IEEE Novosti

One of the singular features of Baotou, an industrial city of 2 million people in Inner Mongolia, is a 10-square-kilometer (4-square-mile) lake that is among the most polluted in the world. The artificial lake, referred to by the name of the dam associated with it, Weikuang Dam (or Baogang Tailings Dam), has for decades collected the effluent from minerals processing for China’s globally dominant iron, steel, and rare-earths industries.Built in the late 1950s, the lake contains a highly toxic stew of industrial chemicals with substantial amounts of radioactive thorium, the latter resulting primarily from the processing of monazite ores for rare earths. China has since greatly tightened its environmental regulations and restrictions on mining and minerals processing. For example, largely because of the environmental disaster that is Weikuang Dam, the country doesn’t permit the development or operation of stand-alone monazite mines—that is, mines where nothing is mined except monazite ore.Now, with industrialized nations in North America, Asia, and Europe spending billions to shore up their rare-earths supply chains, and counting on monazite to help them do it, China’s history and experiences with monazite offer important lessons.This article is a collaboration between Foreign Policy, the global magazine of politics and ideas, and IEEE Spectrum, the flagship magazine of the IEEE.Monazite ore is a pillar of China’s rare-earths industry because it is a prime source of some less common but extremely important rare-earth elements, notably dysprosium, terbium, yttrium, and erbium. The country mines and processes vast quantities of monazite ore; it also imports huge amounts of monazite ore for processing.Most of the domestically mined monazite ore comes from a single mine: the mammoth Bayan Obo complex, 150 kilometers north of Baotou in Inner Mongolia. Bayan Obo, which is primarily an iron-ore mine, also happens to contain the largest known rare-earth deposit in the world. It’s the source of nearly half of all the rare-earth ore mined on the planet in a typical year. Most of the rare-earth ore mined at Bayan Obo is a type called bastnaesite; it’s the mining of that bastnaesite that makes it legal to mine monazite ore at Bayan Obo as well.Related: Can a Radioactive Ore Break China’s Grip on Rare Earths?All told, ore from Bayan Obo was used to produce 170,000 tonnes of rare-earth metal last year, according to David Merriman, research director at the minerals intelligence firm Project Blue. Roughly 30 to 40 percent of the rare-earth ore mined at Bayan Obo is monazite. That amount alone would be enough to make China the world’s largest processor of monazite for rare earths.China’s 2025 Monazite Imports Topped 70,000 TonnesBut a more detailed picture of China’s interest in monazite emerges when you consider the country’s imports of the ore. Last year they amounted to more than 70,000 tonnes, says Thomas Kruemmer, a Singapore-based consultant on minerals and metals. That figure becomes more impressive when you consider the unique challenges of shipping monazite by sea. Because of its radioactivity, concentrated monazite ore is classified as Class 7 dangerous cargo under the International Maritime Dangerous Goods Code. That means, Kruemmer notes, that the ore can be imported through only four ports in China.In addition, importation of some kinds of monazite into China is supposed to go through a joint venture consisting of the China National Nuclear Corp. and Shenghe Resources, which are at least partly state-owned. But lots of imported monazite doesn’t go through those organizations, notes Merriman. That’s because heavy mineral sands, which may contain roughly 2 or 3 percent monazite by weight, are not subject to the same import controls as concentrated monazite ore. So companies import the heavy mineral sands; separate out various profitable concentrates, including the monazite mineral concentrate; and then process the monazite concentrate to recover the rare earths.China has lately stepped up its efforts to secure rare-earth ore around the world. One reason, says Kruemmer, is simply to make up for recently lost supplies. In 2025, the U.S. government invested US $400 million in MP Materials, which operates the Mountain Pass mine and processing facility in California. The deal put an end to longstanding contracts under which MP Materials had been selling tens of thousands of tonnes of rare-earth concentrate annually to a trading affiliate of Shenghe Resources.But other Westerners speculate that China has strategic reasons for stepping up its efforts to acquire heavy mineral sands or other rare-earth assets abroad. By locking down rights to deposits or projects abroad, Chinese organizations are making them unavailable to non-Chinese developers, according to this reasoning.Merriman points out that your view of these activities largely depends on where you sit. The U.S. government recently made a conditional loan commitment of up to $725 million to U.S. monazite processor Energy Fuels, which has been aggressively securing substantial monazite supplies in Australia, Brazil, and Madagascar. To a Chinese observer, those moves probably look rather strategic.China, meanwhile, is doing what it has been doing for decades. “China’s playing its game,” says Merriman.

The global campaigns to break China’s dominance of vital rare-earth markets are shaping up into one of the signature geostrategic struggles of the early 21st century. Hundreds of mining companies, government agencies, and industrial facilities scattered across North America, Australasia, and Europe are striving to wrest share from Chinese firms, which control roughly 90 percent of the markets for rare-earth oxides and rare-earth metals.Rare earths are the ingredients on which vast and crucial manufacturing categories depend: semiconductors, LEDs, lasers, displays, metal alloys, optical lenses, and catalysts for petroleum and chemical processing. Most important, though, are permanent magnets. Essentially all powerful electric motors are based on rare-earth permanent magnets. These motors are the heart of electric vehicles, heating and cooling systems, robots, appliances, and tools. Rare-earth magnets are also in the motors for military systems ranging from tactical drones to ballistic-missile submarines.This article is a collaboration between Foreign Policy, the global magazine of politics and ideas, and IEEE Spectrum, the flagship magazine of the IEEE.Succinctly put, any industrial power that wants to remain competitive in vital, high-tech industries must have access to a steady supply of rare-earth elements or products based on them. The traditional supply chain for these elements, originating in China, is increasingly fraught—so much so that some analysts are beginning to use the term “mineral war.” The countries that are now scrambling to piece together alternative supply chains, whether by themselves or in cooperation with each other, face daunting odds. Never mind the sheer momentum of the Chinese rare earths juggernaut. Few industries can match the rare earths business for instability, risk, intrigue, and sheer frustration.And yet, many rare-earth experts see a way forward, and it depends on a specific kind of ore, called monazite. The ore contains huge amounts of valuable rare earths, including neodymium, praseodymium, and dysprosium.“The future is monazite,” declares Jack Lifton, an analyst, consultant, and author. Lifton, who has been studying the rare earths industry since 1962, has seen countless trends, companies, and technologies come and go, and he isn’t given to sweeping statements. But on the subject of monazites, he’s bullish. “Right now, in my opinion, monazite is the most important rare-earth ore,” says Lifton, who is also cochairman of the Critical Minerals Institute, an organization that’s promoting the creation of a North American supply chain.Related: Chasing China’s Monazite MonopolyThat path forward isn’t going to be smooth or straight, Lifton and others are quick to acknowledge. Monazite typically contains radioactive thorium and uranium, which means its handling, storage, and processing are tightly regulated by government agencies. It can be processed only with special techniques and procedures, and the mildly radioactive wastes generated by processing must be stored in isolation indefinitely.Nevertheless, major investment is now flowing to monazite mines and processing technologies. In the United States, Energy Fuels, which operates the country’s only conventional uranium mill, has developed its own extraction process and is producing mixed oxides of neodymium and praseodymium—and also pilot-scale quantities of dysprosium and terbium oxides, key heavy rare earths that are otherwise produced almost exclusively in China. Energy Fuels is processing monazite into rare-earth oxides at its White Mesa Mill in southeastern Utah. The body of water at left in the image is a tailings impoundment, where radioactive waste from processing is stored.Energy FuelsThe company has spent more than US $200 million to secure monazite assets in Brazil and Madagascar, and plans to scale its rare-earth production to higher commercial levels, at an estimated cost of $410 million. Energy Fuels’ rare-earth ambitions received a significant boost in June, when the Trump administration announced that it had signed a $725 million conditional loan commitment with the company.Meanwhile, though, a government-backed project in Saskatchewan, Canada, stands as a cautionary tale for monazite backers. After six years of construction and expenditures of more than CA $100 million (US $72 million), a processing plant there has produced only minuscule quantities of rare earths. The processing plant, built by the Saskatchewan Research Council, was supposed to be producing 40 tonnes of rare earths per month by the end of 2024. That didn’t happen. “Full operation,” with production of 400 tonnes of metal per year was planned for “early 2025.” That didn’t happen, either. The organization is now targeting late 2027—by which time the project will have cost more than CA $216 million, according to the Saskatchewan Research Council’s CEO, Mike Crabtree.Outside of North America, major monazite projects are also planned by Astron Limited, Hastings Technology Metals, Iluka Resources, Mkango Resources, Sovereign Metals, Steenkampskraal Holdings, and Tronox, among others. Perth, Australia–based Lynas Rare Earths, the largest rare-earths company outside of China, is expanding its monazite mining and processing operations. Most notable is the Lynas mine at Mount Weld, Australia, one of the world’s top monazite resources; the ore is processed at a sprawling facility in Malaysia.Of course, for countries outside of China, all this surging activity doesn’t guarantee a route to rare earths independence. It’s not even close. So the vital questions now are, What else has to happen to broaden and diversify the rare-earths supply chains? And can all of this be done safely?What Are the Advantages of Monazite Ore?Planet Earth has enormous quantities of monazite ore, in two different forms. In Australia, Canada, China, and South Africa, massive deposits of monazite take the form of veins of hard rock that run through mountains and calderas. Much of the new interest in monazite, though, is focused on a second type, called heavy mineral sands. The sands are the result of tens of millions of years of exposure to wind, water, and gravity crumbling mineral-rich rocks into grains, which then accumulated in coastal areas or alongside riverbeds. A particularly desirable form of monazite ore is found in heavy mineral sands, such as these on a beach in Brazil. Travel Pix/AlamyWithin those grains, the monazite is relatively hard and chemically inert. The monazite might be just a few percent by weight, but it typically contains between 45 and 70 percent rare earths by weight, including, in many cases, all of the necessary rare earths for magnets: neodymium, praseodymium, dysprosium, and terbium.“The best distribution of rare-earth elements that you can find anywhere is in monazites,” says Constantine Karayannopoulos, former CEO of Neo Performance Materials and now a director of or adviser to several rare-earths firms. “The real money is in those four elements.” Dysprosium and terbium, both examples of heavy rare earths, are particularly sought after and are usually not present in the most commonly mined ore for rare earths, called bastnaesite.There are vast deposits of monazite-bearing heavy mineral sands in Australia, Brazil, India, Madagascar, Mozambique, and South Africa, with smaller ones in China, Idaho, Indonesia, Malaysia, Sri Lanka, Thailand, the southeastern U.S., and Vietnam. A geologist points out dark veins of hard-rock monazite ore in the Steenkampskraal mine in South Africa [top]. Ore samples from the mine are collected for analysis [above].Rodger Bosch/AFP/Getty ImagesWhat’s more, collecting the ore simply involves dredging or strip-mining the sands, as opposed to the blasting, drilling, and heavy earth moving required to get at hard-rock ores. Then, too, the sands are already in granular form, so they don’t need to be crushed or ground, like rock ores do. Separating out the heavy minerals from the sands can usually be done at the mining site, using relatively cheap magnetic or gravity-based techniques, leaving a relatively high grade of concentrate for further processing.And now the bad news about heavy mineral sands. “The problem is they tend to have a lot of thorium,” which is weakly radioactive, says James Kennedy, president of ThREE consulting, a rare-earths advisory firm based in Missouri. “The thorium is the single biggest problem” with using the sands, he says. “If you were mining and processing monazites for rare earths, you would end up with hundreds and eventually thousands of tons of thorium-containing radioactive residues” per processing facility. The only markets for thorium are a couple of tiny ones for welding rods and some optics, he says, so “you have to store it.” And under the rules for nuclear material set by the U.S. Nuclear Regulatory Commission and the International Atomic Energy Agency, any stored radioactive material that doesn’t have a planned use or disposal pathway is considered nuclear waste. “So the [radioactive] liability suddenly far exceeds the value of anything you’ve mined and sold,” Kennedy says.It wasn’t always this way. Up until the 1950s, monazite was the main ore mined for rare earths. Companies processed heavy mineral sands from Australia, Brazil, and India, and hard rock from South Africa, to produce rare earths for tracer ammunition, steel alloys, cigarette-lighter flints, and, later, color-TV phosphors. The Mountain Pass mine and processing plants, in California, are the only large-scale rare-earth production facilities in the United States.David McNew/Getty ImagesSeveral developments combined to shift rare-earths production from monazite to bastnaesite. In 1949, a vast deposit of bastnaesite ore was discovered at Mountain Pass in California. By the early 1960s, bastnaesite had eclipsed monazite as the primary rare-earth ore. In the 1990s, with rare-earth production shifting to China, the move toward bastnaesite continued with the exploitation of massive deposits in that country, such as those at Maoniuping, that were either entirely or mostly bastnaesite.Now, though, any resurgence of rare-earth production outside of China may hinge on a monazite renaissance, and some observers are taking a hard look at the strict regulations that control the handling of thorium and other by-products of monazite processing. Those regulations, Kennedy insists, will throttle the rare-earth ambitions of the world outside China, unless governments take action now.“You rationalize the regulations around thorium and you will end the rare-earth crisis in the U.S.,” he declares. “You’ll turn rare earths from something that’s always scarce to something that is common and just naturally flows out of other mining.”How to Scale Rare-Earth Processing Using “Waste”A prime example of Kennedy’s vision already exists at Energy Fuels, based in Lakewood, Colo. It’s the only company in the U.S. licensed to process, handle, and store compounds of uranium, and among the very few licensed to store radioactive materials of any kind. Those permits give Energy Fuels the distinction of being the only U.S. entity allowed to process monazite ore for its rare earths. “Supersacks” containing a tonne of neodymium-praseodymium oxides were loaded on pallets at Energy Fuels’ White Mesa Mill.Energy FuelsThe company is moving aggressively to capitalize on that distinction. Energy Fuels’ main business is still uranium—last year it produced nearly 500 tonnes of it. But it has already started selling rare-earth oxides, following a modest $22 million investment in a small rare-earth processing facility at White Mesa Mill in Utah, where it also processes uranium. In September 2025, the company announced that it had sold 1.2 tonnes of neodymium-praseodymium oxide to South Korea–based POSCO International Corp., a leading maker of permanent-magnet assemblies for EV motors. Around the same time, Energy Fuels disclosed that it had raised a further $700 million.In December 2025, the company announced it had produced small quantities of dysprosium oxide. Tiny amounts of dysprosium are needed for motor magnets to ensure they keep functioning at high temperatures. Today, essentially all of the world’s dysprosium comes from China.Energy Fuels says it can produce 1,000 tonnes of rare-earth oxide a year at White Mesa Mill. But its rare-earth production there currently shares facilities with the uranium operations, so they cannot both be produced at the same time. Its monazite comes from the chemical company Chemours, which mines heavy mineral sands in Florida and Georgia to produce titanium and zirconium. The monazite ore sold to Energy Fuels is what remains after Chemours strips out the minerals it needs, namely ilmenite and zircon. The chemical company Chemours mines heavy mineral sands at sites in the U.S. states of Georgia [seen here] and Florida. Monazite extracted from the sands is sold to Lakewood, Colo.-based Energy Fuels, which processes the ore into rare-earth oxides.Chemours“Effectively, monazites are waste,” Karayannopoulos points out. “So you don’t have to dig another mine” to produce them.Chemours and Energy Fuels have agreements under which Chemours ships 500 tonnes of monazite annually to White Mesa Mill. Energy Fuels is also negotiating or already has deals that would give it access to enormous reserves of monazite ore in Australia, Brazil, and Madagascar. These will be essential for its ambitions to become a world-class supplier of rare-earth oxides. Mark Chalmers, former CEO of Energy Fuels, led the uranium company’s entrance into the rare-earths business.Jesse Rieser/The New York Times/Redux“We’re playing a long game,” says Mark Chalmers, who recently retired as Energy Fuels’ CEO and is now a consultant to the company. “We’re doing the engineering right now on a larger facility that will have the capacity to produce up to 6,000 tonnes per year” of rare-earth oxide, he said in an interview. For comparison, Lynas Rare Earths produced around 12,000 tonnes of rare-earth oxide in its most recent fiscal year.In announcing the $725 million conditional loan commitment with Energy Fuels, the U.S. Defense Department’s Office of Strategic Capital said the deal would help Energy Fuels scale up its output of rare-earth oxides and also help realize the company’s plans to turn the oxides into metals, a key step preceding the fabrication of magnets.What Are the Risks in the Rare-Earth Supply Chain?To see what can go wrong with a monazite project, consider the project in Saskatoon, Canada, where the Saskatchewan Research Council (SRC) has built and demonstrated a pilot plant to produce rare-earth oxides from monazite ore. It’s the only rare-earth extraction and separation plant in the country. In an email, a spokesperson said the plant “is scheduled to be commissioned by year’s end and after a production ramp-up phase, operational within the calendar year 2027.” When in “full operation,” at a time not specified by the spokesperson, the plant will have a capacity somewhere between 400 and 600 tonnes of rare-earth metal per year.But even that relatively modest output will depend on a reliable supply or “feedstock” of monazite. In an industry small enough that many of the top people are on a first-name basis, SRC’s inability to secure such a supply is an open secret. “Their problem is they haven’t got enough feedstock to run on a continuous basis, so they’re making sample runs,” a well-connected industry observer told IEEE Spectrum. (SRC declined to make an official available for this story.)In April 2024, SRC announced it had reached agreements with a Vietnamese organization, the Hung Thinh Group, for 3,000 tonnes of rare-earth carbonate per year for five years. It’s unclear how much of that feedstock has made it to the SRC facility. Vietnamese government restrictions on the export of monazite and bastnaesite have been in effect since 2022 and became law this past January.But the real twist here is that one of the largest, highest-grade monazite deposits in the world is just 800 kilometers from Saskatoon, near Alces Lake in Saskatchewan. It’s likely that the SRC facility was conceived and built with that monazite deposit in mind. After all, as a government-owned entity, SRC says its mission is “to help the people of Saskatchewan strengthen the economy with quality jobs and a secure environment.”The Alces Lake deposit is owned by Appia Rare Earths & Uranium Corp. In an interview, Appia’s CEO, Tom Drivas, talked up the possibility of a link between SRC and Appia. “SRC would be our logical option because, you know, it’s in the same area, same province. They need the monazite. And if we can supply the monazite, that’s sort of a natural, right?”It probably won’t happen, though. The logic of rare-earth mining is utterly different from the logic of mining a commodity like, say, copper. Multiple experts contacted by Spectrum suggested that the cost of developing a monazite mine at Alces Lake, including such necessary infrastructure as roadways, electricity, and water, would be in the hundreds of millions of dollars. Such costs, and the mercurial exigencies of the rare-earths market, make it unlikely that the government of Saskatchewan would commit that kind of money, even with help from the Canadian federal government. These factors help explain why Canada, which has some of the most attractive deposits of rare-earth ores on the planet, along with impressive mining companies and resources, has no operating mines for rare earths.Meanwhile, SRC is pursuing other options. Under a deal announced in May with REalloys, in Euclid, Ohio, REalloys will spend $20.6 million for upgrades to SRC’s Saskatoon facility, while SRC develops technology to produce rare-earth metals from rare-earth oxides. The deal also gives REalloys the right to purchase up to 80 percent of the SRC plant’s output. Where will the feedstock come from? Hard to say, but REalloys also struck a deal with U.S. Critical Materials Corp. for 10 percent of the hypothetical output of a proposed mine at Sheep Creek, in Montana. Sheep Creek reportedly has substantial amounts of hard-rock monazite that’s unusually low in thorium. But it’s far from certain that those deposits will ever be mined. The proposal for developing a mine there, in a breathtakingly scenic area near the headwaters of the Bitterroot River, has attracted fierce opposition from environmental groups, outdoor enthusiasts, and local officials.Taken together, SRC’s recent moves have left industry veterans nonplussed. “I’m not sure what their business model is,” said one. “They say, ‘we’re a research institute. Our mandate is to help industry.’ The reality is, they’re competing with industry.”SRC has also drawn scrutiny for some questionable business practices. In 2022, SRC board member Dennis Fitzpatrick was fired after raising concerns about the qualifications of an engineer hired to manage and direct operations at SRC’s Saskatoon plant. The president and CEO of SRC at the time, Mike Crabtree, was responsible for the hire. A subsequent investigation of Fitzpatrick’s claims by the Canadian Broadcasting Corp. detailed a troubling web of relationships and practices surrounding the SRC’s rare-earths initiative. (Fitzpatrick did not respond to multiple emails.) For example, past business associates of Crabtree’s were reportedly appointed to positions of authority without any other candidates being considered and without their connections to Crabtree being made public.In one case, in 2021, Crabtree disclosed that he was offering a venture capital firm, Westbridge Capital, an opportunity to invest in the rare-earths plant. But according to Fitzpatrick, Crabtree never mentioned that the VC firm was run by an SRC board member who also happened to be one of Crabtree’s business associates—or that Crabtree himself was a trustee of Westbridge and an investor in the company, as well as a paid advisor.Crabtree remains as SRC’s CEO.Managing Toxic By-products From Rare Earths Putting aside SRC’s missteps, the organization does have one advantage: It holds multiple licenses from the Canadian Nuclear Safety Commission to handle, process, and manage radioactive materials. And that’s huge.Most of the radioactive burden associated with processing monazite ore comes from substances produced during processing. In one of the early steps, pulverized, powdery ore is dried and then leached in strong acids—a process called cracking—to produce a rare-earth concentrate. Waste products of this step include a slurry containing thorium hydroxide and much smaller amounts of uranium hydroxide. The rare-earth concentrate then undergoes an extraction process to isolate specific rare earths, such as neodymium or dysprosium. More thorium and also radium are produced during these steps. Waste barrels were photographed in 2024 at Energy Fuels’ White Mesa Mill, where ores containing uranium and rare-earths are processed and refined.Jesse Rieser/The New York Times/ReduxThe thorium hydroxide slurry, meanwhile, is typically deposited in heavily regulated waste cells, called tailings impoundments or tailings ponds. The thorium hydroxide, either in the form of a slurry or a powder, must be stored in isolation from the environment indefinitely. Energy Fuels stores waste from its longstanding uranium production operations, as well as its new rare-earth operations, in tailings impoundments at its White Mesa Mill facility. The impoundments are just 10 kilometers from the community center of the Ute Mountain Ute Tribe, a group of Indigenous people, which has vigorously opposed operations at White Mesa Mill for more than 20 years.Virtually all of the thorium is the isotope thorium-232, which is only weakly radioactive. Its radiation is alpha particles, which cannot penetrate human skin. Nevertheless, improper storage can have serious consequences, particularly if the thorium is inhaled or ingested.“Have a look at the gravitational separation of HMS [heavy mineral sands] in Nigeria,” Thomas Kruemmer, a rare-earth consultant and analyst, wrote in an email. “There is lots of dust in the Nigerian processing sites, which the workers inevitably breathe in. And inside your respiratory system there isn’t any protective skin, so alpha radiation develops its full potential. This is something many people do not consider.” In 2012, protestors in Malaysia [top] rallied against the Lynas rare earths processing facility [above], then under construction near Kuantan. Left: AFP/Getty Images; Right: Goh Seng Chong/Bloomberg/Getty ImagesMoreover, adds Kruemmer, “it is not thorium itself. It is the decay isotopes of thorium that are highly radioactive.” One decay product of thorium is polonium, he points out, “the preferred poison for [killing] Russian dissidents.”In 1982, a company called Asia Rare Earth, a subsidiary of Mitsubishi Chemical, began extracting yttrium from monazite ore in Perak, Malaysia. Local residents soon began complaining of various illnesses. With the consent of the Malaysian government, the company had built a waste channel to a nearby disposal site, where the waste was stored in open steel barrels and in an unlined waste pond. The waste overflowed from the pond whenever it rained heavily. In 1984, a Japanese professor measured radiation levels at and near the pond and found them to be, in the most extreme cases, hundreds of times higher than internationally accepted maximums. A subsequent investigation found extremely high levels of birth defects and eight cases of leukemia in the small community near the waste dump. In 2011, at age 69, Lai Kwan prepared to bathe her 29-year-old son, Cheah Kok Leong [top]. Cheah Kok was born with severe mental disabilities that Lai Kwan believed were caused by exposure to radiation that she received while working at a rare earths processing plant in Bukit Merah, Malaysia [bottom, photographed during its disassembly].Rahman Roslan/The New York Times/ReduxA protracted legal fight among local residents and Asia Rare Earth and the Malaysian government culminated in the closure of the plant in 1992. By that time, so much radioactive material had been carelessly dumped that cleanup took more than two decades and cost Mitsubishi an estimated $100 million. The episode still haunts the rare-earths industry in Malaysia, where Lynas now processes monazite concentrate at the largest rare earths processing facility in the world. That plant, in Kuantan in the state of Pahang, has been the subject of organized and active local opposition that began even before its commissioning in 2012. The Malaysian government has sporadically tightened its regulations on the plant, stipulating that, for example, radioactive wastes be shipped out of Malaysia or that the processing stages that create radioactive residues be accomplished outside of Malaysia.Kennedy notes that if monazite ore is to become a major source of rare earths, much larger quantities of thorium will need to be disposed, probably more than could be economically accommodated in lined and isolated tailings ponds.One of the very few options for using such large amounts of thorium involves nuclear power. Years ago, countries including the U.S., Canada, Norway, and Germany had thorium-reactor research programs, and India has had a slow-moving program for decades. But today, the only operating thorium power reactor is in China. The Shanghai Institute of Applied Physics of the Chinese Academy of Sciences has been operating, since 2023, a 2-megawatt molten-salt-cooled reactor that is thorium-fueled. The world’s only operating thorium reactor is a 2-megawatt unit at the Shanghai Institute of Applied Physics of the Chinese Academy of Sciences. In October 2025, technicians used mechanical arms to analyze samples of the reactor’s molten-salt fuel.Jin Liwang/Xinhua/Getty ImagesA couple of Western startups are keeping the thorium reactor dream alive. A Franco-Dutch company called Thorizon says it has designed a reactor that would be fueled by a mixture of thorium and spent nuclear fuel. Startup Copenhagen Atomics is working on a 1-MW reactor that’s a prototype for a 100-MW, mass-manufacturable, molten-salt reactor.Processing of monazite ore could provide fuel for an enormous number of thorium reactors far into the future. Consider a single large facility producing 5,000 tonnes of rare-earth oxide a year. If the monazite contains 7 percent thorium, the plant would also be producing roughly 700 tonnes of thorium a year. According to Rusty Towell, a professor at Abilene Christian University in Texas who directs an advanced-reactor project there, a tonne of thorium would be sufficient to generate about 350 MW (electric) for a year. So the large processing facility alone would be throwing off enough thorium annually to keep 2,450 100-MW reactors operating for a year.That amount of power—245 gigawatts—is about half of all the generating capacity growth forecast for the United States over the next decade, a period during which analysts expect accelerating electricity growth due to increasing numbers of data centers and electric vehicles.What Could Strangle Rare-Earth Supply Chains? Virtually all rare-earths experts foresee a growing role for monazite. But given the problems with thorium, local opposition to some projects, and political instabilities connected to others, some have muted expectations. “The sole and single monazite hope rests with Iluka’s expensive project in Australia, IMHO,” writes Kruemmer. Iluka Resources, based in Perth, is now building a massive rare-earths refining facility at Eneabba, 275 km north of Perth. It will process a million-tonne monazite stockpile that accumulated over decades from the company’s other mining operations. The cost of the project is estimated at AU $1.8 billion (about US $1.3 billion). In a US $1.3 billion project, Iluka Resources is building a massive facility in Eneabba, Australia, to extract and separate rare earths from a million-tonne stockpile of monazite, visible in the background of this photo.Iluka ResourcesFor other experts, though, the biggest challenges facing monazite projects isn’t thorium, or radiation, or regulations. It’s the blend of quirks, misconceptions, and self-dealing that dog the entire rare-earths industry.“The rare-earth mining business is the Afghanistan of mining,” quips Kennedy. It’s a place where people and companies go with grand ideas, only to become ensnared in quagmires.One fundamental problem is the highly speculative nature of almost every aspect of the rare-earths business, which lends itself to backroom deals. In particular, the industry is plagued by speculators who buy stakes in a project with no intention of staying with the project through completion; they cash out as soon as they can profit from any future favorable news—a technique known as “pump and dump.”Consider the Trump administration’s decision to take a 10 percent stake in USA Rare Earth in exchange for $277 million and to provide a $1.3 billion secured loan to the company. USA Rare Earth has the rights to a deposit called Round Top near Sierra Blanca, Texas. The company hired the Wall Street firm Cantor Fitzgerald to raise $1 billion in equity financing for the deal. Cantor Fitzgerald, as it happens, is run by the sons of Howard Lutnick, Donald Trump’s Commerce Secretary.Meanwhile, there is no rare-earths mine at Round Top, and it’s a distinct possibility that there never will be.“The current administration consistently fails to seek expert advice and is recklessly pouring billions of taxpayer dollars into speculative [rare-earth element] processes and projects, like Round Top, that have little chance of commercial success,” says Terry McNulty, a mining consultant based in Tucson.Geoff Atkins, vice president of business development of the Canadian rare-earths firm Ucore Rare Metals, had advice for his colleagues at a convention of the Critical Minerals Institute in 2022. “Instead of focusing on your speculative value,” he said, “instead of being able to say we’ve got the biggest project in the world, instead of, ‘we’ve got a [net present value] of $3 billion or $4 billion,’ how about you say, ‘we’ve got a project which is going to get into operation, and this operation is going to provide the greatest value to our shareholders in the long term, not just in the short term when we can pump this up and, you know, the [early] investors can sell out.“Because unless there is that shift,” he warned, “we’re not going to have any hope of getting the volumes we need to give the certainty to the downstream supply chain.”In short, the rare-earths industry outside of China needs to grow up. And sooner rather than later.

