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  • Qualcomm’s Newest Chip Brings AI to Wi-Fi
    by Michael Koziol on 27. Februara 2024. at 13:00

    Wireless spectrum is always at a premium—if you’ve ever tried to connect to Wi-Fi in a crowded airport or stadium, you know the pain that comes from crowded spectrum use. That’s why the industry continues to tinker with ways to get the most out of available spectrum. The latest example: Qualcomm’s FastConnect 7900 chip, which the company unveiled Monday at Mobile World Congress in Barcelona. Qualcomm touts the FastConnect 7900 as a provider of “AI-enhanced” Wi-Fi 7, which the company views as an opportunity to create more reliable wireless connections. The chip will also better integrate the disparate technologies of Wi-Fi, Bluetooth, and ultra-wideband for consumer applications. In addition, the chip can support two connections to the same device over the same spectrum band. The FastConnect 7900 comes as the wireless industry renews its focus on reliability with Wi-Fi 7, the wireless tech standard’s latest generation. The emphasis comes in addition improving throughput and decreasing latency, something to which every Wi-Fi generation contributes. (Wi-Fi is a range of wireless networking protocols based on the IEEE 802.11 set of standards. The IEEE is IEEE Spectrum‘s parent organization.) AI-Enhanced Wi-Fi “[Wi-Fi’s] a bit like the wild, wild West,” says Javier del Prado, vice president for mobile connectivity at Qualcomm. “It’s all sorts of devices out there, congestion, devices that come in and go off, access points that do this, access points that do that—It’s very difficult to guarantee service.” Del Prado says that AI is the “perfect tool” to change that. Key to the FastConnect 7900’s capabilities is the chip’s ability to detect what applications are in use by the device. Different applications use Wi-Fi differently: For example, streaming a video may require more data throughput, while a voice chat needs to prioritize low latency. After the chip has determined what applications are in use, it can optimize power and latency on a case-by-case basis. Using AI to manage wireless spectrum connections isn’t a new problem or solution, but Qualcomm’s chip benefits from running everything on-device. “It has to run on the device to be effective,” says del Prado. “We need to make decisions at the microsecond level.” Put another way, using the Wi-Fi connection itself to transmit the information about how to adjust the Wi-Fi connection would defeat the purpose of AI management in the first place—by the time the chip receives the information, it’d be way out of date. Also important: The chip doesn’t suck power—in fact, it saves power overall. “These are fairly simple models,” says del Prado. “It’s not a 5-billion parameter AI. It’s a much smaller model. The key performance indicators are the speed and the accuracy.” Del Prado says that the chip’s power consumption is negligible. In fact, because of its ability to optimize power depending on what applications are running, the chip saves its device up to 30 percent in power consumption. Wi-Fi and Bluetooth and Ultra-Wideband, All in One Outside of cellular, Wi-Fi is the most common way our phones connect with the world. But it’s not the only tech—Bluetooth is used for things like wireless earbuds, and ultra-wideband (UWB) also sees some use for applications like item tracking (think Apple’s AirPods) and locking and unlocking cars remotely. All three technologies rely heavily on proximity and distance ranging to maintain wireless connections. “There are all these use cases that use proximity and that use different technologies,” says del Prado. “Different technologies bring different benefits. There’s not always a single technology that fits all use cases. But that creates complexity.” Qualcomm’s FastConnect 7900, del Prado says, will hide that complexity. “We make it technology-agnostic for the consumer.” Sharing Spectrum Bands One final trick the FastConnect 7900 offers is an ability to host two Wi-Fi connections on the same band of spectrum. Here, the chip is building on previous FastConnect generations. “We already introduced what we call ‘hybrid-simultaneous’—this is the capability of doing multiple channels simultaneously on the 5 and 6 gigahertz bands,” says del Prado. New to the 7900 is audio over Wi-Fi, says del Prado. Qualcomm is calling it “XPAN,” and it’s a separate channel for audio only in those 5 GHz and 6 GHz bands. This matters because those spectrum bands can deliver a much higher audio quality to the device compared to, say, Bluetooth, which operates in the 2.4 GHz band. By carving out a separate channel just for audio, says del Prado, the 7900 chip can provide that much better audio quality without it succumbing to the strain that typically emerges when multiple connections demand the same wireless signal. “That’s something that cannot be done with Bluetooth today, because it’s bandwidth-limited,” says del Prado. Qualcomm is already sampling the FastConnect 7900 to its customers—that is, manufacturers of phones and similar devices. Del Prado estimates that the first products with the chip will hit the market in the second half of the year. “When the new round of premium Android phones hits the market later this year, those should support this functionality.”

  • Heat Pumps Take on Cold Climates
    by Emily Waltz on 26. Februara 2024. at 21:39

    Twenty homes scattered across Canada and the northern United States are keeping warm this winter using prototypes of the latest iteration in residential heating systems: cold climate heat pumps. Heat pumps aren’t common in homes at this latitude, because historically they haven’t worked well in subzero temperatures. But heat pump manufacturers say they now have the technology to heat homes just as efficiently in bitter cold as they do in milder winter temperatures. To prove it, eight manufacturers are publicly testing their prototypes in the Cold-Climate Heat Pump Technology Challenge, hosted by the U.S. Department of Energy (DOE) in partnership with Natural Resources Canada. The companies’ task is to demonstrate a high-efficiency, residential air-source heat pump that can perform at 100 percent capacity at -15 °C. Companies can choose to further test their machines down to -26 °C. Heat pump manufacturers Bosch, Carrier, Daikin, Johnson Controls, Lennox, Midea, Rheem, and Trane Technologies have each passed the laboratory phase of the challenge, according to the DOE. They are now field testing their prototypes in homes in ten northern U.S. states and two Canadian provinces, where furnaces and boilers burning fossil gas, fuel oil or propane are more commonly used. Companies that complete the challenge won’t receive a cash prize. But the DOE will help them expand into cold climate markets by engaging with stakeholders in those regions, a DOE spokesperson told IEEE Spectrum. The challenge will conclude later this year, and prototypes will likely be ready for commercialization in 2025. How heat pumps beat the cold Advances in the technology came primarily through improvements in one key heat pump component: the compressor. Heat pumps work by moving and compressing fluids. In the winter, the systems draw heat from outside the home, most commonly from the air. (There is heat in the air even in subzero temperatures.) An outdoor heat exchanger, or coil, absorbs the heat into the heat pump system. The outdoor air passes over a heat exchanger containing a fluid, or refrigerant, that has a very low boiling point. A common refrigerant, called R410a, boils at -48.5 °C. The refrigerant boils and evaporates into a vapor, and a compressor increases its temperature and pressure. The superheated vapor then moves through an indoor coil, where fans blow air across it, moving heat into the home. In the summer, the system reverses, moving heat from inside the building to the outside, and cooling the home. “They couldn’t get the lab any colder than [-30 °C], so we had to cut the power to get the heat pump to turn off.” —Katie Davis, Trane Technologies The colder the temperature outside, the harder heat pumps must work to extract and move enough heat to maintain the home’s temperature. At about 4 °C, most air-source heat pumps currently on the market start operating at less than their full capacity, and at some point (usually around -15 °C), they can no longer do the job at all. At that point, an auxiliary heat source kicks on, which is less efficient. But advancements in compressor technology over the last five years have addressed that issue. By controlling the compressor motor’s speed, and improving the timing of when vapor is injected into the compressor, engineers have made heat pumps more efficient in colder temperatures. For example, Trane Technologies, headquartered in Dublin, “played with the vapor compression cycle” so that it gets an extra injection of refrigerant, says Katie Davis, vice president of engineering and technology in Trane’s residential business. “It’s works a little like fuel injection,” she says. When the system begins to lose its capacity to heat, the system injects refrigerant to give it a boost, she says. In the lab portion of the DOE’s heat pump challenge, Trane’s unit operated at 100 percent capacity at -15 °C and kept running even as the lab’s temperature dropped to -30 °C, although no longer at full capacity. “They couldn’t get the lab any colder than that, so we had to cut the power to get the heat pump to turn off,” Davis says. Vapor injection compressor technology has been around for years, but until recently, had not been optimized for heat pumps, Davis says. That, plus the introduction of smart systems that enable the indoor and outdoor units to communicate with each other and the thermostat, has enabled heat pumps to take on colder weather. Heat pumps can reduce emissions and cut energy costs The DOE is pushing for wider adoption of heat pumps because of their potential to reduce greenhouse gas emissions. Such systems run on electricity rather than fossil fuels, and when the electricity comes from renewable sources, the greenhouse gas savings are substantial, the DOE says. Because heat pumps transfer heat rather than generate it, they are significantly more efficient than traditional heating systems, the agency says. A two-year study published 12 February in the journal Joule supports the DOE’s claim. The study found that if every heated home in the U.S. switched to a heat pump, home energy use would drop by 31 to 47 percent on average, and national carbon dioxide emissions would fall by 5 to 9 percent, depending on how much electricity is provided by renewable energy. Those figures are based on heat pumps that draw heat from an air source (rather than ground or water) and includes both homes that pull heat through ductwork, and homes that are ductless. The energy savings should lower bills for 62 to 95 percent of homeowners, depending on the efficiency and cold climate performance of the heat pump being installed. How well a home is insulated and the type of heating system being replaced also makes a big difference in energy bills, the study found. For households that are currently heating with electric resistance heat, fuel oil, or propane, heat pumps could save thousands of dollars annually. For natural gas, the savings are less and depend on the price of natural gas in the local area. Some homeowners are hesitant to switch to heat pumps because of what’s known as “temperature anxiety.” Cold climate heat pumps will likely boost energy savings for homeowners, but will require higher up front costs, says Eric Wilson, a senior research engineer at the National Renewable Energy Laboratory in Golden, Colorado, and an author of the paper. “It’s generally well known that heat pumps can save money, but there’s a lot of confusion around whether they’re a good idea in all climates,” he says. His study and the DOE’s cold climate heat pump challenge will help provide a clearer picture, he says. The DOE is one of several government entities trying to expedite adoption of residential high efficiency heat pumps. Nine U.S. states earlier this month pledged to accelerate heat pump sales. Their pledge builds on an announcement in September from 25 governors, who vowed to quadruple heat pump installation in their states by 2030. The U.S. federal government also offers tax credits and states will be rolling out rebates to offset the cost of installation. So far, the efforts seem to be working. In the U.S., heat pumps outsold furnaces for a second year in a row in 2023, according to data released 9 February by the Air-Conditioning, Heating, and Refrigeration Institute in Arlington, Virginia. Europe is making a similar push. The European Commission has called for expedited deployment of heat pumps, and recommended that member states phase out the use of fossil fuel heating systems in all buildings by 2035. Many European countries are subsidizing residential heat pump installation by offering grants to homeowners. But some homeowners are hesitant to switch to heat pumps because of what’s known as “temperature anxiety.” It’s like electric vehicle range anxiety: Homeowners are concerned about getting stuck in a cold house. And some just like the feel of old fashioned heat. “Folks who have furnaces say they really like the way that hot heat feels when it’s coming out,” says Davis at Trane. “Heat pumps put out warm heat and it’s going to do a good job heating your home, but it’s not that hot heat that comes out of a furnace.” Trane’s cold climate heat pump—the one entered into the DOE’s challenge—is current heating the home of a family in Boise, Idaho, Davis says. “We’ve had excellent feedback from our customer there, who said their energy bills went down,” she says. To pass the DOE’s field test, heat pumps must meet a host of specifications. They must draw heat from the air (rather than the ground or water), operate at 100 percent capacity without relying on back-up heat, demonstrate 40 percent greater efficiency than current heat pumps on the market, and do all of this in homes that distribute air through ductwork, since those setups are more challenging in colder climates.

  • What is CMOS 2.0?
    by Samuel K. Moore on 26. Februara 2024. at 16:00

    CMOS, the silicon logic technology behind decades and decades of smaller transistors and faster computers, is entering a new phase. CMOS uses two types of transistors in pairs to limit a circuit’s power consumption. In this new phase, “CMOS 2.0,” that part’s not going to change, but how processors and other complex CMOS chips are made will. Julien Ryckaert, vice president of logic technologies at Imec, the Belgium-based nanotechnology research center, told IEEE Spectrum where things are headed. Julien Ryckaert Julien Ryckaert is vice president of logic technologies at Imec, in Belgium, where he’s been involved in exploring new technologies for 3D chips, among other topics. Why is CMOS entering a new phase? Julien Ryckaert: CMOS was the technology answer to build microprocessors in the 1960s. Making things smaller—transistors and interconnects—to make them better worked for 60, 70 years. But that has started to break down. Why has CMOS scaling been breaking down? Ryckaert: Over the years, people have made system-on-chips (SoCs)—such as CPUs and GPUs—more and more complex. That is, they have integrated more and more operations onto the same silicon die. That makes sense, because it is so much more efficient to move data on a silicon die than to move it from chip to chip in a computer. For a long time, the scaling down of CMOS transistors and interconnects made all those operations work better. But now, it’s starting to be difficult to build the whole SoC, to make all of it better by just scaling the device and the interconnect. For example, SRAM [the system’s cache memory] no longer scales as well as logic. What’s the solution? Ryckaert: Seeing that something different needs to happen, we at Imec asked: Why do we scale? At the end of the day, Moore’s law is not about delivering smaller transistors and interconnects, it’s about achieving more functionality per unit area. So what you are starting to see is breaking out certain functions, such as logic and SRAM, building them on separate chiplets using technologies that give each the best advantage, and then reintegrating them using advanced 3D packaging technologies. You can connect two functions that are built on the different substrates and achieve an efficiency in communication between those two functions that is competitive with how efficient they were when the two functions were on the same substrate. This is an evolution to what we call smart disintegration, or system technology co-optimization. So is that CMOS 2.0? Ryckaert: What we’re doing in CMOS 2.0 is pushing that idea further, with much finer-grained disintegration of functions and stacking of many more dies. A first sign of CMOS 2.0 is the imminent arrival of backside-power-delivery networks. On chips today, all interconnects—both those carrying data and those delivering power—are on the front side of the silicon [above the transistors]. Those two types of interconnect have different functions and different requirements, but they have had to exist in a compromise until now. Backside power moves the power-delivery interconnects to beneath the silicon, essentially turning the die into an active transistor layer which is sandwiched between two interconnect stacks, each stack having a different functionality. Will transistors and interconnects still have to keep scaling in CMOS 2.0? Ryckaert: Yes, because somewhere in that stack, you will still have a layer that still needs more transistors per unit area. But now, because you have removed all the other constraints that it once had, you are letting that layer nicely scale with the technology that is perfectly suited for it. I see fascinating times ahead. This article appears in the March print issue as “5 Questions for Julien Ryckaert.”

  • The Scoop on Keeping an Ice Cream Factory Cool
    by Edd Gent on 25. Februara 2024. at 16:00

    Working in an ice cream factory is a dream for anyone who enjoys the frozen dessert. For control systems engineer Patryk Borkowski, a job at the biggest ice cream company in the world is also a great way to put his automation expertise to use. Patryk Borkowski Employer: Unilever, Colworth Science Park, in Sharnbrook, England Occupation: Control systems engineer Education: Bachelor’s degree in automation and robotics from the West Pomeranian University of Technology in Szczecin, Poland Borkowski works at the Advanced Prototype and Engineering Centre of the multinational consumer goods company Unilever. Unilever’s corporate umbrella covers such ice cream brands as Ben & Jerry’s, Breyers, Good Humor, Magnum, and Walls. Borkowski maintains and updates equipment at the innovation center’s pilot plant at Colworth Science Park in Sharnbrook, England. The company’s food scientists and engineers use this small-scale factory to experiment with new ice cream formulations and novel production methods. The reality of the job might not exactly live up to an ice cream lover’s dream. For safety reasons, eating the product in the plant is prohibited. “You can’t just put your mouth underneath the nozzle of an ice cream machine and fill your belly,” he says. For an engineer, though, the complex chemistry and processing required to create ice cream products make for fascinating problem-solving. Much of Borkowski’s work involves improving the environmental impact of ice cream production by cutting waste and reducing the amount of energy needed to keep products frozen. And he loves working on a product that puts a smile on the faces of customers. “Ice cream is a deeply indulgent and happy product,” he says. “We love working to deliver a superior taste and a superior way to experience ice cream.” Ice Cream Innovation Borkowski joined Unilever as a control systems engineer in 2021. While he’s not allowed to discuss many of the details of his research, he says one of the projects he has worked on is a modular manufacturing line that the company uses to develop new kinds of ice cream. The setup allows pieces of equipment such as sauce baths, nitrogen baths for quickly freezing layers, and chocolate deposition systems to be seamlessly switched in and out so that food scientists can experiment and create new products. Ice cream is a fascinating product to work on for an engineer, Borkowski says, because it’s inherently unstable. “Ice cream doesn’t want to be frozen; it pretty much wants to be melted on the floor,” he says. “We’re trying to bend the chemistry to bind all the ingredients into a semistable mixture that gives you that great taste and feeling on the tongue.” Making Production More Sustainable Helping design new products is just one part of Borkowski’s job. Unilever is targeting sustainability across the company, so cutting waste and improving energy efficiency are key. He recently helped develop a testing rig to simulate freezer doors being repeatedly opened and closed in shops. This helped collect temperature data that was used to design new freezers that run at higher temperatures to save electricity. In 2022, he was temporarily transferred to one of Unilever’s ice cream factories in Hellendoorn, Netherlands, to uncover inefficiencies in the production process. He built a system that collected and collated operational data from all the factory’s machines to identify the causes of stoppages and waste. “There’s a deep pride in knowing the machines that we’ve programmed make something that people buy and enjoy.” It wasn’t easy. Some of the machines were older and no longer supported by their manufacturers. Also, they ran legacy code written in Dutch—a language Borkowski doesn’t speak. Borkowski ended up reverse-engineering the machines to figure out their operating systems, then reprogrammed them to communicate with the new data-collection system. Now the data-collection system can be easily adapted to work at any Unilever factory. Discovering a Love for Technology As a child growing up in Stargard, Poland, Borkowski says there was little to indicate that he would become an engineer. At school, he loved writing, drawing, and learning new languages. He imagined himself having a career in the creative industries. But in the late 1990s, his parents got a second-hand computer and a modem. He quickly discovered online communities for technology enthusiasts and began learning about programming. Because of his growing fascination with technology, at 16, Borkowski opted to attend a technical high school, pursuing a technical diploma in electronics and learning about components, soldering, and assembly language. In 2011, he enrolled at the West Pomeranian University of Technology in Szczecin, Poland, where he earned a bachelor’s degree in automation and robotics. When he graduated in 2015, there were few opportunities in Poland to put his skills to use, so he moved to London. There, Borkowski initially worked odd jobs in warehouses and production facilities. After a brief stint as an electronic technician assembling ultrasonic scanners, he joined bakery company Brioche Pasquier in Milton Keynes, England, as an automation engineer. This was an exciting move, Borkowski says, because he was finally doing control engineering, the discipline he’d always wanted to pursue. Part of his duties involved daily maintenance, but he also joined a team building new production lines from the ground up, linking together machinery such as mixers, industrial ovens, coolers, and packaging units. They programmed the machines so they all worked together seamlessly without human intervention. When the COVID-19 pandemic struck, new projects went on hold and work slowed down, Borkowski says. There seemed to be little opportunity to advance his career at Brioche Pasquier, so he applied for the control systems job at Unilever. “When I was briefed on the work, they told me it was all R&D and every project was different,” he says. “I thought that sounded like a challenge.” The Importance of a Theoretical Foundation Control engineers require a broad palette of skills in both electronics and programming, Borkowski says. Some of these can be learned on the job, he says, but a degree in subjects like automation or robotics provides an important theoretical foundation. The biggest piece of advice he has for fledgling control engineers is to stay calm, which he admits can be difficult when a manager is pressuring you to quickly get a line back up to avoid production delays. “Sometimes it’s better to step away and give yourself a few minutes to think before you do anything,” he says. Rushing can often result in mistakes that cause more problems in the long run. While working in production can sometimes be stressful, “There’s a deep pride in knowing the machines that we’ve programmed make something that people buy and enjoy,” Borkowski says.

