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Odors are all around us, and often disperse fast—in hazardous situations like wildfires, for example, wind conditions quickly carry any smoke (and the smell of smoke) away from its origin. Sending people to check out disaster zones is always a risk, so what if a robot equipped with an electronic nose, or e-nose, could track down a hazard by “smelling” for it? This concept motivated a recent study in Science Advances, in which researchers built an e-nose that can not only detect odors at the same speed as a mouse’s olfactory system, but also distinguish between odors by the specific patterns they produce over time when interacting with the e-nose’s sensor. “When odorants are carried away by turbulent airflow, they get chopped into smaller packets,” says Michael Schmuker, a professor at the University of Hertfordshire in the United Kingdom. Schmuker says that these odor packets can rapidly change, which means that an effective odor-sensing system needs to be fast to detect them. And the way in which packets change—and how frequently that happens—can give clues about how far away the odor’s source is. How the E-nose Works The e-nose uses metal oxide gas sensors with a sensing surface heated and cooled to between 150 °C and 400 °C at up to 20 times per second. Redox reactions take place on the sensing surface when it comes into direct contact with an odorant. The new electronic nose is smaller than a credit card, and includes several sensors such as the one on the right.Nik Dennler et al. The e-nose is smaller than a credit card, with a power consumption of only 1.2 to 1.5 watts (including the microprocessor and USB readout). The researchers built the system with off-the-shelf components, with custom-designed digital interfaces to allow odor dynamics to be probed more precisely when they encounter the heated electrodes making up the sensing surface. “Odorants flow around us in the air and some of them react with that hot surface,” says Schmuker. “How they react with it depends on their own chemical composition—they might oxidize or reduce the surface—but a chemical reaction takes place.” As a result, the resistance of the metal oxide electrodes changes, which can be measured. The amount and dynamics of this change are different for different combinations of odorants and sensor materials. The e-nose uses two pairs of four distinct sensors to build a pattern of resistance response curves. Resistance response curves illustrate how a sensor’s resistance changes over time in response to a stimulus, such as an odor. These curves capture the sensor’s conversion of a physical interaction—like an odor molecule binding to its surface—into an electrical signal. Because each odor generates a distinct response pattern, analyzing how the electrical signal evolves over time enables the identification of specific odors. “We discovered that rapidly switching the temperature back and forth between 150°C and 400°C about 20 times per second produced distinctive data patterns that made it easier to identify specific odors,” says Nik Dennler, a dual Ph.D. student at the University of Hertfordshire and Western Sydney University. By building up a picture of how the odorant reacts at these different temperatures, the response curves can be plugged into a machine learning algorithm to spot the patterns that relate to a specific odor. While the e-nose does not “sniff” like a regular nose, the periodic heating cycle for detecting odors is reminiscent of the periodic sniffing that mammals perform. Using the E-nose in Disaster Management A discovery in 2021 by researchers at the Francis Crick Institute in London and the University College London showed that mice can discriminate odor fluctuations up to 40 times per second—contrary to a long-held belief that mammals require one or several sniffs to obtain any meaningful odor information. In the new work—conducted in part by the same researchers behind the 2021 discovery—the researchers found that the e-nose can detect odors as quickly as a mouse can, with the ability to resolve and decode odor fluctuations up to 60 times per second. The e-nose can currently differentiate between 5 different odors when presented individually or in a mixture of two odors. The e-nose could detect additional odors if it is trained to do so. “We found it could accurately identify odors in just 50 milliseconds and decode patterns between odors switching up to 40 times per second,” says Dennler. For comparison, recent research in humans suggests the threshold for distinguishing between two odors binding to the same olfactory receptors is about 60 ms. The small scale and moderate power requirements could enable the e-nose to be deployed in robots used to pinpoint an odor’s source. “Other fast technologies exist, but are usually very bulky and you would need a large battery to power them,” says Schmuker. “We can put our device on a small robot and evaluate its use in applications that you use a sniffer dog for today.” “As soon as you’re driving, walking, or flying around, you need to be really fast at sensing,” says Dennler. “With our e-nose, we can capture odor information at high speeds. Primary applications could involve odor-guided navigation tasks, or, more generally, collecting odor information while on the move.” The researchers are looking at using these small e-nose robots in disaster management applications, including locating wildfires and gas leaks, and finding people buried in rubble after an earthquake.