This White Paper shows RF and microwave engineers and technicians how compact analog signal generators support core amplifier and receiver test tasks, from gain and distortion measurements to blocker rejection, power calibration, and local oscillator substitution.What you will learn about:How to measure amplifier gain and gain compression, including the P1dB point that marks the transition from linear operation into saturation.How two-tone intermodulation distortion testing reveals unwanted signal products created by amplifier nonlinearities, and why third-order intercept (IP3) is used to compare amplifier linearity.How to test receiver performance under blocker and interferer conditions, using high output power and fast switching speed to simulate real-world interference.How power calibration and RSSI testing depend on accurate, repeatable signal sources across multiple frequency channels and bands.How signal generators can substitute for local oscillator and clock subsystems during early-stage receiver development, before final circuit prototypes exist.Download this free whitepaper now!

Progress continues in restructuring IEEE’s geographic regions, as outlined in The Institute’s February 2023 article “IEEE Is Realigning Its Geographic Regions.” The “Region 1” designation will no longer be used.The new Region 2 will merge what is currently Region 1 (Northeastern U.S.) with the current Region 2 (Eastern U.S.). It will be called Region 2 (Northeastern and Eastern U.S.).The current Region 10 (Asia Pacific) will be divided into two separate regions. The new Region 10 will encompass North Asia, and the new Region 11 will cover South Asia and the Pacific.The restructuring is defined in the image of the redrawn map in this article. What’s underwayAlthough the new regional alignment will not officially take place until 1 January 2028, several activities have begun within IEEE Member and Geographic Activities (MGA) and across other IEEE organizational units. Within MGA, volunteers and staff have been actively addressing system enhancements, director/director-elect elections, governance updates, communications, and regional finances and offerings.No changes to membership Work will continue from now until the effective date of realignment in 2028. The change will not affect your IEEE membership, nor will it change either your local IEEE section or chapter.If you have any questions regarding the realignment, contact Reg-Realignment@ieee.org.

If you’ve ever wondered how Claude and similar AI agents can answer complex questions in seconds and where they get the information, you’re not alone.Gurmeet Saran works to find that answer every day as an engineer at Anthropic in San Francisco, the AI research company behind Claude. The IEEE senior member is developing an infrastructure to understand the mechanics behind the chatbot’s behavior and to ensure its underlying architecture produces reliable outputs.Gurmeet SaranEmployer Anthropic in San FranciscoTitle Member of technical staffMember grade IEEE senior memberAlma maters University of Kalyani, in India; Birla Institute of Technology and Science, in Pilani, India “My job is to make sure the data is accurate, structured, and trustworthy, and that private data stays private,” he says. “As I learned earlier in my career, privacy can’t be a layer you bolt on—it has to be part of the architecture from the start.” If you can’t trust the logic, he adds, you can’t make reliable claims about the behavior, and that has real safety implications.Safety is a priority for Anthropic, he says. According to Claude’s mission statement, the company wants the AI agent to be “genuinely helpful to the people it works with or on behalf of, as well as to society, while avoiding actions that are unsafe, unethical, or deceptive.” Crucial to the mission is ensuring that researchers can observe and understand how AI models behave, a challenge that is especially acute for large language models, Saran says.“We have decent tools to monitor databases and web services,” he says, “but for LLMs, we’re still figuring it out.”Solving data quality problems at Yahoo, Intuit, and MetaSaran’s interest in computing systems dates back to his childhood in Kolkata, where he logged as much time as he could in the school computer lab.“I remember the first time I used one of the computers,” he says. “What struck me was that you could write instructions, and the machine would follow them exactly. No ambiguity.“That felt magical to me.”After graduating from high school in 1999, he stayed in India to pursue advanced degrees. He earned a bachelor’s degree in electronics and telecommunication engineering in 2004 at the University of Kalyani. In 2010, he earned a master’s degree in software engineering from the Birla Institute of Technology and Science in Pilani.While a graduate student, he began his career in 2007 at Yahoo as a technical lead of the data team in Bengaluru, India. While there, he encountered the project that shaped his career trajectory: working on web analytics and business intelligence reporting solutions.“We kept running into a frustrating problem,” he recalls. “Different teams would calculate the same website metric—something like daily active site users—using slightly different logic, and end up with conflicting answers. It sounds like a small issue, but when leadership is making decisions about resource allocation and product development using that data, not knowing which number is correct becomes a problem.”The team built a central data repository with defined metrics, essentially serving as a single source of truth. Saran created a model that defined what “daily active site users” means, breaking down which events counted, how to handle edge cases (or when standard code fails), and more.With the new model, data teams across the company could rely on a standard method to determine the number of daily active users for the projects and sites they were responsible for and obtain comparable results using the same logic.From there, Saran and his team built Yahoo! Digits, an internal web analytics tool used for all the company’s products.Saran left Yahoo in 2010 to join Intuit as a staff engineer in Mountain View, Calif. He helped build the analytics infrastructure that unified data across the company’s products, including TurboTax and QuickBooks accounting software. During his tenure, Intuit developed a feature that detected fraudulent tax filings in real time. It required Saran and other engineers to build a system to protect taxpayer data while adhering to strict U.S. Internal Revenue Service policies.“IEEE operates in a lot of countries where engineers don’t have easy pathways into the global professional community. That kind of connection matters as AI development spreads beyond a handful of organizations.”“This taught me that privacy and governance can’t be layers you bolt onto software after the fact,” he says. “They have to be part of the architecture from the start.”Saran joined Meta (known as Facebook at the time) in 2016 as a lead software engineer in Menlo Park, Calif. The company was facing controversy over its video metrics: Meta publicly acknowledged an error that caused it to overstate the number of views for posted videos.“This wasn’t a coding bug; it was an architectural problem,” Saran says. He built a system that worked similarly to what he had developed at Intuit, whereby one single truth was defined to govern all video view metrics across the site.That system, Metric360, eventually became part of the site’s architecture. By the time he left Meta in 2021, he says, Metric360 was being used across the company to calculate video metrics.He left the company to become a principal software engineer at Wealthfront, a financial services company in Palo Alto, Calif. He helped build the company’s data infrastructure.Building trustworthy AI systems at AnthropicSaran joined Anthropic in 2025 as a member of the technical staff.To describe the implications of his work, he uses a medical analogy: “Imagine a doctor is monitoring a patient’s vital signs and making treatment decisions based on those numbers. What if one or a few of the medical devices used were slightly miscalibrated? Even if most readings are fine, it’s a serious problem because the difficult decisions happen in the margins.“It’s the same with AI systems. My job is essentially to make sure the instruments are giving accurate readings—that the data means what it says it means.”His job comes with high stakes and complex problems to solve.“AI agents are especially tricky to observe,” he says. “When a system is taking sequences of actions [generating conversational responses with different users], you need a way to capture what it did, what information it was working from, and whether outcomes matched what the end user expected. And you have to do all of that without exposing anyone’s private information—the systems are designed to recognize sensitive data and keep it protected as it flows through. It’s a much more demanding observability problem than anything I’ve worked on before.”Solving for system reliability while AI is still in its early days of adoption is both Saran’s passion and the problem that keeps him up at night, he says.“I think making AI systems genuinely auditable and transparent is one of the more important engineering problems of the next decade,” he says, “and it doesn’t get talked about as much as model capabilities do.”IEEE can play a role in shaping AI developmentSaran joined IEEE in March 2025.“After more than 20 years of building data infrastructure across organizations like Yahoo, Intuit, Meta, and Wealthfront,” he says, “I wanted to contribute back to the technical community through peer review, publishing, and engaging with the broader engineering research ecosystem.”He says he believes IEEE can play an essential role in shaping the future of AI through its standards work.“We have standards for everything, from building codes to software testing,” he says. “AI infrastructure needs something similar, and IEEE has the credibility and the reach to do that.”Another problem that IEEE can solve for, he says, is access: “IEEE operates in a lot of countries where engineers don’t have easy pathways into the global professional community. That kind of connection matters, especially as AI development spreads beyond a handful of organizations in a few cities.”

This article was originally published by Canary Media.The geothermal startup Fervo Energy is now selling power to the grid from its Cape Station project in Utah—marking the first time a utility-scale, next-generation geothermal facility has achieved such a feat worldwide. The Houston-based firm turned on an initial 33-megawatt unit late last week and has been testing and ramping up output since. On Thursday, Fervo will officially begin commercial operations, supplying carbon-free electricity to Shell Energy, a retail power provider in California. Cape Station, a $2 billion project that will eventually be 500 MW, is unique among the world’s commercial geothermal plants. While other facilities draw on naturally occurring hot water and steam, Fervo has engineered its own geothermal resource deep underground. The startup uses drilling techniques from the oil and gas industry to create reservoirs in hot, dry rocks that generate steam to produce electricity. “Traditional geothermal would have never been done in southern Utah,” says Dawn Owens, Fervo’s senior vice president and head of development and commercial markets. With the first unit online, Fervo says it is working to commission two more 33-MW power blocks by 1 January, 2027. The project’s remaining 400 MW—which is supported by a new agreement with Google—is under construction and slated to come online in 2028. Fervo’s Next-Generation Geothermal JourneyFervo already operates a 3.5-MW commercial pilot plant in Nevada, which went online in late 2023. Owens says the pilot, called Project Red, was important for demonstrating the technology itself. But firing up Cape Station proves that Fervo’s approach can achieve utility scale and export power to wider markets. “This is massive for both Fervo and the geothermal industry at large,” she says. Fervo, which went public in May, is leading the fast-growing movement to develop clean, always-on geothermal power to meet soaring demand from data centers and balance growing supplies of intermittent solar and wind energy. Next-generation geothermal firms have raised billions of dollars in private capital in recent years to develop and deploy technologies that can harness energy in harsh, deep environments. Drilling continues at Fervo Energy’s Cape Station geothermal plant in Beaver County, Utah, where the company plans to ramp up production to 500 MW. Fervo EnergyToday, geothermal represents just 0.4 percent of total U.S. electricity generation every year. Expanding geothermal’s share of the mix will require not just building more projects like Cape Station but also tackling high upfront drilling costs and other economic and regulatory barriers—all while working to limit potential impacts on communities and the environment, experts say. “Fervo’s first power achievement shows investors the size and scale of the opportunity to fuel many more such milestones across developers and sites in the future,” says Mitchell Smith, who serves as grid lead for the Mountain West Geothermal Consortium. The bipartisan initiative is calling for increased public and private financing “to help bring more projects online responsibly and at the speed of demand,” he added. Enhanced Geothermal Energy Gets CheaperFervo has declined to disclose the terms of its power purchase agreements for in-progress and future development, which together represent about 1,000 MW, or enough to power roughly 1 million U.S. homes every year. Phase 1 of Cape Station is set to deliver power at $7,000 per kilowatt, while Phase 2 should come at $5,500 per kilowatt, the company previously told Canary Media. (By comparison, the average cost to build a natural gas–fired power plant was $2,157 per kilowatt in 2025.) Along with slashing drilling costs, enhanced geothermal projects could improve their economics by delivering power when it’s most valuable to utilities, according to researchers. While geothermal plants traditionally provide around-the-clock, baseload power, next-generation facilities might operate flexibly during certain periods—like in the evening, when solar output declines—to reduce overall grid costs. Geothermal plants could also operate “behind the meter” to directly power data centers so that the costs of adding new electricity capacity aren’t passed on to utility customers, Owens says. “We’ve been saying all along that our technology makes geothermal more scalable and affordable in more places,” she says. On Cape Station’s powering-up, she adds, “That’s just something no next-generation geothermal company has ever been able to do before.”

This White Paper provides roboticists and control engineers with a comprehensive overview of how motion planning and motion tracking can be decoupled to preserve professional athletic style in humanoid robots, and why adaptive, speed-aware control is essential for closing the gap between simulation and real-world deployment.What you will learn about: Why combining a professional athlete’s motion style with reliable task performance remains an open challenge in humanoid ball-sports robotics, and how separating a high-level planner from a low-level tracker addresses this trade-off.How motion data extracted from broadcast video and motion-capture recordings can be reconstructed, retargeted, and physically corrected into reference motions a humanoid robot can safely track.Why the sim-to-real transition introduces compounding tracking errors, and how training a tracker on randomized execution speeds, paired with a learned motion-speed adapter in the planner, improves robustness on physical hardware.How this adaptive framework was validated across two humanoid platforms, including full-size, in-the-wild serving performed without any motion-capture system.Download this free whitepaper now!