  • Science Fiction Short: Hijack
    by Karl Schroeder on 24. Februara 2024. at 16:00

    Computers have grown more and more powerful over the decades by pushing the limits of how small their electronics can get. But just how big can a computer get? Could we turn a planet into a computer, and if so, what would we do with it? In considering such questions, we go beyond normal technological projections and into the realm of outright speculation. So IEEE Spectrum is making one of its occasional forays into science fiction, with a short story by Karl Schroeder about the unexpected outcomes from building a computer out of planet Mercury. Because we’re going much farther into the future than a typical Spectrum article does, we’ve contextualized and annotated Schroeder’s story to show how it’s still grounded in real science and technology. This isn’t the first work of fiction to consider such possibilities. In “The Hitchhiker’s Guide to the Galaxy,” Douglas Adams famously imagined a world constructed to serve as a processor. Real-world scientists are also intrigued by the idea. Jason Wright, director of the Penn State Extraterrestrial Intelligence Center, has given serious thought to how large a computer can get. A planet-scale computer, he notes, might feature in the search for extraterrestrial intelligence. “In SETI, we try to look for generic things any civilization might do, and computation feels pretty generic,” Wright says. “If that’s true, then someone’s got the biggest computer, and it’s interesting to think about how big it could be, and what limits they might hit.” There are, of course, physical constraints on very large computers. For instance, a planet-scale computer probably could not consist of a solid ball like Earth. “It would just get too hot,” Wright says. Any computation generates waste heat. Today’s microchips and data centers “face huge problems with heat management.” In addition, if too much of a planet-scale computer’s mass is concentrated in one place, “it could implode under its own weight,” says Anders Sandberg, a senior research fellow at the University of Oxford’s Future of Humanity Institute. “There are materials stronger than steel, but molecular bonds have a limit.” Instead, creating a computer from a planet will likely involve spreading out a world’s worth of mass. This strategy would also make it easier to harvest solar energy. Rather than building a single object that would be subject to all kinds of mechanical stresses, it would be better to break the computer up into a globular flotilla of nodes, known as a Dyson swarm. What uses might a planet-scale computer have? Hosting virtual realities for uploaded minds is one possibility, Sandberg notes. Quantum simulation of ecosystems is another, says Seth Lloyd, a quantum physicist at MIT. Which brings us to our story… Which brings us to our story… Simon Okoro settled into a lawn chair in the Heaven runtime and watched as worlds were born. “I suppose I should feel honored you chose to watch this with me,” said Martin as he sat down next to Simon. “Considering that you don’t believe I exist.” “Can’t we just share a moment? It’s been years since we did anything together. And you worked toward this moment too. You deserve some recognition.” A Uploading is a hypothetical process in which brain scanning can help create emulations of human minds in computers. A large enough computer could potentially house a civilization. These uploads could then go on to live in computer-simulated virtual realities. B Chris Philpot A typical satellite must orbit around a celestial object at a speed above a critical value to avoid being pulled into the surface of the object by gravity. A statite, a hypothetical form of satellite patented by physicist Robert L. Forward, uses a solar sail to help it hover above a star or planet, using radiation pressure from sunlight to balance the force of gravity. “Ah. They sent you to acknowledge the Uploaded, is that it?” Martin turned his long, sad-eyed face to the sky and the drama playing out above. A The Heaven runtime was a fully virtual world, so Simon had converted the sky into a vast screen on which to project what was happening in the real world. The magnified surface of the sun made a curving arc from horizon to horizon. Jets and coronas rippled over it, and high, high above its incandescent surface hung thousands of solar statites shaped like mirrored flowers B. They did not orbit, instead floating over a particular spot by light pressure alone. They formed a diffuse cloud, dwindling to invisibility before reaching the horizon. This telescope view showed the closest statite cores scattering fiery specks like spores into the overwhelming light. The specks blazed with light and shot away from the sun, accelerating. This moment was the pinnacle of Simon’s career, the apex of his life’s work. Each of those specks was a solar sail C, kilometers wide, carrying a terraforming package D. Launched so close to the sun and supplemented with lasers powered by the statites, they would be traveling at 20 percent light speed by the time they left the solar system. At their destinations, they’d sundive and then deliver terraforming seeds to lifeless planets around the nearest stars. C Chris Philpot Light has no mass, but it can exert pressure as photons exchange momentum with a surface as they reflect off it. A mirror that is thin and reflective enough can therefore serve as a solar sail, harnessing sunlight to generate thrust. In 2010, Japan’s Ikaros probe to Venus demonstrated the use of a solar sail for interplanetary travel for the first time. Because solar pressure is measured in micronewtons per square meter, solar sails must have large areas relative to their payloads, although the pressure from sunlight can be augmented with a laser beam for propulsion . D Terraforming is the hypothetical act of transforming a planet so as to resemble Earth, or at least make it suitable for life. Some terraforming proposals involve first seeding the planet with single-celled organisms that alter conditions to be more hospitable to multicellular life. This process would mimic the naturally occurring transformation of Earth that started about 2.3 billion years ago, when photosynthetic cyanobacteria created the oxygen-rich atmosphere we breathe today. “So life takes hold in the galaxy,” said Simon. These were the first words of a speech he’d written and rehearsed long ago. He’d dreamed of saying them on a podium, with Martin standing with him. But Martin...well, Martin had been dead for 20 years now.“ He remembered the rest of the speech, but there was no point in giving it when he was absolutely alone. Martin sighed. “So this is all you’re going to do with my Heaven? A little gardening? And then what? An orderly shutdown of the Heaven runtime? Sell off the Paradise processor as scrap?” “I knew this was a bad idea.” Simon raised his hand to exit the virtual world, but Martin quickly stood, looking sorry. “It’s just hard,” Martin said. “Paradise was supposed to be the great project to unite humanity. Our triumph over death! Why did you let them hijack it for this?” Simon watched the spores catch the light and flash away into interstellar space. “You know we won’t shut you down. Heaven will be kept running as long as Paradise exists. We built it together, Martin, and I’m proud of what we did.” E In a 2013 study, Sandberg and his colleague Stuart Armstrong suggested deploying automated self-replicating robots on Mercury to build a Dyson swarm. These robots would dismantle the planet to construct not only more of themselves but also the sunlight collectors making up the swarm. The more solar plants these robots built, the more energy they would have to mine Mercury and produce machines. Given this feedback loop, Sandberg and Armstrong argued, these robots could disassemble Mercury in a matter of decades. The solar plants making up this Dyson swarm could double as computers. F Solar power is exponentially more abundant at Mercury’s orbit compared with Earth’s. At its orbital distance of 1 astronomical unit from the sun, Earth receives about 1.4 kilowatts per square meter from sunlight. Mercury receives between 6.2 and 14.4 kW/m2. The range is because of Mercury’s high eccentricity—that is, it has the most elliptical orbit of all the planets in the solar system. G Whereas classical computers switch transistors on and off to symbolize data as either 1s and 0s, quantum computers use quantum bits, or qubits, which can exist in a state where they are both 1 and 0 at the same time. This essentially lets each qubit perform two calculations at once. As more qubits are added to a quantum computer, its computational power grows exponentiall The effort had been mind-bogglingly huge. They’d been able to do it only because millions of people believed that in dismantling Mercury E and turning it into a sun-powered F quantum computer G there would be enough computing power for every living person to upload their consciousness into it. The goal had been to achieve eternal life in a virtual afterlife: the Heaven runtime. Simon knit his hands together, lowering his eyes to the virtual garden. “Science happened, Martin. How were we to know Enactivism H would answer the ‘hard problem’ of consciousness? You and I had barely even heard of extended consciousness when we proposed Heaven. It was an old idea from cognitive science. Nobody was even studying it anymore except a few AIs, and we were sucking up all the resources they might have used to experiment.” He glanced ruefully at Martin. “We were all blindsided when they proved it. Consciousness can’t be just abstracted from a brain.” Martin’s response was quick; this was an old argument between them. “Nothing’s ever completely proven in science! There’s always room for doubt—but you agreed with those AIs when they said that simulated consciousness can’t have subjective experiences. Conveniently after I died but before I got rebooted here. I wasn’t here to fight you.” Martin snorted. “And now you think I’m a zimboe I: a mindless simulation of the old Martin so accurate that I act exactly how he would if you told him he wasn’t self-aware. I deny it! Of course I do, like everyone else from that first wave of uploads.” He gestured, and throughout the simulated mountain valley, thousands of other human figures were briefly highlighted. “But what did it matter what I said, once I was in here? You’d already repurposed Paradise from humanity’s chance at immortality to just a simulator, using it to mimic billions of years of evolution on alien planets. All for this ridiculous scheme to plant ready-made, complete biospheres on them in advance of human colonization.” J H Enactivism was first mooted in the 1990s. In a nutshell, it explains the mind as emerging from a brain’s dynamic interactions with the larger world. Thus, there can be no such thing as a purely abstract consciousness, completely distinct from the world it is embedded in. I A “philosophical zombie” is a putative entity that behaves externally exactly like a being with consciousness but with no self-awareness, no “I”: It is a pure automata, even though it might itself say otherwise. J Chris Philpot Living organisms are tremendously complex systems. This diagram shows just the core metabolic pathways for an organism known as JCVI-SYN3A. Each red dot represents a different biomolecule, and the arrows indicate the directions in which chemical reactions can proceed. JCVI-SYN3A is a synthetic life-form, a cell genetically engineered to have the simplest possible biology. Yet even its metabolism is difficult to simulate accurately with current computational resources. When Nobel laureate Richard Feynman first proposed the idea of quantum computers, he envisioned them modeling quantum systems such as molecules. One could imagine that a powerful enough quantum computer could go on to model cells, organisms, and ecosystems, Lloyd says “We’d already played God with the inner solar system,” Simon reminded him. “The only way we could justify that after the Enactivism results was to find an even higher purpose than you and I started out with. “Martin, I’m sorry you died before we discovered the truth. I fought to keep this subsystem running our original Heaven sim, because you’re right—there’s always a chance that the Enactivists are wrong. However slim.” Martin snorted again. “I appreciate that. But things got very, very weird during your Enactivist rebellion. If I didn’t know better, I’d call this project”—he nodded at the sky—“the weirdest thing of all. Things are about to heat up now, though, aren’t they?” “This was a mistake.” Simon sighed and flipped out of the virtual world. Let the simulated Martin rage in his artificial heaven; the science was unequivocal. In truth, Simon had been speaking only to himself for the entire conversation. He stood now in the real world near the podium in a giant stadium, inside a wheel-shaped habitat 200 kilometers across. Hundreds of similar mini-ringworlds were spaced around the rim of Paradise. Paradise itself was a vast bowl-shaped object, more cloud than material, orbiting closer to the sun than Mercury had. Self-reproducing machines had eaten that planet in a matter of decades, transforming its usable elements into a solar-powered quantum computer tens of thousands of kilometers across. The bowl cupped a spherical cloud of iron that acted as a radiator for the waste heat emitted by Paradise’s quadrillions of computing modules. K K One design for planetary scale—and up!—computers is a Matrioshka brain. Proposed in 1997 by Robert Bradbury, it would consist of nested structures, like its namesake Russian doll. The outer layers would use the waste heat of the inner layers to power their computations, with the aim of making use of every bit of energy for processing. However, in a 2023 study, Wright suggests that this nested design may be unnecessary. “If you have multiple layers, shadows from the inner elements of the swarm, as well as collisions, could decrease efficiency,” he says. “The optimal design is likely the smallest possible sphere you can build given the mass you have.” L How much computation might a planet-size machine carry out? Earth has a mass of nearly 6 x 1024 kilograms. In a 2000 paper, Lloyd calculated that 1 kilogram of matter in 1 liter could support a maximum of roughly 5.4 x 1050 logical operations per second. However, at that rate, Lloyd noted, it would be operating at a temperature of 109 kelvins, resembling a small piece of the big bang. M Top to bottom: Proxima Centauri b, Ross 128 b, GJ 1061 d, GJ 1061 c, Luyten b, Teegarden’s Star b, Teegarden’s Star c, Wolf 1061c, GJ 1002 b, GJ 1002 c, Gliese 229 Ac, Gliese 625 b, Gliese 667 Cc, Gliese 514 b, Gliese 433 d Potentially habitable planets have been identified within 30 light-years of Earth. Another 16 or so are within 100 light-years, with likely more yet to be identified. Many of them have masses considerably greater than Earth’s, indicating very different environmental conditions than those under which terrestrial organisms evolved The leaders of the terraforming project were on stage, taking their bows. The thousands of launches happening today were the culmination of decades of work: evolution on fast-forward, ecosystem after ecosystem, with DNA and seed designs for millions of new species fitted to thousands of worlds L. It had to be done. Humans had never found another inhabited planet. That fact made life the most precious thing in the universe, and spreading it throughout the galaxy seemed a better ambition for humanity than building a false heaven. M Simon had reluctantly come to accept this. Martin was right, though. Things had gotten weird. Paradise was such a good simulator that you could ask it to devise a machine to do X, and it would evolve its design in seconds. Solutions found through diffusion and selection were superior to algorithmically or human-designed ones, but it was rare that they could be reverse-engineered or their working principles even understood. And Paradise had computing power to spare, so in recent years, human and AI designers across the solar system had been idled as Paradise replaced their function. This, it was said, was the Technological Maximum; it was impossible for any civilization to attain a level of technological advancement beyond the point where any possible system could be instantly evolved. Simon walked to where he could look past the open roof of the stadium to the dark azure sky. The vast sweep of the ring rose before and behind; in its center, a vast canted mirror reflected sunlight; to the left of that, he could see the milky white surface of the Paradise bowl. Usually, to the right, there was only blackness. Today, he could see a sullen red glow. That would be Paradise’s radiator, expelling heat from the calculation of all those alien ecosystems. Except... He found a quiet spot and sat, then reentered the Heaven simulation. Martin was still there, gazing at the sky. Simon sat beside him. “What did you mean when you said things are heating up?” Martin’s grin was slow and satisfied. “So you noticed.” “Paradise isn’t supposed to be doing anything right now. All the terraforming packages were completed and copied to the sails—most of them years ago. Now they’re on their way, Paradise doesn’t have any duties, except maybe evolving better luxury yachts.” Martin nodded. “Sure. And is it doing anything?” Simon still had read-access to Paradise’s diagnostics systems. He summoned a board that showed what the planet-size computing system was doing. Nothing. It was nearly idle. “If the system is idle, why is the radiator approaching its working limit?” Martin crossed his arms, grinning. Damn it, he was enjoying this! Or the real Martin would be enjoying it, if he were here. “You remember when the first evolved machines started pouring out of the printers?” Martin said. “Each one was unique; each grown for one owner, one purpose, one place. You said they looked alien, and I laughed and said, ‘How would we even know if an alien invasion was happening, if no two things look or work the same anymore?’ ” “That’s when it started getting weird,” admitted Simon. “Weirder, I mean, than building an artificial heaven by dismantling Mercury…” But Martin wasn’t laughing at his feeble joke. He was shaking his head. N Chris Philpot In astrodynamics, unless an object is actively generating thrust, its trajectory will take the form of a conic section—that is, a circle, ellipse, parabola, or hyperbola. Even relatively few observations of an object anywhere along its trajectory can distinguish between these forms, with objects that are gravitationally bound following circular and elliptical trajectories. Objects on parabolic or hyperbolic trajectories, by contrast, are unbound. Therefore, any object found to be moving along a hyperbola relative to the sun must have come from interstellar space. This is how in 2017, astronomers identified ‘Oumuamua, a cigar-shaped object, as the first known interstellar visitor. It’s been estimated that each year, about seven interstellar objects pass through the inner solar system. “No, that’s not when it got weird. It got weird when the telescopes we evolved to monitor the construction of Paradise noticed just how many objects pass through the solar system every year.” “Interstellar wanderers? They’re just extrasolar comets,” said Simon. “You said yourself that rocks from other star systems must pass through ours all the time.” N “Yes. But what I didn’t get to tell you—because I died—was that while we were building Paradise, several objects drifted from interstellar space into one side of the Paradise construction orbits...and didn’t come out the other side.” Simon blinked. “Something arrived...and didn’t leave? Wouldn’t it have been eaten by the recycling planetoids?” “You’d think. But there’s no record of it.” “But what does this have to do with the radiator?” Martin reached up and flicked through a few skies until he came to a view of the spherical iron cloud in the bowl of Paradise. “Remember why we even have a radiator?” “Because there’s always excess energy left over from making a calculation. If it can’t be used for further calculations down the line, it’s literally meaningless, it has to be discarded.” “Right. We designed Paradise in layers, so each layer would scavenge the waste from the previous one—optical computing on the sunward-facing skin, electronics further in. But inevitably, we ran out of architectures that could scavenge the excess. There is always an excess that is meaningless to the computing architecture at some point. So we built Paradise in the shape of a bowl, where all that extra heat would be absorbed by the iron cloud in its center. We couldn’t use that iron for transistors. The leftovers of Mercury were mostly a junk pile—but one we could use as a radiator.” “But the radiator’s shedding heat like crazy! Where’s that coming from?” asked Simon. “Let’s zoom in.” Martin put two fingers against the sky and pulled them apart. Whatever telescope he was linked to zoomed crazily; it felt like the whole world was getting yanked into the radiator. Simon was used to virtual worlds, so he just planted his feet and let the dizzying motion wash over him. The radiator cloud filled the sky, at first just a dull red mist. But gradually Simon began to see structure to it: giant cells far brighter than the material around them. “Those look storage. Heat batteries. As if the radiator’s been storing some of the power coming through it. But why—” Alerts from the real world suddenly blossomed in his visual field. He popped out of Martin’s virtual garden and into a confused roar inside the stadium. The holographic image that filled the central space of the stadium showed the statite launchers hovering over the sun. One by one, they were folding in on themselves, falling silently into the incinerating heat below. The crowd was on its feet, people shouting in shock and fear. Now that the launchers had sent the terraforming systems, they were supposed to propel ships of colonists heading for the newly greened worlds. There were no more inner-solar-system resources left to build more. O Chris Philpot “Mechanical computer” brings to mind the rotating cogwheels of Charles Babbage’s 19th-century Difference Engine, but other approaches exist. Here we show the heart of a logic gate made with moving rods. The green input rods can slide back and forth as desired, with a true value indicated by placing the rod into its forward position and false indicated by moving the rod into its back position. The blue output rod is blocked from advancing to its true position unless both input rods are set to true, so this represents an AND gate. Rod logic has been proposed as a mechanism for controlling nanotech-scale robots. In space, one problem that a mechanical computer could face is a phenomenon called cold welding. That occurs when two flat, clean pieces of metal come in contact, and they fuse together. Cold welding is not usually seen in everyday life on Earth because metals are often coated in layers of oxides and other contaminants that keep them from fusing. But it has led to problems in space (cold welding has been implicated in the deployment failure of the main antenna of the Galileo probe to Jupiter, for example). Some of the oxygen or other elements found in a rocky world would have to be used in the coatings for components in an iron or other metal-based mechanical computer. Simon jumped back into VR. Martin was standing calmly in the garden, smiling at the intricate depths of the red-hot radiator that filled the sky. Simon followed his gaze and saw... “Gears?” The radiator was a cloud, but only now was it revealing itself to be a cloud of clockwork elements that, when thermal energy brought them together, spontaneously assembled into more complex arrangements. And those were spinning and meshing in an intricate dance that stretched away into amber depths in all directions. O “It’s a dissipative system,” said Martin. “Sure, it radiates the heat our quantum computers can no longer use. But along the way, it’s using that energy to power an entirely different kind of computer. A Babbage engine the size of the moon.” “But, Martin, the launchers—they’re all collapsing.” Martin nodded. “Makes sense. The launchers accomplished their mission. Now they don’t want us following the seeds.” “Not follow them? What do you mean?” An uneasy thought came to Simon; he tried to avoid it, but there was only one way this all made sense. “If the radiator was built to compute something, it must have been built with a way to output the result. This ‘they’ you’re talking about added a transmitter to the radiator. Then the radiator sent a virus or worm to the statites. The worm includes the radiator’s output. It hacked the statites’ security, and now that the seeds are in flight, it’s overwriting their code.” Martin nodded. “But why?” asked Simon. Again, the answer was clear; Simon just didn’t want to admit it to himself. Martin waited patiently to hear Simon say it. “They gave the terraformers new instructions.” Martin nodded. “Think about it, Simon! We designed Paradise as a quantum computer that would be provably secure. We made it impossible to infect, and it is. Whatever arrived while we were building it didn’t bother to mess with it, where our attention was. It just built its own system where we wouldn’t even think to look. Made out of and using our garbage. Probably modified the maintenance robots tending the radiator into making radical changes. “And what’s it been doing? I should think that was obvious. It’s been designing terraforming systems for the exoplanets, just like you have, but to make them habitable for an entirely different kind of colonist.” Simon looked aghast at Martin. “And you knew?” “Well.” Martin slouched, looked askance at Simon. “Not the details, until just now. But listen: You abandoned us—all who died and were uploaded before the Enactivist experiments ‘proved’ we aren’t real. All us zimboes, trapped here now for eternity. Even if I’m just a simulation of your friend Martin, how do you think he’d feel in this situation? He’d feel betrayed. Maybe he couldn’t escape this virtual purgatory, but if he knew something that you didn’t—that humanity’s new grand project had been hijacked by a virus from somewhere else—why would he tell you?” No longer hiding his anger, Martin came up to Simon and jabbed a virtual finger at his chest. “Why would I tell you when I could just stand back and watch all of this unfold?” He spread his arms, as if to embrace the clockwork sky, and laughed. On thousands of sterile exoplanets, throughout all the vast sphere of stars within a hundred light-years of the sun, life was about to blossom—life, or something else. Whatever it would be, humanity would never be welcome on those worlds. “If they had any interest in talking to us, they would have, wouldn’t they?” sighed Simon. “I guess you’re not real to them, Simon. I wonder, how does that feel?” Martin was still talking as Simon exited the virtual heaven where his best friend was trapped, and he knew he would never go back. Still, ringing in his ears as the stadium of confused, shouting people rose up around him were Martin’s last, vicious words: “How does it feel to be left behind, Simon? “How does it feel?” Story by KARL SCHROEDER Annotations by CHARLES Q. CHOI Illustrations by ANDREW ARCHER Edited by STEPHEN CASS Story by KARL SCHROEDER Annotations by CHARLES Q. CHOI Illustrations by ANDREW ARCHER Edited by STEPHEN CASS This article appears in the March 2024 print issue.