This is a sponsored article brought to you by Amazon. The journey of a package from the moment a customer clicks “buy” to the moment it arrives at their doorstep is one of the most complex and finely tuned processes in the world of e-commerce. At Amazon, this journey is constantly being optimized, not only for speed and efficiency, but also for sustainability. This optimization is driven by the integration of cutting-edge technologies like artificial intelligence (AI), machine learning (ML), and robotics, which allow Amazon to streamline its operations while working towards minimizing unnecessary packaging. The use of AI and ML in logistics and packaging is playing an increasingly vital role in transforming the way packages are handled across Amazon’s vast global network. In two interviews — one with Clay Flannigan, who leads manipulation robotics programs at Amazon, and another with Callahan Jacobs, an owner of the Sustainable Packaging team’s technology products — we gain insights into how Amazon is using AI, ML, and automation to push the boundaries of what’s possible in the world of logistics, while also making significant strides in sustainability-focused packaging. The Power of AI and Machine Learning in Robotics One of the cornerstones of Amazon’s transformation is the integration of AI and ML into its robotics systems. Flannigan’s role within the Fulfillment Technologies Robotics (FTR) team, Amazon Robotics, centers around manipulation robotics — machines that handle the individual items customers order on amazon.com. These robots, in collaboration with human employees, are responsible for picking, sorting, and packing millions of products every day. It’s an enormously complex task, given the vast diversity of items in Amazon’s inventory. “Amazon is uniquely positioned to lead in AI and ML because of our vast data,” Flannigan explained. “We use this data to train models that enable our robots to perform highly complex tasks, like picking and packing an incredibly diverse range of products. These systems help Amazon solve logistics challenges that simply wouldn’t be possible at this scale without the deep integration of AI.” Learn more about becoming part of Amazon’s Team → At the core of Amazon’s robotic systems is machine learning, which allows the machines to “learn” from their environment and improve their performance over time. For example, AI-powered computer vision systems enable robots to “see” the products they are handling, allowing them to distinguish between fragile items and sturdier ones, or between products of different sizes and shapes. These systems are trained using expansive amounts of data, which Amazon can leverage due to its immense scale. One particularly important application of machine learning is in the manipulation of unstructured environments. Traditional robotics have been used in industries where the environment is highly structured and predictable. But Amazon’s warehouses are anything but predictable. “In other industries, you’re often building the same product over and over. At Amazon, we have to handle an almost infinite variety of products — everything from books to coffee makers to fragile collectibles,” Flannigan said. “There are so many opportunities to push the boundaries of what AI and robotics can do, and Amazon is at the forefront of that change.” —Clay Flannigan, Amazon In these unstructured environments, robots need to be adaptable. They rely on AI and ML models to understand their surroundings and make decisions in real-time. For example, if a robot is tasked with picking a coffee mug from a bin full of diverse items, it needs to use computer vision to identify the mug, understand how to grip it without breaking it, and move it to the correct packaging station. These tasks may seem simple, but they require advanced ML algorithms and extensive data to perform them reliably at Amazon’s scale. Sustainability and Packaging: A Technology-Driven Approach While robotics and automation are central to improving efficiency in Amazon’s fulfillment centers, the company’s commitment to sustainability is equally important. Callahan Jacobs, product manager on FTR’s Mechatronics & Sustainable Packaging (MSP) team, is focused on preventing waste and aims to help reduce the negative impacts of packaging materials. The company has made significant strides in this area, leveraging technology to improve the entire packaging experience. Amazon “When I started, our packaging processes were predominantly manual,” Jacobs explained. “But we’ve moved toward a much more automated system, and now we use machines that custom-fit packaging to items. This has drastically reduced the amount of excess material we use, especially in terms of minimizing the cube size for each package, and frees up our teams to focus on harder problems like how to make packaging out of more conscientious materials without sacrificing quality.” Since 2015, Amazon has decreased its average per-shipment packaging weight by 43 percent, which represents more than 3 million metric tons of packaging materials avoided. This “size-to-fit” packaging technology is one of Amazon’s most significant innovations in packaging. By using automated machines that cut and fold boxes to fit the dimensions of the items being shipped, Amazon is able to reduce the amount of air and unused space inside packages. This not only reduces the amount of material used but also optimizes the use of space in trucks, planes, and delivery vehicles. “By fitting packages as closely as possible to the items they contain, we’re helping to reduce both waste and shipping inefficiencies,” Jacobs explained. Advanced Packaging Technology: The Role of Machine Learning AI and ML play a critical role in Amazon’s efforts to optimize packaging. Amazon’s packaging technology doesn’t just aim to prevent waste but also ensures that items are properly protected during their journey through the fulfillment network. To achieve this balance, the company relies on advanced machine learning models that evaluate each item and determine the optimal packaging solution based on various factors, including the item’s fragility, size, and the route it needs to travel. “We’ve moved beyond simply asking whether an item can go in a bag or a box,” said Jacobs. “Now, our AI and ML models look at each item and say, ‘What are the attributes of this product? Is it fragile? Is it a liquid? Does it have its own packaging, or does it need extra protection?’ By gathering this information, we can make smarter decisions about packaging, helping to result in less waste or better protection for the items.” “By fitting packages as closely as possible to the items they contain, we’re helping to reduce both waste and shipping inefficiencies.” —Callahan Jacobs, Amazon This process begins as soon as a product enters Amazon’s inventory. Machine Learning models analyze each product’s data to determine key attributes. These models may use computer vision to assess the item’s packaging or natural language processing to analyze product descriptions and customer feedback. Once the product’s attributes have been determined, the system decides which type of packaging is most suitable, helping to prevent waste while ensuring the item’s safe arrival. “Machine learning allows us to make these decisions dynamically,” Jacobs added. “For example, an item like a t-shirt doesn’t need to be packed in a box—it can go in a paper bag. But a fragile glass item might need additional protection. By using AI and ML, we can make these decisions at scale, ensuring that we’re always prioritizing for the option that aims to benefits the customer and the planet.” Dynamic Decision-Making With Real-Time Data Amazon’s use of real-time data is a game-changer in its packaging operations. By continuously collecting and analyzing data from its fulfillment centers, Amazon can rapidly adjust its packaging strategies, optimizing for efficiency at scale. This dynamic approach allows Amazon to respond to changing conditions, such as new packaging materials, changes in shipping routes, or feedback from customers. “A huge part of what we do is continuously improving the process based on what we learn,” Jacobs explained. “For example, if we find that a certain type of packaging isn’t satisfactory, we can quickly adjust our criteria and implement changes across our delivery network. This real-time feedback loop is critical in making our system more resilient and keeping it aligned with our team’s sustainability goals.” This continuous learning process is key to Amazon’s success. The company’s AI and ML models are constantly being updated with new data, allowing them to become more accurate and effective over time. For example, if a new type of packaging material is introduced, the models can quickly assess its effectiveness and make adjustments as needed. Jacobs also emphasized the role of feedback in this process. “We’re always monitoring the performance of our packaging,” she said. “If we receive feedback from customers that an item arrived damaged or that there was too much packaging, we can use that information to improve model outputs, which ultimately helps us continually reduce waste.” Robotics in Action: The Role of Gripping Technology and Automation One of the key innovations in Amazon’s robotic systems is the development of advanced gripping technology. As Flannigan explained, the “secret sauce” of Amazon’s robotic systems is not just in the machines themselves but in the gripping tools they use. These tools are designed to handle the immense variety of products Amazon processes every day, from small, delicate items to large, bulky packages. Amazon “Our robots use a combination of sensors, AI, and custom-built grippers to handle different types of products,” Flannigan said. “For example, we’ve developed specialized grippers that can handle fragile items like glassware without damaging them. These grippers are powered by AI and machine learning, which allow them to plan their movements based on the item they’re picking up.” The robotic arms in Amazon’s fulfillment centers are equipped with a range of sensors that allow them to “see” and “feel” the items they’re handling. These sensors provide real-time data to the machine learning models, which then make decisions about how to handle the item. For example, if a robot is picking up a fragile item, it will use gentler strategy, whereas it might optimize for speed when handling a sturdier item. Flannigan also noted that the use of robotics has significantly improved the safety and efficiency of Amazon’s operations. By automating many of the repetitive and physically demanding tasks in fulfillment centers, Amazon has been able to reduce the risk of injuries among its employees while also increasing the speed and accuracy of its operations. It also provides the opportunity to focus on upskilling. “There’s always something new to learn,” Flannigan said, “there’s no shortage of training and advancement options.” Continuous Learning and Innovation: Amazon’s Culture of Growth Both Flannigan and Jacobs emphasized that Amazon’s success in implementing these technologies is not just due to the tools themselves but also the culture of innovation that drives the company. Amazon’s engineers and technologists are encouraged to constantly push the boundaries of what’s possible, experimenting with new solutions and improving existing systems. “Amazon is a place where engineers thrive because we’re always encouraged to innovate,” Flannigan said. “The problems we’re solving here are incredibly complex, and Amazon gives us the resources and freedom to tackle them in creative ways. That’s what makes Amazon such an exciting place to work.” Jacobs echoed this sentiment, adding that the company’s commitment to sustainability is one of the things that makes it an attractive place for engineers. “Every day, I learn something new, and I get to work on solutions that have a real impact at a global scale. That’s what keeps me excited about my work. That’s hard to find anywhere else.” The Future of AI, Robotics, and Innovation at Amazon Looking ahead, Amazon’s vision for the future is clear: to continue innovating in the fields of AI, ML, and robotics for maximum customer satisfaction. The company is investing heavily in new technologies that are helping to progress its sustainability initiatives while improving the efficiency of its operations. “We’re just getting started,” Flannigan said. “There are so many opportunities to push the boundaries of what AI and robotics can do, and Amazon is at the forefront of that change. The work we do here will have implications not just for e-commerce but for the broader world of automation and AI.” Jacobs is equally optimistic about the future of the Sustainable Packaging team. “We’re constantly working on new materials and new ways to reduce waste,” she said. “The next few years are going to be incredibly exciting as we continue to refine our packaging innovations, making them more scalable without sacrificing quality.” As Amazon continues to evolve, the integration of AI, ML, and robotics will be key to achieving its ambitious goals. By combining cutting-edge technology with a deep commitment to sustainability, Amazon is setting a new standard for how e-commerce companies can operate in the 21st century. For engineers, technologists, and environmental advocates, Amazon offers an unparalleled opportunity to work on some of the most challenging and impactful problems of our time. Learn more about becoming part of Amazon’s Team.