Today’s engineers face an unprecedented acceleration in AI hardware complexity, as explained in the recent research article “Revisiting Edge AI: Opportunities and Challenges.” The article examines the rapid growth of edge AI and the challenges it creates, including resource constraints, model architecture limitations, and network demands across edge-AI deployments.The acceleration is driven by a fundamental shift in how modern AI models are built and scaled. As the models have become much larger and more complex, they are computationally more demanding because they contain more parameters and require more calculations.To meet the demands of scaling deep neural networks, the industry is increasingly developing AI chips that are designed for specific tasks.One major reason is that moving data between memory and the processor has become a major limitation on AI performance.The movement to confront the hardware bottleneck—the AI memory wall—has altered the trajectory of semiconductor innovation, shifting architectural priorities toward domain-specific accelerator platforms.No longer can engineers evaluate systems statically; they must master joint hardware design and network-algorithm co-optimization to navigate the critical trade‑offs between throughput, latency, and operational efficiency.The challenges are addressed in the new AI Processor Architecture, Design Principles, and Performance program, developed by IEEE Educational Activities with support from the IEEE Computer Society.Topics coveredThe five-course program provides a structured exploration of AI processor technologies, from fundamental design principles to advanced architectures and real-world deployment.The topics are:Fundamental principles of design and functionality.Practical insights into advanced architectures.Understanding neural processing units for industry deployment.Emerging trends and evolving architectures.Designing for edge, cloud, quantum, and the Internet of Things (IoT).The program addresses the needs of professionals across the AI hardware ecosystem, including hardware architects, chip designers, embedded systems developers, data‑center hardware engineers, and innovators exploring next‑generation processor ecosystems.It is also valuable for those transitioning into AI chip design or seeking to understand the architectural forces shaping modern machine learning acceleration. For many, it provides the bridge between theoretical knowledge and the collaborative, cross‑disciplinary reasoning required in engineering environments.The program is designed to explore how modern AI processors are conceived, structured, and optimized. The architectural layers that define contemporary AI hardware will be covered, including compute units, memory hierarchies, dataflows, and the performance characteristics that emerge from design decisions. The curriculum bridges theory and application, enabling participants to interpret architectural foundations, analyze trade‑offs, and understand how hardware structures shape computational efficiency across diverse environments.By the end of the courses, learners will be able to evaluate processor behavior with the analytical precision expected of professionals working at the frontier of AI hardware design.AI-generated avatars explain conceptsThe program also uses a dialogue‑driven learning approach. Learners view conversations between AI-generated avatars that engage in scenario‑based dialogues. The avatars represent engineers tackling the same problems from various perspectives based on their different roles. The simulated storytelling and dialogues make advanced engineering concepts approachable without sacrificing depth or interactivity, because the user is occasionally challenged to decide the correct answer that leads to the best course of action.A hardware engineer might push back against a systems engineer’s demands, for example, revealing the friction between physical constraints and algorithmic ambition. A computational validation specialist might interrogate a chip performance engineer’s optimism, exposing the gap between theoretical throughput and real‑world behavior. A heterogeneous systems architect could debate a multiprocessor coordination specialist about synchronization overheads, while a standards development engineer discusses regulatory implications with a technology strategy and compliance architect.In the final course, an IoT systems architect and an embedded AI optimization engineer dissect the realities of deploying AI in constrained environments.By the end of the course, learners will be able to evaluate processor behavior with the analytical precision expected of professionals working at the frontier of AI hardware design.The avatar-driven learning environment creates a psychologically safer environment for learners, who might feel intimidated by traditional expert‑led videos. Research published in 2024 in IEEE Transactions on Learning Technologies showed that avatar‑based instruction can increase a learner’s confidence by up to 25 percent and improve retention of complex technical material.Each course concludes with a module in which the two experts from different disciplines debate, question, and challenge each other’s assumptions.Modeling expert reasoning through dialogueThe cross‑disciplinary conversations can do more than explain concepts; they can model how experts think. They can reveal the negotiations behind architectural choices, the competing priorities that shape system design, and the analytical rigor required to balance performance, efficiency, scalability, and compliance.Learners observe engineering discourse, gaining insight into the reasoning patterns that drive innovation in AI processor development.The program goes further by interrupting the dialogue at key moments and inviting the learner to step in. Instead of passively absorbing information, the learner decides how to resolve a trade‑off, predict the outcome of a design choice, or select the most defensible engineering path. The experience can feel less like a course and more like an apprenticeship inside an engineering team.A new standard for advanced engineering educationBy merging rigorous technical content with an innovative, dialogue‑driven delivery model, the program sets a standard for teaching advanced engineering. It captures the complexity of real‑world problem‑solving, humanizes the learning experience, and challenges participants to think like the engineers who are defining the future of AI hardware. It is not simply a new course; it is a new way of learning.For individual access, visit the IEEE Learning Network.For customized organizational options, contact a content specialist to discuss volume pricing.

In mid-September, Anthropic CEO Dario Amodei joined a growing group of researchers calling for the AI industry to slow down. While some have questioned these researchers’ motivation for voicing concerns about the rapid pace of development, the plea highlighted a key tension between safety and speed. The underlying problem is not simply a technical error, but a sociotechnical failure: Pressure to move quickly has made it hard for engineers to fully assess and manage new risks of increasingly powerful AI systems. It is a clear warning that responsible engineering requires more than technical expertise; engineers must also understand workplace pressures, moral and ethical rules, and the social consequences rooted in the systems they create.Cases like this, alongside invasive surveillance technologies and biased AI tools, have exposed serious shortcomings in how engineers recognize and address the ethical and social implications of their work. While the need to educate graduates in ethics and social responsibility is widely recognized, a persistent gap remains—these topics are too often treated as afterthoughts, disconnected from the technical content of engineering coursework. This siloed approach leaves graduates ill-equipped to address real-world challenges; indeed, only about a third of engineering professionals in the United States report having received training on their public welfare responsibilities during their formal engineering education.We are working to bridge this gap by designing two complementary instructional approaches, one that integrates social issues directly into foundation technical coursework and a second that prepares students to act as “public welfare watchdogs” through a standalone engineering course. By embedding ethics and social responsibility training directly into the core of undergraduate engineering coursework, we equip future professionals with the agency they will need to protect the public good.The gap in instruction about ethics and social responsibilityAcross the engineering profession, there is a persistent belief that technical skill-building is central to engineering, while ethical, social, and public welfare concerns are peripheral or distracting. This technical/social dualism is evident even in the evaluation of scholarly research. For instance, we recently submitted an academic position paper discussing this ethics education gap to a leading peer-reviewed engineering journal. It was dismissed by reviewers along the very same dualism it described. One reviewer warned that it would be “highly irresponsible” to challenge technical skill-building as “the foundation of any kind of engineering” by adding ethics and social responsibility training into engineering classrooms. Another reviewer objected that integrating these considerations into foundational courses risks “distracting the students from fully absorbing the technology.” These comments miss the core argument of our work: that ethics and social responsibility are integral to responsible technical practice.Today, accreditation agencies like ABET (formerly known as the Accreditation Board for Engineering and Technology) and professional licensing bodies like the National Society of Professional Engineers mandate student outcomes related to social and public welfare consequences. But these curriculum requirements tend to be unhelpfully generic and minimally enforced. As a result, engineering students often receive ethics instruction through introductory or non-engineering coursework, where the focus is on codes of conduct and academic integrity, rather than deeper topics of social responsibility. This instruction is almost always disconnected from the complex real-world dilemmas engineers face. Indeed, we find that only ethics instruction in actual engineering courses (not in other parts of the curriculum) can effectively instill ethical awareness as professionals. Concerningly, only about 4 in 10 engineers in the United States recall receiving this kind of training, and 3 in 10 report never receiving training on their public welfare responsibilities at any point in their careers. This leaves most of the profession underprepared to handle the complex challenges of engineering practice.A new approach: Integrating technical and social dimensionsTo advance a new paradigm—one that embeds ethics and social responsibility issues within the undergraduate engineering curriculum—we’ve designed two innovative course-based approaches: a library of one-hour sociotechnical modules for foundational circuits courses (described here and here) and a new engineering course dedicated to public welfare responsibilities.The first approach integrates discussions about relevant social issues directly into foundational engineering courses, where students begin to form their identities as engineers. “Introduction to Circuits” is typically the first course in the electrical engineering major and is a requirement for many other engineering disciplines, making it a powerful site for introducing social content and ethical reasoning. We designed several one-hour modules for this course, where social issues provide context for a technical topic, offering a manageable entry point for faculty who may feel ill-prepared to teach about ethics and social responsibility.While many engineering faculty recognize the importance of teaching students about these topics, they struggle to integrate them into their courses amid heavy teaching and research demands. To overcome these challenges, each module provides ready-to-use materials, including lecture slides with scripts and homework and exam problems. Integrating these modules directly into the course signals to students that sociotechnical awareness is not an “add-on,” but a required professional competency.Our module on hospital power prioritization, for instance, connects the technical topics of power and equivalent circuits to the ethical challenges of healthcare infrastructure. Students learn about the reality of “red outlets” in medical settings, debating the power needs of specific wards (e.g., the intensive care unit, operating rooms, and physiotherapy rooms) to decide which branches should receive emergency power during a hypothetical outage.Our conflict minerals module connects the technical topic of capacitors to the global tantalum supply chain. Students calculate the amount of tantalum used in cell phones and identify mining locations in regions like the Democratic Republic of the Congo, where revenue often fuels armed conflict and human rights abuses. Students research the conflict-mineral policies of major companies such as Apple and Samsung and reflect on their future roles as ethical designers and consumers.Student responses to these modules have been overwhelmingly positive. In interviews, students told us that the content helped them get away from the “stigma that engineers only worry about math” and made the work more meaningful. We plan to share the modules widely to help circuits instructors across the globe bridge the technical-social divide.Beyond these integrated modules, our second approach is a standalone engineering course—Public Welfare Responsibilities in Engineering (PWRE)—that helps round out students’ sociotechnical education by preparing them to fulfill their professional obligations as engineers. While most dedicated ethics courses end by introducing engineers’ abstract responsibilities, PWRE helps students recognize specific public welfare concerns, enhances their motivation to act when they encounter them, and teaches concrete, practical intervention strategies.In addition to teaching students about professional codes, such as the IEEE Code of Ethics, PWRE engages students in discussion of where these formal definitions may fall short, and how structural barriers such as workplace culture can hinder engineers from fulfilling these responsibilities. Then, the course equips students with intervention strategies. Students learn about their rights and responsibilities as whistleblowers, review tactics for seeking advice from professional societies, and practice writing op-eds to raise the alarm about ethical issues to the broader public. Our research shows that the course produces notable shifts in students’ ethical agency, strengthening their ability to serve as “public welfare watchdogs” in the modern workplace.Ethical engineers for the futureToday’s engineering graduates face complex sociotechnical challenges beyond AI expansion, including climate change and global supply chains; they need tools to navigate ambiguity and act ethically in uncertain contexts. To meet these evolving demands, engineering education must move beyond outdated modes of instruction that focus only on building technical skills—some of which may soon be outsourced to AI. We must fundamentally reimagine the role of ethics and social responsibility, embedding them across the engineering curriculum and throughout the professional formation of engineers.All engineers learn to solve problems, but ethical engineers can also discern which problems are worth solving and anticipate the consequences of innovation for society. By centering ethics and social responsibility as core priorities of the profession, we can prepare engineers to be stewards of technology and trusted voices in public discourse.As one student reflected after participating in our modules, “We are a part of the issue if we don’t decide to fix it.”

By 2028, genetic engineering and gene therapy will be used frequently to treat a variety of diseases, thanks to advances in artificial intelligence, according to the recently released 2030 Technology Megatrends Report from IEEE.“We are seeing a fundamental shift in how technology evolves,” says IEEE Fellow Dejan Milojicic, chair of the IEEE Future Directions Committee’s Industry Advisory Board, the group responsible for the report. “Artificial intelligence is no longer operating in a vacuum; it is now deeply connected to our energy, infrastructure, and physical systems.” Milojicic, a Hewlett Packard Enterprise Fellow, is a vice president at HPE Labs in Milpitas, Calif.AI underpins many of the report’s 30 technologies and five “megatrends,” which are technology shifts with the potential to reshape industries and everyday life for decades. A megatrend isn’t a single breakthrough but a collection of related technologies.For the 2030 report, 166 experts—from 38 countries across six continents—drawn from industry, academia, and government identified 30 breakthrough technologies across the five megatrends: AI, energy, health, space, and physical AI. The experts graded the trends against five criteria: likelihood of success, impact to humanity, maturity, market adoption, and horizon to adoption.AI as a common threadInterconnected systems drive the megatrends, and the connection among them is AI.The report notes that AI technology is advancing faster than any previous revolution, including industrial, electrical, and digital. The pace has turned the technology into general-purpose infrastructure, Milojicic says.That evolution carries risks, though, the experts say. The report cites the lack of computing infrastructure and energy sources as constraints on the ability to scale up AI, and it says the technology’s growth will require reshaping the workforce.Workers have always needed to adapt to stay competitive, Milojicic says, adding that AI adoption isn’t an all-or-nothing approach. Businesses should decide when and how to apply it, he says.The experts say long-term success will require both technical capabilities and attention to the human-AI relationship. Conditions including trust, security, explainability, and policymaking will determine which AI-supported technologies are most likely to scale, they say.“This report shifts the conversation to something far more important: how these advances will intersect with one another to shape human lives,” Mary Ellen Randall, 2026 IEEE president and CEO, said in a news release about the report. “The data makes clear that trust, safety, and human connection must guide every major breakthrough in the decade ahead.”Health care is a high-impact betThe experts scored personalized medicine as the study’s highest-impact technology, at 4.93 out of 5, for its potential to improve human life.The report credits AI-driven breakthroughs in personalized diagnostics and therapeutic treatments, driven by innovative biotech startups. Within two years, the experts predict, genetic engineering and gene therapy will be used in therapeutic areas.Within five years, earlier disease diagnoses will move out of the lab and into day-to-day medical practice, the report predicts. Meanwhile, it says, researchers likely will be able to manufacture simple synthetic proteins—molecules engineered from scratch, rather than found in nature—to perform a specific job in the body, such as targeting a tumor or supplying a missing enzyme.Energy wakes up for AIIncreased power demand by AI is outpacing the energy sector’s ability to keep up. Tech infrastructure—data centers, networks, and computing hardware—already uses an estimated 10 percent of electricity globally, according to the news release.The experts predict that within two to three years, energy storage will double in developed countries.The energy industry has been relatively dormant for years, Milojicic says, with little breakthrough innovation since the times of Thomas Edison and Nikola Tesla.“Data centers can ramp up within milliseconds, yet it takes months for the power and energy sector to ramp up,” he says. “The governments of various countries will have to rethink their approach to power generation to stay ahead of demand.”The experts cite the Jevons paradox—the idea that efficiency gains tend to increase overall consumption. As AI chip efficiency improves, organizations will run more hardware to build ever-larger frontier models—those advanced AI systems currently in development—the experts say. Data centers will draw as much power as grids can supply, leaving no surplus, the experts predict.Physical AI moves the fastestPhysical AI refers to systems embedded within machines that sense and act in the world, including robots and autonomous vehicles. In the report, the category originally was called “hyper-automation and robotics,” a broad, software-driven process automation paired with physical machines. The Industry Advisory Board later folded it into the single physical AI megatrend.The experts found that human-AI interaction has the shortest adoption horizon of any of the 30 scored technologies, at two to three years. They also say physical AI has the biggest chance of technical advancement of any megatrend during the next four years.“The data makes clear that trust, safety, and human connection must guide every major breakthrough in the decade ahead.” —Mary Ellen Randall, 2026 IEEE president and CEOSmarter, more efficient robots will substantially cut energy consumption in the next two years, the experts say.Communications between humans and AI—whether in an android or another device—will shift from text-based commands to predominantly audio and video instructions within two to three years, the experts predict.Androids used in factories will use tactile and haptic feedback within two to five years, letting them sense what they’re touching and adjust their grip or force in response, the experts say. Bio-inspired skin, tissues, and sensors will advance in the same timeframe, the experts predict, giving robots perception that better mirrors human touch.Space is just getting startedSpace technology, as the report defines it, covers more than exploration. It spans satellite communications, in-space manufacturing, launch technology, and other infrastructure. It is one of the lower-profile megatrends in the report, not for lack of promise but because of where the sector presently sits.“It’s not lagging,” Milojicic says. “It’s the current state of maturity. People are talking about satellite communication right now because it’s very cool and practical. But it’s a small segment compared to the attention AI gets.”He says he believes the space sector will be more prominent in the coming years: “This is an area of technology that will become increasingly important, especially as terrestrial communication and transportation push the boundaries for space as well.”The report highlights two technologies on the near horizon: semiconductor manufacturing that uses parts built in space and more affordable reusable rockets. It also sees clean energy generation in space and the commercialization of orbital garbage collection as potential technologies to watch for in the next decade.What’s next?The report recommends that academia, industry, governments, and professional associations work together.To build a well-trained workforce, the report says, industry should partner with professional organizations on training programs and revive apprenticeships in chemistry, physics, and other physical sciences. It also calls on businesses to treat AI infrastructure—computing, data, and energy—as a long-term strategic asset.Other recommendations include expanding access to affordable, quality education in STEM coursework and AI competence.Governments should lead on policy and implement reintegration programs for workers displaced by AI, the report says.The experts say professional organizations including IEEE should develop new products and services using AI, strengthen safety and data governance standards, and serve as a collaboration platform for industry, academia, and policymakers.The report also offers recommendations for end users, content creators, investors, and company leadership on strategies for succeeding in an AI-focused future.The same thread runs through all the report’s recommendations: Don’t lose sight of the human-AI relationship.“As a public charity dedicated to advancing technology for the benefit of humanity, IEEE believes progress must be measured by more than technology development and implementation speed,” Sophia A. Muirhead, IEEE executive director and chief operating officer, said in the news release. “Whether retraining the global workforce or integrating AI responsibly into daily life, the success of these megatrends depends on ensuring innovation strengthens human well-being and public trust.”

This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free!When I changed careers at 30 to become a software engineer, I expected it to be hard. I expected to struggle to fit in, and I expected to bomb a lot of interviews (I did). What I didn’t expect was how few people would support me.As a parent, I get it now.A career change is a risk, and the people who care about you tend to project their fears onto you. They don’t want to watch you struggle or fail, so they discourage you or just don’t offer much enthusiasm.Right now is a strange time to be in engineering, or knowledge work in general. Plenty of us are excited about what AI means for our jobs. More of us are nervous, and I think that fear is justified. Employment for entry-level software developers has fallen nearly 20 percent since late 2022, according to Stanford’s 2026 AI Index, and it can feel like the knowledge we spent our careers building is being commoditized. Lately I’ve seen a flood of social media content telling software developers to go into the trades. YouTubers are telling you to become a plumber or a goat herder. But before you do that, I want to warn you, there is no EASY path. Even if you want to become a goat herder.I’ve changed careers once so far, but my business has forced me through several pivots that felt a lot like career changes—new audiences, new skills, new identity. Here’s what I’d tell anyone considering a change, whether you’re moving into engineering, leaving it, or finding a new speciality.Find someone who’s done it and ask howThis is exactly what I told my oldest son, who’s getting ready to enter the job market full-time. Outside of the primary social media feeds, the internet is full of free and paid communities on Facebook, LinkedIn, and other sites. Reddit works, too, if you can stomach the trolls, because there’s valuable information buried in there. Many of these spaces are flooded with people trying to break in, so look for the ones full of people who already do the work and talk openly about what is and isn’t going well. Then find one or two people in your own circle who’ve done it and ask what the job takes. You’ll often find the degree matters less than you assumed. Indeed Hiring Lab found the share of U.S. postings requiring a bachelor’s degree fell from 20.4 percent in 2019 to 17.8 percent in early 2024, and more than half listed no education requirement at all.Build proof of work, even if it’s unpaidYes, you’ll probably need to do some work for free, or at least far cheaper than you’re used to. I’ve had to do this in my business over and over. Nobody cares that I’ve been a software engineer for 12 years. They want to know whether I can do the thing they need.When I started pitching businesses, I offered to build something for free as a case study, then used it to land clients who paid well. You can copy this in most fields. Say you want to get into marketing: Learn how Facebook ads work, find a friend or small business that needs help, and run the campaigns at a low price. Now you’ve picked up the jargon, and you have something concrete to show a hiring manager.Stability is a mythIt may be corny, but change is the only constant. If you’re a software engineer, you could argue you’re in the middle of a career change already, whether you opted in or not. Programmers in the 1990s had the same experience when the web showed up and they had to quickly learn new tools just to stay relevant.If you’re looking for stability, you’re in the wrong place, and I think it will get scarcer as AI and other forces reshape work. The best move is to build a strong foundation so you can adapt when the ground shifts. I wrote about the foundational skills to focus on if you’re interested.—BrianBarbara Mazzolai Wants to Build a New Field of RoboticsRoboticist Barbara Mazzolai is a prime example of someone who advanced her career by switching fields—and now, she’s helping to create a new one. Learn how her background in biology led her to co-author a manifesto on “sustainability robotics.”Read more here. Turning Tech Talent Into Leadership LegacyThe transition from a technical role to a leadership position can be challenging. To help seasoned professionals navigate the shift, IEEE is hosting its first International Leadership Conference in Budapest. Read more here.IEEE Celebrates Innovators Shaping the FutureEvery year, IEEE recognizes engineers whose technical achievements and leadership have made a lasting impact with its Technical Field Awards. See who’s on the 2027 list of awardees, which was announced in late September. Read more here.