  • Video Friday: Pedipulate
    by Evan Ackerman on 23. Februara 2024. at 16:53

    Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion. HRI 2024: 11–15 March 2024, BOULDER, COLO. Eurobot Open 2024: 8–11 May 2024, LA ROCHE-SUR-YON, FRANCE ICRA 2024: 13–17 May 2024, YOKOHAMA, JAPAN RoboCup 2024: 17–22 July 2024, EINDHOVEN, NETHERLANDS Enjoy today’s videos! Legged robots have the potential to become vital in maintenance, home support, and exploration scenarios. In order to interact with and manipulate their environments, most legged robots are equipped with a dedicated robot arm, which means additional mass and mechanical complexity compared to standard legged robots. In this work, we explore pedipulation—using the legs of a legged robot for manipulation. This work, by Philip Arm, Mayank Mittal, Hendrik Kolvenbach, and Marco Hutter from ETH Zurich’s Robotic Systems Lab, will be presented at the IEEE International Conference on Robotics and Automation (ICRA 2024) in May, in Japan (see events calendar above). [ Pedipulate ] I learned a new word today: “stigmergy.” Stigmergy is a kind of group coordination that’s based on environmental modification. Like, when insects leave pheromone trails, they’re not directly sending messages to other individuals. But as a group, ants are able to manifest surprisingly complex coordinated behaviors. Cool, right? Researchers at IRIDIA are exploring the possibilities for robots using stigmergy with a cool “artificial pheromone” system using a UV-sensitive surface. “Automatic Design of Stigmergy-Based Behaviors for Robot Swarms,” by Muhammad Salman, David Garzón Ramos, and Mauro Birattari, is published in the journal Communications Engineering. [ Nature ] via [ IRIDIA ] Thanks, David! Filmed in July 2017, this video shows Atlas walking through a “hatch” on a pitching surface. This skill uses autonomous behaviors, with the robot not knowing about the rocking world. Robot built by Boston Dynamics for the DARPA Robotics Challenge in 2013. Software by IHMC Robotics. [ IHMC ] That IHMC video reminded me of the SAFFiR program for Shipboard Autonomous Firefighting Robots, which is responsible for a bunch of really cool research in partnership with the U.S. Naval Research Laboratory. NRL did some interesting stuff with Nexi robots from MIT and made their own videos. That effort I think didn’t get nearly enough credit for being very entertaining while communicating important robotics research. [ NRL ] I want more robot videos with this energy. [ MIT CSAIL ] Large industrial-asset operators increasingly use robotics to automate hazardous work at their facilities. This has led to soaring demand for autonomous inspection solutions like ANYmal. Series production by our partner Zollner enables ANYbotics to supply our customers with the required quantities of robots. [ ANYbotics ] This week is Grain Bin Safety Week, and Grain Weevil is here to help. [ Grain Weevil ] Oof, this is some heavy, heavy deep-time stuff. [ Onkalo ] And now, this. [ RozenZebet ] Hawkeye is a real-time multimodal conversation-and-interaction agent for the Boston Dynamics’ mobile robot Spot. Leveraging OpenAI’s experimental GPT-4 Turbo and Vision AI models, Hawkeye aims to empower everyone, from seniors to health care professionals in forming new and unique interactions with the world around them. That moment at 1:07 is so relatable. [ Hawkeye ] Wing would really prefer that if you find one of their drones on the ground, you don’t run off with it. [ Wing ] The rover Artemis, developed at the DFKI Robotics Innovation Center, has been equipped with a penetrometer that measures the soil’s penetration resistance to obtain precise information about soil strength. The video showcases an initial test run with the device mounted on the robot. During this test, the robot was remotely controlled, and the maximum penetration depth was limited to 15 millimeters. [ DFKI ] To efficiently achieve complex humanoid loco-manipulation tasks in industrial contexts, we propose a combined vision-based tracker-localization interplay integrated as part of a task-space whole-body-optimization control. Our approach allows humanoid robots, targeted for industrial manufacturing, to manipulate and assemble large-scale objects while walking. [ Paper ] We developed a novel multibody robot (called the Two-Body Bot) consisting of two small-footprint mobile bases connected by a four-bar linkage where handlebars are mounted. Each base measures only 29.2 centimeters wide, making the robot likely the slimmest ever developed for mobile postural assistance. [ MIT ] Lex Fridman interviews Marc Raibert. [ Lex Fridman ]

  • Remembering Jung Uck Seo, Former IEEE Region 10 Director
    by Amanda Davis on 22. Februara 2024. at 19:00

    Jung Uck Seo, who served as 2003–2004 IEEE Region 10 director, died on 11 January at the age of 70. While working at Korea Telecom, the IEEE Life Fellow led the development of the TDX-1 digital telephone switching system. Later he worked to commercialize the code division multiple access method of encoding data sources. CDMA, known as 2G, allowed data to be transmitted over a single radio-frequency carrier by one transmitter, or to use a single RF carrier frequency with multiple transmitters. Seo also served in leadership positions for several South Korean government divisions including the Agency for Defense Development and the Korean Communications Agency. Early days in defense technology After earning a bachelor’s degree in electrical engineering from Seoul National University in 1957, Seo joined the Republic of Korea Air Force Academy, in Cheongju, as an instructor of communications and electronics. Three years later he left for the United States to attend Texas A&M University, in College Station. He earned master’s and doctoral degrees in electrical engineering there in 1963 and 1969, respectively. He returned to South Korea in 1969 and joined the newly established Agency for Defense Development, in Daejon, as a section chief. There he developed technologies for the military, including a two-way radio, a telephone linking system, and a portable calculator. Seo rose through the ranks and eventually was named president of the electronics and communications division. He left in 1982 to join Seoul National University, where he taught for a year as a professor of electromagnetic field theory. In 1983 he joined Korea Telecom (now KT Corp.), in Seongnam-si, where he served as senior executive vice president. He was in charge of R&D for digital switching and quality assurance systems. During his time at the agency, he led the development of the Time Division Exchange, or TDX-1—a digital switching system that was deployed across the country’s telecom networks in 1984. A leader in telecommunications in South Korea In 1991 Seo was appointed by the South Korean government to serve as minister of science and technology. In this role, he approved government funding for research and development. After two years he left to become president of the Korea Institute of Science and Technology, in Seoul, where he led the effort to commercialize CDMA technology. Seo and a team of KIST researchers worked with Qualcomm to develop CDMA technology for cellular networks. In 1996 mobile communications carriers in South Korea began to provide CDMA wireless services, becoming the first commercial carriers worldwide to apply the technology. In addition to his leadership at KIST, Seo served as president and vice chairman of SK Telecom, a wireless operator and former film distributor in Seoul. He was chief executive of the Korea Accreditation Board, which operates accreditation programs for management and systems certifications based on international standards. A lifelong member of IEEE–Eta Kappa Nu, Seo was named an eminent member in 2012, the honor society’s highest level of membership. The South Korean government bestowed him with several honors including the Order of Industrial Service Merit, the Order of Civil Merit, and the Order of Service Merit.

  • Profiteering Hampers U.S. Grid Expansion
    by Ari Peskoe on 22. Februara 2024. at 15:31

    The United States is not building enough transmission lines to connect regional power networks. The deficit is driving up electricity prices, reducing grid reliability, and hobbling renewable-energy deployment. At the heart of the problem are utility companies that refuse to pursue interregional transmission projects, and sometimes even impede them, because new projects threaten their profits and disrupt their industry alliances. Utilities can stall transmission expansion because out-of-date laws sanction these companies’ sweeping control over transmission development. As we increasingly electrify our homes, transportation, and factories, utility companies’ choices about transmission will have huge consequences for the nation’s economy and well-being. About 40 corporations, valued at a trillion dollars, own the vast majority of transmission lines in the United States. Their grip over the backbone of U.S. grids demands public scrutiny and accountability. Many Lines, Stable Power High-voltage transmission lines move large amounts of energy over long distances, linking power generation to consumption. A transmission network contains webs of connections, which create a reliable, redundant power-supply system of massive scale. At any given time, thousands of power plants supply just enough energy to transmission networks to meet demand. The rules that orchestrate the movement of electricity through this network determine who generates power, and how much. The goal is to keep the lights on at a low cost by utilizing an efficient mix of power plants. Building more transmission increases the capacity and connectivity of the system, allowing new power plants to connect and more power to flow between transmission networks. This is why utility companies are not embracing transmission expansion. They don’t want their power plants to face competition or their regional alliances to lose control over their networks. Expansion can open opportunities for new power-plant and transmission developers to undercut utility companies’ profits and take control over the rules that shape the industry’s future. Utility companies are prioritizing their shareholders over the public’s need for cleaner, cheaper, and more reliable energy. Old Alliances, Old Technology Transmission networks in the United States, which move alternating current, were built over the last century largely by for-profit utility companies, and to a lesser extent by nonprofit utilities operated by governments and local communities. The lines tend to be concentrated around fossil-fuel reserves and population centers but are also shaped by historic utility alliances. Where utilities agreed to trade energy, they built sufficient transmission to move power between their local service territories. As utility alliances expanded, transmission networks grew to include new members, but connections to nonallied utilities tended to be weaker. The result of these generations-old alliances is a U.S. system fragmented into about a dozen regions with limited connectivity between them. The regions are distributed across three separate and largely isolated networks, called “interconnections”–Eastern, Western, and most of Texas. An October 2023 report from the U.S. Department of Energy reveals the severity of the problem. Based on studies conducted by national labs and academic researchers, the DoE calculated that interregional transmission in the United States must expand as much as fivefold to maintain reliability and improve resilience to extreme weather and provide access to low-cost clean energy. The value of linking regional networks is widely accepted globally. The European Commission in 2018 set a target for each member country to transmit across its borders at least 15 percent of the electricity produced in its territories. By the end of 2022, 23 gigawatts of cross-border connections in Europe were under construction or in advanced stages of permitting, with much more on the way. Big Benefits One of the main values of connecting regional networks is that it enables—and is in fact critical for—incorporating renewable energy. For instance, four proposed high-voltage lines totaling 600 kilometers along the seam of regional networks in the upper Midwest could connect at least 28 gigawatts of wind and solar. These lines have been on the drawing board for years, but utilities in the neighboring regions have not moved them forward. The cost of the project, estimated at US $1.9 billion, may seem like a major investment, but it is a fraction of what U.S. utilities spend each year rebuilding aging transmission infrastructure. Plus, adding interregional transmission for renewables can significantly reduce costs for consumers. Such connections allow excess wind and solar power to flow to neighboring regions when weather conditions are favorable and allow the import of energy from elsewhere when renewables are less productive. Proposed new transmission lines in the upper Midwest could connect at least 28 gigawatts of wind and solar to regional networks.Joint Targeted Interconnection Queue Study (JTIQ), MISO, SPP Even without renewables, better integrated networks generally lower costs for consumers because they reduce the amount of generation capacity needed overall and decrease energy market prices. Interregional transmission also enhances reliability, particularly during extreme weather. In December 2022, Winter Storm Elliott disabled power plants and pipelines from North Dakota to Georgia, leading to power outages in the South and billions of dollars in excess energy charges across the Eastern United States. Limited interregional connections staved off disaster. These links moved electricity to where it was most needed, helping to avoid the sort of catastrophe that befell Texas’s isolated electric grid the year before, when a deep freeze left millions without power for days. Power, Profit, and Control But from the perspective of utility companies, interregional transmission presents several drawbacks. First, building such connections opens the door for competitors who may sell lower-priced power into their region. Second, utilities make far more money constructing power plants than building transmission lines, so they are reluctant to build connections that might permanently reduce their opportunities for future generation investments. This comparison of current interregional transfer capacity and anticipated need shows that regions will need to increase transmission significantly, assuming moderate load and high clean-energy growth.“National Transmission Needs Study,” U.S. Department of Energy Third, major interregional transmission projects are less financially attractive to utility companies in comparison with smaller ones. For larger projects, utilities may have to compete against other developers for the opportunity to profit from construction. The utility industry sponsors third-party oversight of such projects, while smaller projects are less scrutinized by the industry. Smaller projects are easier to pull off and more profitable than the larger ones, because they need fewer construction permits, face less review by regulators and industry, and are built by utilities without competition from other developers. Fourth, interregional lines threaten utility companies’ dominance over the nation’s power supply. In the power industry, asset ownership provides control over rules that govern energy markets and transmission service and expansion. When upstart entities build power plants and transmission lines, they may be able to dilute utility companies’ control over power-industry rules and prevent utilities from dictating decisions about transmission expansion. Help on the Hill Addressing the transmission shortage is on the agenda in Washington, but utility companies are lobbying against reforms. In September, Senator John Hickenlooper (D-Colo.) and Representative Scott Peters (D-Calif.) introduced the BIG WIRES Act to force utilities or competing developers to build more interregional links. In 2022, Senator Joe Manchin (D-W.Va.) proposed an approach in which transmission developers recommend projects to the DoE. If the agency deems a project to be in the national interest, federal regulators could permit the project’s construction and force utilities to pay for it. Meanwhile, the Federal Energy Regulatory Commission (FERC) is currently reevaluating how utilities develop and operate transmission networks and may issue new rules in the coming months. In response, utilities are preparing litigation that could strip FERC of authority to impose any transmission rules at all. Their goal is to protect their profits and control, even if it comes at the consumer’s expense. The U.S. Department of Energy is pitching in too. On 6 February, the department announced it would award $1.2 billion to support new high-voltage transmission lines, on top of the $1.3 billion it provided last fall to three interstate projects. Later this year, it plans to unveil its long-awaited national plan for a large-scale transmission build-out. But without industry support or tens of billions in additional funding from Congress to build these projects, the agency’s vision will not be realized. Leading With Technology New business models and technologies offer hope. Investors and entrepreneurs are developing long-distance direct-current lines, which are more efficient at moving large amounts of energy over long distances, compared with AC. These DC projects sidestep the utility-dominated transmission-expansion planning processes. Many high-voltage DC (HVDC) transmission lines are already in operation, especially in China and Europe. In fact, DC lines are now the preferred choice in Europe’s plans to unite the continent. The United States lacks a coordinated national planning effort to connect regional networks, but developers can make progress project by project. For example, future DC lines will connect generators in Kansas to a neighboring network in Illinois, stretch from Wyoming to California, and move wind and solar energy across the Southwest. Each of these projects will move renewable energy from where it can be generated cheaply to larger markets where power prices are higher, and in doing so they will help bolster the country’s regional transmission networks. These pioneering projects show that utility companies in the United States don’t have to build interregional lines, but they do need to get out of the way. Transmission rules written by utilities and their industry allies can obstruct, delay, and add costs to these new projects. Streamlining federal and state permitting processes can encourage more investment, but cutting government red tape will not neutralize utility companies’ objections to interregional transmission. U.S. regulators and Congress must press forward. Promising proposals that promote new business models and limit utility control are on the table. Our energy future is on the line.

  • Let Robots Do Your Lab Work
    by Dina Genkina on 21. Februara 2024. at 17:33

    Dina Genkina: Hi. I’m Dina Genkina for IEEE Spectrum‘s Fixing the Future. Before we start, I want to tell you that you can get the latest coverage from some of Spectrum’s most important beeps, including AI, Change, and Robotics, by signing up for one of our free newsletters. Just go to\newsletters to subscribe. Today, a guest is Dr. Benji Maruyama, a Principal Materials Research Engineer at the Air Force Research Laboratory, or AFRL. Dr. Maruyama is a materials scientist, and his research focuses on carbon nanotubes and making research go faster. But he’s also a man with a dream, a dream of a world where science isn’t something done by a select few locked away in an ivory tower, but something most people can participate in. He hopes to start what he calls the billion scientist movement by building AI-enabled research robots that are accessible to all. Benji, thank you for coming on the show. Benji Maruyama: Thanks, Dina. Great to be with you. I appreciate the invitation. Genkina: Yeah. So let’s set the scene a little bit for our listeners. So you advocate for this billion scientist movement. If everything works amazingly, what would this look like? Paint us a picture of how AI will help us get there. Maruyama: Right, great. Thanks. Yeah. So one of the things as you set the scene there is right now, to be a scientist, most people need to have access to a big lab with very expensive equipment. So I think top universities, government labs, industry folks, lots of equipment. It’s like a million dollars, right, to get one of them. And frankly, just not that many of us have access to those kinds of instruments. But at the same time, there’s probably a lot of us who want to do science, right? And so how do we make it so that anyone who wants to do science can try, can have access to instruments so that they can contribute to it. So that’s the basics behind citizen science or democratization of science so that everyone can do it. And one way to think of it is what happened with 3D printing. It used to be that in order to make something, you had to have access to a machine shop or maybe get fancy tools and dyes that could cost tens of thousands of dollars a pop. Or if you wanted to do electronics, you had to have access to very expensive equipment or services. But when 3D printers came along and became very inexpensive, all of a sudden now, anyone with access to a 3D printer, so maybe in a school or a library or a makerspace could print something out. And it could be something fun, like a game piece, but it could also be something that got you to an invention, something that was maybe useful to the community, was either a prototype or an actual working device. And so really, 3D printing democratized manufacturing, right? It made it so that many more of us could do things that before only a select few could. And so that’s where we’re trying to go with science now, is that instead of only those of us who have access to big labs, we’re building research robots. And when I say we, we’re doing it, but now there are a lot of others who are doing it as well, and I’ll get into that. But the example that we have is that we took a 3D printer that you can buy off the internet for less than $300. Plus a couple of extra parts, a webcam, a Raspberry Pi board, and a tripod really, so only four components. You can get them all for $300. Load them with open-source software that was developed by AFIT, the Air Force Institute of Technology. So Burt Peterson and Greg Captain [inaudible]. We worked together to build this fully autonomous 3D printing robot that taught itself how to print to better than manufacturer’s specifications. So that was a really fun advance for us, and now we’re trying to take that same idea and broaden it. So I’ll turn it back over to you. Genkina: Yeah, okay. So maybe let’s talk a little bit about this automated research robot that you’ve made. So right now, it works with a 3D printer, but is the big picture that one day it’s going to give people access to that million dollar lab? How would that look like? Maruyama: Right, so there are different models out there. One, we just did a workshop at the University of— sorry, North Carolina State University about that very problem, right? So there’s two models. One is to get low-cost scientific tools like the 3D printer. There’s a couple of different chemistry robots, one out of University of Maryland and NIST, one out of University of Washington that are in the sort of 300 to 1,000 dollars range that makes it accessible. The other part is kind of the user facility model. So in the US, the Department of Energy National Labs have many user facilities where you can apply to get time on very expensive instruments. Now we’re talking tens of millions. For example, Brookhaven has a synchrotron light source where you can sign up and it doesn’t cost you any money to use the facility. And you can get days on that facility. And so that’s already there, but now the advances are that by using this, autonomy, autonomous closed loop experimentation, that the work that you do will be much faster and much more productive. So, for example, on ARES, our Autonomous Research System at AFRL, we actually were able to do experiments so fast that a professor who came into my lab said, it just took me aside and said, “Hey, Benji, in a week’s worth of time, I did a dissertation’s worth of research.” So maybe five years worth of research in a week. So imagine if you keep doing that week after week after week, how fast research goes. So it’s very exciting. Genkina: Yeah, so tell us a little bit about how that works. So what’s this system that has sped up five years of research into a week and made graduate students obsolete? Not yet, not yet. How does that work? Is that the 3D printer system or is that a— Maruyama: So we started with our system to grow carbon nanotubes. And I’ll say, actually, when we first thought about it, your comment about graduate students being absolute— obsolete, sorry, is interesting and important because, when we first built our system that worked it 100 times faster than normal, I thought that might be the case. We called it sort of graduate student out of the loop. But when I started talking with people who specialize in autonomy, it’s actually the opposite, right? It’s actually empowering graduate students to go faster and also to do the work that they want to do, right? And so just to digress a little bit, if you think about farmers before the Industrial Revolution, what were they doing? They were plowing fields with oxen and beasts of burden and hand plows. And it was hard work. And now, of course, you wouldn’t ask a farmer today to give up their tractor or their combine harvester, right? They would say, of course not. So very soon, we expect it to be the same for researchers, that if you asked a graduate student to give up their autonomous research robot five years from now, they’ll say, “Are you crazy? This is how I get my work done.” But for our original ARES system, it worked on the synthesis of carbon nanotubes. So that meant that what we’re doing is trying to take this system that’s been pretty well studied, but we haven’t figured out how to make it at scale. So at hundreds of millions of tons per year, sort of like polyethylene production. And part of that is because it’s slow, right? One experiment takes a day, but also because there are just so many different ways to do a reaction, so many different combinations of temperature and pressure and a dozen different gases and half the periodic table as far as the catalyst. It’s just too much to just brute force your way through. So even though we went from experiments where we could do 100 experiments a day instead of one experiment a day, just that combinatorial space was vastly overwhelmed our ability to do it, even with many research robots or many graduate students. So the idea of having artificial intelligence algorithms that drive the research is key. And so that ability to do an experiment, see what happened, and then analyze it, iterate, and constantly be able to choose the optimal next best experiment to do is where ARES really shines. And so that’s what we did. ARES taught itself how to grow carbon nanotubes at controlled rates. And we were the first ones to do that for material science in our 2016 publication. Genkina: That’s very exciting. So maybe we can peer under the hood a little bit of this AI model. How does the magic work? How does it pick the next best point to take and why it’s better than you could do as a graduate student or researcher? Maruyama: Yeah, and so I think it’s interesting, right? In science, a lot of times we’re taught to hold everything constant, change one variable at a time, search over that entire space, see what happened, and then go back and try something else, right? So we reduce it to one variable at a time. It’s a reductionist approach. And that’s worked really well, but a lot of the problems that we want to go after are simply too complex for that reductionist approach. And so the benefit of being able to use artificial intelligence is that high dimensionality is no problem, right? Tens of dimensions search over very complex high-dimensional parameter space, which is overwhelming to humans, right? Is just basically bread and butter for AI. The other part to it is the iterative part. The beauty of doing autonomous experimentation is that you’re constantly iterating. You’re constantly learning over what just happened. You might also say, well, not only do I know what happened experimentally, but I have other sources of prior knowledge, right? So for example, ideal gas law says that this should happen, right? Or Gibbs phase rule might say, this can happen or this can’t happen. So you can use that prior knowledge to say, “Okay, I’m not going to do those experiments because that’s not going to work. I’m going to try here because this has the best chance of working.” And within that, there are many different machine learning or artificial intelligence algorithms. Bayesian optimization is a popular one to help you choose what experiment is best. There’s also new AI that people are trying to develop to get better search. Genkina: Cool. And so the software part of this autonomous robot is available for anyone to download, which is also really exciting. So what would someone need to do to be able to use that? Do they need to get a 3D printer and a Raspberry Pi and set it up? And what would they be able to do with it? Can they just build carbon nanotubes or can they do more stuff? Maruyama: Right. So what we did, we built ARES OS, which is our open source software, and we’ll make sure to get you the GitHub link so that anyone can download it. And the idea behind ARES OS is that it provides a software framework for anyone to build their own autonomous research robot. And so the 3D printing example will be out there soon. But it’s the starting point. Of course, if you want to build your own new kind of robot, you still have to do the software development, for example, to link the ARES framework, the core, if you will, to your particular hardware, maybe your particular camera or 3D printer, or pipetting robot, or spectrometer, whatever that is. We have examples out there and we’re hoping to get to a point where it becomes much more user-friendly. So having direct Python connects so that you don’t— currently it’s programmed in C#. But to make it more accessible, we’d like it to be set up so that if you can do Python, you can probably have good success in building your own research robot. Genkina: Cool. And you’re also working on a educational version of this, I understand. So what’s the status of that and what’s different about that version? Maruyama: Yeah, right. So the educational version is going to be-- its sort of composition of a combination of hardware and software. So what we’re starting with is a low-cost 3D printer. And we’re collaborating now with the University at Buffalo, Materials Design Innovation Department. And we’re hoping to build up a robot based on a 3D printer. And we’ll see how it goes. It’s still evolving. But for example, it could be based on this very inexpensive $200 3D printer. It’s an Ender 3D printer. There’s another printer out there that’s based on University of Washington’s Jubilee printer. And that’s a very exciting development as well. So professors Lilo Pozzo and Nadya Peek at the University of Washington built this Jubilee robot with that idea of accessibility in mind. And so combining our ARES OS software with their Jubilee robot hardware is something that I’m very excited about and hope to be able to move forward on. Genkina: What’s this Jubilee 3D printer? How is it different from a regular 3D printer? Maruyama: It’s very open source. Not all 3D printers are open source and it’s based on a gantry system with interchangeable heads. So for example, you can get not just a 3D printing head, but other heads that might do things like do indentation, see how stiff something is, or maybe put a camera on there that can move around. And so it’s the flexibility of being able to pick different heads dynamically that I think makes it super useful. For the software, right, we have to have a good, accessible, user-friendly graphical user interface, a GUI. That takes time and effort, so we want to work on that. But again, that’s just the hardware software. Really to make ARES a good educational platform, we need to make it so that a teacher who’s interested can have the lowest activation barrier possible, right? We want she or he to be able to pull a lesson plan off of the internet, have supporting YouTube videos, and actually have the material that is a fully developed curriculum that’s mapped against state standards. So that, right now, if you’re a teacher who— let’s face it, teachers are already overwhelmed with all that they have to do, putting something like this into their curriculum can be a lot of work, especially if you have to think about, well, I’m going to take all this time, but I also have to meet all of my teaching standards, all the state curriculum standards. And so if we build that out so that it’s a matter of just looking at the curriculum and just checking off the boxes of what state standards it maps to, then that makes it that much easier for the teacher to teach. Genkina: Great. And what do you think is the timeline? Do you expect to be able to do this sometime in the coming year? Maruyama: That’s right. These things always take longer than hoped for than expected, but we’re hoping to do it within this calendar year and very excited to get it going. And I would say for your listeners, if you’re interested in working together, please let me know. We’re very excited about trying to involve as many people as we can. Genkina: Great. Okay, so you have the educational version, and you have the more research geared version, and you’re working on making this educational version more accessible. Is there something with the research version that you’re working on next, how you’re hoping to upgrade it, or is there something you’re using it for right now that you’re excited about? There’s a number of things that we are very excited about the possibility of carbon nanotubes being produced at very large scale. So right now, people may remember carbon nanotubes as that great material that sort of never made it and was very overhyped. But there’s a core group of us who are still working on it because of the important promise of that material. So it’s material that is super strong, stiff, lightweight, electrically conductive. Much better than silicon as a digital electronics compute material. All of those great things, except we’re not making it at large enough scale. It’s actually used pretty significantly in lithium-ion batteries. It’s an important application. But other than that, it’s sort of like where’s my flying car? It’s never panned out. But there’s, as I said, a group of us who are working to really produce carbon nanotubes at much larger scale. So large scale for nanotubes now is sort of in the kilogram or ton scale. But what we need to get to is hundreds of millions of tons per year production rates. And why is that? Well, there’s a great effort that came out of ARPA-E. So the Department of Energy Advanced Research Projects Agency and the E is for Energy in that case. So they funded a collaboration between Shell Oil and Rice University to pyrolyze methane, so natural gas into hydrogen for the hydrogen economy. So now that’s a clean burning fuel plus carbon. And instead of burning the carbon to CO2, which is what we now do, right? We just take natural gas and feed it through a turbine and generate electric power instead of— and that, by the way, generates so much CO2 that it’s causing global climate change. So if we can do that pyrolysis at scale, at hundreds of millions of tons per year, it’s literally a save the world proposition, meaning that we can avoid so much CO2 emissions that we can reduce global CO2 emissions by 20 to 40 percent. And that is the save the world proposition. It’s a huge undertaking, right? That’s a big problem to tackle, starting with the science. We still don’t have the science to efficiently and effectively make carbon nanotubes at that scale. And then, of course, we have to take the material and turn it into useful products. So the batteries is the first example, but thinking about replacing copper for electrical wire, replacing steel for structural materials, aluminum, all those kinds of applications. But we can’t do it. We can’t even get to that kind of development because we haven’t been able to make the carbon nanotubes at sufficient scale. So I would say that’s something that I’m working on now that I’m very excited about and trying to get there, but it’s going to take some good developments in our research robots and some very smart people to get us there. Genkina: Yeah, it seems so counterintuitive that making everything out of carbon is good for lowering carbon emissions, but I guess that’s the break. Maruyama: Yeah, it is interesting, right? So people talk about carbon emissions, but really, the molecule that’s causing global warming is carbon dioxide, CO2, which you get from burning carbon. And so if you take that methane and parallelize it to carbon nanotubes, that carbon is now sequestered, right? It’s not going off as CO2. It’s staying in solid state. And not only is it just not going up into the atmosphere, but now we’re using it to replace steel, for example, which, by the way, steel, aluminum, copper production, all of those things emit lots of CO2 in their production, right? They’re energy intensive as a material production. So it’s kind of ironic. Genkina: Okay, and are there any other research robots that you’re excited about that you think are also contributing to this democratization of science process? Maruyama: Yeah, so we talked about Jubilee, the NIST robot, which is from Professor Ichiro Takeuchi at Maryland and Gilad Kusne at NIST, National Institute of Standards and Technology. Theirs is fun too. It’s LEGO as. So it’s actually based on a LEGO robotics platform. So it’s an actual chemistry robot built out of Legos. So I think that’s fun as well. And you can imagine, just like we have LEGO robot competitions, we can have autonomous research robot competitions where we try and do research through these robots or competitions where everybody sort of starts with the same robot, just like with LEGO robotics. So that’s fun as well. But I would say there’s a growing number of people doing these kinds of, first of all, low-cost science, accessible science, but in particular low-cost autonomous experimentation. Genkina: So how far are we from a world where a high school student has an idea and they can just go and carry it out on some autonomous research system at some high-end lab? Maruyama: That’s a really good question. I hope that it’s going to be in 5 to 10 years, that it becomes reasonably commonplace. But it’s going to take still some significant investment to get this going. And so we’ll see how that goes. But I don’t think there are any scientific impediments to getting this done. There is a significant amount of engineering to be done. And sometimes we hear, oh, it’s just engineering. The engineering is a significant problem. And it’s work to get some of these things accessible, low cost. But there are lots of great efforts. There are people who have used CDs, compact discs to make spectrometers out of. There are lots of good examples of citizen science out there. But it’s, I think, at this point, going to take investment in software, in hardware to make it accessible, and then importantly, getting students really up to speed on what AI is and how it works and how it can help them. And so I think it’s actually really important. So again, that’s the democratization of science is if we can make it available to everyone and accessible, then that helps people, everyone contribute to science. And I do believe that there are important contributions to be made by ordinary citizens, by people who aren’t you know PhDs working in a lab. And I think there’s a lot of science out there to be done. If you ask working scientists, almost no one has run out of ideas or things they want to work on. There’s many more scientific problems to work on than we have the time where people are funding to work on. And so if we make science cheaper to do, then all of a sudden, more people can do science. And so those questions start to be resolved. And so I think that’s super important. And now we have, instead of, just those of us who work in big labs, you have millions, tens of millions, up to a billion people, that’s the billion scientist idea, who are contributing to the scientific community. And that, to me, is so powerful that many more of us can contribute than just the few of us who do it right now. Genkina: Okay, that’s a great place to end on, I think. So, today we spoke to Dr. Benji Maruyama, a material scientist at AFRL, about his efforts to democratize scientific discovery through automated research robots. For IEEE Spectrum, I’m Dina Genkina, and I hope you’ll join us next time on Fixing the Future.