In 1965, the United States and other nuclear powers committed to the Comprehensive Nuclear-Test-Ban Treaty, which prohibited nuclear tests. The National Nuclear Security Administration (NNSA), a successor to the Manhattan Project, now tests nukes only in simulation. To that end, the NNSA yesterday unveiled the world’s fastest supercomputer to air in its mission to maintain a safe, secure, and reliable nuclear stockpile. El Capitan was announced yesterday at the SC Conference for supercomputing in Atlanta, Georgia, and it debuted at #1 in the newest Top500 list, a twice-yearly ranking of the world’s highest performing supercomputers. El Capitan, housed at Lawrence Livermore National Laboratory in Livermore, Calif., can perform over 2700 quadrillion operations per second at its peak. The previous record holder, Frontier, could do just over 2000 quadrillion peak operations per second. Alongside El Capitan, the NNSA announced its unclassified cousin, Toulumne, which debuted at #10 on the Top500 list and can perform a peak of 288 quadrillion operations per second. The NNSA—which oversees Lawrence Livermore as well as Los Alamos National Laboratory and Sandia National Laboratories—plans to use El Capitan to “model and predict nuclear weapon performance, aging effects, and safety,” says Corey Hinderstein, acting principal deputy administrator at NNSA. Hinderstein says the 3D modeling of multiple physics processes will be significantly enhanced by the new supercomputer’s speed. The team also plans to use El Capitan to aid in its inertial confinement fusion efforts, as well as to train artificial intelligence in support of both of those efforts. Planning for El Capitan began in 2018, and construction has been ongoing for the past four years. The system is built by Hewlett Packard Enterprise, which has built all of the current top 3 supercomputers on the Top500 list. El Capitan uses AMD’s MI300a chip, dubbed an accelerated processing unit, which combines a CPU and GPU in one package. In total, the system boasts 44,544 MI300As, connected together by HPE’s Slingshot interconnects. El Capitan uses 44,544 of AMD’s MI300A chips, which combine a CPU and GPU in one package.Garry McLeod/Lawrence Livermore National Laboratory Scientists are already at work porting their code over to the new machine, and they are enthusiastic about its promise. “We’re seeing significant speed ups compared to running on old chips versus this new thing,” says Luc Peterson, computational physicist at Lawrence Livermore National Laboratory. “We are at the point where our time to science is shrinking. We can do things in a few days that would have taken a few months. So we’re pretty excited about the applications.” Yet the appetite for ever larger supercomputers lives on. “We are already working on the next [high performance computing] acquisition,” says Thuc Hoang, director of the advanced simulation and computing program at NNSA.

A year after introducing the first in-ear, silicon-based earbuds, xMEMS has unveiled a prototype of the latest version of its microspeakers—this time, for use as an open-air speaker, which is a more challenging task. The Silicon Valley-based startup’s previous microspeakers brought microelectromechanical systems (MEMS) to wireless earbuds and boasted excellent sound quality. By modulating ultrasound signals, the speakers create high-fidelity sound in a light and compact device. The clarity of sound that the new silicon-and-piezoelectric chip, called Sycamore, produces is more like that of a smartphone speaker—decent, but far from the sound quality of in-ear alternatives. And like a smartphone speaker, Sycamore is intended for open-air audio produced by devices near or on the body. In particular, the speaker could be used in various wearable devices, like smart watches, XR glasses, or open earbuds, which clip around the ear instead of nestling within it. For these applications, the advantage of using MEMS drivers instead of a conventional speaker is less about sound quality, and more about size. The microspeaker is about 1 millimeter thick, one-third the thickness of a coil driver, and removing the magnetic coils of conventional speakers brings its weight down by roughly 70 percent to 150 milligrams. Other speakers also require empty space behind the diaphragm, called back volume. The MEMS-based speakers significantly reduce the back volume needed. For wearables, every millimeter and milligram matters; a heavy or bulky design could deter users, says Mike Housholder, xMEMS’ vice president of marketing and business development. That’s why the microspeakers are “perfect for smart watches.” Users seeking excellent audio quality would likely opt for in-ear buds or over-ear headphones. The thin, open-air microspeakers instead help deliver a sleek, “fashion-forward” product, Housholder says. Sound From Ultrasound Sycamore uses the same “sound from ultrasound” technology introduced in Cypress, xMEMS’ in-ear microspeaker. This tech produces ultrasound by vibrating robust silicon flaps coated in piezoelectric material. It then modulates the ultrasound to generate a full range of audible frequencies. What’s new with Sycamore is a more efficient chip design. This enhanced efficiency means the speakers can deliver more decibels, making open air listening possible. The speaker also performs well in the bass frequency range, historically a weak spot for MEMS speakers. (In the first commercial headphones to use xMEMS technology, the silicon microspeaker was used only for the high-frequency “tweeter”; it was paired with a conventional dynamic driver “woofer” to produce mid-range and bass audio.) In the company’s tests of its prototype speaker, Sycamore emitted similar or louder audio compared to the speaker on an Apple Watch Series 8 across most frequencies. Compared to Bose open earbuds, it lagged in mid-range but had stronger bass and treble frequencies. The new speaker will be made with the same fabrication process as the earbud chip, Cypress. xMEMS will continue to partner with TSMC to manufacture the speakers, though they are now also using Bosch, a leading MEMS foundry. Housholder says that by further improving the efficiency of the cell design, MEMS speakers may become loud enough for other applications, like phone or laptop speakers. But there are fundamental size limitations for the microspeakers, which are manufactured on a 300 millimeter wafer. Combining multiple chips can also bring up the volume, but it’s unlikely that your next loudspeakers will be made of MEMS. xMEMS plans to begin sampling Sycamore in early 2025, with mass production expected in January 2026. In the meantime, the company’s full-range in-ear microspeakers will begin mass production in June 2025, followed by its all-silicon fan-on-a-chip in October of the same year.