This White Paper gives robotics researchers and engineers an overview of a new large-scale motion capture dataset built to close the data gap limiting humanoid robot learning. It also shows how policies trained on the dataset transfer to a real humanoid robot.What you will learn about: Why humanoid robot learning, a central problem in embodied AI and Physical AI, needs data that internet video and existing motion capture datasets cannot provide. How FrameNet, a linguistic framework for human action, can guide motion capture collection to systematically cover a broad range of whole-body motion. Why synchronized object trajectories and meshes make human-object interaction data useful for teaching robots real-world tasks such as carrying, pushing, and pulling. How reinforcement learning policies trained on this motion capture data improve with scale, and how sim-to-real transfer carries them onto a physical humanoid robot.Download this free whitepaper now!

Happy IEEE Day 2026!IEEE Day is an annual event that celebrates the first time in history when engineers worldwide gathered to share their technical ideas in 1884.One of the IEEE Day’s objectives is to show the ways IEEE members, in local communities, join together to collaborate on ideas that leverage technology for a better tomorrow. IEEE Day 2026 - October 6, 2026. Stay tuned as we continue this journey of innovation, connection, and global celebration.IEEECelebrate IEEE Day with colleagues from IEEE Sections, Student Branches, Affinity groups, and Society Chapters. Events happen both virtually and in person all around the world.IEEE Day 2026 EventsExplore IEEE Day 2026 events taking place around the world from 4 - 17 October 2026. The event listing is dynamically updated as new events are registered.View events →Compete in contests and win prizes!Have some fun and compete in the photo and video contests. Get your phone and camera ready when you attend one of the events. This year we will have both Photo and Video Contests. You can submit your entries in technovation, raise your flag, and social categories.View contests →Special Offers & ActivitiesCheck out our special offers and activities for IEEE members and future members. And share these with your friends and colleagues.View offers →

For the first 10 years of my career, I worked in product management and data analytics by myself. I wrote database queries that pulled numbers out of corporate systems, built statistical models to predict what customers would buy, and shipped data pipelines that moved information between business systems.I built and scaled analytics teams at Best Buy and Target, studying how customers shop and what stores should stock. Today I lead enterprise AI transformation at Lowe’s, the Fortune 100 home improvement retailer.The goal is not to sell artificial intelligence; it is to use it to deliver useful expertise at the moment a customer needs it. In retail and other customer-facing industries, virtual assistants can help people address everyday questions—such as how to repair a leaky faucet—while guiding them toward relevant products, services, or next steps. As these capabilities become more common, technology roles are changing. The work is no longer limited to building AI systems; it also includes defining how they operate: which decisions they can make autonomously, when they must escalate to a person, and which actions must remain off-limits.That shift—from building AI systems to governing them—is coming for anyone who is accountable for what such systems produce. Not the casual user typing into a chatbot but the engineers, product managers, analysts, and business operators who sign off on work a machine drafted.It is the subject of the book I recently coauthored, The Enterprise Brain. I call the change the “governor shift,” from executing tasks yourself to setting the intent, principles, and boundaries within systems that execute them for you.Business operators might not write code; they will decide which pricing exceptions an agent may approve and which it must escalate.That is governing.A 2025 report from MIT Media Lab’s Project NANDA found that, despite an estimated US $30 billion to $40 billion in enterprise generative-AI investment, the vast majority of organizations in its dataset had not yet demonstrated measurable profit-and-loss impact. The report estimated that only about 5 percent of integrated pilots were generating substantial value, underscoring how difficult it remains to move from experimentation to scaled business outcomes.Researchers named the pattern the GenAI Divide, the term I adopted for the book.The companies rarely lack technology; they use the same models as the 5 percent that are winners. But they lack people who can direct the systems and stand behind the results. Guidelines to followHere are six guidelines.Recognize when you have become “human middleware.” In software, “middleware” is the code that sits between two systems and passes information back and forth. Many of us have become its human version. Take an honest look at your week. How much time is spent pulling data out of one tool, reformatting it, and routing it to another team? I call this the “administrator trap,” which is set by the architecture, not by the people caught in it.Relaying is what AI agents now do well. But they cannot judge which numbers deserve attention, which risks are real, or which compromises are worth making.Trade rules for principles. For many years, workers used rules to manage their work. Refunds for a product over a certain amount needed a signature from upper management, for example. Writing code needed two reviewers. Rules work at human speed. But rules break when a system makes thousands of decisions per hour and meets situations no rulebook anticipated, such as a complaint covered by three different policies. A rule says to do exactly this specific thing; a principle says to achieve the outcome without crossing certain lines.Governing AI means writing those principles in priority order so the system settles its own conflicts the way a well-led team does when the manager is not available. Never harm the customer. Tell the truth even if the company loses a sale. Protect the economics, and then move quickly. Underneath sits a question of decision rights: the formal authority over who or what may make a given call. Writing down the answers in what I call a “library of principles” is now core leadership work, whether you’re a technologist or a business owner.Write your culture into your code. Many companies have turned their values into posters that hang on office walls. But an AI agent cannot read the posters. Instead, write your governance as code. Include your values and policies as machine-readable instructions that the AI agent will follow automatically.Do so in three layers. The top is the constitution, which states the rules an agent may never break, and never state a fact it cannot support. The second layer is the doctrine: how the business competes and the acceptable trade-offs to get there, such as protecting a long-term relationship over a short-term sale. At the bottom sits the playbook, which has the tactics used for one task.Install a trust thermostat, not a trust switch. The question that stalls nearly every company’s AI deployment is some version of: “What if it tells our biggest customer something wrong, or quotes a price we will not honor?” It might. Treating trust as a switch leaves two bad options: an unsupervised system or a human reviewing every transaction—which would cost more than the automation would save.The alternative is a thermostat. Every decision an agent makes carries a confidence score measured against the principles set. Above an agreed threshold, it proceeds alone; below it, a human decides. That person’s answer is fed back into the learning loop so the next similar case clears the threshold on its own. Every decision stays transparent, auditable, and explainable—which is what I call a glass box.Fix context before you govern. You cannot govern a system that cannot see the whole picture. Ask your best employee about a project, and they will pull together the budget, the contract clause, and the customer’s last complaint because they know it all by heart. Most enterprise AI fails that test, because the information sits scattered across applications that store it in incompatible formats. I describe the full loop as Connections, Context, Reasoning, Actions, and Governance (CCRAG).Connections feed in raw information such as transactions and service records. Context weaves it into a context graph, which is a single connected picture of the business that gives agents something close to memory. Reasoning makes the decisions. Actions carry them back into the business systems. Governance keeps things aligned with the company’s intent.Most organizations obsess over the reasoning in the middle and underinvest in context and governance, which is exactly where humans play a role. Context compounds: Every interaction makes the graph richer and harder to reproduce.Learn to lead by exception. The most important change in habit comes last. Most of us have been trained to check every report and every number because we never knew where an error could surface. In a governed system, the machine tells you which cases it could not resolve confidently. Routine workflows go untouched, and your attention goes to the small portion that is ambiguous, unfamiliar, or high stakes.At first, that might feel like losing control, but it is the opposite. It is what makes a self-scaling enterprise possible, an organization whose output grows without its head count growing in proportion. People were not removed from the loop; they were raised above it.The identity questionWhen I talk with people about the shift, their resistance is rarely about technical issues. More often it is about identity: If the AI does the doing, what do I do?I have watched capable people freeze on that question. I’ve also asked myself the question.Doing was never really the job, though. Judgment was. Doing was just how we expressed it.AI has not made judgment less valuable. It has made it the scarcest resource in the organization because, for the first time, one person’s judgment, written down well, can guide thousands of decisions each day.Judgment has a twin we talk less about: taste. Judgment tells you whether an answer is sound. Taste tells you whether the question was worth asking and which of a hundred defensible options to offer the customer. A machine will happily generate all 100 options, but it cannot tell you which is best.The AI transition rewards instincts many IEEE members already have: systems thinking, precision about requirements, and honesty about failure modes. The tools have changed, but the discipline has not.Governing is where taste and judgment stop being soft words and become the work itself.The people who treat it that way, rather than as a step away from engineering, will define the profession in the age of AI.

Christos Cassandras has spent more than 40 years studying how computers and machines make decisions. Lately, his work has focused on a problem nearly every driver knows well: sitting at a red light with no cross traffic in sight, wondering why it takes so long for the light to turn green.Cassandras, an IEEE Life Fellow, is a professor at Boston University, where he headed its systems engineering division, a graduate program he helped create.Christos CassandrasEmployer Boston UniversityTitle ProfessorMember grade Life FellowAlma maters Yale, Stanford, and HarvardHe is the recipient of this year’s IEEE Intelligent Transportation Systems Society Outstanding Research Award for his work on autonomous vehicles and the systems that help them drive—work that could one day get rid of traffic lights altogether.That idea—proving with mathematical models and intelligent algorithms using cars’ relative speeds, mass, and distance as inputs that self-driving cars will behave safely before they are ever let loose on the streets—has become the foundation of Cassandras’s career and the reason his name keeps coming up in conversations about transportation.In the 1990s he theorized that a machine such as a car could be understood as two things at once: an object obeying the laws of physics and a computer processing information and making decisions. This led to what we now call cyber physical systems. That framework is still the standard way engineers describe a car’s mechanical behavior.“Ultimately, the key word is safety,” Cassandras says. “You would not buy a self-driving car unless the manufacturer could guarantee that it’s safe for you.” Growing up in Greece during a dictatorshipBorn in Athens, Cassandras was a teenager during Greece’s military dictatorship, a period he says scarred him and many others but also taught him some hard lessons. His father was a partner in an insulation manufacturing company, and his mother was a homemaker.His friends pulled Cassandras toward engineering. As a teenager, he ran with a group of classmates who read about philosophy, history, and science. They were especially struck by the 1969 Apollo 11 moon landing and the questions it raised about the technology that made it possible.That same year also marked the first time a piece of data was sent from one computer to another—an early step toward the creation of the Internet. Several inspiring teachers pushed his curiosity about technology even further.He was awarded a scholarship to study in the United States, and he enrolled at Yale, where he first studied physics and philosophy before switching to engineering. He earned his bachelor’s degree in engineering and applied science in 1977. At the time, Yale didn’t yet offer separate degrees in fields such as electrical or mechanical engineering.From there, Cassandras went to Stanford, earning a master’s degree in electrical engineering in 1978. There, he became interested in game theory, the mathematical study of decision-making and strategy.A mentor pointed him to Harvard, home to a leading game theory researcher at the time. His new mentor, Professor Yu-Chi “Larry” Ho, now an IEEE Life Fellow, advised him to abandon game theory, which Ho considered a scientific dead end at the time. Ho suggested a newer, less-explored area of research, that of emerging dynamic systems in modern technology whose behavior could be understood and managed through occurrences of discrete events.Cassandras took the advice. He earned a second master’s degree from Harvard the following year and completed his Ph.D. in applied mathematics there in 1982. He and Ho remain friends and colleagues. Fiat assembly line births of a new kind of engineeringAsk people in intelligent transportation systems circles why Cassandras’s name carries so much weight, and the answer traces back to a theoretical shift he helped pioneer starting in the 1980s. Cassandras recognized that human-made systems—computers, factory machines and, eventually, vehicles—operate on a different kind of logic than the one that governs the natural world.The physical world runs on time-driven physics, the same math Isaac Newton used centuries ago. But machines built by people, he realized, are better understood as event-driven: They mostly sit still until something happens, like a button click or a part arriving on a conveyor belt.That insight didn’t come from a textbook. It took shape during Cassandras’s early graduate work, when he was handed a real-world problem. He was assigned to figure out how to manage the buffers—the temporary holding areas for car parts—on an assembly line for Fiat, the Italian carmaker.Solving that concrete, practical puzzle helped him see how event-driven thinking could describe an entire class of systems that classical physics-based math couldn’t handle well. This came to be known as discrete event systems.By the early 1990s, that insight had matured into a formal framework: hybrid systems theory, now known as cyber physical systems theory. The idea is that a machine can be understood as an object obeying the laws of physics and as a computer obeying event-driven dynamics.Building “event-driven” systemsAfter completing his doctorate, Cassandras wanted to test himself outside of academia. He spent about a year and a half as a systems engineer at Information and Technology for Production (ITP), a small manufacturing-automation startup in the Boston area. The experience convinced him that the “real world” wasn’t so different from academic life, and he returned to research while continuing to consult for the company for roughly a decade. He has kept up the habit of staying connected to industry throughout his career.In 1984 he joined the University of Massachusetts Amherst as a faculty member in the electrical and computer engineering department. Building on the ideas born out of the Fiat project, he helped pioneer research into event-driven systems.That was a shift away from the traditional math used to describe the physical world toward systems built around discrete events. The transition, driven by the rise of computers, became the backbone of Cassandras’s research for the next three decades.In 1997, after years of commuting weekly between Amherst and Boston for his consulting work, he joined Boston University as a tenured full professor in what was then its manufacturing engineering department. As the field evolved in the early 2000s with the rise of the Internet and sensor technology, Cassandras helped establish the university’s systems engineering division. In 2008 he became the research program’s first director, a role he held until stepping down two months ago. Using math to show that self-driving cars are safeCassandras’s recent research has focused on mathematically proving vehicle safety before a car is road-tested. He has developed models built around measurable quantities: a vehicle’s position, speed, and distance to nearby vehicles. That lets connected and automated vehicles cooperate during tricky maneuvers such as changing lanes on a highway in traffic.He has extended the approach to a bigger idea: eliminating traffic lights. One of his more striking projects involves a chronically congested intersection near his office in Boston, where Commonwealth Avenue meets the Boston University Bridge. Using computer simulations, he and his team modeled what would happen if they removed all the traffic lights, and cars simply coordinated with each other instead. The results, he says, showed improvements not only in how smoothly traffic would flow but also in safety and energy efficiency.Cassandras acknowledges that widespread adoption is still far off. For now, only a small percentage of vehicles on the road are capable of that kind of communication. But he’s testing similar coordination systems using small robots in his BU lab, working to bring the idea closer to reality and to exploit the intelligence of cooperating autonomous vehicles even when they are only a small fraction of the actual cars on the road.He says his perspective has evolved in recent years from viewing humans as obstacles to smooth automation toward designing technology that actually works for people.Through projects with Boston on smart city initiatives, he learned that not everyone can afford or access new technology—a lesson that reshaped how he approaches his research.“It’s not just about technology,” he says. “It’s really about technology and people.”A legacy passed on through his studentsHis influence in the field has been amplified by some of his students. Several of his Ph.D. students have developed safety algorithms at companies including Aptiv and Zoox, both of which build technology for autonomous and connected vehicles. He wrote a book, titled Safe Autonomy with Control Barrier Functions: Theory and Applications, with one of his former students. It lays out the mathematical foundations of autonomous-vehicle safety, with specific applications to intelligent transportation.A long relationship with IEEECassandras joined IEEE as a student member while he was a graduate student. He was drawn in by the discounted student conference registration fees, he says.That early, practical decision grew into something bigger: a professional community with which he has stayed connected for more than 40 years.He has served as president of the IEEE Control Systems Society, his primary professional home within IEEE. He has served on the conference publications committee and is currently involved with IEEE Publishing Ethics.IEEE’s greatest value to his career, he says, has come through the relationships he has built at its conferences and serving on committees. Connections with colleagues and IEEE leadership have shaped his thinking and opened doors throughout his career, he says.Looking back on more than four decades of work, Cassandras says the goal has never really been about the technology for its own sake.“After all,” he says, “we’re doing all this to make society better and facilitate the comfortable lives of all of humanity.”

Dielectric metasurfaces have moved to the forefront of nanophotonics, offering flat, low-loss alternatives to conventional bulk optical elements for controlling the amplitude, phase, and polarization of light. These structures are of growing interest to researchers and engineers working on sensing, energy harvesting, and flat optics, as their performance hinges on precisely engineered optical resonances. Full-wave finite element simulation, combined with semianalytical multipole decomposition in the COMSOL Multiphysics® software, gives us a way to not only predict these resonances but also uncover their underlying physical origins.In this webinar, Dr. Pavel Terekhov, postdoctoral researcher at National Institute of Standards and Technology, will trace how a single quadrumer meta-atom, originally studied for its magnetic octupole response, evolves into two distinct light manipulation regimes. He will first revisit the foundational single-particle results that motivated this work, then show how arranging quadrumers into a periodic crystalline silicon metasurface produces anomalous absorption enhancement, governed by two independent multipole mechanisms coexisting in the same structure. Building on this, he will then introduce ongoing work on a gallium nitride metasurface, where the complex interplay of four different multipoles is used to sculpt reflection and transmission spectra including the quasi-bound-states-in-the-continuum (q-BIC) manipulation.Attendees will see how COMSOL Multiphysics® and multipole decomposition connect full-wave simulation and analytical insight, turning abstract resonance behavior into physically interpretable design rules. The broader takeaway is that multipole-based simulation is not just a diagnostic tool but a design strategy: It enables the on-demand tailoring of absorption, reflection, and transmission in dielectric metasurfaces, with direct relevance to sensing, energy harvesting, and future optical device applications.Key Takeaways:Learn about modeling multipole resonances in dielectric metasurfaces using full-wave finite element simulation and semianalytical multipole decomposition in COMSOL Multiphysics®.See how multipole-based simulation can be used to understand and control absorption, reflection, and transmission in silicon and gallium nitride metasurfaces.Explore how different multipole mechanisms, including quasi-bound states in the continuum (q-BICs), can be engineered to tailor optical responses for sensing, energy harvesting, and flat optics.Gain insights into how simulation and multipole decomposition can help researchers and engineers turn complex resonance behavior into practical design strategies for future optical devices.Register now for this free webinar!

It’s disheartening how much power gets generated and then promptly lost as it travels through grid networks. This leaking of electricity happens when it vanishes as heat as well as when it is pilfered by thieves and nonpaying customers. More than half of the countries that track these metrics lost at least 10 percent of their electricity in 2023, according to the World Bank. Losses topped 20 percent for 24 of those nations. Two countries lost more than half of what they generated. With numbers this high, cutting down on losses makes sense. Electricity demand is rising beyond what many grid operators can supply; reducing waste would help meet some of that demand without having to build new power plants. Plus, when the power comes from fossil fuels, any loss means emitting even more greenhouse gases into the atmosphere. And electric losses hit the bottom lines of power providers, which ultimately pass those costs on to everyone else. The trouble is, reducing electricity losses is a hard and expensive process that takes a long time. Typically, the less maintained the grid infrastructure, the more electricity that’s lost. And the more fragile the region’s law enforcement and government, the more prevalent the power theft. Natural disasters and war make things worse. Delhi’s Power Grid ComebackFixing a power grid requires a systemic approach across many sectors. There’s no one technology that will solve the problem. At the outset, the obstacles to success may feel insurmountable. Equipment across entire grid networks must be updated. Multiple arms of government must agree to reforms and coordinate to ensure power providers are set up to succeed. Regulations must be written or revised, investments made, cultures changed.The city of Delhi did all those things. Over the past 25 years, it cut its electricity losses from about 50 to 5 percent. How the city pulled off that impressive feat is the focus of “The Epic Comeback of Delhi’s Power Grid” by Mini Shaji Thomas, an electrical engineer at the university Jamia Millia Islamia who has lived in Delhi since the 1990s. She gives us a view from the inside—as a resident and a power systems expert. Delhi is a shining example, but some other regions have significantly lowered electricity losses over the last quarter century too. The country of Georgia went from losses of over 16 percent in 2002 to about 8 percent in 2023. In Singapore, losses dropped from 6.6 percent to a nearly nonexistent 0.2 percent over the same time period. Global Electricity Theft CrisisBut there are many parts of the world where electricity losses remain a problem or have gotten worse. In Jamaica, where power theft is rampant, losses have hovered between 21 and 28 percent for years. Argentina’s losses nearly doubled between 2015 and 2023, going from an all-time low of about 12 percent to an all-time high of nearly 24 percent. The main problem: Transmission and distribution companies lacked the capital to maintain and upgrade their networks, which left equipment operating under stress. A delay in the installation of smart meters has allowed thieves to more easily siphon power and tamper with meters. Thomas says she hopes her account of Delhi’s grid comeback will serve as a blueprint for others. It’s possible to replicate the sweeping changes Delhi made, she says. But it “requires a concerted effort from all stakeholders, customers, the utility, the government, and their employees.”