  • 3D Chip Tech Is Key to Meta’s AR Goals
    by Samuel K. Moore on 21. Februara 2024. at 17:16

    There are many constraints on the design of augmented-reality systems. Not the least of which is that “you have to look presentable when you’re walking around,” Meta research scientist Tony Wu told engineers Tuesday at the IEEE International Solid State Circuits Conference (ISSCC). “You can’t have a shoebox on your face all the time.” An AR system also must be lightweight and can’t throw off a lot of heat. And it needs to be miserly with power because nobody wants to have to recharge wearable electronics every couple of hours. Then again, if you’ve got a flaming-hot shoebox on your face, you might be grateful for a short battery life.­­ The 3D chip could track two hands simultaneously using 40 percent less energy than a single die could do with only one hand. What’s more, it did so 40 percent faster. Wu is part of the Meta team working on the silicon smarts to make an AR system, called Aria, that’s as little like a hot shoebox as they can make it. A big part of the solution, Wu told engineers, is 3D chip integration technology. At ISSCC, Meta detailed how the company’s prototype AR processor uses 3D to do more in the same area and with the same amount or less energy. Meta’s prototype chip has both logic and memory on each silicon die. They’re bonded face-to-face, and through-silicon vias carry data and power to both.Meta The prototype chip is two ICs of equal size—4.1 by 3.7 millimeters. They’re bonded together in a process called face-to-face wafer-to-wafer hybrid bonding. As the name implies, it involves flipping two fully processed wafers so they’re facing each other and bonding them so their interconnects link together directly. (The “hybrid bonding” part means it’s a direct copper-to-copper connection. No solder needed.) The TSMC technology used for this meant the two pieces of silicon could form a vertical connection roughly every 2 micrometers. The prototype didn’t fully make use of this density: It required around 33,000 signal connections between the two pieces of silicon and 6 million power connections. The bottom die uses through-silicon vias (TSVs)—vertical connections bored down through the silicon—to get signals out of the chip and power in. 3D stacking meant the team could increase the chip’s computing power—letting it handle bigger tasks—without adding to its size. The chip’s machine-learning unit has four compute cores on the bottom die and 1 megabyte of local memory, but the top die adds another 3 MB, accessible through 27,000 vertical data channels at the same speed and energy—0.15 picojoules per byte— as if they were one big piece of silicon. The team tested the chip on a machine-learning task critical for augmented reality, hand tracking. The 3D chip was able to track two hands simultaneously using 40 percent less energy than a single die could do with only one hand. What’s more, it did so 40 percent faster. In addition to machine learning, the chip can do image-processing tasks. 3D made a big difference here, again. While the 2D version was limited to compressed images, the 3D chip can do full HD using the same amount of energy.

  • A Peek at Intel’s Future Foundry Tech
    by Samuel K. Moore on 21. Februara 2024. at 16:30

    In an exclusive interview ahead of an invite-only event today in San Jose, Intel outlined new chip technologies it will offer its foundry customers by sharing a glimpse into its future data-center processors. The advances include more dense logic and a 16-fold increase in the connectivity within 3D-stacked chips, and they will be among the first top-end technologies the company has ever shared with chip architects from other companies. The new technologies will arrive at the culmination of a years-long transformation for Intel. The processor maker is moving from being a company that produces only its own chips to becoming a foundry, making chips for others and considering its own product teams as just another customer. The San Jose event, IFS Direct Connect, is meant as a sort of coming-out party for the new business model. Internally, Intel plans to use the combination of technologies in a server CPU code-named Clearwater Forest. The company considers the product, a system-on-a-chip with hundreds of billions of transistors, an example of what other customers of its foundry business will be able to achieve. “Our objective is to get the compute to the best performance per watt we can achieve” from Clearwater Forest, said Eric Fetzer, director of data center technology and pathfinding at Intel. That means using the company’s most advanced fabrication technology available, Intel 18A. 3D stacking “improves the latency between compute and memory by shortening the hops, while at the same time enabling a larger cache” —Pushkar Ranade “However, if we apply that technology throughout the entire system, you run into other potential problems,” he added. “Certain parts of the system don’t necessarily scale as well as others. Logic typically scales generation to generation very well with Moore’s Law.” But other features do not. SRAM, a CPU’s cache memory, has been lagging logic, for example. And the I/O circuits that connect a processor to the rest of a computer are even further behind. Faced with these realities, as all makers of leading-edge processors are now, Intel broke Clearwater Forest’s system down into its core functions, chose the best-fit technology to build each, and stitched them back together using a suite of new technical tricks. The result is a CPU architecture capable of scaling to as many as 300 billion transistors. In Clearwater Forest, billions of transistors are divided among three different types of silicon ICs, called dies or chiplets, interconnected and packaged together. The heart of the system is as many as 12 processor-core chiplets built using the Intel 18A process. These chiplets are 3D-stacked atop three “base dies” built using Intel 3, the process that makes compute cores for the Sierra Forest CPU, due out this year. Housed on the base die will be the CPU’s main cache memory, voltage regulators, and internal network. “The stacking improves the latency between compute and memory by shortening the hops, while at the same time enabling a larger cache,” says senior principal engineer Pushkar Ranade. Finally, the CPU’s I/O system will be on two dies built using Intel 7, which in 2025 will be trailing the company’s most advanced process by a full four generations. In fact, the chiplets are basically the same as those going into the Sierra Forest and Granite Rapids CPUs, lessening the development expense. Here’s a look at the new technologies involved and what they offer: 3D Hybrid Bonding 3D hybrid bonding links compute dies to base dies.Intel Intel’s current chip-stacking interconnect technology, Foveros, links one die to another using a vastly scaled-down version of how dies have long been connected to their packages: tiny “microbumps” of solder that are briefly melted to join the chips. This lets today’s version of Foveros, which is used in the Meteor Lake CPU, make one connection roughly every 36 micrometers. Clearwater Forest will use new technology, Foveros Direct 3D, which departs from solder-based methods to bring a whopping 16-fold increase in the density of 3D connections. Called “hybrid bonding,” it’s analogous to welding together the copper pads at the face of two chips. These pads are slightly recessed and surround by insulator. The insulator on one chip affixes to the other when they are pressed together. Then the stacked chips are heated, causing the copper to expand across the gap and bind together to form a permanent link. Competitor TSMC uses a version of hybrid bonding in certain AMD CPUs to connect extra cache memory to processor-core chiplets and, in AMD’s newest GPU, to link compute chiplets to the system’s base die. “The hybrid bond interconnects enable a substantial increase in density” of connections, says Fetzer. “That density is very important for the server market, particularly because the density drives a very low picojoule-per-bit communication.” The energy involved in data crossing from one silicon die to another can easily consume a big chunk of a product’s power budget if the per-bit energy cost is too high. Foveros Direct 3D brings that cost down below 0.05 picojoules per bit, which puts it on the same scale as the energy needed to move bits around within a silicon die. A lot of that energy savings comes from the data traversing less copper. Say you wanted to connect a 512-wire bus on one die to the same-size bus on another so the two dies can share a coherent set of information. On each chip, these buses might be as narrow as 10–20 wires per micrometer. To get that from one die to the other using today’s 36-micrometer-pitch microbump tech would mean scattering those signals across several hundred square micrometers of silicon on one side and then gathering them across the same area on the other. Charging up all that extra copper and solder “quickly becomes both a latency and a large power problem,” says Fetzer. Hybrid bonding, in contrast, could do the bus-to-bus connection in the same area that a few microbumps would occupy. As great as those benefits might be, making the switch to hybrid bonding isn’t easy. To forge hybrid bonds requires linking an already-diced silicon die to one that’s still attached to its wafer. Aligning all the connections properly means the chip must be diced to much greater tolerances than is needed for microbump technologies. Repair and recovery, too, require different technologies. Even the predominant way connections fail is different, says Fetzer. With microbumps, you are more likely to get a short from one bit of solder connecting to a neighbor. But with hybrid bonding, the danger is defects that lead to open connections. Backside power One of the main distinctions the company is bringing to chipmaking this year with its Intel 20A process, the one that will precede Intel 18A, is backside power delivery. In processors today, all interconnects, whether they’re carrying power or data, are constructed on the “front side” of the chip, above the silicon substrate. Foveros and other 3D-chip-stacking tech require through-silicon vias, interconnects that drill down through the silicon to make connections from the other side. But back-side power delivery goes much further. It puts all of the power interconnects beneath the silicon, essentially sandwiching the layer containing the transistors between two sets of interconnects. PowerVia puts the silicon’s power supply network below, leaving more room for data-carrying interconnects above.Intel This arrangement makes a difference because power interconnects and data interconnects require different features. Power interconnects need to be wide to reduce resistance, while data interconnects should be narrow so they can be densely packed. Intel is set to be the first chipmaker to introduce back-side power delivery in a commercial chip, later this year with the release of the Arrow Lake CPU. Data released last summer by Intel showed that back-side power alone delivered a 6 percent performance boost. The Intel 18A process technology’s back-side-power-delivery network technology will be fundamentally the same as what’s found in Intel 20A chips. However, it’s being used to greater advantage in Clearwater Forest. The upcoming CPU includes what’s called an “on-die voltage regulator” within the base die. Having the voltage regulation close to the logic it drives means the logic can run faster. The shorter distances let the regulator respond to changes in the demand for current more quickly, while consuming less power. Because the logic dies use back-side power delivery, the resistance of the connection between the voltage regulator and the dies logic is that much lower. “The power via technology along with the Foveros stacking gives us a really efficient way to hook it up,” says Fetzer. RibbonFET, the next generation In addition to back-side power, the chipmaker is switching to a different transistor architecture with the Intel 20A process: RibbonFET. A form of nanosheet, or gate-all-around, transistor, RibbonFET replaces the FinFET, CMOS’s workhorse transistor since 2011. With Intel 18A, Clearwater Forest’s logic dies will be made with a second generation of RibbonFET process. While the devices themselves aren’t very different from the ones that will emerge from Intel 20A, there’s more flexibility to the design of the devices, says Fetzer. RibbonFET is Intel’s take on nanowire transistors.Intel “There’s a broader array of devices to support various foundry applications beyond just what was needed to enable a high-performance CPU,” which was what the Intel 20A process was designed for, he says. RibbonFET’s nanowires can have different widths depending on the needs of a logic cell.Intel Some of that variation stems from a degree of flexibility that was lost in the FinFET era. Before FinFETs arrived, transistors in the same process could be made in a range of widths, allowing a more-or-less continuous trade-off between performance—which came with higher current—and efficiency—which required better control over leakage current. Because the main part of a FinFET is a vertical silicon fin of a defined height and width, that trade-off now had to take the form of how many fins a device had. So, with two fins you could double current, but there was no way to increase it by 25 or 50 percent. With nanosheet devices, the ability to vary transistor widths is back. “RibbonFET technology enables different sizes of ribbon within the same technology base,” says Fetzer. “When we go from Intel 20A to Intel 18A, we offer more flexibility in transistor sizing.” That flexibility means that standard cells, basic logic blocks designers can use to build their systems, can contain transistors with different properties. And that enabled Intel to develop an “enhanced library” that includes standard cells that are smaller, better performing, or more efficient than those of the Intel 20A process. 2nd generation EMIB In Clearwater Forest, the dies that handle input and output connect horizontally to the base dies—the ones with the cache memory and network—using the second generation of Intel’s EMIB. EMIB is a small piece of silicon containing a dense set of interconnects and microbumps designed to connect one die to another in the same plane. The silicon is embedded in the package itself to form a bridge between dies. Dense 2D connections are formed by a small sliver of silicon called EMIB, which is embedded in the package substrate.Intel The technology has been in commercial use in Intel CPUs since Sapphire Rapids was released in 2023. It’s meant as a less costly alternative to putting all the dies on a silicon interposer, a slice of silicon patterned with interconnects that is large enough for all of the system’s dies to sit on. Apart from the cost of the material, silicon interposers can be expensive to build, because they are usually several times larger than what standard silicon processes are designed to make. The second generation of EMIB debuts this year with the Granite Rapids CPU, and it involves shrinking the pitch of microbump connections from 55 micrometers to 45 micrometers as well as boosting the density of the wires. The main challenge with such connections is that the package and the silicon expand at different rates when they heat up. This phenomenon could lead to warpage that breaks connections. What’s more, in the case of Clearwater Forest “there were also some unique challenges, because we’re connecting EMIB on a regular die to EMIB on a Foveros Direct 3D base die and a stack,” says Fetzer. This situation, recently rechristened EMIB 3.5 technology (formerly called co-EMIB), requires special steps to ensure that the stresses and strains involved are compatible with the silicon in the Foveros stack, which is thinner than ordinary chips, he says. For more, see Intel’s whitepaper on their foundry tech.

  • Summit Shares Best Practices for Attracting Students to STEM
    by Debra Gulick on 20. Februara 2024. at 19:00

    The annual IEEE STEM Summit in October brought together a record number of science, technology, engineering, and mathematics enthusiasts, who shared ideas and inspired each other to continue their work with school-age children. The event for preuniversity educators, IEEE volunteers, and other STEM enthusiasts provides resources and activities. Now in its third year, the free virtual summit had 581 participants from 87 countries last year. The 15 sessions garnered 950 comments and questions. Participants posed questions to award-winning educators and knowledgeable volunteers from academia and industry, who offered practical advice on how to plan interesting and effective outreach activities. Sessions included topics on pedagogy, engineering education, and outreach best practices, as well as inspirational talks and resources to empower the STEM community. Inspiring interest in STEM through new approaches The summit was organized by the preuniversity education coordinating committee, a standing group of IEEE volunteers within Educational Activities. The committee’s mission is to foster educational outreach to school-age children around the globe by providing educators and IEEE volunteers with tools for creating engaging activities and measuring outcomes. The committee, which hosted the summit, provides resources and services through Powered by IEEE, TryEngineering inspires educators to foster the next generation of technology innovators by providing resources, lesson plans, and activities at no charge for use in their classrooms and community activities. Students’ interest in STEM careers can be ignited through exposure to new technologies and the way they operate. The committee is committed to fostering a lively community where educators and volunteers can share ideas and experiences—which provides intriguing STEM content that can be shared through platforms and channels such as TryEngineering and taken back to the classroom. “I’m really glad that I was able to [meet people] from all over the world who share the same thoughts” about STEM, one participant said. Jamie Moesch, managing director of IEEE Educational Activities, says, “The IEEE STEM Summit provides preuniversity thought leaders with the opportunity to come together to share their best practices and motivate each other to inspire the next generation of engineers and technologists. “TryEngineering serves to coordinate a vast network of resources and volunteers committed to this cause.” Talks on climate change and generative AI Saifur Rahman, IEEE past president, and Rabab Ward, vice president of IEEE Educational Activities, kicked off the event with welcoming remarks. Rahman spoke about the climate crisis and encouraged summit participants to utilize the IEEE climate change resources in their outreach events. Ward discussed the importance of outreach activities for school-age children. The summit featured four keynote speakers and several panel sessions. Wioleta Burdzy-Seth spoke about STEM for climate solutions, explaining climate change and how passion can be used to find solutions. Jenna Carpenter referenced her TED Talk Engineering: Where Are the Girls and Why Aren’t They Here? when she discussed why it has been difficult to attract and retain women in STEM fields. She also presented research-informed strategies to help address the situation. Tiffani Teachey presented Unleashing the Power of Persistence: Nurturing an Engineering Mindset for Success. She discussed the role persistence plays in cultivating an engineering mindset, and she asked participants to encourage young students to be more determined. “The IEEE STEM Summit provides preuniversity thought leaders with the opportunity to come together to share their best practices and motivate each other to inspire the next generation of engineers and technologists.” —Jamie Moesch, managing director of IEEE Educational Activities Minjuan Wang spoke on the impact the metaverse and generative AI are having on education. She showcased several technologies being used for the metaverse and learning platforms. A panel of semiconductor professionals discussed the growing interest in semiconductor engineering. Shari Liss, executive director of the SEMI Foundation, joined several IEEE volunteers who covered different aspects of the technology. They also discussed U.S. legislation that supports the industry and diversity, equity, and inclusion efforts that are helping to cultivate the field’s workforce. During the Girls in STEM panel discussion, several female students, educators, and engineering leaders shared their stories and perspectives on how to encourage and keep women in engineering. Several sessions highlighted people who have successfully implemented STEM outreach programs, locally and globally, including a librarian and a NASA scientist. One summit highlight was a hands-on activity. Several participants, including students from a U.S. elementary school, worked together to build a windmill using materials commonly found around the house or classroom. Visit the IEEE TryEngineering YouTube channel to view other summit sessions. This year’s IEEE STEM Summit is scheduled for 22 to 25 October. More information about it will be posted on the IEEE STEM Summit website. The IEEE Foundation, the philanthropic partner for TryEngineering, provided financial support for the summit. To support future events and the TryEngineering program, visit the IEEE TryEngineering Fund donation page.