Machine learning chips that use analog circuits instead of digital ones have long promised huge energy savings. But in practice they’ve mostly delivered modest savings, and only for modest-sized neural networks. Silicon Valley startup Sageance says it has the technology to bring the promised power savings to tasks suited for massive generative AI models. The startup claims that its systems will be able to run the large language model Llama 2-70B at one-tenth the power of an Nvidia H100 GPU-based system, at one-twentieth the cost and in one-twentieth the space. “My vision was to create a technology that was very differentiated from what was being done for AI,” says Sageance CEO and founder Vishal Sarin. Even back when the company was founded in 2018, he “realized power consumption would be a key impediment to the mass adoption of AI…. The problem has become many, many orders of magnitude worse as generative AI has caused the models to balloon in size.” The core power-savings prowess for analog AI comes from two fundamental advantages: It doesn’t have to move data around and it uses some basic physics to do machine learning’s most important math. That math problem is multiplying vectors and then adding up the result, called multiply and accumulate. Early on, engineers realized that two foundational rules of electrical engineers did the same thing, more or less instantly. Ohm’s Law—voltage multiplied by conductance equals current—does the multiplication if you use the neural network’s “weight” parameters as the conductances. Kirchoff’s Current Law—the sum of the currents entering and exiting a point is zero—means you can easily add up all those multiplications just by connecting them to the same wire. And finally, in analog AI, the neural network parameters don’t need to be moved from memory to the computing circuits—usually a bigger energy cost than computing itself—because they are already embedded within the computing circuits. Sageance uses flash memory cells as the conductance values. The kind of flash cell typically used in data storage is a single transistor that can hold 3 or 4 bits, but Sageance has developed algorithms that let cells embedded in their chips hold 8 bits, which is the key level of precision for LLMs and other so-called transformer models. Storing an 8-bit number in a single transistor instead of the 48 transistors it would take in a typical digital memory cell is an important cost, area, and energy savings, says Sarin, who has been working on storing multiple bits in flash for 30 years. Digital data is converted to analog voltages [left]. These are effectively multiplied by flash memory cells [blue], summed, and converted back to digital data [bottom].Analog Inference Adding to the power savings is that the flash cells are operated in a state called “deep subthreshold.” That is, they are working in a state where they are barely on at all, producing very little current. That wouldn’t do in a digital circuit, because it would slow computation to a crawl. But because the analog computation is done all at once, it doesn’t hinder the speed. Analog AI Issues If all this sounds vaguely familiar, it should. Back in 2018 a trio of startups went after a version of flash-based analog AI. Syntiant eventually abandoned the analog approach for a digital scheme that’s put six chips in mass production so far. Mythic struggled but stuck with it, as has Anaflash. Others, particularly IBM Research, have developed chips that rely on nonvolatile memories other than flash, such as phase-change memory or resistive RAM. Generally, analog AI has struggled to meet its potential, particularly when scaled up to a size that might be useful in datacenters. Among its main difficulties are the natural variation in the conductance cells; that might mean the same number stored in two different cells will result in two different conductances. Worse still, these conductances can drift over time and shift with temperature. This noise drowns out the signal representing the result, and the noise can be compounded stage after stage through the many layers of a deep neural network. Sageance’s solution, Sarin explains, is a set of reference cells on the chip and a proprietary algorithm that uses them to calibrate the other cells and track temperature-related changes. Another source of frustration for those developing analog AI has been the need to digitize the result of the multiply and accumulate process in order to deliver it to the next layer of the neural network where it must then be turned back into an analog voltage signal. Each of those steps requires analog-to-digital and digital-to-analog converters, which take up area on the chip and soak up power. According to Sarin, Sageance has developed low-power versions of both circuits. The power demands of the digital-to-analog converter are helped by the fact that the circuit needs to deliver a very narrow range of voltages in order to operate the flash memory in deep subthreshold mode. Systems and What’s Next Sageance’s first product, to launch in 2025, will be geared toward vision systems, which are a considerably lighter lift than server-based LLMs. “That is a leapfrog product for us, to be followed very quickly [by] generative AI,” says Sarin. Future systems from Sageance will be made up of 3D-stacked analog chips linked to a processor and memory through an interposer that follows the universal chiplet interconnect (UCIe) standard.Analog Inference The generative AI product would be scaled up from the vision chip mainly by vertically stacking analog AI chiplets atop a communications die. These stacks would be linked to a CPU die and to high-bandwidth memory DRAM in a single package called Delphi. In simulations, a system made up of Delphis would run Llama2-70B at 666,000 tokens per second consuming 59 kilowatts, versus a 624 kW for an Nvidia H100-based system, Sageance claims.