Every year, IEEE recognizes individual researchers, innovators, and collaborative teams whose technical achievements and leadership have made a lasting impact on technology, engineering, and society worldwide with its Technical Field Awards.The honorees are selected by the IEEE Awards Board based on their extraordinary technical achievements, societal impact, and leadership in specialized engineering disciplines and are approved by the IEEE Board of Directors.“The 2027 IEEE Technical Field Award recipients embody the relentless spirit of innovation and ingenuity that defines our global community,” said Mary Ellen Randall, 2026 IEEE president and CEO in a news release. “Across microelectronics, artificial intelligence, medical robotics, wireless networks, energy conversion, and technical education, their groundbreaking accomplishments continue to push the boundaries of science and solve critical challenges for the benefit of humanity.”The recipients are:IEEE Biomedical Engineering AwardSponsors: IEEE Engineering in Medicine and Biology, IEEE Circuits and Systems, and IEEE Signal Processing societiesIEEE Life Fellow Kullervo HynynenSunnybrook Research Institute in Toronto, and the University of Toronto“For pioneering contributions to the development, advancement, and clinical translation of therapeutic ultrasound.”IEEE Cledo Brunetti AwardSponsor: The Brunetti BequestIEEE Member Edward Charles GageIEEE Fellow Ganping JuIEEE Fellow Jan-Ulrich ThieleSeagate Technology in Cupertino, Calif.“For contributions to heat-assisted magnetic technology powering ultra-high-capacity hard disk drives and enabling greater storage of digital information.”IEEE Roger W. Brockett Control Systems AwardSponsors: IEEE Control Systems Society and Friends and Family of Roger W. BrockettIEEE Life Fellow Arjan van der SchaftBernoulli Institute at the University of Groningen in the Netherlands (Retired)“For contributions to robust and geometric approaches to nonlinear control, hybrid systems, and the control of physical systems.”IEEE Electromagnetics AwardSponsors: IEEE Antennas and Propagation, IEEE Electromagnetic Compatibility, IEEE Geoscience and Remote Sensing, and IEEE Microwave Theory and Technology societiesIEEE Fellow Zoya PopovićUniversity of Colorado Boulder“For contributions to microwave and millimeter-wave circuit designs, including power amplifiers, power-combining, and wireless power systems.”IEEE James L. Flanagan Speech and Audio Processing AwardSponsor: Mitsubishi Electric Research Laboratories (MERL)IEEE Life Fellow Chin-Hui LeeGeorgia Tech“For contributions to and leadership in technologies that enable automatic speech and speaker recognition, including innovations in adaptive learning and discriminative training.”IEEE Fourier Award for Signal ProcessingSponsor: Mitsubishi Electric Research Laboratories (MERL)IEEE Fellow Nicholas Dimitrios SidiropoulosUniversity of Virginia in Charlottesville“For contributions to signal processing methods for analyzing complex data and identifying patterns in multiple dimensions.”IEEE Andrew S. Grove AwardSponsor: IEEE Electron Devices SocietyIEEE Fellow John Robert BrewsUniversity of Arizona in Tucson (Retired)“For foundational contributions to the physics and technology underlying MOS devices and circuits.”IEEE Masaru Ibuka Consumer Technology AwardSponsor: Sony Group CorporationIEEE Honorary Member Mike LazaridisQuantum Valley Investments in Waterloo, Ont., Canada“For transformative contributions to mobile communication through visionary leadership that reshaped global consumer technology.”IEEE Internet AwardSponsor: Google LLCIEEE Fellow Matthew RoughanAdelaide University in Australia“For foundational contributions to analyzing Internet traffic and network structures, enabling better network management and understanding.”IEEE Richard Harold Kaufmann AwardSponsor: IEEE Industry Applications SocietyIEEE Fellow Akshay Kumar RathoreNational University of Singapore“For developing innovative current-fed and multilevel power electronics converters.”IEEE Joseph F. Keithley Award in Instrumentation and MeasurementSponsors: Tektronix Inc. and the IEEE Instrumentation and Measurement SocietyIEEE Life Fellow Pasquale DaponteUniversity of Sannio in Benevento, Italy“For contributions to the measurement and standardization of analog-to-digital and digital-to-analog converters.”IEEE Gustav Robert Kirchhoff AwardSponsor: IEEE Circuits and Systems SocietyIEEE Life Fellow Janusz RajskiSiemens Digital Industries Software in Wilsonville, Ore.“For contributions to testing digital integrated circuits at the silicon level.”IEEE Leon K. Kirchmayer Graduate Teaching AwardSponsor: The IEEE Leon K. Kirchmayer Memorial Fund IEEE Life Fellow Rama ChellappaJohns Hopkins University in Baltimore“For contributions to multi-disciplinary graduate education and mentoring of doctoral students in image processing, computer vision, and pattern recognition.”IEEE Koji Kobayashi Computers and Communications AwardSponsor: NEC CorporationIEEE Member Amin VahdatGoogle in Mountain View, Calif.“For developing network systems that scale data centers to support modern cloud computing and machine learning.”IEEE Arun N. Netravali Video Analytics, Technology, and Systems AwardSponsor: Nokia Bell LabsIEEE Life Member Jens-Rainer OhmRWTH Aachen University in Germany (Professor Emeritus)IEEE Life Fellow Gary J. SullivanDolby Laboratories in San Francisco IEEE Member Benjamin BrossFraunhofer Institute for Telecommunications Heinrich-Hertz-Institut in Berlin“For leadership in and contributions to developing the High Efficiency Video Coding (HEVC) international standard, which improves the efficiency of digital video compression.”IEEE William E. Newell Power Electronics AwardSponsor: IEEE Power Electronics SocietyIEEE Fellow Paolo MattavelliUniversity of Padova in Padua, Italy “For contributions to advanced modeling and control of power electronics converters.”IEEE Donald O. Pederson Award in Solid-State CircuitsSponsor: IEEE Solid-State Circuits SocietyIEEE Life Fellow Wanda Kay GassTexas Instruments in Bellevue, Wash. (Retired)“For pioneering contributions to programmable digital signal processing systems through innovations in circuit design, system architecture, and silicon compilers.”IEEE Frederik Philips AwardSponsor: The IEEE Frederik Philips Award FundRecipient: IEEE Member Ann B. KelleherIntel in Portland, Ore. (Retired)“For leading the historic advancement of CMOS process technology, culminating in the first node with backside power delivery.”IEEE Photonics AwardSponsor: IEEE Photonics SocietyIEEE Life Fellow Martin David DawsonUniversity of Strathclyde in Glasgow“For contributions to microstructured and wide-bandgap semiconductor optoelectronics and for leadership in technological innovation.”IEEE Robotics and Automation AwardSponsor: IEEE Robotics and Automation SocietyIEEE Life Fellow Russell H. TaylorJohns Hopkins University in Baltimore“For contributions to, leadership in, and the clinical translation of surgical and medical robotics.”IEEE Frank Rosenblatt AwardSponsor: IEEE Computational Intelligence SocietyIEEE Life Fellow Shun-ichi AmariTeikyo University in Tokyo“For foundational contributions to neural network theory and information geometry.”IEEE Marie Sklodowska-Curie AwardSponsor: IEEE Nuclear and Plasma Sciences SocietyVictor MalkaWeizmann Institute of Science in Rehovot, Israel“For leadership in the development of laser-driven particle sources and advancing the technology toward practical applications.”IEEE Innovation in Societal Infrastructure AwardSponsors: Hitachi, Ltd. and the IEEE Computer SocietyIEEE Fellow Jaideep VaidyaRutgers University in New Brunswick, N.J.IEEE Senior Member Lucila Ohno-MachadoYale School of MedicineIEEE Senior Member Xiaoqian JiangThe University of Texas Health Science Center at Houston“For leadership in designing and deploying privacy-preserving distributed clinical research infrastructures that enable secure, large-scale collaboration using health data.”IEEE Charles Proteus Steinmetz AwardSponsor: IEEE Standards AssociationIEEE Life Fellow Mehmet UlemaManhattan University in New York City (Professor Emeritus)“For leadership in the development and standardization of architecture, management, and protocols used in wireless communications and data networks.”IEEE Eric E. Sumner AwardSponsor: Nokia Bell LabsIEEE Fellow Gerhard FettweisTechnische Universität Dresden Barkhausen Institut in Germany“For contributions to physical-layer communication technology that have had an impact on the global wireless industry.”IEEE Nikola Tesla AwardSponsors: Wolong Electric Group Co. Ltd. and the IEEE Industry Applications and IEEE Power & Energy societiesIEEE Life Fellow Pragasen PillayConcordia University in Montreal“For contributions to the modeling, design, and application of electric machines in industrial, transportation, and renewable energy applications.”IEEE Kiyo Tomiyasu AwardSponsors: The late Dr. Kiyo Tomiyasu and the IEEE Geoscience and Remote Sensing and the IEEE Microwave Theory and Technology societiesIEEE Fellow Yun Raymond FuNortheastern University in Boston“For contributions to computer vision and augmented human-machine interaction.”IEEE Transportation Technologies AwardSponsors: IEEE Industry Applications, IEEE Industrial Electronics, IEEE Intelligent Transportation Systems, IEEE Microwave Theory and Technology, IEEE Power Electronics, IEEE Power & Energy, and IEEE Vehicular Technology societiesIEEE Fellow Chunting Chris MiSan Diego State University“For contributions to advancing electric and hybrid electric vehicle technologies.”IEEE Rao R. Tummala Electronics Packaging AwardSponsors: IEEE Electronics Packaging Society and Friends of Rao R. TummalaIEEE Fellow Ravi MahajanIntel in Chandler, Ariz.“For contributions to the development of technology that connects multiple chips within advanced electronic packages.”IEEE Undergraduate Teaching AwardSponsor: IEEE Education SocietyIEEE Fellow Rhonda Rene FranklinUniversity of Minnesota in Minneapolis“For leadership in advancing inclusive workforce development initiatives that create clear and accessible career pathways via undergraduate engineering education.”IEEE Lotfi A. Zadeh Award for Emerging TechnologiesSponsor: IEEE Systems, Man, and Cybernetics SocietyIEEE Life Fellow Peng ShiAdelaide University in Australia“For contributions to artificial intelligence-based methods for robust control and filtering in complex systems.”Nominate a colleague Nominations are now open for the 2028 Technical Field Awards. The deadline to submit a nomination is 15 January.For more information on the awards, please visit the IEEE Awards webpage.

Financial markets have always relied on timely information, and the drive for timeliness has always adapted to the latest technology. From clipper ships transiting the oceans to telegraph wires connecting cities to fiber-optic cables conducting trades in microseconds, traders have embraced any advantage to get the most up-to-date information. Indeed, the history of finance is really a story about how fast you can move information and who controls the interface.It’s only natural that people also figured out a way to profit by supplying that market intel. In 1841, for example, the Mercantile Exchange (predecessor to Dun & Bradstreet) began selling proprietary business information to its U.S. clients. The following decade, Paul Julius Reuter began selling news services and stock price information. To supplement the company’s telegraph dispatches, he sent pigeons between Aachen, Germany, and Brussels; each bird carried a cylinder containing slips of paper with that day’s stock prices. In 1867, an inventor named Edward Calahan introduced the first telegraphic ticker-tape machine, which spooled out stock price information in near real time; Thomas Edison improved upon the design with his patented version in 1871.The Dow Jones Industrial Average debuted in 1896 as an index of 12 key businesses listed on U.S. stock exchanges. It included gas, oil, coal, and electric companies, as well as enterprises dealing in leather, rubber, and tobacco. Messengers delivered quotes from the trading floor to brokerage offices, while stock tickers kept investors informed of prices. By the time New York City held its first official ticker-tape parade, in 1919, telegraphy in Western Europe and the United States had become the chief means for quick transmission of vital stock information.In 1960, the first paperless financial service debuted, when Quotron introduced its electronic screens for displaying market quotes. Over the next two decades, other companies rolled out similar innovations for distributing financial news and data.So when Michael Bloomberg decided to enter this well-established industry in 1981, the big question was: How would his new company stand out?The Birth of the Bloomberg TerminalBloomberg had cofounded Innovative Market Systems (IMS) after being fired from the investment bank Salomon Brothers. Landing on his feet with his US $10 million equity payout and joined by former Salomon colleagues Thomas Secunda, Duncan MacMillan, and Charles Zegar, Bloomberg pursued his belief that Wall Street would pay a premium for specialized financial data. He’d earned an electrical engineering degree from Johns Hopkins University and an MBA from Harvard, and he’d built computerized financial systems for Salomon. IMS focused on developing a computer terminal that not only provided up-to-date information but could also do instant quantitative analysis based on historical data. Michael Bloomberg believed Wall Street would pay a premium for access to specialized financial data. Karjean Levine/Getty ImagesAt the time, most financial data still circulated through telephone calls, printed price sheets, and specialist publications, and analysis involved a fair amount of gut instinct guided by human expertise. Companies such as Reuters and Dow Jones provided subscription-based services for access to business news. But traders still had to assemble information from multiple sources and perform their own calculations and analysis.IMS proposed an integrated system with a single interface. Its Market Master terminal consisted of a monochrome CRT monitor, a custom keyboard, and a communications/controller unit that connected to the company’s private network. At launch, it provided only U.S. government bond prices and bond-calculation tools, but the dream was much bigger: a dedicated terminal that would sit on a trader’s desk and run different market scenarios, produce yield curves, and support investment calculations.IMS initially had just one client, Merrill Lynch, which invested $30 million (about $110 million today) in exchange for a 30 percent stake in the company and exclusive rights to the terminals for five years; Merrill waived that right in 1984. The first 22 Market Master terminals were delivered to Merrill in 1982, in the middle of a global recession. The timing was fortuitous. Worldwide, stock markets were transitioning to electronic trading, and the U.S. Federal Reserve was allowing more freely floating interest rates. Bond prices were more volatile, and investors were eager to figure out how to value them accurately. Bloomberg’s specialized financial terminals provided the data and the analytical tools to process and comprehend those sweeping changes.Five years after its launch, IMS rebranded as Bloomberg LP and expanded its clientele, and the Market Master became known as the Bloomberg Terminal.How Did the Bloomberg Terminal Work?The Bloomberg Terminal’s keyboard was designed with traders and analysts in mind. The function keys were color-coded and given labels specifying their usage, so that users didn’t have to remember. The original keyboard, affectionately referred to as “the Chiclet,” was hand assembled. A cable ran from the keyboard to the Bloomberg Controller, which had a dedicated phone line to connect to a local hub. The internet wasn’t commercially available yet, so the company basically built its own closed network, with centralized computers that maintained large databases and performed most of the calculations. Commands entered on the keyboard sent a request to the hub, which processed the information and sent back the result. The Chiclet keyboard for the Bloomberg Terminal was introduced around 1983. Although it looks like a generic keyboard, its function keys were finance-specific hot keys.National Museum of American History/Smithsonian InstitutionHot keys let traders easily call up data on government securities, corporate debt, and currency markets, among other things. A series of keystrokes would pull up other historical and real-time data, run an analysis, or place a trade. Learning how to use the terminal and digest the vast amount of information, which was presented mostly in tabular form, became a rite of passage for users.In 1990, Bloomberg added a trackball to the keyboard, which helped the user navigate the multiple windows and menus typically displayed on screen. Two years later, the keyboard gained a built-in speaker, to support multimedia information; this design also included telephone, headphone, and microphone jacks. One of the most popular features was Instant Bloomberg, which allowed users to chat directly with fellow Bloomberg Terminal users over the proprietary network. By 1996, Bloomberg had keyboards that supported 23 different languages. In the early 2000s, the company began incorporating biometric authentication for terminal login, via a fingerprint reader on the keyboard.As the company’s business model evolved, the Bloomberg Terminal added services well beyond its initial offerings. In 1990, for example, worried that Dow Jones would stop providing access to its news stories, Bloomberg set up its own news service. It recruited Wall Street Journal reporter Matthew Winkler to oversee a dozen reporters; their stories on market and securities movements used graphs and calculations that served as advertisements for the terminal’s capabilities. These days, the Bloomberg news empire includes Bloomberg Businessweek, Bloomberg Radio, and Bloomberg Television.Bloomberg’s subscription-based financial model included the leasing of a Bloomberg Terminal with its specialized keyboard and other hardware, access to a dedicated private network, and a suite of services. In 1999, a subscription to a single Bloomberg Terminal cost $1,600 per month with a minimum two-year contract and a discount on each additional terminal. Today the annual price is upwards of $32,000 (trending a little below inflation). In 1995, the company launched a suite of “Open Bloomberg” software products that ran on the customer’s own PC; five years later, it stopped leasing dedicated terminals. Current customers also have access to mobile applications that allow terminal functions to run on phones and tablets. Today, “Bloomberg Terminal” has come to refer to the integrated data, analytics, news, communications, and trading environment.The Legacy of the Bloomberg TerminalAlthough the shift away from dedicated terminals was a logical response to the rise of the internet and publicly available market data, it altered the material culture of financial work. For nearly two decades, Bloomberg Terminals commanded an aura of power and financial prowess. They were emblems of market mastery, with a brand that was distinct from other office computers. With Open Bloomberg, users were no longer tied to a single desk or a fixed set of monitors.And so, cast-off Bloomberg Terminals found their way into museum collections. They’re a physical embodiment of the ethereal nature of financial markets, and a manifestation of mathematical calculations, network infrastructure, and business culture. The Bloomberg keyboard used by “Bond King” Bill Gross has his login and password taped on the front.National Museum of American History/Smithsonian InstitutionThe Smithsonian Institution’s National Museum of American History has a number of Bloomberg keyboards in its collection, but my favorite is object number 2014.0012.02, which was used by “Bond King” Bill Gross during the 1990s and 2000s at Pacific Investment Management. Gross had cofounded PIMCO in 1971 and built it into a $2 trillion bond investment firm. I especially love that Gross taped his login and password directly on his keyboard, which makes the object more relatable. I may never know what it’s like to manage billions in assets from a Bloomberg Terminal, but I absolutely understand the trial of remembering my passwords.Part of a continuing series looking at historical artifacts that embrace the boundless potential of technology.An abridged version of this article appears in the October 2026 print issue as “The Keyboard That Moved Markets.”ReferencesBloomberg Professional Services has a nice timeline showing the evolution of its keyboards.For an economic analysis of how computers like the Bloomberg Terminal changed financial markets, see Gerben Bakker’s 2025 paper “The Terminal Revolution: Reuters and Bloomberg as global providers of financial and economic news, 1960–2020,” published by the London School of Economics and Political Science.Bloomberg Terminals are in the collections of many museums, including the Computer History Museum and the National Museum of American History.