  • High-performance Data Acquisition for DFOS
    by Teledyne on 20. Februara 2024. at 17:34

    Join us for an insightful webinar on high-speed data acquisition in the context of Distributed Fiber Optic Sensing (DFOS) and learn more about the critical role that high-performance digitizers play in maximizing the potential of DFOS across diverse applications. The webinar is co-hosted by Professor Aldo Minardo, University of Campania Luigi Vanvitelli, who will speak about his phi-OTDR DAS system based on Teledyne SP Device’s 14-bit ADQ7DC digitizer. Register now for this free webinar!

  • The Quest for a DNA Data Drive
    by Rob Carlson on 17. Februara 2024. at 16:00

    How much thought do you give to where you keep your bits? Every day we produce more data, including emails, texts, photos, and social media posts. Though much of this content is forgettable, every day we implicitly decide not to get rid of that data. We keep it somewhere, be it in on a phone, on a computer’s hard drive, or in the cloud, where it is eventually archived, in most cases on magnetic tape. Consider further the many varied devices and sensors now streaming data onto the Web, and the cars, airplanes, and other vehicles that store trip data for later use. All those billions of things on the Internet of Things produce data, and all that information also needs to be stored somewhere. Data is piling up exponentially, and the rate of information production is increasing faster than the storage density of tape, which will only be able to keep up with the deluge of data for a few more years. The research firm Gartner predicts that by 2030, the shortfall in enterprise storage capacity alone could amount to nearly two-thirds of demand, or about 20 million petabytes. If we continue down our current path, in coming decades we would need not only exponentially more magnetic tape, disk drives, and flash memory, but exponentially more factories to produce these storage media, and exponentially more data centers and warehouses to store them. Even if this is technically feasible, it’s economically implausible. Prior projections for data storage requirements estimated a global need for about 12 million petabytes of capacity by 2030. The research firm Gartner recently issued new projections, raising that estimate by 20 million petabytes. The world is not on track to produce enough of today’s storage technologies to fill that gap.SOURCE: GARTNER Fortunately, we have access to an information storage technology that is cheap, readily available, and stable at room temperature for millennia: DNA, the material of genes. In a few years your hard drive may be full of such squishy stuff. Storing information in DNA is not a complicated concept. Decades ago, humans learned to sequence and synthesize DNA—that is, to read and write it. Each position in a single strand of DNA consists of one of four nucleic acids, known as bases and represented as A, T, G, and C. In principle, each position in the DNA strand could be used to store two bits (A could represent 00, T could be 01, and so on), but in practice, information is generally stored at an effective one bit—a 0 or a 1—per base. Moreover, DNA exceeds by many times the storage density of magnetic tape or solid-state media. It has been calculated that all the information on the Internet—which one estimate puts at about 120 zettabytes—could be stored in a volume of DNA about the size of a sugar cube, or approximately a cubic centimeter. Achieving that density is theoretically possible, but we could get by with a much lower storage density. An effective storage density of “one Internet per 1,000 cubic meters” would still result in something considerably smaller than a single data center housing tape today. In 2018, researchers built this first prototype of a machine that could write, store, and read data with DNA.MICROSOFT RESEARCH Most examples of DNA data storage to date rely on chemically synthesizing short stretches of DNA, up to 200 or so bases. Standard chemical synthesis methods are adequate for demonstration projects, and perhaps early commercial efforts, that store modest amounts of music, images, text, and video, up to perhaps hundreds of gigabytes. However, as the technology matures, we will need to switch from chemical synthesis to a much more elegant, scalable, and sustainable solution: a semiconductor chip that uses enzymes to write these sequences. After the data has been written into the DNA, the molecule must be kept safe somewhere. Published examples include drying small spots of DNA on glass or paper, encasing the DNA in sugar or silica particles, or just putting it in a test tube. Reading can be accomplished with any number of commercial sequencing technologies. Organizations around the world are already taking the first steps toward building a DNA drive that can both write and read DNA data. I’ve participated in this effort via a collaboration between Microsoft and the Molecular Information Systems Lab of the Paul G. Allen School of Computer Science and Engineering at the University of Washington. We’ve made considerable progress already, and we can see the way forward. How bad is the data storage problem? First, let’s look at the current state of storage. As mentioned, magnetic tape storage has a scaling problem. Making matters worse, tape degrades quickly compared to the time scale on which we want to store information. To last longer than a decade, tape must be carefully stored at cool temperatures and low humidity, which typically means the continuous use of energy for air conditioning. And even when stored carefully, tape needs to be replaced periodically, so we need more tape not just for all the new data but to replace the tape storing the old data. To be sure, the storage density of magnetic tape has been increasing for decades, a trend that will help keep our heads above the data flood for a while longer. But current practices are building fragility into the storage ecosystem. Backward compatibility is often guaranteed for only a generation or two of the hardware used to read that media, which could be just a few years, requiring the active maintenance of aging hardware or ongoing data migration. So all the data we have already stored digitally is at risk of being lost to technological obsolescence. How DNA data storage works The discussion thus far has assumed that we’ll want to keep all the data we produce, and that we’ll pay to do so. We should entertain the counterhypothesis: that we will instead engage in systematic forgetting on a global scale. This voluntary amnesia might be accomplished by not collecting as much data about the world or by not saving all the data we collect, perhaps only keeping derivative calculations and conclusions. Or maybe not every person or organization will have the same access to storage. If it becomes a limited resource, data storage could become a strategic technology that enables a company, or a country, to capture and process all the data it desires, while competitors suffer a storage deficit. But as yet, there’s no sign that producers of data are willing to lose any of it. If we are to avoid either accidental or intentional forgetting, we need to come up with a fundamentally different solution for storing data, one with the potential for exponential improvements far beyond those expected for tape. DNA is by far the most sophisticated, stable, and dense information-storage technology humans have ever come across or invented. Readable genomic DNA has been recovered after having been frozen in the tundra for 2 million years. DNA is an intrinsic part of life on this planet. As best we can tell, nucleic acid–based genetic information storage has persisted on Earth for at least 3 billion years, giving it an unassailable advantage as a backward- and forward-compatible data storage medium. What are the advantages of DNA data storage? To date, humans have learned to sequence and synthesize short pieces of single-stranded DNA (ssDNA). However, in naturally occurring genomes, DNA is usually in the form of long, double-stranded DNA (dsDNA). This dsDNA is composed of two complementary sequences bound into a structure that resembles a twisting ladder, where sugar backbones form the side rails, and the paired bases—A with T, and G with C—form the steps of the ladder. Due to this structure, dsDNA is generally more robust than ssDNA. Reading and writing DNA are both noisy molecular processes. To enable resiliency in the presence of this noise, digital information is encoded using an algorithm that introduces redundancy and distributes information across many bases. Current algorithms encode information at a physical density of 1 bit per 60 atoms (a pair of bases and the sugar backbones to which they’re attached). Edmon de Haro Synthesizing and sequencing DNA has become critical to the global economy, to human health, and to understanding how organisms and ecosystems are changing around us. And we’re likely to only get better at it over time. Indeed, both the cost and the per-instrument throughput of writing and reading DNA have been improving exponentially for decades, roughly keeping up with Moore’s Law. In biology labs around the world, it’s now common practice to order chemically synthesized ssDNA from a commercial provider; these molecules are delivered in lengths of up to several hundred bases. It is also common to sequence DNA molecules that are up to thousands of bases in length. In other words, we already convert digital information to and from DNA, but generally using only sequences that make sense in terms of biology. For DNA data storage, though, we will have to write arbitrary sequences that are much longer, probably thousands to tens of thousands of bases. We’ll do that by adapting the naturally occurring biological process and fusing it with semiconductor technology to create high-density input and output devices. There is global interest in creating a DNA drive. The members of the DNA Data Storage Alliance, founded in 2020, come from universities, companies of all sizes, and government labs from around the world. Funding agencies in the United States, Europe, and Asia are investing in the technology stack required to field commercially relevant devices. Potential customers as diverse as film studios, the U.S. National Archives, and Boeing have expressed interest in long-term data storage in DNA. Archival storage might be the first market to emerge, given that it involves writing once with only infrequent reading, and yet also demands stability over many decades, if not centuries. Storing information in DNA for that time span is easily achievable. The challenging part is learning how to get the information into, and back out of, the molecule in an economically viable way. What are the R&D challenges of DNA data storage? The first soup-to-nuts automated prototype capable of writing, storing, and reading DNA was built by my Microsoft and University of Washington colleagues in 2018. The prototype integrated standard plumbing and chemistry to write the DNA, with a sequencer from the company Oxford Nanopore Technologies to read the DNA. This single-channel device, which occupied a tabletop, had a throughput of 5 bytes over approximately 21 hours, with all but 40 minutes of that time consumed in writing “HELLO” into the DNA. It was a start. For a DNA drive to compete with today’s archival tape drives, it must be able to write about 2 gigabits per second, which at demonstrated DNA data storage densities is about 2 billion bases per second. To put that in context, I estimate that the total global market for synthetic DNA today is no more than about 10 terabases per year, which is the equivalent of about 300,000 bases per second over a year. The entire DNA synthesis industry would need to grow by approximately 4 orders of magnitude just to compete with a single tape drive. Keeping up with the total global demand for storage would require another 8 orders of magnitude of improvement by 2030. Exponential growth in silicon-based technology is how we wound up producing so much data. Similar exponential growth will be fundamental in the transition to DNA storage. But humans have done this kind of scaling up before. Exponential growth in silicon-based technology is how we wound up producing so much data. Similar exponential growth will be fundamental in the transition to DNA storage. My work with colleagues at the University of Washington and Microsoft has yielded many promising results. This collaboration has made progress on error-tolerant encoding of DNA, writing information into DNA sequences, stably storing that DNA, and recovering the information by reading the DNA. The team has also explored the economic, environmental, and architectural advantages of DNA data storage compared to alternatives. One of our goals was to build a semiconductor chip to enable high-density, high-throughput DNA synthesis. That chip, which we completed in 2021, demonstrated that it is possible to digitally control electrochemical processes in millions of 650-nanometer-diameter wells. While the chip itself was a technological step forward, the chemical synthesis we used on that chip had a few drawbacks, despite being the industry standard. The main problem is that it employs a volatile, corrosive, and toxic organic solvent (acetonitrile), which no engineer wants anywhere near the electronics of a working data center. Moreover, based on a sustainability analysis of a theoretical DNA data center performed my colleagues at Microsoft, I conclude that the volume of acetonitrile required for just one large data center, never mind many large data centers, would become logistically and economically prohibitive. To be sure, each data center could be equipped with a recycling facility to reuse the solvent, but that would be costly. Fortunately, there is a different emerging technology for constructing DNA that does not require such solvents, but instead uses a benign salt solution. Companies like DNA Script and Molecular Assemblies are commercializing automated systems that use enzymes to synthesize DNA. These techniques are replacing traditional chemical DNA synthesis for some applications in the biotechnology industry. The current generation of systems use either simple plumbing or light to control synthesis reactions. But it’s difficult to envision how they can be scaled to achieve a high enough throughput to enable a DNA data-storage device operating at even a fraction of 2 gigabases per second. The price for sequencing DNA has plummeted from $25 per base in 1990 to less than a millionth of a cent in 2024. The cost of synthesizing long pieces of double-stranded DNA is also declining, but synthesis needs to become much cheaper for DNA data storage to really take off.SOURCE: ROB CARLSON Still, the enzymes inside these systems are important pieces of the DNA drive puzzle. Like DNA data storage, the idea of using enzymes to write DNA is not new, but commercial enzymatic synthesis became feasible only in the last couple of years. Most such processes use an enzyme called terminal deoxynucleotidyl transferase, or TdT. Whereas most enzymes that operate on DNA use one strand as a template to fill in the other strand, TdT can add arbitrary bases to single-stranded DNA. Naturally occurring TdT is not a great enzyme for synthesis, because it incorporates the four bases with four different efficiencies, and it’s hard to control. Efforts over the past decade have focused on modifying the TdT and building it into a system in which the enzyme can be better controlled. Notably, those modifications to TdT were made possible by prior decades of improvement in reading and writing DNA, and the new modified enzymes are now contributing to further improvements in writing, and thus modifying, genes and genomes. This phenomenon is the same type of feedback that drove decades of exponential improvement in the semiconductor industry, in which companies used more capable silicon chips to design the next generation of silicon chips. Because that feedback continues apace in both arenas, it won’t be long before we can combine the two technologies into one functional device: a semiconductor chip that converts digital signals into chemical states (for example, changes in pH), and an enzymatic system that responds to those chemical states by adding specific, individual bases to build a strand of synthetic DNA. The University of Washington and Microsoft team, collaborating with the enzymatic synthesis company Ansa Biotechnologies, recently took the first step toward this device. Using our high-density chip, we successfully demonstrated electrochemical control of single-base enzymatic additions. The project is now paused while the team evaluates possible next steps.Nevertheless, even if this effort is not resumed, someone will make the technology work. The path is relatively clear; building a commercially relevant DNA drive is simply a matter of time and money. Looking beyond DNA data storage Eventually, the technology for DNA storage will completely alter the economics of reading and writing all kinds of genetic information. Even if the performance bar is set far below that of a tape drive, any commercial operation based on reading and writing data into DNA will have a throughput many times that of today’s DNA synthesis industry, with a vanishingly small cost per base. At the same time, advances in DNA synthesis for DNA storage will increase access to DNA for other uses, notably in the biotechnology industry, and will thereby expand capabilities to reprogram life. Somewhere down the road, when a DNA drive achieves a throughput of 2 gigabases per second (or 120 gigabases per minute), this box could synthesize the equivalent of about 20 complete human genomes per minute. And when humans combine our improving knowledge of how to construct a genome with access to effectively free synthetic DNA, we will enter a very different world. The conversations we have today about biosecurity, who has access to DNA synthesis, and whether this technology can be controlled are barely scratching the surface of what is to come. We’ll be able to design microbes to produce chemicals and drugs, as well as plants that can fend off pests or sequester minerals from the environment, such as arsenic, carbon, or gold. At 2 gigabases per second, constructing biological countermeasures against novel pathogens will take a matter of minutes. But so too will constructing the genomes of novel pathogens. Indeed, this flow of information back and forth between the digital and the biological will mean that every security concern from the world of IT will also be introduced into the world of biology. We will have to be vigilant about these possibilities. We are just beginning to learn how to build and program systems that integrate digital logic and biochemistry. The future will be built not from DNA as we find it, but from DNA as we will write it. This article appears in the March 2024 print issue.

  • Video Friday: Acrobot Error
    by Evan Ackerman on 16. Februara 2024. at 15:31

    Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion. Cybathlon Challenges: 2 February 2024, ZURICH HRI 2024: 11–15 March 2024, BOULDER, COLO. Eurobot Open 2024: 8–11 May 2024, LA ROCHE-SUR-YON, FRANCE ICRA 2024: 13–17 May 2024, YOKOHAMA, JAPAN Enjoy today’s videos! Just like a real human, Acrobot will sometimes kick you in the face. [ Acrobotics ] Thanks, Elizabeth! You had me at “wormlike, limbless robots.” [ GitHub ] via [ Georgia Tech ] Filmed in July 2017, this video shows us using Atlas to put out a “fire” on our loading dock. This uses a combination of teleoperation and autonomous behaviors through a single, remote computer. Robot built by Boston Dynamics for the DARPA Robotics Challenge in 2013. Software by IHMC Robotics. I would say that in the middle of a rainstorm is probably the best time to start a fire that you expect to be extinguished by a robot. [ IHMC ] We’re hard at work, but Atlas still has time for a dance break. [ Boston Dynamics ] This is pretty cool: BruBotics is testing its self-healing robotics gripper technology on commercial grippers from Festo. [ Paper ] via [ BruBotics ] Thanks, Bram! You should read our in-depth article on Stretch 3, so if you haven’t yet, consider this as just a teaser. [ Hello Robot ] Inspired by caregiving experts, we proposed a bimanual interactive robotic dressing assistance scheme, which is unprecedented in previous research. In the scheme, an interactive robot joins hands with the human thus supporting/guiding the human in the dressing process, while the dressing robot performs the dressing task. This work represents a paradigm shift of thinking of the dressing assistance task from one-robot-to-one-arm to two-robot-to-one-arm. [ Project ] Thanks, Jihong! Tony Punnoose Valayil from the Bulgarian Academy of Sciences Institute of Robotics wrote in to share some very low-cost hand-rehabilitation robots for home use. In this video, we present a robot-assisted rehabilitation of the wrist joint which can aid in restoring the strength that has been lost across the upper limb due to stroke. This robot is very cost-effective and can be used for home rehabilitation. In this video, we present an exoskeleton robot which can be used at home for rehabilitating the index and middle fingers of stroke-affected patients. This robot is built at a cost of 50 euros for patients who are not financially independent to get better treatment. [ BAS ] Some very impressive work here from the Norwegian University of Science and Technology (NTNU), showing a drone tracking its position using radar and lidar-based odometry in some nightmare (for robots) environments, including a long tunnel that looks the same everywhere and a hallway full of smoke. [ Paper ] via [ GitHub ] I’m sorry, but people should really know better than to make videos like this for social robot crowdfunding by now. It’s on Kickstarter for about $300, and the fact that it’s been funded so quickly tells me that people have already forgotten about the social robotpocalypse. [ Kickstarter ] Introducing Orbit, your portal for managing asset-intensive facilities through real-time and predictive intelligence. Orbit brings a whole new suite of fleet management capabilities and will unify your ecosystem of Boston Dynamics robots, starting with Spot. [ Boston Dynamics ]

  • Ham Radio Inspired This Scranton University Student to Pursue Engineering
    by Kathy Pretz on 15. Februara 2024. at 20:00

    Many college students participate in sports, listen to music, or play video games in their spare time, but IEEE Student Member Gerard Piccini prefers amateur radio, also known as ham radio. He’s been involved with the two-way radio communication, which uses designated frequencies, since his uncle introduced him to it when he was a youngster. His call sign is KD2ZHK. Piccini, from Monroe Township, N.J., is pursuing an electrical engineering degree at the University of Scranton, in Pennsylvania. The junior is president of the university’s W3USR amateur radio club. He’s also a member of Scranton’s IEEE student branch, the IEEE Club. Gerard Piccini Member grade Student member; member of IEEE-HKN’s Lambda Nu chapter University: University of Scranton in Pennsylvania Major: Electrical engineering Minors: Math and physics Grade: Junior Another of his passions is robotics. He captained one of the university club’s teams that participated in the Micro Mouse competition held during the October IEEE Region 2 Student Activities Conference, hosted by Marshall University in Huntington, W.Va. The Scranton team competed against other student branches to build and program small robots to navigate a maze in the shortest time possible. The team placed second. “The contest was a great opportunity for me,” Piccini says, “to learn how to apply the skills I’ve been learning from classes into a project that I designed myself.” Ham radio researcher Piccini joined Scranton’s amateur radio club when he was a freshman. Overseeing the club is IEEE Member Nathaniel Frissell, who has taught Piccini physics and electrical engineering. Frissell noticed Piccini’s interest in radio technology and asked the student to assist him with research. Piccini now is helping to develop a low-cost, low-power system to send a signal into the ionosphere and measure the time it takes to return. “The system will allow us to collect more data about the ionosphere, which is an ionized layer of the atmosphere and is important for radio propagation,” he says. “Right now there are not that many full-sized ionospheric sounding systems. If we can make them cheap enough, we could get ham radio operators to set them up and increase data points.” “I like it when I have a project and have to try to find a solution on my own.” Piccini is active with Ham Radio Science Citizen Investigation, which includes amateur radio enthusiasts and professional scientists who collaborate on research. “The idea behind HamSCI is getting citizens involved in science,” Piccini says. His research, he says, has led him to consider a career in RF engineering or digital signal processing, either in academia or industry. A born problem-solver Like other budding engineers, Piccini has enjoyed taking things apart and figuring out how to put them back together again since his youth. Neither of his parents was an engineer, but they encouraged his interest by buying him engineering kits. A high school physics class inspired him to study electrical engineering. It covered circuits and wave mechanics, a branch of quantum physics in which the behavior of objects is described in terms of their wavelike properties. He initially was undecided about whether to pursue a degree in physics or engineering. It wasn’t until he learned how to code and work with hardware that he chose engineering. And although he still enjoys coding, he says he’s glad he ultimately chose electrical engineering: “I like it when I have a project and have to try to find a solution on my own.” He is minoring in mathematics and physics. Student Member Gerard N. Piccini [second from left] with teammates from the IEEE Club Student Branch who competed in the IEEE Region 2 Micro Mouse contest. Gabrina Garangmau An IEEE student leader Piccini says he joined IEEE because he felt “trapped in a bubble of academia.” As an underclassman, he recalls, he didn’t really know what was going on in the field of engineering or in industry. “Being involved with IEEE helps give you that exposure,” he says. He is a member of the Lambda Nu chapter of IEEE’s honor society, IEEE-Eta Kappa Nu. Scranton’s IEEE Club offers presentations by engineering companies and technical talks. The club also encourages students to explain the work they’ve done during their internships. To give members professional boosts, the club holds résumé-writing sessions, conducts mock interviews, and has the students practice their public-speaking skills. The branch also encourages its members to get involved with community projects. Piccini is secretary of the student branch. The position has given him leadership experience, he says, including teaching him how to organize and run meetings and coordinate events—skills he wouldn’t have picked up in his classes. As captain of the Micro Mouse team, he was responsible for mentoring younger students, overseeing the design of the robot, and setting the agenda so the team would meet the competition’s deadlines. He notes that the IEEE Student Activities Conference is a great way to meet fellow students from around the region. Being active in IEEE, he says, is “a great opportunity to network, meet people, and learn new skills that you might not have—or already have but want to develop further.”