Timothy Ayelagbe dreams of using technology to advance health care and make other improvements across Africa. Ayelagbe calls microelectronics his “joy and passion” and says he wants to use the expertise he’s gaining in the field to help others. “My ultimate goal,” he says, “is to uplift my fellow Africans.” Timothy Ayelagbe Volunteer Roles: IEEE Youth Endeavors for Social Innovation Using Sustainable Technology ambassador, 2025 vice president of the IEEE Robotics and Automation Society student branch chapter University: Obafemi Awolowo University in Ile-Ife, Nigeria Major: Electronics and electrical engineering Minor: Microelectronics He is pursuing an electronics and electrical engineering degree, specializing in microelectronics, at Obafemi Awolowo University (OAU), in Ile-Ife, Nigeria. He says he believes learning how to employ field-programmable gate arrays (FPGAs) is the path to mastering the hardware description languages that will let him develop affordable, sustainable medical electronics. He says he hopes to apply his growing technical expertise and leadership abilities to address the continent’s challenges in health care, infrastructure, and natural resources management. Ayelagbe is passionate about mentoring aspiring African engineers as well. Early this year, he became an IEEE Youth Endeavors for Social Innovation Using Sustainable Technology (YESIST) ambassador. The YESIST 12 program provides students and young professionals with a platform to showcase ideas for addressing humanitarian and social issues affecting their communities. As an ambassador, Ayelagbe made online webinar sessions in his student branch while also mentoring pre-university students through activities encouraging service-oriented engineering practice. A technologist right out of the gate Born in Lagos, Nigeria, Ayelagbe was captivated by how things worked from a young age. As a child, he would dismantle and reassemble his toys to learn how they worked. His mother, a trader, and his father, then a quality control officer in the metal processing industry, nurtured his curiosity. While the conventional path to upward mobility in Nigeria might have led him to becoming a doctor or nurse, his parents supported his pursuit of technology. As it turns out, he is poised to advance the state of health care in Nigeria and around the globe. For now, he is focused on his undergraduate studies and on gaining practical experience. He recently completed a six-week student work experience program as part of his university’s engineering curriculum. He and fellow OAU students developed an angular speed measurement system using Hall effect sensors, which calculates the speed when its Hall’s element moves in relation to a magnetic field. Changes in the voltage and current running through the Hall element can be used to calculate the strength of the magnetic field at different locations or to track changes in its position. One common use of Hall effect sensors is to monitor wheel speed in a vehicle’s antilock braking system. “I want to apply the things I’m learning to make Africa great.” Like commercialized versions, the students’ device was designed to withstand harsh weather and unfavorable road conditions. But theirs is certain to have a significantly lower price point than the magnetic devices it emulates, while producing more accurate readings than traditional mechanical versions, Ayelagbe says. “We did some data processing and manipulation via Arduino programming using an ATmega microcontroller and a liquid crystal display to show the angular speed and frequency of rotation,” he says. Because the measurement system has potential applications in automotive and other industries, Ayelagbe’s OAU team is seeking partnerships with other researchers to further develop and commercialize it. The team also hopes to publish its findings in an IEEE journal. “In the future, I hope to work with semiconductor giant industries like TSMC, Nvidia, Intel, and Qualcomm,” he says. Volunteering provides valuable experience Despite Ayelagbe’s academic success, he has faced challenges in finding semiconductor internships, citing some companies’ geographical inaccessibility to African students. Instead, he says, he has been gaining valuable experience through volunteering. He serves as a social media manager for the Paris-based Human Development Research Initiative (HDRI), an organization that works to inspire young people to help achieve the 17 sustainable U.N. development goals known collectively as Agenda 2030. He has been promoting environmental and climate action through LinkedIn posts. Ayelagbe is an active IEEE volunteer and is involved in his student branch. He is the incoming vice president of the branch’s IEEE Robotics and Automation Society chapter and says he would love to take on more roles in the course of his leadership journey. He organizes webinars, meetings, and other initiatives, including connecting fellow student members with engineering professionals for mentorship. Through his work with HDRI and IEEE, he has the opportunity to network with students, professionals, and industry experts. The connections, he hopes, can help him achieve his ambitions. African nations “need engineers in the leadership sector,” he says, “and I want to apply the things I’m learning to make Africa great.”

