autonomous driving technology, U.S. driverless vehicle company Waymo has created a virtual representation of human driver behavior in near-crash situations.
The photoacoustic effect has been known for more than 150 years: Gases exposed to light heat up. Pulsing the light generates periodic pressure fluctuations, or sound waves, with frequencies that can be uniquely assigned to individual gases. This photoacoustic effect forms the basis for a measurement method that is highly precise even at low gas concentrations. Despite its high sensitivity, the method has previously only occupied a niche, primarily because it relies on a resonator for acoustic amplification. This resonator is highly sensitive to even the slightest changes in air pressure, temperature or mechanical stress. However, ensuring an accurate measurement requires the system to precisely hit the correct resonant frequency.
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