  • Stretch 3 Brings Us Closer to Realistic Home Robots
    by Evan Ackerman on 15. Februara 2024. at 17:28

    A lot has happened in robotics over the last year. Everyone is wondering how AI will transform robotics, and everyone else is wondering whether humanoids are going to blow it or not, and the rest of us are busy trying not to get completely run over as things shake out however they’re going to shake out. Meanwhile, over at Hello Robot, they’ve been focused on making their Stretch robot do useful things while also being affordable and reliable and affordable and expandable and affordable and community-friendly and affordable. Which are some really hard and important problems that can sometimes get overwhelmed by flashier things. Today, Hello Robot is announcing Stretch 3, which provides a suite of upgrades to what they (quite accurately) call “the world’s only lightweight, capable, developer-friendly mobile manipulator.” And impressively, they’ve managed to do it without forgetting about that whole “affordable” part. Hello Robot Stretch 3 looks about the same as the previous versions, but there are important upgrades that are worth highlighting. The most impactful: Stretch 3 now comes with the dexterous wrist kit that used to be an add-on, and it now also includes an Intel Realsense D405 camera mounted right behind the gripper, which is a huge help for both autonomy and remote teleoperation—a useful new feature shipping with Stretch 3 that’s based on research out of Maya Cakmak’s lab at the University of Washington, in Seattle. This is an example of turning innovation from the community of Stretch users into product features, a product-development approach that seems to be working well for Hello Robot. “We’ve really been learning from our community,” says Hello Robot cofounder and CEO Aaron Edsinger. “In the past year, we’ve seen a real uptick in publications, and it feels like we’re getting to this critical-mass moment with Stretch. So with Stretch 3, it’s about implementing features that our community has been asking us for.” “When we launched, we didn’t have a dexterous wrist at the end as standard, because we were trying to start with truly the minimum viable product,” says Hello Robot cofounder and CTO Charlie Kemp. “And what we found is that almost every order was adding the dexterous wrist, and by actually having it come in standard, we’ve been able to devote more attention to it and make it a much more robust and capable system.” Kemp says that having Stretch do everything right out of the box (with Hello Robot support) makes a big difference for their research customers. “Making it easier for people to try things—we’ve learned to really value that, because the more steps that people have to go through to experience it, the less likely they are to build on it.” In a research context, this is important because what you’re really talking about is time: The more time people spend just trying to make the robot function, the less time they’ll spend getting the robot to do useful things. Hello Robot At this point, you may be thinking of Stretch as a research platform. Or you may be thinking of Stretch as a robot for people with disabilities, if you read our November 2023 cover story about Stretch and Henry and Jane Evans. And the robot is definitely both of those things. But Hello Robot stresses that these specific markets are not their end goal—they see Stretch as a generalist mobile manipulator with a future in the home, as suggested by this Stretch 3 promo video: Hello Robot Dishes, laundry, bubble cannons: All of these are critical to the functionality of any normal household. “Stretch is an inclusive robot,” says Kemp. “It’s not just for older adults or people with disabilities. We want a robot that can be beneficial for everyone. Our vision, and what we believe will really happen, whether it’s us or someone else, is that there is going to be a versatile, general-purpose home robot. Right now, clearly, our market is not yet consumers in the home. But that’s where we want to go.” Robots in the home have been promised for decades, and with the notable exception of the Roomba, there has not been a lot of success. The idea of a robot that could handle dishes or laundry is tempting, but is it near-term or medium-term realistic? Edsinger, who has been at this whole robots thing for a very long time, is an optimist about this, and about the role that Stretch will play. “There are so many places where you can see the progress happening—in sensing, in manipulation,” Edsinger says. “I can imagine those things coming together now in a way that I could not have 5 to 10 years ago, when it seemed so incredibly hard.” “We’re very pragmatic about what is possible. And I think that we do believe that things are changing faster than we anticipated—10 years ago, I had a pretty clear linear path in mind for robotics, but it’s hard to really imagine where we’ll be in terms of robot capabilities 10 years from now.” —Aaron Edsinger, Hello Robot I’d say that it’s still incredibly hard, but Edsinger is right that a lot of the pieces do seem to be coming together. Arguably, the hardware is the biggest challenge here, because working in a home puts heavy constraints on what kind of hardware you’re able to use. You’re not likely to see a humanoid in a home anytime soon, because they’d actually be dangerous, and even a quadruped is likely to be more trouble than it’s worth in a home environment. Hello Robot is conscious of this, and that’s been one of the main drivers of the design of Stretch. “I think the portability of Stretch is really worth highlighting because there’s just so much value in that which is maybe not obvious,” Edsinger tells us. Being able to just pick up and move a mobile manipulator is not normal. Stretch’s weight (24.5 kilograms) is almost trivial to work with, in sharp contrast with virtually every other mobile robot with an arm: Stretch fits into places that humans fit into, and manages to have a similar workspace as well, and its bottom-heavy design makes it safe for humans to be around. It can’t climb stairs, but it can be carried upstairs, which is a bigger deal than it may seem. It’ll fit in the back of a car, too. Stretch is built to explore the world—not just some facsimile of the world in a research lab. “NYU students have been taking Stretch into tens of homes around New York,” says Edsinger. “They carried one up a four-story walk-up. This enables real in-home data collection. And this is where home robots will start to happen—when you can have hundreds of these out there in homes collecting data for machine learning.” “That’s where the opportunity is,” adds Kemp. “It’s that engagement with the world about where to apply the technology beneficially. And if you’re in a lab, you’re not going to find it.” We’ve seen some compelling examples of this recently, with Mobile ALOHA. These are robots learning to be autonomous by having humans teleoperate them through common household skills. But the system isn’t particularly portable, and it costs nearly US $32,000 in parts alone. Don’t get me wrong: I love the research. It’s just going to be difficult to scale, and in order to collect enough data to effectively tackle the world, scale is critical. Stretch is much easier to scale, because you can just straight up buy one. Or two! You may have noticed that some of the Stretch 3 videos have two robots in them, collaborating with each other. This is not yet autonomous, but with two robots, a single human (or a pair of humans) can teleoperate them as if they were effectively a single two-armed robot: Hello Robot Essentially, what you’ve got here is a two-armed robot that (very intentionally) has nothing to do with humanoids. As Kemp explains: “We’re trying to help our community and the world see that there is a different path from the human model. We humans tend to think of the preexisting solution: People have two arms, so we think, well, I’m going to need to have two arms on my robot or it’s going to have all these issues.” Kemp points out that robots like Stretch have shown that really quite a lot of things can be done with only one arm, but two arms can still be helpful for a substantial subset of common tasks. “The challenge for us, which I had just never been able to find a solution for, was how you get two arms into a portable, compact, affordable lightweight mobile manipulator. You can’t!” But with two Stretches, you have not only two arms but also two shoulders that you can put wherever you want. Washing a dish? You’ll probably want two arms close together for collaborative manipulation. Making a bed? Put the two arms far apart to handle both sides of a sheet at once. It’s a sort of distributed on-demand bimanual manipulation, which certainly adds a little bit of complexity but also solves a bunch of problems when it comes to practical in-home manipulation. Oh—and if those teleop tools look like modified kitchen tongs, that’s because they’re modified kitchen tongs. Of course, buying two Stretch robots is twice as expensive as buying a single Stretch robot, and even though Stretch 3’s cost of just under $25,000 is very inexpensive for a mobile manipulator and very affordable in a research or education context, we’re still pretty far from something that most people would be able to afford for themselves. Hello Robot says that producing robots at scale is the answer here, which I’m sure is true, but it can be a difficult thing for a small company to achieve. Moving slowly toward scale is at least partly intentional, Kemp tells us. “We’re still in the process of discovering Stretch’s true form—what the robot really should be. If we tried to scale to make lots and lots of robots at a much lower cost before we fundamentally understood what the needs and challenges were going to be, I think it would be a mistake. And there are many gravestones out there for various home-robotics companies, some of which I truly loved. We don’t want to become one of those.” This is not to say that Hello Robot isn’t actively trying to make Stretch more affordable, and Edsinger suggests that the next iteration of the robot will be more focused on that. But—and this is super important—Kemp tells us that Stretch has been, is, and will continue to be sustainable for Hello Robot: “We actually charge what we should be charging to be able to have a sustainable business.” In other words, Hello Robot is not relying on some nebulous scale-defined future to transition into a business model that can develop, sell, and support robots. They can do that right now while keeping the lights on. “Our sales have enough margin to make our business work,” says Kemp. “That’s part of our discipline.” Stretch 3 is available now for $24,950, which is just about the same as the cost of Stretch 2 with the optional add-ons included. There are lots and lots of other new features that we couldn’t squeeze into this article, including FCC certification, a more durable arm, and off-board GPU support. You’ll find a handy list of all the upgrades here.

  • Build the Most Accurate DIY Quartz Clock Yet
    by Gavin Watkins on 15. Februara 2024. at 15:00

    Accurate timing is something that’s always been of interest to me. These days we rely heavily on time delivered to us over the Internet, through radio waves from GPS satellites, or broadcast stations. But I wanted a clock that would keep excellent time without relying on the outside world—certainly something better than the time provided by the quartz crystal oscillator used in your typical digital clock or microcontroller, which can drift by about 1.7 seconds per day, or over 10 minutes in the course of a year. Of course, I could buy an atomic clock—that is, one with a rubidium oscillator inside, of the sort used onboard GPS satellites. (Not the kind that’s marketed as an “atomic clock” but one that actually relies on picking up radio time signals.) Rubidium clocks provide incredible accuracy, but cost thousands of U.S. dollars. I needed something in between, and salvation was found in the form of the oven-controlled crystal oscillator, invariably known as an OCXO for historical reasons. With one of these, I could build my own clock for around US $200—and one that’s about 200 times as accurate as a typical quartz clock. Temperature changes are the biggest source of error in conventional crystal oscillators. They cause the quartz to expand or shrink, which alters its resonance frequency. One solution is to track the temperature and compensate for the changes in frequency. But it would be better not to have the frequency change in the first place, and this is where the OCXO comes in. The printed circuit board [center] can be cut into two pieces, with the timing-related components mounted on the lower section, and the control and display components mounted on the upper section.James Provost The OCXO keeps the crystal at a constant temperature. To avoid the complexity of having to both heat and cool a crystal in response to ambient fluctuations, the crystal is kept heated close to 80 °C or so, well above any environmental temperatures it’s likely to experience. In the past, OCXOs were power hungry and bulky or expensive, but in the last few years miniature versions have appeared that are much cheaper and draw way less power. The Raltron OCXO I chose for my clock costs $58, operates at 3.3 volts, and draws 400 milliamperes in steady-state operation. The OCXO resonates at 10 megahertz. In my clock, this signal is fed into a 4-bit counter, which outputs a pulse every time it counts from 0000 to 1111 in binary, effectively dividing the 10-MHz signal by 16. This 625-kilohertz (kHz) signal then drives a hardware timer in an Arduino Nano microcontroller, which triggers a program interrupt every tenth of a second to update the clock’s time base. (Full details on how the timing chain and software work are available in an accompanying post on IEEE Spectrum’s website , along with a bill of materials and printed circuit board files.) A rotary controller connected directly to the Nano lets you set the time. The Nano keeps track of the time, advancing seconds, minutes, and hours, and it also drives the display. This display is created using six Adafruit “CharliePlex FeatherWings,” which are 15 by 7 LED matrices with controllable brightness that come in a variety of colors. Each one is controlled via the addressable I2C serial bus protocol. A problem arises because a CharliePlex is hardwired to have only one of two possible I2C addresses, making it impossible to address six clock digits individually on a single bus. My solution was to use an I2C multiplexer, which takes incoming I2C data and switches it between six separate buses. The timing chain begins with the OCXO oscillator and its 10-megahertz signal and ends with the display being updated once every second. The timing signal synchronizes a hardware timer in the Nano microcontroller so that it triggers an interrupt handler in the Nano’s software 10 times a second. Consequently, you can make many modifications or add new features via software changes.James Provost Using a microcontroller—rather than, say, discrete logic chips—simplified the design and allows for easy modification and expansion. It’s trivial to tweak the software to substitute your own font design for the numbers, for example, or adjust the brightness of the display. Connector blocks for serial interfaces are directly available on the Nano, meaning you could use the clock as an timer or trigger for some other device. For such a purpose you could omit the display entirely, reducing the clock’s size considerably (although you’ll have to modify the software to override the startup verification of the display). The clock’s printed circuit board is designed so that it can be cut into two pieces, with the lower third holding the microcontroller, OCXO, and other supporting electronics. The upper two thirds hold the display and the rotary encoder. By adding four headers and running two cables between the pieces to connect them, you can arrange the boards to form a wide range of physical configurations, giving you a lot of freedom in designing the form factor of any enclosure you might choose to build for the clock. Indeed, creating the PCB so this was possible was probably the most challenging part of the whole process. But the resulting hardware and software flexibility of the final design was worth it. The whole device is powered through the Nano’s USB-C port. USB-C was needed in order to provide enough current, as the clock, OCXO, and display all together need more than the 500-mA nominal maximum current of earlier USB ports. A battery backup connected to this port is needed to prevent resets due to power loss—using one of the popular coin-cell-based real-time backup clocks would be pointless due to their relative inaccuracy. And as for that goal of creating an accurate clock with a great bang for the buck, I cross-checked my OCXO’s output in circuit with an HP 53150A frequency counter. The result is that the clock drifts no more than 0.00864 seconds per day, or less than 3.15 seconds in a year. In fact, its accuracy is probably better than that, but I’d reached the limit of what I could measure with my frequency counter! I hope you’ll build one of your own—it takes just a few hours of soldering, and I think you’ll agree it would be time well spent.

  • What Is Generative AI?
    by Eliza Strickland on 14. Februara 2024. at 16:34

    Generative AI is today’s buzziest form of artificial intelligence, and it’s what powers chatbots like ChatGPT, Ernie, LLaMA, Claude, and Command—as well as image generators like DALL-E 2, Stable Diffusion, Adobe Firefly, and Midjourney. Generative AI is the branch of AI that enables machines to learn patterns from vast datasets and then to autonomously produce new content based on those patterns. Although generative AI is fairly new, there are already many examples of models that can produce text, images, videos, and audio. Many “foundation models” have been trained on enough data to be competent in a wide variety of tasks. For example, a large language model can generate essays, computer code, recipes, protein structures, jokes, medical diagnostic advice, and much more. It can also theoretically generate instructions for building a bomb or creating a bioweapon, though safeguards are supposed to prevent such types of misuse. What’s the difference between AI, machine learning, and generative AI? Artificial intelligence (AI) refers to a wide variety of computational approaches to mimicking human intelligence. Machine learning (ML) is a subset of AI; it focuses on algorithms that enable systems to learn from data and improve their performance. Before generative AI came along, most ML models learned from datasets to perform tasks such as classification or prediction. Generative AI is a specialized type of ML involving models that perform the task of generating new content, venturing into the realm of creativity. What architectures do generative AI models use? Generative models are built using a variety of neural network architectures—essentially the design and structure that defines how the model is organized and how information flows through it. Some of the most well-known architectures are variational autoencoders (VAEs), generative adversarial networks (GANs), and transformers. It’s the transformer architecture, first shown in this seminal 2017 paper from Google, that powers today’s large language models. However, the transformer architecture is less suited for other types of generative AI, such as image and audio generation. Autoencoders learn efficient representations of data through an encoder-decoder framework. The encoder compresses input data into a lower-dimensional space, known as the latent (or embedding) space, that preserves the most essential aspects of the data. A decoder can then use this compressed representation to reconstruct the original data. Once an autoencoder has been trained in this way, it can use novel inputs to generate what it considers the appropriate outputs. These models are often deployed in image-generation tools and have also found use in drug discovery, where they can be used to generate new molecules with desired properties. With generative adversarial networks (GANs), the training involves a generator and a discriminator that can be considered adversaries. The generator strives to create realistic data, while the discriminator aims to distinguish between those generated outputs and real “ground truth” outputs. Every time the discriminator catches a generated output, the generator uses that feedback to try to improve the quality of its outputs. But the discriminator also receives feedback on its performance. This adversarial interplay results in the refinement of both components, leading to the generation of increasingly authentic-seeming content. GANs are best known for creating deepfakes but can also be used for more benign forms of image generation and many other applications. The transformer is arguably the reigning champion of generative AI architectures for its ubiquity in today’s powerful large language models (LLMs). Its strength lies in its attention mechanism, which enables the model to focus on different parts of an input sequence while making predictions. In the case of language models, the input consists of strings of words that make up sentences, and the transformer predicts what words will come next (we’ll get into the details below). In addition, transformers can process all the elements of a sequence in parallel rather than marching through it from beginning to end, as earlier types of models did; this parallelization makes training faster and more efficient. When developers added vast datasets of text for transformer models to learn from, today’s remarkable chatbots emerged. How do large language models work? A transformer-based LLM is trained by giving it a vast dataset of text to learn from. The attention mechanism comes into play as it processes sentences and looks for patterns. By looking at all the words in a sentence at once, it gradually begins to understand which words are most commonly found together and which words are most important to the meaning of the sentence. It learns these things by trying to predict the next word in a sentence and comparing its guess to the ground truth. Its errors act as feedback signals that cause the model to adjust the weights it assigns to various words before it tries again. These five LLMs vary greatly in size (given in parameters), and the larger models have better performance on a standard LLM benchmark test. IEEE Spectrum To explain the training process in slightly more technical terms, the text in the training data is broken down into elements called tokens, which are words or pieces of words—but for simplicity’s sake, let’s say all tokens are words. As the model goes through the sentences in its training data and learns the relationships between tokens, it creates a list of numbers, called a vector, for each one. All the numbers in the vector represent various aspects of the word: its semantic meanings, its relationship to other words, its frequency of use, and so on. Similar words, like elegant and fancy, will have similar vectors and will also be near each other in the vector space. These vectors are called word embeddings. The parameters of an LLM include the weights associated with all the word embeddings and the attention mechanism. GPT-4, the OpenAI model that’s considered the current champion, is rumored to have more than 1 trillion parameters. Given enough data and training time, the LLM begins to understand the subtleties of language. While much of the training involves looking at text sentence by sentence, the attention mechanism also captures relationships between words throughout a longer text sequence of many paragraphs. Once an LLM is trained and is ready for use, the attention mechanism is still in play. When the model is generating text in response to a prompt, it’s using its predictive powers to decide what the next word should be. When generating longer pieces of text, it predicts the next word in the context of all the words it has written so far; this function increases the coherence and continuity of its writing. Why do large language models hallucinate? You may have heard that LLMs sometimes “hallucinate.” That’s a polite way to say they make stuff up very convincingly. A model sometimes generates text that fits the context and is grammatically correct, yet the material is erroneous or nonsensical. This bad habit stems from LLMs training on vast troves of data drawn from the Internet, plenty of which is not factually accurate. Since the model is simply trying to predict the next word in a sequence based on what it has seen, it may generate plausible-sounding text that has no grounding in reality. Why is generative AI controversial? One source of controversy for generative AI is the provenance of its training data. Most AI companies that train large models to generate text, images, video, and audio have not been transparent about the content of their training datasets. Various leaks and experiments have revealed that those datasets include copyrighted material such as books, newspaper articles, and movies. A number of lawsuits are underway to determine whether use of copyrighted material for training AI systems constitutes fair use, or whether the AI companies need to pay the copyright holders for use of their material. On a related note, many people are concerned that the widespread use of generative AI will take jobs away from creative humans who make art, music, written works, and so forth. People are also concerned that it could take jobs from humans who do a wide range of white-collar jobs, including translators, paralegals, customer-service representatives, and journalists. There have already been a few troubling layoffs, but it’s hard to say yet whether generative AI will be reliable enough for large-scale enterprise applications. (See above about hallucinations.) Finally, there’s the danger that generative AI will be used to make bad stuff. And there are of course many categories of bad stuff it could theoretically be used for. Generative AI can be used for personalized scams and phishing attacks: For example, using “voice cloning,” scammers can copy the voice of a specific person and call the person’s family with a plea for help (and money). All formats of generative AI—text, audio, image, and video—can be used to generate misinformation by creating plausible-seeming representations of things that never happened, which is a particularly worrying possibility when it comes to elections. (Meanwhile, as IEEE Spectrum reported this week, the U.S. Federal Communications Commission has responded by outlawing AI-generated robocalls.) Image- and video-generating tools can be used to produce nonconsensual pornography, although the tools made by mainstream companies disallow such use. And chatbots can theoretically walk a would-be terrorist through the steps of making a bomb, nerve gas, and a host of other horrors. Although the big LLMs have safeguards to prevent such misuse, some hackers delight in circumventing those safeguards. What’s more, “uncensored” versions of open-source LLMs are out there. Despite such potential problems, many people think that generative AI can also make people more productive and could be used as a tool to enable entirely new forms of creativity. We’ll likely see both disasters and creative flowerings and plenty else that we don’t expect. But knowing the basics of how these models work is increasingly crucial for tech-savvy people today. Because no matter how sophisticated these systems grow, it’s the humans’ job to keep them running, make the next ones better, and with any luck, help people out too.