It’s time to start preparing your organization and employees for the effects of artificial general intelligence, sustainability, and digital transformation. According to IEEE’s 2024 Technology Megatrends report, the three technologies will change how companies, governments, and universities operate and will affect what new skills employees need. A megatrend, which integrates multiple tendencies that evolve over two decades or so, is expected to have a substantial effect on society, technology, ecology, economics, and more. More than 50 experts from Asia, Australia, Europe, Latin America, the Middle East, and the United States provided their perspectives for the report. They represent all 47 of IEEE’s fields of interest and come from academia, the public sector, and the private sector. The report includes insights and opportunities about each megatrend and how industries could benefit. The experts compared their insights to technology predictions from Google Trends; the IEEE Computer Society and the IEEE Xplore Digital Library; and the U.S. Patent and Trademark Office. “We made predictions about technology and megatrends and correlated them with other general megatrends such as economical, ecological, and sociopolitical. They’re all intertwined,” says IEEE Fellow Dejan Milojicic, a member of the IEEE Future Directions Committee and vice president at Hewlett Packard Labs in Milpitas, Calif. He is also a Hewlett Packard Enterprise Fellow. The benefits and drawbacks of artificial general intelligence Artificial general intelligence (AGI) includes ChatGPT, autonomous robots, wearable and implantable technologies, and digital twins. Education, health care, and manufacturing are some of the sectors that can benefit most from AGI, the report says. For academia, the technology can help expand remote learning, potentially replacing physical classrooms and leading to more personalized education for students. In health care, the technology could lead to personalized medicine, tailored patient treatment plans, and faster drug discovery. AGI also could help reduce costs and increase efficiencies, the report says. Manufacturing can use the technology to improve quality control, reduce downtime, and increase production. The time to market could be significantly shortened, the report says. Today’s AI systems are specialized and narrow, so to reap the benefits, experts say, the widespread adoption of curated datasets, advances in AI hardware, and new algorithms will be needed. It will require interdisciplinary collaborations across computer science, engineering, ethics, and philosophy, the report says. The report points out drawbacks with AGI, including a lack of data privacy, ethical challenges, and misuse of content. Another concern is job displacement and the need for employees to be retrained. AGI requires more AI programmers and data scientists but fewer support staff and system administrators, the report notes. Adopting digital technologies Digital transformation tech includes autonomous technologies, ubiquitous connectivity, and smart environments. The areas that would benefit most from expanding their use of computers and other electronic devices, the experts say, are construction, education, health care, and manufacturing. The construction industry could use building information modeling (BIM), which generates digital versions of office buildings, bridges, and other structures to improve safety and efficiency. Educational institutions already use electronics such as digital whiteboards, laptops, tablets, and smartphones to enhance the learning experience. But the experts point out that schools aren’t using the tools yet for continuing education programs needed to train workers on how to use new tools and technology. “Most education processes are the same now as they were in the last century, at a time when we need to change to lifelong learning,” the experts say. “We made predictions about technology and megatrends, but we correlated them with other general megatrends such as economical, ecological, and sociopolitical. They’re all intertwined.” —Dejan Milojicic The report says the digital transformation will need more employees to supervise automation, as well as those with experience in analytics, but fewer operators and workers responsible for maintaining old systems. The health field has started converting to electronic records, but more could be done, the report says, such as using computer-aided design to develop drugs and prosthetics and using BIM tools to design hospitals. Manufacturing could benefit by using computer-aided-design data to create digital representations of product prototypes. There are some concerns with digital transformation, the experts acknowledge. There aren’t enough chips and batteries to build all the devices and systems needed, for example, and not every organization or government can afford the digital tools. Also, people in underdeveloped areas who lack connectivity would not have access to them, leading to a widening of the digital divide. Other people might resist because of privacy, religious, or lifestyle concerns, the experts note. Addressing the climate crisis Technology can help engineer social and environmental change. Sustainability applications include clean renewable energy, decarbonization, and energy storage. Nearly half of organizations around the world have a company-wide sustainability strategy, but only 18 percent have well-defined goals and a timetable for how to implement them, the report says. About half of companies lack the tools or expertise to deploy sustainable solutions. Meanwhile, information and communication technologies’ energy consumption is growing, using about 10 percent of worldwide electricity. The experts