Seventeen years ago, cell biologist Donald Ingber and his colleagues at Harvard University’s Wyss Institute for Biologically Inspired Engineering submitted a paper to the journal Science describing their model human lung. It was smaller than a USB stick and made of a clear polymer slab containing narrow channels, which were lined with the type of cells that line a lung’s air sacs and blood vessels. When air was pumped through hollow chambers beside the channels, the device rhythmically expanded and contracted—it “breathed.”This lifelike movement was a dramatic change from previous generations of lung models, which typically used static cultures of lung tissue that were unable to simulate the movements essential to lung function. When exposed to inflammatory proteins and bacteria, Ingber’s artificial lung reacted much as living lungs would. And exposure to silica nanoparticles used to model the effects of ultrafine particulates revealed that movement affected how tissues absorbed them. Donald Ingber led the team that developed the first human lung-on-a-chip at Harvard University’s Wyss Institute. Sam OgdenIt was a powerful proof-of-principle demonstration of a system that could be used to test drugs and other chemicals, providing a complement and even an alternative to testing in tissue cultures or in animals. Even so, the editors at Science were hesitant. They rejected the paper and suggested that Ingber’s team also run the tests in mice.It wasn’t an unreasonable request: Harvard’s lung system was new and comparing the results it generated to results from mice would help validate it. Ingber’s team ran the suggested experiments and resubmitted their study a year later, in 2010, at which point it was published. (It has since been cited by nearly 5,400 other papers.) Still, the incident spoke to how animal models have been the default of modern biomedical research. An early lung-on-a-chip developed at Harvard’s Wyss Institute used microfluidic channels lined with human cells to reproduce key features of lung function. Wyss Institute at Harvard UniversityA recent story told by Ilka Maschmeyer, a translational toxicology researcher and executive at the German biotech company TissUse, shows how much things have changed. TissUse specializes in building organ-on-a-chip systems—the conversational name for systems like Ingber’s lung—that are used by pharmaceutical companies for research. A few months ago, says Maschmeyer, a pharmaceutical company approached TissUse after being denied permission by the U.S. Food and Drug Administration to run a clinical trial of a new drug. The problem: It had presented animal data, but the FDA wanted data from organs-on-a-chip or some comparable alternative. The standards had come full circle.A Breathing Lung-on-a-ChipThe moment spoke to a trend, perhaps even the early days of a fundamental shift, away from the use of animals in toxicology and drug development. “It’s rare still,” says Maschmeyer, “but I think it’s going to be more and more frequent.”A host of these kinds of alternatives to experiments on animals have been developed over the years. Collectively they’re known as NAMs, an acronym that stands, depending on whom you’re talking to, for new approach methodologies, novel alternative methods, or nonanimal methods. Most NAMs have yet to be rigorously tested, but early studies suggest their potential.As NAMs have become more sophisticated, the question of how they will be implemented has become less about their technical qualities and more about the practical next steps needed to realize their potential. Validating NAMs—standardizing the systems, conducting head-to-head comparisons with animal experiments—is an enormous challenge. Moreover, simply outperforming animal models is necessary but not sufficient. The adoption of NAMs will require changes in policy, training, and culture.“This transition process is much more complicated than you would think,” says Thomas Hartung, a toxicologist and director of the Center for Alternatives to Animal Testing at Johns Hopkins University. “It is more about change management than it is about the technology.”The Technologies Replacing Animal TestingFor decades, animal advocates and many scientists have criticized both the morality and usefulness of experimenting on animals. An estimated 92 percent of all drugs that enter U.S. clinical trials fail to reach the market, sometimes for business reasons but often because the drugs prove ineffective or unsafe in ways that were not predicted by animal experiments. Failure rates are even higher in drugs for heart disease, cancer, and diseases of the brain.These statistics don’t automatically mean that a reliance on animals is to blame. Flawed study designs are a problem too, and also the sheer confounding complexity of disease. But there’s little question that animals have made poor surrogates for many conditions. And just as animal experiments may mistakenly suggest efficacy or fail to predict harm in humans, they might also erroneously suggest that drugs are ineffective or harmful when they could actually work in humans. Some researchers argue that if aspirin or acetaminophen had been discovered after the advent of modern testing requirements, they might have been abandoned. TissUse’s Humimic systems use microfluidic chips to culture human tissues and model interactions between organs. A researcher images tissues in a chip during an experiment [top], Humimic chips sit in a temperature-controlled unit [center], and a researcher prepares chips for use [bottom].TissUse (3)Researchers developing NAMs have pushed these systems far beyond old-fashioned tissue cultures. The new technologies include organoids that more closely mimic the structure, composition, and function of human organs. More humanlike still are organ-on-a-chip systems; alongside Ingber’s lung-on-a-chip are brains, hearts, kidneys, and even placentas on a chip. As many as 10 such organs have been linked together, yielding multi-organ systems that promise to recapitulate many aspects of human physiology—not perfectly, but better than a mouse or a monkey would. Supporting these systems are computational simulations of organs and organisms, and also artificial intelligence tools that analyze data generated by other systems and inform future experiments in a high-powered iterative loop.Yet even as studies piled up and some pharmaceutical companies started using NAMs in-house, the U.S. regulatory system governing drug developing and testing remained an obstacle to their wider use. NAM proponents were overjoyed, then, when in late 2022 the FDA Modernization Act 2.0 passed into law. It explicitly authorized the use of NAMs in the preclinical studies required of new drugs before they could enter human trials. Previous regulations had mandated animal testing; now the door was open to alternatives. It was a landmark moment. “That was something I didn’t expect to see in my life,” says Maschmeyer.Although immediate in-the-lab impact was limited, the FDA’s decision was a harbinger of things to come. In 2025, the FDA pledged “to make animal studies the exception rather than the norm” for drug safety testing. Then, in September 2026, the agency followed up by issuing a rule that, if it takes effect, will replace references to “animal tests” in its drug-development regulations with the broader term “nonclinical tests.” The change makes explicit that validated alternatives such as human-cell systems, organs-on-chips, and computer models can be used when appropriate.Also in 2025, the U.S. National Institutes of Health, the world’s largest public biomedical research funder, announced that researchers applying for grants to study animal models would also need to incorporate nonanimal research, such as real-world data or studies of NAMs. Meanwhile, the European Commission and United Kingdom have announced their own plans to phase out animal testing, and the intergovernmental Organisation for Economic Co-operation and Development updated its influential guidelines to allow for expanded use of NAMs.NAM proponents say these shifts were essential: If regulators won’t accept NAM results, there’s less incentive to adopt them, especially for researchers already working with animals. Maschmeyer says TissUse’s clients increasingly include scientists whose research has been focused on animals. “I see, within the last year, a change,” says Maschmeyer. “It’s more people who are working with animal models who now have to also add in vitro models.” She traces it mainly to the regulatory shift—a trend Ingber calls “game-changing.”Proving That NAMs WorkIt’s not enough for regulators to say that NAMs can or should be used, though. Even more important is the regulatory apparatus dedicated to assessing how they should be used. This begins with their validation: the process by which experimental methodologies and devices are determined to be reliable and trustworthy. A prototype brain-on-a-chip designed to model a rare neurological disease might work fine in the lab that developed it—but to be validated, the system needs to work in the real world.“You read about all the organ chips that come out of academic labs, which is great—but that’s not going to change their uptake by the FDA, because you have to get the same results anywhere in the world. It has to be a commercial product. It has to be mass-produced and meet very fine performance criteria,” says Ingber. For example, even minute variations in the hydrogels used as tissue scaffolds in organ chips can produce very different growth patterns. Emulate’s Organ-Chips are connected to the company’s automated culture system, which supplies the chips with nutrients and controls the flow of fluid through them. EmulateWorkflows and procedures need to be uniform, too. One obstacle to wider use of vascularized tumor-on-a-chip platforms in developing cancer therapies, for example, is the different metrics used by different research groups to characterize blood-vessel function and geometry. Experimental guidelines, workflows, checkpoints, metrics, reporting criteria: All need to be standardized in order for researchers to compare their work and collaborate across platforms. Members of Ingber’s lab coach industry researchers on how to use chips developed by Emulate, a company founded by Ingber. But even with instructions, they still need help with the finer points of tending to stem-cell cultures.When a NAM is ready for commercial use and researchers know how to use it, the most important test—whether it provides clinical benefit—still remains. A rare-disease organ chip might be reliable, but are the biomarkers it measures actually relevant? If so, are the algorithms that extrapolate chip results to the drug’s in-body effects truly predictive? Microscopic images reveal the human tissues grown inside Emulate’s Organ-Chips. Bacteria, shown in magenta, interact with mucus and airway cells in a LungChip [top]; an IntestineChip develops structures resembling those that absorb nutrients in the small intestine [center]; and tiny hairlike cilia grow on cells in another LungChip, where they help move mucus and trapped particles out of the airway [bottom].Emulate (3)Such questions have been answered for some NAMs. For example, a liver-on-a-chip system from Emulate correctly flagged about seven out of every eight drugs that had safely passed animal trials but proved toxic to human livers. A similar study was conducted by researchers from Oxford University and Janssen Pharmaceutica (later renamed Johnson & Johnson Innovative Medicine). That team showed that their computational simulations of human heart cells flagged compounds that caused a type of dangerous heart arrhythmia with 89 percent accuracy, compared to animal studies that were 75 percent accurate.Such studies, however, are complicated and costly. Emulate’s study required 870 chips and the labor equivalent of 16 full-time employees working for 16 weeks—efforts far beyond the reach of the average lab. If the researchers wanted regulatory approval to use their chip to predict large-molecule drugs rather than the small-molecule drugs they tested, they would have needed to run another such study for that particular use. And comparable studies ostensibly need to be conducted for every commercially available NAM and every context in which they would be used—a vast undertaking. “That’s a challenge,” says Kathy Archibald, founder of Safer Medicines Trust, a United Kingdom–based group that considers animals to be poor models of human biology. “It takes too long and costs too much, and small companies can’t afford to do it.”Ingber thinks that academic scientists need to collaborate more with industry researchers on NAMs, and that governments should fund those projects. He and other NAM proponents also stress the importance of having access to the necessary data: Without information from preclinical animal studies and human clinical trials, comparisons are difficult, but much of that data is now proprietary. Pharmaceutical companies and regulators need to share it, they say, and the FDA has called for an open-access repository of drug toxicity data. Hartung of Johns Hopkins also suggests that new animal experiments be run in parallel with NAMs, producing side-by-side comparisons.To Christian Maass, a computational biologist at the German biotechnology company ESQlabs, NAMS are overdue for a showdown with animal models. His company makes “digital twin” systems in which data from organ chip systems inform whole-human simulations of drug outcomes and disease progression. “I love what we are doing,” says Maass, speaking not only of his company but of the whole field. But he adds that researchers have not yet provided “the evidence and the proof that we are doing better or as good as the animal models.”Maass thinks that head-to-head comparisons are essential to good science. After all, if a NAM doesn’t outperform an animal model, or works best as a complement rather than a replacement, that needs to be known. He also believes such studies could convince skeptics. Maass mentions the debut of the iPhone, when people saw for the first time how well a phone could work without buttons. “That was an ‘aha!’ moment,” he says. But for NAMs, “that moment is still lacking.”Changing Scientific HabitsEven when those head-to-head comparisons are made, though, and regulations are appropriately changed, adoption can be slow. In the mid-1990s, researchers developed and validated the monocyte activation test—an assay that uses human blood cells to predict immune response—to replace the rabbit pyrogen test, which involves injecting a compound into a rabbit’s ear and monitoring the animal’s rectal temperature. But it wasn’t until 2010 that the European Pharmacopeia—the official Europe-wide standards for such testing—accepted the monocyte activation test as a replacement. And rabbits are still widely used for this test worldwide.Why the slow pace of change? In part because updates to guidance documents referring to animal tests lagged behind, but also because of inertia within the culture and institutions of science. “The formal requirement may disappear, but the informal expectation persists,” says Kathrin Herrmann, a veterinary scientist and colleague of Hartung’s at the Center for Alternatives to Animal Testing. Regulators, grant reviewers, peer reviewers, journal editors—the human infrastructure of science—often still expect to see animal data and are unfamiliar with NAMs.Herrmann is now overseeing a survey of early-career researchers working with, or trying to make the switch to, NAMs. “We consistently hear concerns that NAM-only proposals are perceived as risky by funders, that there is pressure to ‘add an animal experiment’ for credibility, that access to NAM infrastructure is limited, and that career trajectories become uncertain when departing from established animal models,” says Herrmann. The vast majority of drugs entering clinical trials in the United States fail to reach FDA approval, with particularly high failure rates in some therapeutic areas.Animal models are embedded in databases, training programs, and the very culture of research. Scientists who use animals may be reluctant to change; their identities as researchers are tied to animals and, more practically, they’ve spent their careers learning the techniques. A toxicologist who has used rats for decades might understandably look askance when asked to take a chance on unfamiliar chunks of polymer and stem cells—especially when human well-being, or millions of dollars, may ride on the choice. Likewise, an academic scientist whose career was built on animal models may not welcome NAMs; a switch may represent the loss of jobs for lab members whose expertise is no longer relevant. “I could see why it’s a hard thing for people to take it up,” says Ingber.Education and training is vital, say NAM proponents. The NIH and FDA now offer resources for researchers interested in NAMs, as do their counterparts in other countries embracing the technologies. Herrmann helps run webinars where researchers and regulators learn to use and evaluate NAMs; Hartung’s modules on Coursera, the online learning platform, have been taken by about 12,000 students so far. “These trainees will set up their own labs. They will go to industry. They will replace the old guard,” says Joseph Wu, director of Stanford University’s Cardiovascular Institute.Wu is also a cofounder of Greenstone Biosciences, a company that uses stem-cell-derived human tissues and AI to model disease and predict drug responses. He’s used that position to introduce researchers to NAMs, helping convince the company’s directors to freely share Greenstone’s large library of stem-cell lines with any academic researchers who want to use them. “I really believe that people should understand how this platform works,” says Wu. “At the end of the day, we’re just trying to advance science.”With enough time—and funding, incentives, training, education, collaboration, and generational turnover—the research culture of drug development and safety testing may shift. Whether NAMs will be used in other areas of science, though, is an open question. Early-stage drug development and regulatory testing account for roughly 30 percent of animals used in experiments; the rest are used in basic biological research. Replacing those animals is less straightforward, but it may be possible: Ingber describes organ-on-a-chip-based insights into inflammatory bowel disease, preterm birth, and treating viral infections that couldn’t have been made in animals. Hartung calls the adoption of NAMs in toxicology a “lighthouse function,” helping guide the way for other types of research.“Suddenly, all the dams have opened,” he says.

Learn how engineers can shift from manually managing tools and handoffs to defining objectives and supervising AI-driven execution across the silicon development lifecycle.Key TakeawaysExplore four critical technologies enabling silicon design autonomyUnderstand how autonomous AI accelerates complex engineering workflowsSee real-world impact across semiconductor design and verificationGain practical insights from Ambiq’s deployment of autonomous AI in productionRegister now for this free webinar!

Building useful robots starts with understanding the people who use them. For Charlie Kemp, cofounder and chief technology officer of Hello Robot, that means developing assistive robots that can help people with everyday tasks and support greater independence.In this Robots Guide profile, Kemp shares his path from studying artificial intelligence at MIT to building Stretch, explains how working with people with disabilities has shaped his approach, and offers advice for aspiring roboticists. Read the full profile on IEEE’s Robots Guide.

IEEE TryEngineering is dedicated to inspiring intellectual curiosity in children.The technologies shaping our world, including in the realms of artificial intelligence, electric vehicles, and ocean exploration, are evolving rapidly. Helping young learners understand the concepts is essential to preparing the next generation of problem-solvers, creators, and engineers.TryEngineering has introduced a STEM book series for youngsters ages 8 to 12 through the Lerner Publishing Group.The series, Tomorrow’s Technology With TryEngineering, Powered by IEEE, makes complex topics more approachable and engaging, with each book combining age-appropriate explanations, real-world examples, and design challenges that encourage curiosity and critical thinking. The series is based on ebooks and videos available at tryengineering.org.For the series, TryEngineering partnered with several other IEEE groups including the Communications, Computer, and Oceanic Engineering societies and the Transportation Electrification Council.Whether used in the classroom, a library, or at home, the books can help pupils connect STEM concepts to the technologies they encounter every day, including computers and smartphones. Six topics in the collectionHere are the books in the new collection: Artificial Intelligence: The Future of Smart Technology explores the systems behind streaming services, search engines, and health care. Readers learn how AI works while exploring ethical concerns such as bias, deepfakes, hallucinations, and privacy. Pupils can better understand one of the most influential technologies of our time as it evolves.Communication Technology: From Morse Code to Smartphones teaches readers about smoke signals, semaphore towers, telephones, and wireless networks. The pupils can gain an understanding of how engineers are changing communications technology through innovations such as 6G and space-based networks. The book highlights career opportunities in the aerospace and telecommunications fields.Electric Vehicles: Powering the Future of Transportation covers how e-cars, e-bikes, e-scooters, and electrified trains are transforming the way people travel. Readers can discover how hybrid and fully electric vehicles operate and how the batteries that power them work. They also can learn about the roles engineers play in developing smarter transit systems.Ocean Engineering: Protecting Our Ocean Environments highlights the vital ecosystem role played by the world’s oceans, which require careful stewardship. play in our ecosystem. Readers can learn how engineers study the underwater world using submersibles and floats, how they address pollution, and how they protect marine environments.Semiconductors: The Building Blocks of Modern Electronics focuses on the technology behind nearly every electronic tool we use. Microchips power smartphones, computers, and countless other products, all thanks to semiconductors. Readers can learn about insulators and conductors, how microchips are manufactured, and why semiconductor engineering is a fertile field for innovation.Signal Power: The Hidden Waves Behind Modern Tech explores how engineers analyze and manipulate signals to make technologies work more effectively. Whether it is a phone call reaching the correct person despite background noise or a medical device monitoring a patient, signal processing plays crucial roles in modern life. The book introduces different wave types, the signal processing workflow, and careers in the field.The book series can help children understand the technologies shaping the world around them while encouraging them to think like engineers and innovators. By connecting STEM concepts to real-world applications, the series can make learning more meaningful and engaging.The Tomorrow’s Technology With TryEngineering series is available through Amazon, Bookshop, and Lerner. More about the collection may be found here.