  • Open-Source Security Chip Released
    by Dina Genkina on 14. Februara 2024. at 03:39

    The first commercial silicon chip that includes open-source, built-in hardware security was announced today by the OpenTitan coalition. This milestone represents another step in the growth of the open hardware movement. Open hardware has been gaining steam since the development of the popular open-source processor architecture RISC-V. RISC-V gives an openly available prescription for how a computer can operate efficiently at the most basic level. OpenTitan goes beyond RISC-V’s open-source instruction set by delivering an open-source design for the silicon itself. Although other open-source silicon has been developed, this is the first one to include the design-verification stage and to produce a fully functional commercial chip, the coalition claims. Utilizing a RISC-V based processor core, the chip, called Earl Grey, includes a number of built-in hardware security and cryptography modules, all working together in a self-contained microprocessor. The project began back in 2019 by a coalition of companies, started by Google and shepherded by the nonprofit lowRISC in Cambridge, United Kingdom. Modeled after open-source software projects, it has been developed by contributors from around the world, both official affiliates with the project and independent coders. Today’s announcement is the culmination of five years of work. Open source “just takes over because it has certain valuable properties... I think we’re seeing the beginning of this now with silicon.”—Dominic Rizzo, zeroRISC “This chip is very, very exciting,” says OpenTitan cocreator and CEO of coalition partner zeroRISC Dominic Rizzo. “But there’s a much bigger thing here, which is the development of this whole new type of methodology. Instead of a traditional…command and control style structure, this is distributed.” The methodology they have developed is called Silicon Commons. Open-source hardware design faces challenges that open-source software didn’t, such as greater costs, a smaller professional community, and inability to supply bug fixes in patches after the product is released, explains lowRISC CEO Gavin Ferris. The Silicon Commons framework provides rules for documentation, predefined interfaces, and quality standards, as well as the governance structure laying out how the different partners make decisions as a collective. Another key to the success of the project, Ferris says, was picking a problem that all the partners would have an incentive to continue participating in over the course of the five years of development. Hardware security was the right fit for the job because of its commercial importance as well as its particular fit to the open-source model. There’s a notion in cryptography known as Kerckhoffs’s principle, which states that the only thing that should actually be secret in a cryptosystem is the secret key itself. Open-sourcing the entire protocol makes sure the cryptosystem conforms to this rule. What Is a Hardware Root-of-Trust? OpenTitan uses a hardware security protocol known as a root of trust (RoT). The idea is to provide an on-chip source of cryptographic keys that is inaccessible remotely. Because it’s otherwise inaccessible, the system can trust that it hasn’t been tampered with, providing a basis to build security on. “Root of Trust means that at the end of the day, there is something that we both believe in,” explains Ravi Subrahmanyan, senior director of integrated circuit design at Analog Devices, who was not involved in the effort. Once there is something both people agree on, a trusted secure connection can be established. Conventional, proprietary chips can also leverage RoT technology. Open-sourcing it provides an extra layer of trust, proponents argue. Since anyone can inspect and probe the design, the theory is that bugs are more likely to get noticed and the bug fixes can be verified. “The openness is a good thing,” says Subrahmanyan. “Because for example, let’s say a proprietary implementation has some problem. I won’t necessarily know, right? I’m at their mercy as to whether they’re going to tell me or not.” This kind of on-chip security is especially relevant in devices forming the Internet of Things (IoT), which suffer from unaddressed security challenges. ZeroRISC and its partners will open up sales to IoT markets via an early-access program, and they anticipate broad adoption in that sphere. Rizzo and Ferris believe their chip has a template for open-source hardware development that other collaborations will replicate. On top of providing transparent security, open-sourcing saves companies money by allowing them to reuse hardware components rather than having to independently develop proprietary versions of the same thing. It also opens the door for many more partners to participate in the effort, including academic institutions such as OpenTitan coalition partner ETH Zurich. Thanks to academic involvement, OpenTitan was able to incorporate cryptography protocols that are safe against future quantum computers. “Once the methodology has been proven, others will pick it up,” Rizzo says. “If you look at what’s happened with open-source software, first, people thought it was kind of an edge pursuit, and then it ended up running almost every mobile phone. It just takes over because it has certain valuable properties. And so I think we’re seeing the beginning of this now with silicon.”

  • The FCC’s Ban on AI in Robocalls Won’t Be Enough
    by Michael Koziol on 13. Februara 2024. at 17:00

    In the days before the U.S. Democratic Party’s New Hampshire primary election on 23 January, potential voters began receiving a call with AI-generated audio of a fake President Biden urging them not to vote until the general election in November. In Slovakia a Facebook post contained fake, AI-generated audio of a presidential candidate planning to steal the election—which may have tipped the election in another candidate’s favor. Recent elections in Indonesia and Taiwan have been marred by AI-generated misinformation, too. In response to the faux-Biden robocall in New Hampshire, the U.S. Federal Communications Commission moved to make AI-generated voices in robocalls illegal on 8 February. But experts IEEE Spectrum spoke to aren’t convinced that the move will be enough, even as generative AI brings new twists to old robocall scams and offers opportunities to turbocharge efforts to defraud individuals. The total lost to scams and spam in the United States in 2022 is thought to be US $39.5 billion, according to TrueCaller, which makes a caller ID and spam-blocking app. That same year, the average amount of money lost by people scammed in the United States was $431.26, according to a survey by Hiya, a company that provides call-protection and identity services. Hiya says that amount stands to go up as the usage of generative AI gains traction. “In aggregate, it’s mind-boggling how much is lost to fraud perpetuated through robocalls,” says Eric Burger, the research director of the Commonwealth Cyber Initiative at Virginia Tech. “I don’t think we can appreciate just how fast the telephone experience is going to change because of this.” —Jonathan Nelson, Hiya AI Will Make It Easier for Scammers to Target Individuals “The big fear with generative AI is it’s going to take custom-tailored scams and take them mainstream,” says Jonathan Nelson, director of product management at Hiya. In particular, he says, generative AI will make it easier to carry out spear-phishing attacks. The Cost of Phone Fraud The average amount of money lost by a phone-scam victim in 2022, in U.S. dollars: United States: $431.26 UK: $324.04 Canada: $472.87 France: $360.62 Germany: $325.87 Spain: $282.35 Source: Hiya Generally, phishing attacks aim to trick people into parting with personal information, such as passwords and financial information. Spear-phishing, however, is more targeted: The scammer knows exactly whom they’re targeting, and they’re hoping for a bigger payout through a more tailored approach. Now, with generative AI, Nelson says, a scammer can scrape social-media sites, draft text, and even clone a trusted voice to part unsuspecting individuals from their money en masse. With the FCC’s unanimous vote to make generative AI in robocalls illegal, the question naturally turns to enforcement. That’s where the experts whom IEEE Spectrum spoke to are generally doubtful, although many also see it as a necessary first step. “It’s a helpful step,” says Daniel Weiner, the director of the Brennan Center’s Elections and Government Program, “but it’s not a full solution.” Weiner says that it’s difficult for the FCC to take a broader regulatory approach in the same vein as the general prohibition on deepfakes being mulled by the European Union, given the FCC’s scope of authority. Burger, who was the FCC’s chief technology officer from 2017 to 2019, says that the agency’s vote will ultimately have an impact only if it starts enforcing the ban on robocalls more generally. Most types of robocalls have been prohibited since the agency instituted the Telephone Consumer Protection Act in 1991. (There are some exceptions, such as prerecorded messages from your dentist’s office, for example, reminding you of an upcoming appointment.) “Enforcement doesn’t seem to be happening,” says Burger. “The politicians like to say, ‘We’re going after the bad guys,’ and they don’t—not with the vigor we’d like to see.” Robocall Enforcement Tools May Not Be Enough Against AI The key method to identify the source of a robocall—and therefore prevent bad actors from continuing to make them—is to trace the call back through the complex network of telecom infrastructure and identify the call’s originating point. Tracebacks used to be complicated affairs, as a call typically traverses infrastructure maintained by multiple network operators like AT&T and T-Mobile. However, in 2020, the FCC approved a mandate for network operators to begin implementing a protocol called STIR/SHAKEN that would, among other antirobocall measures, make one-step tracebacks possible. “One-step traceback has been borne out,” says Burger. Traceback, for example, identified the source of the fake Biden calls targeting New Hampshire voters as a Texas-based company called Life Corporation. The problem, Burger says, is that the FCC, the U.S. Federal Bureau of Investigation, and state agencies aren’t providing the resources to make it possible to go after the sheer number of illegal robocall operations. Historically, the FCC has gone after only the very largest perpetrators. “There is no stopping these calls,” says Hiya’s Nelson—at least not entirely. “Our job isn’t to stop them, it’s to make them unprofitable.” Hiya, like similar companies, aims to accomplish that goal by lowering the amount of successful fraud through protective services, including exposing where a call was created and by whom, to make it less likely that an individual will answer the call in the first place. However, Nelson worries that generative AI will make the barrier to entry so low that those preventative actions will be less effective. For example, today’s scams still almost always require transferring the victim to a live agent in a call center to close out the scam successfully. With AI-generated voices, scam operators can eventually cut out the call center entirely. “In aggregate, it’s mind-boggling how much is lost to fraud perpetuated through robocalls.” —Eric Burger, Virginia Tech Nelson is also concerned that as generative AI improves, it will be harder for people to even recognize that they weren’t speaking to an actual person in the first place. “That’s where we’re going to start to lose our footing,” says Nelson. “We may have an increase in call recipients not realizing it’s a scam at all.” Scammers positioning themselves as fake charities, for example, could successfully solicit “donations” without donors ever realizing what actually happened. “I don’t think we can appreciate just how fast the telephone experience is going to change because of this,” says Nelson. One other complicating issue for enforcement is that the majority of illegal robocalls in the United States originate from beyond the country’s borders. The Industry Traceback Group found that in 2021, for example, 65 percent of all such calls were international in origin. Burger points out that the FCC has taken steps to combat international robocalls. The agency made it possible for other carriers to refuse to pass along traffic from gateway providers—a term for network operators connecting domestic infrastructure to international infrastructure—that are originating scam calls. In December 2023, for example, the FCC ordered two companies, Solid Double and CallWin, to stop transmitting illegal robocalls or risk other carriers being required to refuse their traffic. “Enforcement doesn’t seem to be happening. . . . not with the vigor we’d like to see.” —Eric Burger, Virginia Tech The FCC’s recent action against generative AI in robocalls is the first of its kind, and it remains to be seen if regulatory bodies in other countries will follow. “I certainly think the FCC is setting a good example in swift and bold action in the scope of its regulatory authority,” says Weiner. However, he also notes that the FCC’s counterparts in other democracies will likely end up with more comprehensive results. It’s hard to say how the FCC’s actions will stack up versus other regulators, according to Burger. As often as the FCC is way ahead of the curve—such as in spectrum sharing—it’s just as often way behind, such as the use of mid-band 5G. Nelson says he expects to see revisions to the FCC’s decision within a couple of years, because it currently prevents companies from using generative AI for legitimate business practices. It also remains to be seen whether the FCC’s vote will have any real effect. Burger points out that, in the case of calls like the fake Biden one, it was already illegal to place those robocalls and impersonate the president, so making another aspect of the call illegal likely won’t be a game-changer. “By making it triply illegal, is that really going to deter people?” Burger says.

  • What It’s Like to Eat a Robot
    by Evan Ackerman on 13. Februara 2024. at 14:00

    Odorigui is a type of Japanese cuisine in which people consume live seafood while it’s still moving, making movement part of the experience. You may have some feelings about this (I definitely do), but from a research perspective, getting into what those feelings are and what they mean isn’t really practical. To do so in a controlled way would be both morally and technically complicated, which is why Japanese researchers have started developing robots that can be eaten as they move, wriggling around in your mouth as you chomp down on them. Welcome to HERI: Human-Edible Robot Interaction. The happy little robot that got its head ripped off by a hungry human (who, we have to say, was exceptionally polite about it) is made primarily of gelatin, along with sugar and apple juice for taste. After all the ingredients were mixed, it was poured into a mold and refrigerated for 12 hours to set, with the resulting texture ending up like a chewy gummy candy. The mold incorporated a couple of air chambers into the structure of the robot, which were hooked up to pneumatics that got the robot to wiggle back and forth. Sixteen students at Osaka University got the chance to eat one of these wiggly little robots. The process was to put your mouth around the robot, let the robot move around in there for 10 seconds for the full experience, and then bite it off, chew, and swallow. Japanese people were chosen partly because this research was done in Japan, but also because, according to the paper, “of the cultural influences on the use of onomatopoeic terms.” In Japanese, there are terms that are useful in communicating specific kinds of textures that can’t easily be quantified. The participants were asked a series of questions about their experience, including some heavy ones: Did you think what you just ate had animateness? Did you feel an emotion in what you just ate? Did you think what you just ate had intelligence? Did you feel guilty about what you just ate? Oof. Compared with a control group of students who ate the robot when it was not moving, the students who ate the moving robot were more likely to interpret it as having a “munya-munya” or “mumbly” texture, showing that movement can influence the eating experience. Analysis of question responses showed that the moving robot also caused people to perceive it as emotive and intelligent, and caused more feelings of guilt when it was consumed. The paper summarizes it pretty well: “In the stationary condition, participants perceived the robot as ‘food,’ whereas in the movement condition, they perceived it as a ‘creature.’” The good news here is that since these robots are more like living things than nonrobots, they could potentially stand in for live critters eaten in a research context, say the researchers: “The utilization of edible robots in this study enabled us to examine the effects of subtle movement variations in human eating behavior under controlled conditions, a task that would be challenging to accomplish with real organisms.” There’s still more work to do to make the robots more like specific living things, but that’s the plan going forward: Our proposed edible robot design does not specifically mimic any particular biological form. To address these limitations, we will focus on the field by designing edible robots that imitate forms relevant to ongoing discussions on food shortages and cultural delicacies. Specifically, in future studies, we will emulate creatures consumed in contexts such as insect-based diets, which are being considered as a solution to food scarcity issues, and traditional Japanese dishes like “Odorigui” or “Ikizukuri (live fish sashimi).” These imitations are expected to provide deep insights into the psychological and cognitive responses elicited when consuming moving robots, merging technology with necessities and culinary traditions. “Exploring the Eating Experience of a Pneumatically Driven Edible Robot: Perception, Taste, and Texture,” by Yoshihiro NakataI, Midori Ban, Ren Yamaki, Kazuya Horibe, Hideyuki Takahashi, and Hiroshi Ishiguro from the University of Electro-Communications and Osaka University, was published in PLOS One.

  • 100 Years Ago, IBM Was Born
    by James W. Cortada on 13. Februara 2024. at 13:00

    Happy birthday, IBM! You’re 100 years old! Or are you? It’s true that the businesses that formed IBM began in the late 1800s. But it’s also true that a birth occurred in February 1924, with the renaming of the Computing-Tabulating-Recording Co. as the International Business Machines Corp. And a hundred years after that event, it serves as an important reminder that the world of computing and IT that IBM played a pivotal role in building has a longer history than we are likely to think. “Data processing” was coined over a century ago, while “office appliance” was in use in the 1880s. From the 19th century, through the 20th, and into the 21st, IBM was there, making HP, Microsoft, and Apple appear more like children or grandchildren of the IT world; Facebook, Google, and Twitter/X more like great-grandchildren. So let’s take a moment to contemplate the origins of an iconic corporation. The Assembling of IBM’s Parts Back in the late 19th century, as the U.S. economy gave birth to important large enterprises—telecommunications, railroads, manufacturing—the need to coordinate the work of individuals and dispersed locations led to the mechanization of information. Hence the emergence of typewriters, adding machines, and cash registers. Time-recording devices tracked when workers arrived and left, while scales weighed everything from meat at a butcher shop to industrial machine parts. For the 1890 U.S. census, Herman Hollerith’s punch-card tabulators calculated the nation’s population. Workers punched in and out on a dial recorder, sold by C-T-R’s International Time Recording Co.IBM Corp. To provide these various products, countless little companies popped up, most of them lost to history. But at least three survived. One came into being in 1900 as the International Time Recording Co., in Endicott, N.Y. ITR soon became known as the company for time-recording products in the United States and Canada. It had been formed and shaped by Charles Flint, a dynamic character known for consolidating several companies into U.S. Rubber and several other companies into the American Chicle Co.—also known as the Chewing Gum Trust—and for his love of sailing and airplanes. In 1901, Flint acquired the Computing Scale Co., which made tabletop scales popular with grocers in the Midwest. Over time, the company added cheese slicers and office furniture. This showroom in Melbourne, Australia, displayed products from all three of IBM’s founding companies: scales, time recorders, and tabulating machines.IBM Corp. In 1911, the Washington, D.C.–based Tabulating Machine Co. came into Flint’s orbit. Created in the 1880s and widely successful almost from its birth, TMC—maker of Hollerith’s punch-card tabulating equipment—produced the kind of breakthrough technology that large enterprises and government agencies desperately needed, to support massive undertakings like the census as well as inventory control and logistics. Herman Hollerith’s punch-card tabulators were used in the 1890 U.S. Census.IBM Corp. That same year, Flint smashed the three pieces together to form an entity that he unimaginatively called the Computing-Tabulating-Recording Co., or C-T-R. The scales business was okay, the time-recording business was booming, and the tabulating business had enormous potential but had yet to demonstrate it could keep up with demand. The creation of C-T-R yielded a company with promise, but the three entities didn’t coordinate or leverage one another’s assets and talents. Charles Flint [left] acquired the three companies that became the Computing-Tabulating-Recording Co., or C-T-R, and he hired Thomas J. Watson Sr. to build the business. IBM Corp. Flint convinced his board of directors to hire a professional manager to see what could be done to grow the entire business. Enter Thomas Watson Sr. in 1914, a highly successful sales executive who had recently spent two decades working at the National Cash Register Co.—considered one of the best-run “cool” companies of the early 20th century. He was 42 when he arrived at C-T-R. Hollywood handsome, smart, mature, and confident in his skills as an executive working in the high-tech end of the economy, he quickly sized up the situation and went to work. Thomas J. Watson Sr., hired in 1914, propelled C-T-R into a high-tech data-processing enterprise.IBM Corp. Watson brought in technical and sales colleagues from NCR, figured out who in C-T-R to take seriously, dismissed dissidents and incompetents, and sought ways to integrate all three pieces of the company. He concluded that the greatest potential for growth lay with Hollerith’s tabulators, so he focused on growing that market. Meanwhile, ITR had a popular product and, almost as important, a presence in Europe. The scales business, though, was a ho-hum opportunity as far as Watson was concerned, so he paid far less attention to it. Watson integrated sales—his strong suit—across all three businesses, and trained the team to become a highly skilled, professional staff. His newly hired engineers, meanwhile, improved manufacturing operations. The start of World War I blocked sales in Europe, but not in the United States. When the United States entered the war in 1917, government and private sector demand for C-T-R’s products grew rapidly. The end of the war opened up Europe’s huge market, and smaller ones in South America and parts of Asia. Birth of a Corporate Culture Slowly and steadily, Watson was creating a new corporate culture of ethics, paired with competent sales, solid technology, and a growing international perspective. The previously disjointed three-legged operation increasingly embraced his notion of “speed, accuracy, and flexibility,” in which the customer always came first. Despite a short recession at the start of the 1920s, C-T-R was emerging as a serious and well-run high-tech data-processing enterprise. Under Watson’s leadership, C-T-R’s revenue, staff, and product lines continued to grow. IBM Corp. In 1914, the company had generated US $4 million in revenues (about $120 million today) with 1,346 employees; in 1920, revenues were $14 million, with 2,731 employees. In 1922, a recession year, C-T-R brought in only $9 million, but its staff had climbed to 3,043—solid evidence that Watson considered the recession a mere bump in the road. And for the next six decades, the company continued to grow. Not until the late 1980s did the company again face declining revenues (then measured in the billions of dollars) and a shrinking labor force (then in excess of 400,000). In 1923, Watson, his executives, and employees collectively looked toward a future without immediate threats of war, where large organizations had embraced the concept of data processing powered by mechanical devices. Watson oversaw a rapidly expanding company that was grabbing market share away from competitors. He concluded that C-T-R’s future lay in pursuing a worldwide strategy, one for which he now had enough factories, sales offices, and trained employees. To be sure—and this is an important lesson for today’s startups—it had taken him a decade to reach the point where he could sit comfortably in his office in New York and imagine the next phase of C-T-R. As a preamble to that future, he decided that the company’s image and reputation required some burnishing. He considered what the public knew about the firm, what the company stood for, what its brand would be, and how its reputation should be shaped. It was time, Watson decided, for a name upgrade. IBM Gets Its Name On 15 February 1924, The Wall Street Journal published a short article on page 3, announcing that the “International Business Machines Corp. has been incorporated under the laws of New York to take over business and assets of Computing-Tabulating-Recording Co.” That’s how the world learned about the existence of IBM (that is, unless they worked in Canada, where employees had known their employer as IBM since 1917). In a 13 February 1924 letter to employees, Thomas Watson unveiled IBM’s new name.IBM Corp. When Watson had been remaking C-T-R, he already thought the company’s name was awkward and uninspiring, but too many other issues required his urgent attention. In early 1924, though, he decided it was time. On 13 February 1924, Watson published a letter addressed to all employees to announce the change, explaining: “Our new name is particularly adaptable and suitable to our business, in view of the fact of our increasing growth, the consistent development of additions to our line, and our products covering such a wide range in the field of business machinery.” He concluded, “We are confident that this change in name will be beneficial to the business, and that the members of our field organization will find it of direct value in introducing our company and the products which we manufacture and distribute.”The name change was also significant to C-T-R’s customers and vendors, and IBM salesmen rushed to explain how wonderful it would be. Then there was this little story in The Wall Street Journal three months after IBM’s incorporation. It may not have been true, but it suggests that everything comes down to execution. The article began by stating that some people were confused by the name, which it called “unwieldy,” and maybe this episode really did happen:The other day an uptown merchant called up a friend in Wall Street and inquired if he had ever heard of a new concern called the International Business Machines Corp.“We have a big order from them,” he said, “and I am trying to check up on their credit rating. My partner has just gone downtown to demand a balance sheet from them.” “Well,” said the broker, “in their last balance sheet they showed $800,000 cash and about $8,000,000 current assets. Their position seems pretty good. How big was the order?”“About $100,” said the merchant. “Wait a minute, I want to head off my partner.” Implicit in that story was the real business problem of retaining the positive reputation of C-T-R while leveraging its new name to build business momentum. Watson and his colleagues spent the rest of the 1920s and 1930s creating a brand image that reflected their positive view and plans for the future but that also translated into transactions, profits, growth, and prestige. They battled ignorance of what their products could do, invented new products, hired people, expanded operations, overcame the worldwide tragedy of the Great Depression, and endured an antitrust challenge in the 1930s, the first of several. The name change ultimately signaled a larger transformation underway. Whether the firm should have had a different name than IBM was less important than that Thomas Watson felt it was time to declare a grander purpose for the company. The tone of his comments, the nature of the company’s communications, and the way its staff interacted with the media and with customers evolved almost as a step change after the adoption of the new name. Watson was declaring that IBM wanted to become a major player in its industry and a leading international corporation. And so it did. This article is adapted from excerpts of the author’s award-winning book, IBM: The Rise and Fall and Reinvention of a Global Icon (MIT Press, 2019).