It’s 6 a.m. on a cold January morning in 2002 in New Delhi. It’s still dark outside, and I’m in the kitchen preparing breakfast, packing lunches, and getting my two children ready to catch the school bus when, for the third time in a week, the power goes out. No lights, no mixer to finish my daughter’s puttu—her favorite rice dish—no kettle, no toaster. The bathroom is dark, and the kids are upset.It will probably be hours before the power comes back on, so I grab a flashlight and light the candles that are set up around the house for these occasions. We’re behind schedule now. We pack the food we have, bundle up as the house turns chilly, and head outside, leaving a mess in the kitchen. We make our way to the bus stop in the dark—the streetlights are out, too—only to discover my daughter has missed her ride. Again. I’ll be late for work at Jamia Millia Islamia, a university where I am a professor of electrical engineering and teach power systems and smart grids. I just hope the power is on there.This was a common scene for my family and all of Delhi in the early 2000s. Power outages happened almost daily and lasted hours. When the power was on, the quality was so poor that it would dim lights, flicker screens, and wreak havoc on appliances. Customer service at the power utilities essentially didn’t exist. A child walks in July 2007 past a store in New Delhi specializing in reconditioned generators. The fear of power cuts during summer heat spurs demand for these generators so that residents can produce their own power.Nicholas Bradley/AFP/Getty ImagesThese problems had been getting worse through the 1980s and 1990s. The cause: an aging distribution grid bereft of crucial technologies, and electricity providers with little accountability. The situation became so bad that the city was losing more than half of its power through obsolete equipment and theft. These staggering losses meant that utilities got paid for only a fraction of the electricity they were trying to deliver. And the lack of funds prevented them from investing in better grid infrastructure.But over the last quarter century, a remarkable effort by the government and the city’s distribution utilities has turned Delhi’s grid into a reliable, modern system. Power losses have shrunk from over 50 percent in 2002 to 5 to 6 percent in 2026—on par with France and Belgium, and better than Greece and Serbia. Delhi’s grid reliability index, a measure of how often electricity can be counted on, stood at around 70 percent in 2002 and has now topped 99.9 percent. The bustling Main Bazar in the Paharganj neighborhood of Delhi increasingly uses more nighttime electricity, but reductions in electricity loss help counter demand. iStock With reliable power, businesses across the city have blossomed. The streetlights are bright. The number of electric vehicles, including city buses, is growing daily. Quality of life has improved. Today, my family is comfortable year-round in our home despite Delhi’s scorching summers and cold winters. The chaos of losing power no longer hinders me from getting to work. The city still has problems—pollution, overcrowding, noise—but thankfully, reliable power is no longer among them.The transformation of Delhi’s grid can serve as a model for other cities that suffer from decrepit power infrastructure. Regions of Albania, Argentina, Bangladesh, Brazil, Estonia, India, Kenya, Pakistan, Sri Lanka, Uganda, and Venezuela are reeling from heavy losses in their distribution grids. Their problems look like Delhi’s 25 years ago. I believe it’s possible to improve electricity in these places by adapting the changes Delhi made. Here’s an inside look at how the city accomplished it.Delhi’s Power Grid and Energy MixThe city of Delhi hosts the capital of the Republic of India, and sits along the Yamuna River in the northern part of the country. It’s home to about 23 million people and is one of the most densely populated areas in the world. Delhi’s grid includes thousands of kilometers of power lines, and peak electricity demand reached an all-time high this year of 8,748 megawatts. The city currently buys 76 percent of its power from central generating companies and private players from neighboring states. Energy generation within the city is restricted to natural gas and renewable sources. Nearly 48.5 percent of the city’s power comes from coal, about 26.5 percent from natural gas, and the rest from carbon-free sources, led by hydropower at 15.6 percent. Tata Power-DDL replaced about 5 kilometers of overhead lines with underground cables, which reduced electricity loss and improved the aesthetics of Delhi’s streets, such as the Janta Flats in the Shalimar Bagh neighborhood.Tata Power-DDLBy the early 2000s, Delhi’s nearly 100-year-old power distribution system was in serious disrepair. Everything was old—lines, transformers, circuit breakers, switches. New grid technologies were needed to keep up with new kinds of electricity loads, but there was little money to upgrade components.The shabby state of the grid caused many problems, most notably high electricity losses, where electricity vanishes primarily as heat. The cause of the losses was a classic electrical problem: too much current flowing through a network that wasn’t designed to carry it efficiently.To understand the problem, it helps to understand how modern power grids work. Typically, they include generation, transmission, and distribution. After power is generated, transformers convert the electricity to high voltage levels—typically 132, 220, 400, or 765 kilovolts in India. Transmission lines then carry the power over long distances to receiving substations that are closer to where customers need electricity. Transformers then step down the voltage (to 66, 33, or 11 kV in India) and distribution lines branch out, carrying the power to customers. The whole grid works primarily on alternating current.Electricity Losses by Country, 2002 vs. 2023Distribution networks carry both active and reactive power. Active power is the energy used to perform useful work (and is measured in watts). Reactive power is the power that flows back and forth in an electric circuit, building electric and magnetic fields (measured in volt-ampere-reactive, or VAR). Although it doesn’t perform useful work, reactive power is necessary for many devices, such as induction motors, transformers, and computers (typically any circuit or device with inductance or capacitance elements).When there are a lot of devices consuming reactive power on the same line, the overall current carried by the line—the sum of the active and reactive current—must increase. The more current in the line, the more the line heats up and the more energy that’s wasted as heat.In addition to current, resistance in the line will increase losses as well. Resistance is when electrons encounter opposition as they move through the conductive material (typically aluminum in a power grid). Longer lines with many branches and connection points will increase resistance. The rule of thumb is that line loss equals the square of the current multiplied by the resistance.Reactive power creates a second problem: It causes the voltage along the line to drop. And when the voltage falls, many modern electrical devices try to maintain roughly the same level of performance by drawing more current. That higher current produces even greater losses in the line and causes the voltage to fall further.Meter Technology Impacts Electricity LossesIn a healthy grid, the utility will take compensatory measures to lower the current and maintain the voltage all the way to the ends of the lines. But in Delhi, this wasn’t happening. The result was a vicious cycle. Reactive loads increased the current, the higher current increased energy losses and lowered the voltages, lower voltages forced devices to draw more current and further increased the losses.In some parts of Delhi, the effect was so severe that residents took matters into their own hands. A colleague of mine who lived in a different part of the city constantly experienced voltage that was too low for her appliances to operate reliably, so she had to install her own voltage stabilizer. At my home, we bought an inverter and battery system to keep a fan and a few lights running during the many outages.Electricity Loss and Theft in DelhiThe losses in Delhi weren’t caused solely by technical problems. Theft of electricity was rampant, by both the powerful and the powerless (in both senses of the word). Businesses, residential customers, and utility employees with vested interests would siphon electricity from the grid. It was easy to illegally hook into a streetlight or a distribution line running close to one’s house or factory. Utilities didn’t have the resources to identify theft or penalize offenders. Even if they could, the courts were already overburdened, and an electricity regulatory commission that could push for reforms had not yet fully formed. Updated meters have made billing easier and more accurate. Tata Power-DDL Making matters worse, the utilities and their employees were rarely held accountable for their actions, and so corruption plagued the system. Junior engineers and line workers, many of them lacking appropriate technical skills, were tasked with handling nearly every issue, including outages, flickering, and bill payment. This was too much authority in the hands of people with too little training.On top of that, customers didn’t pay their bills. Meters were old, frequently faulty, and easily tampered with. Utility employees would take a meter reading by visiting the customer’s property, noting the reading in a book, entering it in a ledger or on a computer back at the office, and converting it into an electricity bill that would get dropped off at the customer’s property. This process left a lot of room for incorrect billing.To pay a bill, customers had to stand in long queues at the utility offices, which had limited business hours. Not wanting to take off a half day of work for this, many customers simply didn’t pay. And there was no penalty for not paying—there were no regulations allowing the utilities to cut off a customer’s power. (I paid my bill by having a family member stand in line for me.)The combined commercial and technical losses left Delhi’s utilities collecting payment for less than half of the electricity they were supplying in the early 2000s.India’s Electricity Act and Power ReformsSuch problems weren’t unique to Delhi. On average in 2002, state utilities across India experienced electricity losses of nearly 37 percent. My country desperately needed systemic reforms, but authority over electricity was split between the central and state governments so any decision-making was fractured. States managed most of the generation, as well as transmission and distribution, while the central government oversaw generation that supplied multiple states, such as hydropower, fossil fuel plants, and nuclear plants. The central government could push reforms, but the states determined whether those reforms would succeed. Making matters worse, most states put a single organization in charge of generation, transmission, and distribution, giving that entity too much control and reducing transparency and competition. A team of technicians with BSES Rajdhani Power maintains an insulator string on a large power transformer in 2011. BSES Rajdhani Power In 2001, India’s central government began writing some historic legislation that became the landmark Electricity Act, 2003. Among the grand reforms aimed at transforming the country’s power industry, it unbundled state oversight of grid networks, creating separate entities for generation, transmission, and distribution. It also opened up the power sector to privatization. It allowed large electricity customers to bypass local distribution companies and purchase electricity from competitors or build their own power plants. It created a central regulatory agency responsible for determining interstate tariffs and promoting market competition in the power sector. And it created mechanisms for prosecuting electricity theft. Hundreds of capacitor banks have been installed in Delhi to supply reactive power at strategic locations and help stabilize voltage.Tata Power-DDLIn 2002, Delhi was already taking drastic action to fix its grid. The organization overseeing Delhi’s distribution, the Delhi Vidyut Board, was broken up and two private companies—BSES (now part of Reliance Infrastructure), and North Delhi Power Limited (now known as Tata Power-DDL)—took over distribution. They faced a Herculean task. Tata Power-DDL, serving the northern half of Delhi, would have to tackle a combined commercial and technical electricity loss of 53.5 percent. BSES, whose territory was split between two subsidiaries, was facing 51.5 percent losses in South Delhi and 63.1 percent losses in East Delhi.“The company inherited a deteriorated and overloaded network, massive power theft, weak billing and collection systems, inaccurate consumer records, and an aging, largely untrained workforce,” Dwijadas Basak, CEO of Tata Power-DDL, told me. There were over 100,000 unresolved billing complaints, 20,000 pending connection applications, and frequent supply failures, which had severely eroded consumer trust, he added. Both Tata Power-DDL and BSES devised sweeping reforms and human resource development initiatives. The companies followed their own paths over the years, but ultimately implemented similar changes, with similar results.Delhi’s Electricity System OverhaulFixing Delhi’s grid was a journey that involved all stakeholders, including customers, city authorities, and utility employees at all levels. The utilities revamped their organizational structures, diminishing the power of junior staff and creating separate teams to focus on specific tasks. Long-term employees of the erstwhile Delhi Vidyut Board received training from the up-and-comers at the new companies.On the technical side, both companies installed digital control systems that let them monitor and operate the grid from a central location. Known as SCADA, or supervisory control and data acquisition, the systems offered a bird’s-eye view of the infrastructure, including the status of equipment, voltage, current, power flow, and switch positions, with updates in seconds. This helped the companies identify areas of high loss and theft and make faster decisions based on accurate information. The SCADA (supervisory control and data acquisition) system at Balaji Estate in Delhi’s Kalkaji neighborhood serves as the nerve center of BSES Rajdhani Power’s distribution network in South and West Delhi. It enables real-time visibility, remote control of grid operations, fault identification and isolation, and load management. BSES Rajdhani PowerThe utilities also replaced aging transformers and circuit breakers and created extensive maintenance plans for equipment. In 2002, 11 percent of the transformers in the region were failing at any given time. That rate is less than 1 percent today, according to Tata Power-DDL. Crucially, the companies installed hundreds of capacitor banks, including some mobile ones, to supply reactive power at strategic locations. This improvement reduced the total current flowing in the distribution lines and helped stabilize the voltage. They also installed voltage regulators at points in the system where voltage tends to drop.To reduce theft, the companies replaced bare distribution wires with insulated lines—a single cable for three phases—which made it harder to tap into the lines. The cables also reduced outages because they’re better at preventing ground faults, which can occur when, say, a tree branch falls on the line.Workers received better sensors and tools to do their jobs safely and accurately. For instance, they were given helmet-mounted voltage sensors, which are safer than handheld ones, and thermal scanning tools to detect hidden defects in the insulation of high-voltage equipment that could otherwise have led to catastrophic failures.To reduce inaccurate billing and meter tampering, the companies replaced the old electromechanical meters with digital ones that are read with handheld devices. In some locations, radio-frequency-based group metering systems were installed by Tata Power-DDL to consolidate multiple customers’ meters into one. The data is then wirelessly transmitted to a central database, eliminating the need for individual meter readings. The companies are now trying smart meters, which give consumers more control over their electricity bills and give utilities remote control of some equipment (with the customer’s consent).To encourage people to pay their bills, the utilities installed kiosks that are available 24 hours a day, and they created a web-based payment system and mobile app. Incentives for early bill payment and community-engagement programs also helped. Assistance from Delhi’s law enforcement considerably reduced electricity theft. Tata Power hired women living in the 223 slums it serves in the northern parts of the city to knock on neighbors’ doors and remind them to pay their power bills. These payment collectors [left and center], known as abhas, were photographed while speaking with a customer [right] in the Sanjay Basti area of New Delhi in 2017. Prashanth Vishwanathan/Bloomberg/Getty Images In areas where theft was particularly rampant and losses were as high as 83 percent, according to Tata Power-DDL, the companies took a different strategy. These pockets of Delhi were predominantly occupied by low-income families. Tata Power-DDL, and later BSES, worked to improve the water supply for these residents and provide educational opportunities, such as instruction in reading and writing in Hindi as well as financial literacy. These efforts focused on the women, who were at home more, and paid them to collect electricity payments from their neighbors. Bill payment rates from these areas are now on par with those of other parts of Delhi.In recent years, some customers have been installing rooftop solar panels to take advantage of subsidies and incentives. This trend can reduce electricity losses further because the energy generated at the customer end reduces current in the distribution lines. Customers are also installing more LED lights and energy-efficient appliances, reducing the load in the system.BSES is using AI to help detect theft. The algorithms analyze consumption patterns in pockets where losses are higher than they should be. The company is also using AI to forecast demand, fine-tune operational efficiency, and provide chatbots for customers.Quality of Life Improves in DelhiLife in Delhi is better than it was 25 years ago. I’m not worried that the power may go out and force me to reschedule my activities. My uninterrupted Wi-Fi gives me peace of mind, and my heating and cooling systems keep me and my family comfortable. I rarely need to use our old inverter and battery. The sharp rise of e-rickshaws in Delhi has increased demand on the power grid. Sajjad Hussain/AFP/Getty Images The number of businesses in Delhi has increased substantially, in part because of the access to quality power. People can confidently buy products that depend on electricity. In fact, the city’s peak electricity demand has tripled since 2002 due to the increase in population, commercial activity, and use of electrical gadgets.And then there’s the benefits to the planet. One unit of electricity that isn’t frittered away is one less unit that must be generated, not to mention the reductions in carbon emissions.Still, there’s work to do. Some areas of Delhi continue to have high losses, driven partly by the illegal charging of e-rickshaws. Elsewhere in India, the states of Himachal Pradesh, Madhya Pradesh, Maharashtra, and Telangana still experience losses of about 17 to 23 percent despite the sweeping Electricity Act, 2003. There are many reasons for the ongoing losses: long distribution lines to remote villages, less digitization, and inefficiencies in billing and collection of payments.These regions, and others around the world, can learn from Delhi’s grid comeback. Recently, power losses have increased substantially in countries such as Argentina, Greece, Jamaica, and Morocco, according to the World Bank, and some of the causes are similar to those that Delhi faced back in 2002.Meanwhile, Australia, most countries in North America and Europe, and a few countries in Asia and Africa experience low electricity losses as they invest regularly in their distribution infrastructure and the ethical enforcement of rules. In China, for example, losses have gradually been cut in half, from 7.1 to 3.4 percent. In Latvia, losses plummeted from 25 to 5.8 percent.What’s important is a comprehensive approach. Technologies like smart metering, AI, and analytics certainly help, but equally important is that people in the field are trained and take responsibility for their jobs, and that laws are enforced and payments collected.“Sustainable loss reduction cannot happen through technology alone,” Abhishek Ranjan, CEO of BSES Rajdhani Power told me. “Technology is an important enabler, but long-term success comes from combining it with disciplined execution, operational accountability, and strong consumer engagement.” This article was updated on 30 September, 2026 with clarification about the full name Tata Power-DDL.

My job is to translatedry and unrelenting codeinto a user interface of surpassing beauty.With my mouse, I roll one pixel after anotherup the vast anthill of the internet.My dream is to translatethe visions of the holy onesinto a communication protocolof universal wonderment.I want to launch shreds of light into the airto fall like a layer of diamondson the endless mountains of the Web.Don’t imagine these dreams are limitedby the LANs of the software lab.Between here and the ultimateunlimited interface of my aspirationslives a dazzling darkness,wide as the universe and thin as a hair.

Even before France approved legislation banning social media for children under 15 last January, 13-year-old Benjamin was already wondering what life without social media would look like. “If we want to play football, we won’t be able to organize it. What will we do? Send letters instead?” he joked in an interview for Le Monde. His reaction captured the central challenge behind the growing wave of youth social media bans: Removing access is one thing; understanding what those platforms mean in children’s lives is another. Within weeks of Australia’s similar ban, the country’s eSafety Commissioner reported that platforms had restricted access to 4.7 million under-16 accounts. Two months later, though, one in five Australian teenagers under 16 was still using TikTok and Snapchat, according to a parental-control data company. But even if all children’s social media accounts were to disappear, do such bans actually make children safer online?Governments are moving ahead without answering that question as they follow Australia’s lead. Indonesia’s child-safety framework, which took effect in March, bars children under 16 from holding accounts on “high-risk” platforms. The U.K. government has announced plans to ban social media for under-16s, add default overnight social media curfews for 16- and 17-year-olds, and extend child-safety rules to cover risky AI features. And on 17 September, the European Commission proposed the EU KIDS Act, which would bar children under 13 from social media, set 15 as the EU-wide minimum age for opening an account independently, and require platforms to show that their services are age appropriate and safe by design.But based on my experience working on Child Online Protection initiatives with the International Telecommunication Union (ITU) across Southeast Asia and the Pacific, I know the bans don’t address the real problems. Instead, we should be paying more attention to the systems that generate harm in the first place—namely, recommender algorithms, engagement-maximizing design, opaque moderation, and extractive data practices.Account removals are not the same as online child safetyMy experience working on protecting children’s online safety has taught me three main lessons: First, the public institutions responsible for child online protection often lack the staff, budget, or technical capacity to enforce complex online safety policies. Indonesia is illustrative. A 2026 UNICEF evaluation found capacity constraints among service providers, long-term funding uncertainty, and a need for specialized personnel. At the local level, some staff lacked digital skills, while budget constraints left some areas reliant on external support. Second, many children, and often their parents, lack the digital literacy and critical thinking skills needed to navigate online risks safely. My policy research on child online protection in Indonesia, published earlier this year in Digital Society, found substantial gaps that account removals cannot repair: Many children lacked guidance on navigating the internet safely, and large numbers did not know how to report harmful experiences. And third, the platforms have limited independent oversight as they identify underage users, design age-verification systems, and report their own compliance. In Indonesia, platforms themselves are responsible for carrying out age verification, while the Ministry of Communication and Digital Affairs oversees compliance. TikTok’s appeals process for users flagged as underage, for instance, can require a government-issued ID and selfies, which is a problem because it involves collecting the additional personal data on an ID card, beyond that needed to confirm age. Will government regulators ensure that TikTok handles that data responsibly? The privacy paradox of proving ageEvery age-based ban creates an engineering problem: How can a platform reliably determine that a user is old enough, without intruding on other information? Governments and companies may use identity documents, parental authorization, app-store checks, or facial age estimation. Each approach has trade-offs among accuracy, privacy, accessibility, and resistance to circumvention. There are also technical issues. One tool, facial age estimation, draws on enormous databases but it is probabilistic, not exact, because people vary so much. It’s also been shown to misclassify both children and adults. The challenge should not merely be to “verify age.” It should be to prove that someone is above a threshold, without disclosing their identity, birth date, or other information third parties might use to create a marketing profile. The European Commission’s age-verification blueprint challenges companies to verify ages without collecting all that additional information.Privacy-preserving technologies offer promising ways to achieve this. Zero-Knowledge Proofs (ZKPs) can confirm that someone meets an age threshold without revealing their identity or exact date of birth. W3C Verifiable Credentials are cryptographically verifiable digital claims that can disclose only the information needed, such as “over 16.” And device-based age signals can allow a phone or app store to share an age range without revealing a user’s exact birth date. But these methods still require rigorous security testing, common standards, independent oversight, and clear limits on data retention. Otherwise, poorly designed child-safety policies risk creating permanent identity infrastructures in which businesses, not people, control personal data.Where connection goes when a platform closesBlocking access to a platform redirects some young people, but not always where expected. Early anecdotal reports in Australia pointed to teenagers migrating to smaller, less-regulated platforms like Yope, a pattern the Cato Institute flagged as a “whack-a-mole” problem for regulators. But industry data collected two months later found no broad-based shift of that kind, aside from a small uptick in WhatsApp use. Many teens simply found a way to stay on the banned platforms.This points to a deeper gap in current society: the erosion of youth “third places“ physical spaces where young people have room to socialize and build identity outside home and school. As those spaces have diminished, commercial communications platforms have absorbed that role. For many teenagers, social media workarounds are merely inconvenient. But for isolated, marginalized, disabled, or LGBTQ+ youth who depend on online communities for support that’s otherwise unavailable, displacement can mean losing certain kinds of belonging, or having to move to a platform with even weaker oversight. How to design safer online systems for childrenIf blanket social media bans don’t work, then what will? The platforms have created many of the conditions that governments are now trying to contain: engagement-optimized recommenders, intrusive data practices, weak safeguards against unwanted contact, and features such as infinite scroll, autoplay, streaks, and persistent notifications. These design patterns increasingly face regulatory scrutiny, including what’s required under the European Union’s Digital Services Act. A 2026 study from the 5Rights Foundation that tracked children’s device use minute by minute found that the user interfaces shape children’s attention, sleep, and well-being in real time. A more durable response would regulate those interfaces directly, treating children as legitimate users whose privacy, agency, and well-being are required protections, not afterthoughts. That means designing for safety from the outset. One example would be for children’s apps to have high-privacy defaults, such as private accounts and location sharing switched off for minors. They could also have recommender systems that explain the main factors shaping a feed and give young users more control over personalization. The European Commission has published age-appropriate interaction guidelines that limit unsolicited contact and prevent minors from being added to groups without consent. Rules could also prohibit engagement-maximizing features that demand users’ attention, such as autoplay, infinite scroll, usage streaks, read receipts, and push notifications, by disabling or limiting them by default.Governments should define measurable outcomes and fund independent evaluation, platforms should give researchers meaningful data access, and engineers should audit age-assurance systems for bias and data leakage. Schools, parents, and children themselves need a seat in designing the technology that’s designed to protect children. If policymakers still decide to remove an infrastructure for youth connection, they should offer something better in return. Social media bans may reduce some forms of exposure to harmful content and may be justified for particular ages, services, or risks. But they are just one tool, not a comprehensive substitute for safer design, accountable platforms, digital literacy, institutional capacity, and noncommercial digital “third places”—moderated communities, creative spaces, and public-interest platforms designed for youth participation rather than profit.The first wave of social media restrictions isn’t enough to keep children safe. Governments are still measuring what’s easiest to count, while neglecting harder-to-measure outcomes such as children’s access to safe third places and meaningful social connection, both online and offline. Until governments can show evidence that harm has actually declined, they will keep mistaking account removal for safety.

In Guadalajara, Mexico, many high schools have motivated teachers and talented students with an interest in science, technology, engineering, and mathematics, but they lack access to advanced tools such as robotics laboratories. The resources shortfall limits the students’ opportunities for hands-on learning on cutting-edge applications.A team from ITESO, Universidad Jesuita de Guadalajara, is working to change that. Through the EPICS in IEEE initiative, a multidisciplinary group of 15 engineering students, faculty advisors, and IEEE Guadalajara Section volunteers developed RoboMeshA. The portable, self-contained educational platform brings robotics and AI experiences into classrooms.EPICS is administered by IEEE Educational Activities and funded by the IEEE Robotics and Automation Society.A mobile laboratoryRather than requiring a school to build a dedicated computer lab or install complex software, RoboMeshA operates as an all-in-one mobile learning network.“RoboMeshA brings robotics and AI to students who don’t have access to specialized facilities or preinstalled software,” says team member Fernando Vidal Luna, an IEEE student member and a mechatronics engineering major at ITESO.Students connect directly to the platform from a user-friendly web browser. They can interact with the robot manually or use its control modes to watch it move and detect and avoid obstacles.“The project combines mechanical design, embedded systems, control engineering, computer vision, and AI into a single robotic system that functions as a mobile learning laboratory,” says faculty advisor Jorge A. Lizarraga.The team says young students are interested in technology, programming, and robotics but don’t have an opportunity to work with systems that combine mechanics, electronics, software, and control.“RoboMeshA allows students to see how all these disciplines work together in a tangible and understandable way,” says team member José S. González, who also is studying mechatronics engineering.The team has built two units and is developing a modular coupling framework to expand the system’s capabilities for research and classroom demonstrations. The structured system design approach connects independent software components while minimizing internal dependencies, enabling four RobotMeshA robots to operate together.Overcoming design challengesThe team faced significant hurdles while designing the project.“One key challenge involved the robot’s structural design,” Luna says. “It wasn’t only about making a chassis where all the components fit and the design had sufficient stability, rigidity, and weight distribution. It was also about ensuring that the electronics were protected while still being accessible for maintenance, testing, and modifications.”“It was also challenging to design a platform that could be used by students with different levels of experience,” González adds.“When students realize the technology they develop can inspire others and improve lives, engineering becomes far more meaningful.” —Luis Fernando Luque-VegaThe team partnered with the CETI Colomos and Prepa ITESO high schools to validate the platform in classroom settings.“We wanted the first interactions with the robot to be simple and intuitive,” González says, “such that students could simply power the robot, connect to its network, and begin interacting with it, rather than having to deal with software installation, extensive configuration, or troubleshooting.”Engineering with social impactMany of the students who participated were from ITESO’s applied professional projects program. The experience offered them practical training in project management, system integration, and user-centered design.The team also presented a research paper and a project poster in May at the Engineering Congress of the Jesuit University System.“Seeing a design move from a digital model to a physical system was invaluable,” González says. “Working with students from different backgrounds taught us to listen to end users and design for their actual needs.”Project lead Luis Fernando Luque-Vega, an IEEE member, says he’d like the venture to serve as a blueprint for engineering education.“I hope RoboMeshA is adopted by schools, universities, and IEEE student branches across Mexico and internationally as a model for integrating technical innovation with community engagement,” Luque-Vega says.By pairing engineering talent with community service, initiatives such as EPICS in IEEE demonstrate how targeted support can turn academic concepts into real-world solutions.“When students realize the technology they develop can inspire others and improve lives, engineering becomes far more meaningful,” Luque-Vega says.For more information on service-learning opportunities, visit the EPICS website.