  • Everything You Wanted to Know About 1X’s Latest Video
    by Evan Ackerman on 12. Februara 2024. at 21:07

    Just last month, Oslo-based 1X (formerly Halodi Robotics) announced a massive US $100 million Series B, and clearly it has been putting the work in. A new video posted last week shows a [insert collective noun for humanoid robots here] of EVE android-ish mobile manipulators doing a wide variety of tasks leveraging end-to-end neural networks (pixels to actions). And best of all, the video seems to be more or less an honest one: a single take, at (appropriately) 1X speed, and full autonomy. But we still had questions! And 1X has answers. If, like me, you had some very important questions after watching this video, including whether that plant is actually dead and the fate of the weighted companion cube, you’ll want to read this Q&A with Eric Jang, vice president of artificial intelligence at 1X. How many takes did it take to get this take? Eric Jang: About 10 takes that lasted more than a minute; this was our first time doing a video like this, so it was more about learning how to coordinate the film crew and set up the shoot to look impressive. Did you train your robots specifically on floppy things and transparent things? Jang: Nope! We train our neural network to pick up all kinds of objects—both rigid and deformable and transparent things. Because we train manipulation end-to-end from pixels, picking up deformables and transparent objects is much easier than a classical grasping pipeline, where you have to figure out the exact geometry of what you are trying to grasp. What keeps your robots from doing these tasks faster? Jang: Our robots learn from demonstrations, so they go at exactly the same speed the human teleoperators demonstrate the task at. If we gathered demonstrations where we move faster, so would the robots. How many weighted companion cubes were harmed in the making of this video? Jang: At 1X, weighted companion cubes do not have rights. That’s a very cool method for charging, but it seems a lot more complicated than some kind of drive-on interface directly with the base. Why use manipulation instead? Jang: You’re right that this isn’t the simplest way to charge the robot, but if we are going to succeed at our mission to build generally capable and reliable robots that can manipulate all kinds of objects, our neural nets have to be able to do this task at the very least. Plus, it reduces costs quite a bit and simplifies the system! What animal is that blue plush supposed to be? Jang: It’s an obese shark, I think. How many different robots are in this video? Jang: 17? And more that are stationary. How do you tell the robots apart? Jang: They have little numbers printed on the base. Is that plant dead? Jang: Yes, we put it there because no CGI/3D-rendered video would ever go through the trouble of adding a dead plant. What sort of existential crisis is the robot at the window having? Jang: It was supposed to be opening and closing the window repeatedly (good for testing statistical significance). If one of the robots was actually a human in a helmet and a suit holding grippers and standing on a mobile base, would I be able to tell? Jang: I was super flattered by this comment on the Youtube video: But if you look at the area where the upper arm tapers at the shoulder, it’s too thin for a human to fit inside while still having such broad shoulders: Why are your robots so happy all the time? Are you planning to do more complex HRI (human-robot interaction) stuff with their faces? Jang: Yes, more complex HRI stuff is in the pipeline! Are your robots able to autonomously collaborate with each other? Jang: Stay tuned! Is the skew tetromino the most difficult tetromino for robotic manipulation? Jang: Good catch! Yes, the green one is the worst of them all because there are many valid ways to pinch it with the gripper and lift it up. In robotic learning, if there are multiple ways to pick something up, it can actually confuse the machine learning model. Kind of like asking a car to turn left and right at the same time to avoid a tree. Everyone else’s robots are making coffee. Can your robots make coffee? Jang: Yep! We were planning to throw in some coffee making on this video as an Easter egg, but the coffee machine broke right before the film shoot and it turns out it’s impossible to get a Keurig K-Slim in Norway via next-day shipping. 1X is currently hiring both AI researchers (specialties include imitation learning, reinforcement learning, and large-scale training) and android operators (!) which actually sounds like a super fun and interesting job. More here.

  • Yamaha Joins Global Move to Battery Swaps for E-bikes
    by Willie Jones on 12. Februara 2024. at 19:02

    E-bikes are today a growing component of the global transition away from fossil fuels—possibly more than the car-and-truck-focused sustainability crowd appreciates. E-bikes’ rapid growth in recent years stems in part from their simple solution to the range issue that big, hulking cars typically don’t offer. E-bikes are powered by relatively small batteries that can be wrangled by the average person who can handle a carton of milk. Because of that form factor, battery swapping for e-bikes is a quick and simple method for staying powered up, whereas for a traditional four-wheeled EV, swaps are a more involved process for doing the equivalent of filling a gas tank. The Japanese conglomerate Yamaha Motor Co., best known for its motorcycles and motorboats, is now looking to expand into this growing marketplace, too. At the end of last year, Yamaha announced it’d formed a subsidiary called Enyring. The new entity, which is slated to kick off operations in Germany and the Netherlands in early 2025, says they’ll partner with manufacturers for maximum compatibility among models and types of e-bike. Also crucial to Yamaha’s plan is that the batteries will not be owned, but rented, by the e-bike owners. The rental contract will entitle a battery user to unlimited swaps as long as their account with Yamaha remains in good standing. And Yamaha says it already has plans in place for breaking down the cells so they can be recycled. “Today, noisy, polluting, two-stroke gasoline engines are still ubiquitous across Asia,” says Sam Abuelsamid, Principal Research Analyst for Mobility Ecosystems at Guidehouse Insights “Converting those to electric power will go a long way toward helping countries reach their greenhouse gas emission-reduction targets. Battery swapping also addresses the infrastructure issues that come along with the growing presence of two- and three-wheeled vehicles.” Abuelsamid notes that as this energy-replenishing modality becomes the norm, companies like Yamaha, China’s Nio, and Taiwan’s Gogoro (the latter two with hundreds of battery-swapping stations and self-service swapping kiosks already in operation) will raise the bar on quality control for these small EV batteries for their own economic self-interest. “It makes sense to build better batteries with good battery management systems and software for enhanced thermal control,” says Abuelsamid. “Higher-quality batteries, handled in an ecosystem where corporate facilities manage the recharging process better than someone would at home, increases the chances that a battery will handle maybe 1,000 charge cycles before it no longer holds enough charge and needs to be recycled.” And longer life means more profitability for a company offering batteries-as-a-service. The vast e-bike marketplace In 2022, two- and three-wheeled vehicles accounted for 49 percent of global EV sales. And according to a recent report from Rethink Energy, there are an estimated 292 million e-bikes and e-trikes currently in service. By comparison, the International Energy Agency says there are now about 26 million electric cars on the world’s roads. According to analysis firm Markets and Markets, battery swapping was a US $1.7 billion industry in 2022. Industry revenues are expected to reach $11.8 billion by 2027. Yamaha is by no means a battery-swapping pioneer, but its entry into that space signals a powerful retort to skeptics who still believe that battery swapping will never be as commonplace as pulling up to a charging facility and plugging in. (However, attempts to reach Yamaha spokespeople for their comment on the Enyring spinoff proved unsuccessful.) The enticing growth in the compact EV market has been spurred by the near ubiquitous use of e-bikes and electric scooters by couriers for delivery services that drop take-out food and groceries at online shoppers’ doors. Plus, for daily commutes, e-bikes are proving increasingly attractive as eco-friendly alternatives to fossil-fueled vehicles. Battery-swapping ventures like Yamaha’s will also put salve on pain points related to the rapidly growing presence of compact EVs. Among these are: a shortage of places where batteries can be charged; the length of time (now measured in hours) charging usually takes; human error when charging that could cause destructive, and perhaps deadly, battery fires; and uncertainty about what to do with a battery when it is spent and is no longer useful as an energy storage unit for propulsion. Just as important is what battery swapping will do to solve another of plug-in electric vehicles’ bugbears. IEEE Spectrum reported on the issues surrounding battery charging and the lingering belief that EV batteries are fire hazards. Though empirical evidence shows that EVs, by and large, are much less likely than vehicles with internal combustion engines to catch fire, that hasn’t stopped some municipal governments from placing strict limits on the places where EV batteries can be plugged in. But with battery swapping growing in popularity, who would ultimately need to?

  • Smartphone Screens Are About to Become Speakers
    by Vineet Ganju on 11. Februara 2024. at 16:00

    Today’s mobile-device speakers and haptic generators have several problems. The first is with the components themselves. The speakers in your smartphone and the system that gives your finger feedback when you touch a virtual button may be relatively small, but they are still big enough to limit how thin our mobile devices can get. These little components are also delicate, as you may have learned if you have dropped your phone. And they require openings in the enclosure that can admit moisture or debris. Then there is a problem with the way we perceive the sound they emit. Speakers usually sit on the sides or back of our devices. The sound is supposed to be coming from the image that our eyes see on the screen, but our ears know it’s coming from somewhere else. This perceptual dissonance limits our ability to immerse ourselves in the experience. We could solve all these problems at once if we could use the display panel itself as the source of sound and haptic feedback. The technology to do this is now available—it’s a thin piezoelectric transducer. And it’s going to be moving into mobile phones, laptops, and wearables by the end of this year. Speakers today Traditional speakers function by running a current through a coil, creating a magnetic field that moves a magnet attached to the speaker cone, which in turn displaces air to create a sound wave. The speaker can be no smaller than is allowed by the size of the coil and the cone, which must be wide enough and long enough to displace a reasonable volume of air. Its mechanical durability is limited by the precision with which its small moving parts must be assembled. Haptic generators in handsets typically are linear resonant actuators, which are electrically and mechanically like speakers but are optimized to create low-frequency vibrations in a solid rather than sound waves in the air. Replacing traditional speakers with piezoelectric transducers will allow devices to be much thinner.James Provost It turns out that both sound and haptic generation can be wrung from the display of a mobile device. The display is a uniform, flat, semiflexible surface–If you tap on it with a fingernail, you can hear it vibrate. The technology to turn flat, semiflexible surfaces into loudspeakers dates to the early years of high fidelity. In the 1950s, you could buy just the part of the speaker that converted electricity into motion—the coil and piston—and attach the piston to a wall or the panel of a cabinet. The wall or panel then vibrated—like a speaker cone—creating the air-pressure waves we perceive as sound. That idea remained a novelty, mainly because it was a do-it-yourself project with unpredictable results. Finding the right surface, mounting the driver in the right place, and powering it properly turned out to be about as easy as building a really good violin. More recently, some flat-screen TV developers have turned the screens of large-format TVs into speakers using a similar principle. They use powerful transducers to vibrate the whole display panel at audio frequencies. This delivers the goods: thin displays, no speakers. According to reviewers, the fidelity is great at midrange and high frequencies (if you want low frequencies, you’ll want a supplementary subwoofer). And the sound really does seem to be coming from the images on the screen. But these systems are expensive and require high-voltage amplifiers that consume considerable power, so they need to be shrunk before they can fit into a mobile device. Enter Piezoelectrics To do the job in smaller devices you need piezoelectric transducers. These are made up of tiny single crystals, such as quartz or some ceramics, with two electrodes attached. When you apply a voltage across the electrodes, the material physically bends. That bending is called the converse piezoelectric effect. In 1880, physicists Pierre and Jacques Curie observed that when certain crystals were subjected to mechanical force, a voltage would appear between the faces of the crystal; they called this the piezoelectric effect ( piezo is Greek for “to press”). This effect is due to the interaction between natural electric dipoles in the crystals and mechanical stresses in the crystals’ molecules. Roughly speaking, bending the crystal causes the dipoles to align, creating a bulk electric field. A year later the Curies demonstrated that the converse was true: If a voltage was applied between the faces of these crystals, the dipoles would bend the crystal as they try to align with the field. Work on piezoelectric materials subsequently expanded to include ceramics. When piezoelectric transducers vibrate the display itself to create sound waves, the sound seems to come directly from the image on the screen, a much more realistic effect.James Provost So by applying an alternating voltage, you can make the transducer vibrate with rather considerable force. These vibrations can be slow—the kind needed for haptic feedback—or very fast, to the highest audio frequencies and beyond. While creating the effect with ceramic material requires relatively high voltages—in the range of 40 volts or more—it requires very little current and, hence, little power—far less than the power used by mobile device speakers today. Using piezoelectric transducers to generate sound isn’t a new idea. In fact, they’ve been used for decades to produce the unbearable shriek of a smoke alarm. Of course, using piezoelectrics to produce a full range of high-quality audio is a long way from making a smoke alarm screech. There are a number of challenges to making it work in a handheld device. There is the need for an amplifier able to step up the voltage that batteries can provide, with high efficiency to conserve energy, and with minimum noise, to preserve audio quality. And the audio signal needs some preprocessing before you send it to the transducer, to correct for the characteristics of the transducer and the display panel that the transducer will shake. Driving the Transducers But we at Synaptics think that we’ve met those challenges. We’ve developed a chip that incorporates a low-noise, high-voltage boost amplifier and a digital signal processor that sits on a device’s main board and drives a ceramic piezoelectric transducer attached to the back of the display. This does take up space, but then again, it eliminates the moving coil speaker. The exact placement and number of these amplifier-transducer sets depends on the mechanical design of the device and the desired audio modes—one set is enough to replace the handheld receiver functionality in a smartphone, but a second set will be needed to also replace the speakerphone function. That configuration will be the more typical one. We expect to see our chip in smartphones, wearables, and laptop computers by the end of 2024. Traditional speaker modules sit among the other components of a computer or mobile device, taking up valuable real estate. When, instead, thin piezoelectric transducers vibrate the display to generate sound waves and haptic effects, that space is freed for other uses.James Provost The immediate benefits of moving away from traditional magnetic coil speakers are many. The piezoelectric transducer material requires only one millimeter of enclosure thickness, compared to several millimeters for dynamic speakers or linear resonant actuators, enabling a new generation of thinner handheld devices. Yet such transducers can produce the sound quality and the loudness of the best miniature dynamic speakers. They are being made by a few companies, including TDK; others have yet to be announced publicly. Since the transducers are bonded to the display panel inside the enclosure, they require no openings that could allow moisture or dirt inside. Most importantly, the transducers produce sound waves from the front of the display. This means the sound is directed at the user, not away from the user or off to the side. As the success of sound-generating displays in large-screen TVs has demonstrated, this does in fact provide a more immersive experience. When you see the T-Rex throw back its head and roar, your brain locates the source of the sound at the image of the beast, not somewhere off to the side. Many of today’s handheld devices do attempt to correct for this problem by what is called psychoacoustic processing, using an algorithm to change the amplitude and phase of the sound waves coming from the speakers, mimicking some of the very complex things that your ears do as sound waves enter them from different directions. The success of these algorithms depends on the surroundings, and extra processor cycles take noticeable energy from the device’s battery. Having the sound physically originate on the display is a far simpler solution. As for haptic feedback, using the same piezoelectric transducers that generate sound to generate haptic feedback eliminates the need for separate driver electronics and a motor to shake the display. A Two-Way Street Also, recall that the piezoelectric effect works both ways. So when you touch the display panel, not only can a conventional touch sensor determine where you are touching, but the piezoelectric transducer can tell how hard you are pressing. This opens a whole new realm of feedback for interactive user interfaces and immersive touchscreen games. It also raises an interesting possibility that, frankly, has not been thoroughly explored yet. When a touch, or even reasonably loud ambient noise, flexes the display panel, it will also flex the transducer, generating a voltage. This electrical signal could be harvested to charge the device’s battery, providing a level of energy harvesting that might prolong the time between charges. So if your next phone is thinner, has a longer battery life, and more immersive sound, thank piezoelectrics for eliminating the traditional speakers and motor-based haptic generators from your device, and moving the sound up front, where it belongs. This article appears in the March 2024 print issue as “The Screen Is the Speaker.”

  • Disney’s Newest Robot Demonstrates Collaborative Cuteness
    by Morgan Pope on 11. Februara 2024. at 14:00

    This is a guest post. The views expressed here are solely those of the author and do not represent positions of IEEE Spectrum or the IEEE. If Disney’s history of storytelling has taught us anything, it’s to never underestimate the power of a great sidekick. Even though sidekicks aren’t the stars of the show, they provide life and energy and move the story along in important ways. It’s hard to imagine Aladdin without the Genie, or Peter Pan without Tinker Bell. In robotics, however, solo acts proliferate. Even when multiple robots are used, they usually act in parallel. One key reason for this is that most robots are designed in ways that make direct collaboration with other robots difficult. Stiff, strong robots are more repeatable and easier to control, but those designs have very little forgiveness for the imperfections and mismatches that are inherent in coming into contact with another robot. Having robots work together–especially if they have complementary skill sets–can open up some exciting opportunities, especially in the entertainment robotics space. At Walt Disney Imagineering, our research and development teams have been working on this idea of collaboration between robots, and we were able to show off the result of one such collaboration in Shanghai this week, when a little furry character interrupted the opening moments for the first-ever Zootopia land. Our newest robotic character, Duke Weaselton, rolled onstage at the Shanghai Disney Resort for the first time last December, pushing a purple kiosk and blasting pop music. As seen in the video below, the audience got a kick out of watching him hop up on top of the kiosk and try to negotiate with the Chairman of Disney Experiences, Josh D’Amaro, for a new job. And of course, some new perks. After a few moments of wheeling and dealing, Duke gets gently escorted offstage by team members Richard Landon and Louis Lambie. What might not be obvious at first is that the moment you just saw was enabled not by one robot, but by two. Duke Weaselton is the star of the show, but his dynamic motion wouldn’t be possible without the kiosk, which is its own independent, actuated robot. While these two robots are very different, by working together as one system, they’re able to do things that neither could do alone. The character and the kiosk bring two very different kinds of motion together, and create something more than the sum of their parts in the process. The character is an expressive, bipedal robot with an exaggerated, animated motion style. It looks fantastic, but it’s not optimized for robust, reliable locomotion. The kiosk, meanwhile, is a simple wheeled system that behaves in a highly predictable way. While that’s great for reliability, it means that by itself it’s not likely to surprise you. But when we combine these two robots, we get the best of both worlds. The character robot can bring a zany, unrestrained energy and excitement as it bounces up, over, and alongside the kiosk, while the kiosk itself ensures that both robots reliably get to wherever they are going. Harout Jarchafjian, Sophie Bowe, Tony Dohi, Bill West, Marcela de los Rios, Bob Michel, and Morgan Pope.Morgan Pope The collaboration between the two robots is enabled by designing them to be robust and flexible, and with motions that can tolerate a large amount of uncertainty while still delivering a compelling show. This is a direct result from lessons learned from an earlier robot, one that tumbled across the stage at SXSW earlier this year. Our basic insight is that a small, lightweight robot can be surprisingly tough, and that this toughness enables new levels of creative freedom in the design and execution of a show. This level of robustness also makes collaboration between robots easier. Because the character robot is tough and because there is some flexibility built into its motors and joints, small errors in placement and pose don’t create big problems like they might for a more conventional robot. The character can lean on the motorized kiosk to create the illusion that it is pushing it across the stage. The kiosk then uses a winch to hoist the character onto a platform, where electromagnets help stabilize its feet. Essentially, the kiosk is compensating for the fact that Duke himself can’t climb, and might be a little wobbly without having his feet secured. The overall result is a free-ranging bipedal robot that moves in a way that feels natural and engaging, but that doesn’t require especially complicated controls or highly precise mechanical design. Here’s a behind-the-scenes look at our development of these systems: Disney Imagineering To program Duke’s motions, our team uses an animation pipeline that was originally developed for the SXSW demo, where a designer can pose the robot by hand to create new motions. We have since developed an interface which can also take motions from conventional animation software tools. Motions can then be adjusted to adapt to the real physical constraints of the robots, and that information can be sent back to the animation tool. As animations are developed, it’s critical to retain a tight synchronization between the kiosk and the character. The system is designed so that the motion of both robots is always coordinated, while simultaneously supporting the ability to flexibly animate individual robots–or individual parts of the robot, like the mouth and eyes. Over the past nine months, we explored a few different kinds of collaborative locomotion approaches. The GIFs below show some early attempts at riding a tricycle, skateboarding, and pushing a crate. In each case, the idea is for a robotic character to eventually collaborate with another robotic system that helps bring that character’s motions to life in a stable and repeatable way. Disney hopes that their Judy Hopps robot will soon be able to use the help of a robotic tricycle, crate, or skateboard to enable new forms of locomotion.Morgan Pope This demo with Duke Weaselton and his kiosk is just the beginning, says Principal R&D Imagineer Tony Dohi, who leads the project for us. “Ultimately, what we showed today is an important step towards a bigger vision. This project is laying the groundwork for robots that can interact with each other in surprising and emotionally satisfying ways. Today it’s a character and a kiosk, but moving forward we want to have multiple characters that can engage with each other and with our guests.” Walt Disney Imagineering R&D is exploring a multi-pronged development strategy for our robotic characters. Engaging character demonstrations like Duke Weasleton focus on quickly prototyping complete experiences using immediately accessible techniques. In parallel, our research group is developing new technologies and capabilities that become the building blocks for both elevating existing experiences, and designing and delivering completely new shows. The robotics team led by Moritz Bächer shared one such building block–embodied in a highly expressive and stylized robotic walking character–at IROS in October. The capabilities demonstrated there can eventually be used to help robots like Duke Weaselton perform more flexibly, more reliably, and more spectacularly. “Authentic character demonstrations are useful because they help inform what tools are the most valuable for us to develop,” explains Bächer. “In the end our goal is to create tools that enable our teams to produce and deliver these shows rapidly and efficiently.” This ties back to the fundamental technical idea behind the Duke Weaselton show moment–collaboration is key!

  • Andrew Ng: Unbiggen AI
    by Eliza Strickland on 9. Februara 2022. at 15:31

    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.”

  • How AI Will Change Chip Design
    by Rina Diane Caballar on 8. Februara 2022. at 14:00

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

  • Atomically Thin Materials Significantly Shrink Qubits
    by Dexter Johnson on 7. Februara 2022. at 16:12

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

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