Andrew Ng has serious street cred in artificial intelligence. He pioneered the use of graphics processing units (GPUs) to train deep learning models in the late 2000s with his students at Stanford University, cofounded Google Brain in 2011, and then served for three years as chief scientist for Baidu, where he helped build the Chinese tech giant’s AI group. So when he says he has identified the next big shift in artificial intelligence, people listen. And that’s what he told IEEE Spectrum in an exclusive Q&A. Ng’s current efforts are focused on his company Landing AI, which built a platform called LandingLens to help manufacturers improve visual inspection with computer vision. He has also become something of an evangelist for what he calls the data-centric AI movement, which he says can yield “small data” solutions to big issues in AI, including model efficiency, accuracy, and bias. Andrew Ng on... What’s next for really big models The career advice he didn’t listen to Defining the data-centric AI movement Synthetic data Why Landing AI asks its customers to do the work The great advances in deep learning over the past decade or so have been powered by ever-bigger models crunching ever-bigger amounts of data. Some people argue that that’s an unsustainable trajectory. Do you agree that it can’t go on that way? Andrew Ng: This is a big question. We’ve seen foundation models in NLP [natural language processing]. I’m excited about NLP models getting even bigger, and also about the potential of building foundation models in computer vision. I think there’s lots of signal to still be exploited in video: We have not been able to build foundation models yet for video because of compute bandwidth and the cost of processing video, as opposed to tokenized text. So I think that this engine of scaling up deep learning algorithms, which has been running for something like 15 years now, still has steam in it. Having said that, it only applies to certain problems, and there’s a set of other problems that need small data solutions. When you say you want a foundation model for computer vision, what do you mean by that? Ng: This is a term coined by Percy Liang and some of my friends at Stanford to refer to very large models, trained on very large data sets, that can be tuned for specific applications. For example, GPT-3 is an example of a foundation model [for NLP]. Foundation models offer a lot of promise as a new paradigm in developing machine learning applications, but also challenges in terms of making sure that they’re reasonably fair and free from bias, especially if many of us will be building on top of them. What needs to happen for someone to build a foundation model for video? Ng: I think there is a scalability problem. The compute power needed to process the large volume of images for video is significant, and I think that’s why foundation models have arisen first in NLP. Many researchers are working on this, and I think we’re seeing early signs of such models being developed in computer vision. But I’m confident that if a semiconductor maker gave us 10 times more processor power, we could easily find 10 times more video to build such models for vision. Having said that, a lot of what’s happened over the past decade is that deep learning has happened in consumer-facing companies that have large user bases, sometimes billions of users, and therefore very large data sets. While that paradigm of machine learning has driven a lot of economic value in consumer software, I find that that recipe of scale doesn’t work for other industries. Back to top It’s funny to hear you say that, because your early work was at a consumer-facing company with millions of users. Ng: Over a decade ago, when I proposed starting the Google Brain project to use Google’s compute infrastructure to build very large neural networks, it was a controversial step. One very senior person pulled me aside and warned me that starting Google Brain would be bad for my career. I think he felt that the action couldn’t just be in scaling up, and that I should instead focus on architecture innovation. “In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn.” —Andrew Ng, CEO & Founder, Landing AI I remember when my students and I published the first NeurIPS workshop paper advocating using CUDA, a platform for processing on GPUs, for deep learning—a different senior person in AI sat me down and said, “CUDA is really complicated to program. As a programming paradigm, this seems like too much work.” I did manage to convince him; the other person I did not convince. I expect they’re both convinced now. Ng: I think so, yes. Over the past year as I’ve been speaking to people about the data-centric AI movement, I’ve been getting flashbacks to when I was speaking to people about deep learning and scalability 10 or 15 years ago. In the past year, I’ve been getting the same mix of “there’s nothing new here” and “this seems like the wrong direction.” Back to top How do you define data-centric AI, and why do you consider it a movement? Ng: Data-centric AI is the discipline of systematically engineering the data needed to successfully build an AI system. For an AI system, you have to implement some algorithm, say a neural network, in code and then train it on your data set. The dominant paradigm over the last decade was to download the data set while you focus on improving the code. Thanks to that paradigm, over the last decade deep learning networks have improved significantly, to the point where for a lot of applications the code—the neural network architecture—is basically a solved problem. So for many practical applications, it’s now more productive to hold the neural network architecture fixed, and instead find ways to improve the data. When I started speaking about this, there were many practitioners who, completely appropriately, raised their hands and said, “Yes, we’ve been doing this for 20 years.” This is the time to take the things that some individuals have been doing intuitively and make it a systematic engineering discipline. The data-centric AI movement is much bigger than one company or group of researchers. My collaborators and I organized a data-centric AI workshop at NeurIPS, and I was really delighted at the number of authors and presenters that showed up. You often talk about companies or institutions that have only a small amount of data to work with. How can data-centric AI help them? Ng: You hear a lot about vision systems built with millions of images—I once built a face recognition system using 350 million images. Architectures built for hundreds of millions of images don’t work with only 50 images. But it turns out, if you have 50 really good examples, you can build something valuable, like a defect-inspection system. In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn. When you talk about training a model with just 50 images, does that really mean you’re taking an existing model that was trained on a very large data set and fine-tuning it? Or do you mean a brand new model that’s designed to learn only from that small data set? Ng: Let me describe what Landing AI does. When doing visual inspection for manufacturers, we often use our own flavor of RetinaNet. It is a pretrained model. Having said that, the pretraining is a small piece of the puzzle. What’s a bigger piece of the puzzle is providing tools that enable the manufacturer to pick the right set of images [to use for fine-tuning] and label them in a consistent way. There’s a very practical problem we’ve seen spanning vision, NLP, and speech, where even human annotators don’t agree on the appropriate label. For big data applications, the common response has been: If the data is noisy, let’s just get a lot of data and the algorithm will average over it. But if you can develop tools that flag where the data’s inconsistent and give you a very targeted way to improve the consistency of the data, that turns out to be a more efficient way to get a high-performing system. “Collecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity.” —Andrew Ng For example, if you have 10,000 images where 30 images are of one class, and those 30 images are labeled inconsistently, one of the things we do is build tools to draw your attention to the subset of data that’s inconsistent. So you can very quickly relabel those images to be more consistent, and this leads to improvement in performance. Could this focus on high-quality data help with bias in data sets? If you’re able to curate the data more before training? Ng: Very much so. Many researchers have pointed out that biased data is one factor among many leading to biased systems. There have been many thoughtful efforts to engineer the data. At the NeurIPS workshop, Olga Russakovsky gave a really nice talk on this. At the main NeurIPS conference, I also really enjoyed Mary Gray’s presentation, which touched on how data-centric AI is one piece of the solution, but not the entire solution. New tools like Datasheets for Datasets also seem like an important piece of the puzzle. One of the powerful tools that data-centric AI gives us is the ability to engineer a subset of the data. Imagine training a machine-learning system and finding that its performance is okay for most of the data set, but its performance is biased for just a subset of the data. If you try to change the whole neural network architecture to improve the performance on just that subset, it’s quite difficult. But if you can engineer a subset of the data you can address the problem in a much more targeted way. When you talk about engineering the data, what do you mean exactly? Ng: In AI, data cleaning is important, but the way the data has been cleaned has often been in very manual ways. In computer vision, someone may visualize images through a Jupyter notebook and maybe spot the problem, and maybe fix it. But I’m excited about tools that allow you to have a very large data set, tools that draw your attention quickly and efficiently to the subset of data where, say, the labels are noisy. Or to quickly bring your attention to the one class among 100 classes where it would benefit you to collect more data. Collecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity. For example, I once figured out that a speech-recognition system was performing poorly when there was car noise in the background. Knowing that allowed me to collect more data with car noise in the background, rather than trying to collect more data for everything, which would have been expensive and slow. Back to top What about using synthetic data, is that often a good solution? Ng: I think synthetic data is an important tool in the tool chest of data-centric AI. At the NeurIPS workshop, Anima Anandkumar gave a great talk that touched on synthetic data. I think there are important uses of synthetic data that go beyond just being a preprocessing step for increasing the data set for a learning algorithm. I’d love to see more tools to let developers use synthetic data generation as part of the closed loop of iterative machine learning development. Do you mean that synthetic data would allow you to try the model on more data sets? Ng: Not really. Here’s an example. Let’s say you’re trying to detect defects in a smartphone casing. There are many different types of defects on smartphones. It could be a scratch, a dent, pit marks, discoloration of the material, other types of blemishes. If you train the model and then find through error analysis that it’s doing well overall but it’s performing poorly on pit marks, then synthetic data generation allows you to address the problem in a more targeted way. You could generate more data just for the pit-mark category. “In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In manufacturing, you might have 10,000 manufacturers building 10,000 custom AI models.” —Andrew Ng Synthetic data generation is a very powerful tool, but there are many simpler tools that I will often try first. Such as data augmentation, improving labeling consistency, or just asking a factory to collect more data. Back to top To make these issues more concrete, can you walk me through an example? When a company approaches Landing AI and says it has a problem with visual inspection, how do you onboard them and work toward deployment? Ng: When a customer approaches us we usually have a conversation about their inspection problem and look at a few images to verify that the problem is feasible with computer vision. Assuming it is, we ask them to upload the data to the LandingLens platform. We often advise them on the methodology of data-centric AI and help them label the data. One of the foci of Landing AI is to empower manufacturing companies to do the machine learning work themselves. A lot of our work is making sure the software is fast and easy to use. Through the iterative process of machine learning development, we advise customers on things like how to train models on the platform, when and how to improve the labeling of data so the performance of the model improves. Our training and software supports them all the way through deploying the trained model to an edge device in the factory. How do you deal with changing needs? If products change or lighting conditions change in the factory, can the model keep up? Ng: It varies by manufacturer. There is data drift in many contexts. But there are some manufacturers that have been running the same manufacturing line for 20 years now with few changes, so they don’t expect changes in the next five years. Those stable environments make things easier. For other manufacturers, we provide tools to flag when there’s a significant data-drift issue. I find it really important to empower manufacturing customers to correct data, retrain, and update the model. Because if something changes and it’s 3 a.m. in the United States, I want them to be able to adapt their learning algorithm right away to maintain operations. In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In manufacturing, you might have 10,000 manufacturers building 10,000 custom AI models. The challenge is, how do you do that without Landing AI having to hire 10,000 machine learning specialists? So you’re saying that to make it scale, you have to empower customers to do a lot of the training and other work. Ng: Yes, exactly! This is an industry-wide problem in AI, not just in manufacturing. Look at health care. Every hospital has its own slightly different format for electronic health records. How can every hospital train its own custom AI model? Expecting every hospital’s IT personnel to invent new neural-network architectures is unrealistic. The only way out of this dilemma is to build tools that empower the customers to build their own models by giving them tools to engineer the data and express their domain knowledge. That’s what Landing AI is executing in computer vision, and the field of AI needs other teams to execute this in other domains. Is there anything else you think it’s important for people to understand about the work you’re doing or the data-centric AI movement? Ng: In the last decade, the biggest shift in AI was a shift to deep learning. I think it’s quite possible that in this decade the biggest shift will be to data-centric AI. With the maturity of today’s neural network architectures, I think for a lot of the practical applications the bottleneck will be whether we can efficiently get the data we need to develop systems that work well. The data-centric AI movement has tremendous energy and momentum across the whole community. I hope more researchers and developers will jump in and work on it. Back to top This article appears in the April 2022 print issue as “Andrew Ng, AI Minimalist.”

The end of Moore’s Law is looming. Engineers and designers can do only so much to miniaturize transistors and pack as many of them as possible into chips. So they’re turning to other approaches to chip design, incorporating technologies like AI into the process.Samsung, for instance, is adding AI to its memory chips to enable processing in memory, thereby saving energy and speeding up machine learning. Speaking of speed, Google’s TPU V4 AI chip has doubled its processing power compared with that of its previous version.But AI holds still more promise and potential for the semiconductor industry. To better understand how AI is set to revolutionize chip design, we spoke with Heather Gorr, senior product manager for MathWorks’ MATLAB platform.How is AI currently being used to design the next generation of chips?Heather Gorr: AI is such an important technology because it’s involved in most parts of the cycle, including the design and manufacturing process. There’s a lot of important applications here, even in the general process engineering where we want to optimize things. I think defect detection is a big one at all phases of the process, especially in manufacturing. But even thinking ahead in the design process, [AI now plays a significant role] when you’re designing the light and the sensors and all the different components. There’s a lot of anomaly detection and fault mitigation that you really want to consider. Heather GorrMathWorksThen, thinking about the logistical modeling that you see in any industry, there is always planned downtime that you want to mitigate; but you also end up having unplanned downtime. So, looking back at that historical data of when you’ve had those moments where maybe it took a bit longer than expected to manufacture something, you can take a look at all of that data and use AI to try to identify the proximate cause or to see something that might jump out even in the processing and design phases. We think of AI oftentimes as a predictive tool, or as a robot doing something, but a lot of times you get a lot of insight from the data through AI.What are the benefits of using AI for chip design?Gorr: Historically, we’ve seen a lot of physics-based modeling, which is a very intensive process. We want to do a reduced order model, where instead of solving such a computationally expensive and extensive model, we can do something a little cheaper. You could create a surrogate model, so to speak, of that physics-based model, use the data, and then do your parameter sweeps, your optimizations, your Monte Carlo simulations using the surrogate model. That takes a lot less time computationally than solving the physics-based equations directly. So, we’re seeing that benefit in many ways, including the efficiency and economy that are the results of iterating quickly on the experiments and the simulations that will really help in the design.So it’s like having a digital twin in a sense?Gorr: Exactly. That’s pretty much what people are doing, where you have the physical system model and the experimental data. Then, in conjunction, you have this other model that you could tweak and tune and try different parameters and experiments that let sweep through all of those different situations and come up with a better design in the end.So, it’s going to be more efficient and, as you said, cheaper?Gorr: Yeah, definitely. Especially in the experimentation and design phases, where you’re trying different things. That’s obviously going to yield dramatic cost savings if you’re actually manufacturing and producing [the chips]. You want to simulate, test, experiment as much as possible without making something using the actual process engineering.We’ve talked about the benefits. How about the drawbacks?Gorr: The [AI-based experimental models] tend to not be as accurate as physics-based models. Of course, that’s why you do many simulations and parameter sweeps. But that’s also the benefit of having that digital twin, where you can keep that in mind—it’s not going to be as accurate as that precise model that we’ve developed over the years.Both chip design and manufacturing are system intensive; you have to consider every little part. And that can be really challenging. It’s a case where you might have models to predict something and different parts of it, but you still need to bring it all together.One of the other things to think about too is that you need the data to build the models. You have to incorporate data from all sorts of different sensors and different sorts of teams, and so that heightens the challenge.How can engineers use AI to better prepare and extract insights from hardware or sensor data?Gorr: We always think about using AI to predict something or do some robot task, but you can use AI to come up with patterns and pick out things you might not have noticed before on your own. People will use AI when they have high-frequency data coming from many different sensors, and a lot of times it’s useful to explore the frequency domain and things like data synchronization or resampling. Those can be really challenging if you’re not sure where to start.One of the things I would say is, use the tools that are available. There’s a vast community of people working on these things, and you can find lots of examples [of applications and techniques] on GitHub or MATLAB Central, where people have shared nice examples, even little apps they’ve created. I think many of us are buried in data and just not sure what to do with it, so definitely take advantage of what’s already out there in the community. You can explore and see what makes sense to you, and bring in that balance of domain knowledge and the insight you get from the tools and AI.What should engineers and designers consider when using AI for chip design?Gorr: Think through what problems you’re trying to solve or what insights you might hope to find, and try to be clear about that. Consider all of the different components, and document and test each of those different parts. Consider all of the people involved, and explain and hand off in a way that is sensible for the whole team.How do you think AI will affect chip designers’ jobs?Gorr: It’s going to free up a lot of human capital for more advanced tasks. We can use AI to reduce waste, to optimize the materials, to optimize the design, but then you still have that human involved whenever it comes to decision-making. I think it’s a great example of people and technology working hand in hand. It’s also an industry where all people involved—even on the manufacturing floor—need to have some level of understanding of what’s happening, so this is a great industry for advancing AI because of how we test things and how we think about them before we put them on the chip.How do you envision the future of AI and chip design?Gorr: It’s very much dependent on that human element—involving people in the process and having that interpretable model. We can do many things with the mathematical minutiae of modeling, but it comes down to how people are using it, how everybody in the process is understanding and applying it. Communication and involvement of people of all skill levels in the process are going to be really important. We’re going to see less of those superprecise predictions and more transparency of information, sharing, and that digital twin—not only using AI but also using our human knowledge and all of the work that many people have done over the years.

Quantum computing is a devilishly complex technology, with many technical hurdles impacting its development. Of these challenges two critical issues stand out: miniaturization and qubit quality.IBM has adopted the superconducting qubit road map of reaching a 1,121-qubit processor by 2023, leading to the expectation that 1,000 qubits with today’s qubit form factor is feasible. However, current approaches will require very large chips (50 millimeters on a side, or larger) at the scale of small wafers, or the use of chiplets on multichip modules. While this approach will work, the aim is to attain a better path toward scalability.Now researchers at MIT have been able to both reduce the size of the qubits and done so in a way that reduces the interference that occurs between neighboring qubits. The MIT researchers have increased the number of superconducting qubits that can be added onto a device by a factor of 100.“We are addressing both qubit miniaturization and quality,” said William Oliver, the director for the Center for Quantum Engineering at MIT. “Unlike conventional transistor scaling, where only the number really matters, for qubits, large numbers are not sufficient, they must also be high-performance. Sacrificing performance for qubit number is not a useful trade in quantum computing. They must go hand in hand.”The key to this big increase in qubit density and reduction of interference comes down to the use of two-dimensional materials, in particular the 2D insulator hexagonal boron nitride (hBN). The MIT researchers demonstrated that a few atomic monolayers of hBN can be stacked to form the insulator in the capacitors of a superconducting qubit.Just like other capacitors, the capacitors in these superconducting circuits take the form of a sandwich in which an insulator material is sandwiched between two metal plates. The big difference for these capacitors is that the superconducting circuits can operate only at extremely low temperatures—less than 0.02 degrees above absolute zero (-273.15 °C). Superconducting qubits are measured at temperatures as low as 20 millikelvin in a dilution refrigerator.Nathan Fiske/MITIn that environment, insulating materials that are available for the job, such as PE-CVD silicon oxide or silicon nitride, have quite a few defects that are too lossy for quantum computing applications. To get around these material shortcomings, most superconducting circuits use what are called coplanar capacitors. In these capacitors, the plates are positioned laterally to one another, rather than on top of one another.As a result, the intrinsic silicon substrate below the plates and to a smaller degree the vacuum above the plates serve as the capacitor dielectric. Intrinsic silicon is chemically pure and therefore has few defects, and the large size dilutes the electric field at the plate interfaces, all of which leads to a low-loss capacitor. The lateral size of each plate in this open-face design ends up being quite large (typically 100 by 100 micrometers) in order to achieve the required capacitance.In an effort to move away from the large lateral configuration, the MIT researchers embarked on a search for an insulator that has very few defects and is compatible with superconducting capacitor plates.“We chose to study hBN because it is the most widely used insulator in 2D material research due to its cleanliness and chemical inertness,” said colead author Joel Wang, a research scientist in the Engineering Quantum Systems group of the MIT Research Laboratory for Electronics. On either side of the hBN, the MIT researchers used the 2D superconducting material, niobium diselenide. One of the trickiest aspects of fabricating the capacitors was working with the niobium diselenide, which oxidizes in seconds when exposed to air, according to Wang. This necessitates that the assembly of the capacitor occur in a glove box filled with argon gas.While this would seemingly complicate the scaling up of the production of these capacitors, Wang doesn’t regard this as a limiting factor.“What determines the quality factor of the capacitor are the two interfaces between the two materials,” said Wang. “Once the sandwich is made, the two interfaces are “sealed” and we don’t see any noticeable degradation over time when exposed to the atmosphere.”This lack of degradation is because around 90 percent of the electric field is contained within the sandwich structure, so the oxidation of the outer surface of the niobium diselenide does not play a significant role anymore. This ultimately makes the capacitor footprint much smaller, and it accounts for the reduction in cross talk between the neighboring qubits.“The main challenge for scaling up the fabrication will be the wafer-scale growth of hBN and 2D superconductors like [niobium diselenide], and how one can do wafer-scale stacking of these films,” added Wang.Wang believes that this research has shown 2D hBN to be a good insulator candidate for superconducting qubits. He says that the groundwork the MIT team has done will serve as a road map for using other hybrid 2D materials to build superconducting circuits.