predict that transitioning to more sustainable information and communication technologies will lead to entirely new businesses. Blockchain technology could be used to optimize surplus energy produced by microgrids, for example, ultimately leading to more jobs, less-expensive energy, and energy security. Early leaders in sustainability are already applying digital technologies such as AI, big data, blockchain, computer vision, and the Internet of Things to help operationalize sustainability. Employees familiar with those technologies will be needed, the report predicts, adding that engineers who can design systems that are more energy efficient and environmentally friendly will be in demand. Some of the challenges that could hinder such efforts include a lack of regulations, an absence of incentives to encourage people to become eco-friendly, and the high cost of sustainable technologies. How organizations can work together All three megatrends should be considered synergistically, the experts say. For example, AGI techniques can be applied to sustainable and digitally transformed technologies. Sustainability is a key aspect of technology, including AGI. And digital transformation needs to be continually updated with AGI and sustainability features, the report says. The report included several recommendations for how academia, governments, industries, and professional organizations can work together to advance the three technologies. To address the need to retrain employees, for example, industry should work with colleges and universities to educate the workforce and train instructors on the technologies. To advance the science that supports the megatrend technologies, academia needs to work more closely with industry on research projects, the experts suggest. In turn, governments should foster research by academia and not-for-profit organizations. Companies should advise government officials on how to best regulate the technologies. To gain widespread acceptance of the technologies, the risks and the benefits should be explained to the public to avoid misinformation, the experts say. In addition, processes, practices, and educational materials need to be created to address ethical issues surrounding the technologies. “As a whole, these megatrends should focus on helping industry,” Milojicic says. “Government and academia are important in their own ways, but if we can make industry successful, everything else will come from that. Industry will fund academia, and governments will help industry.” Professional organizations including IEEE will need to develop technical standards and road maps on the three areas, he says. A road map is a strategic look at the long-term landscape of a technology, what the trends are, and what the possibilities are. The megatrends influence which initiatives IEEE is going to explore, Milojicic says, “which could potentially lead to future road maps and standards. In a way, we are doing the prework to prepare what they could eventually standardize.” Dejan Milojicic discusses findings from IEEE’s 2024 Technology Megatrends report. Dissemination and education are critical The group encourages a broad dissemination of the three megatrends to avoid widening the digital divide. “The speed of change could be faster than most people can adapt to—which could lead to fear and aggression toward technology,” the experts say. “Broad education is critical for technology adoption.”

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. Humanoids 2024: 22–24 November 2024, NANCY, FRANCE Humanoids Summit: 11–12 December 2024, MOUNTAIN VIEW, CA Enjoy today’s videos! Don’t get me wrong, this is super impressive, but I’m like 95% sure that there’s a human driving it. For robots like these to be useful, they’ll need to be autonomous, and high speed autonomy over unstructured terrain is still very much a work in progress. [ Deep Robotics ] Dung beetles impressively coordinate their six legs simultaneously to effectively roll large dung balls. They are also capable of rolling dung balls varying in the weight on different terrains. The mechanisms underlying how their motor commands are adapted to walk and simultaneously roll balls (multitasking behavior) under different conditions remain unknown. Therefore, this study unravels the mechanisms of how dung beetles roll dung balls and adapt their leg movements to stably roll balls over different terrains for multitasking robots. [ Paper ] via [ Advanced Science News ] Subsurface lava tubes have been detected from orbit on both the Moon and Mars. These natural voids are potentially the best place for long-term human habitations, because they offer shelter against radiation and meteorites. This work presents the development and implementation of a novel Tether Management and Docking System (TMDS) designed to support the vertical rappel of a rover through a skylight into a lunar lava tube. The TMDS connects two rovers via a tether, enabling them to cooperate and communicate during such an operation. [ DFKI Robotics Innovation Center ] Ad Spiers at Imperial College London writes, “We’ve developed a $80 barometric tactile sensor that, unlike past efforts, is easier to fabricate and repair. By training a machine learning model on controlled stimulation of the sensor we have been able to increase the resolution from 6mm to 0.28mm. We also implement it in one of our E-Troll robotic grippers, allowing the estimation of object position and orientation.” [ Imperial College London ] via [ Ad Spiers ] Thanks Ad! A robot, trained for the first time to perform surgical procedures by watching videos of robotic surgeries, executed the same procedures—but with considerably more precision. [ Johns Hopkins University ] Thanks, Dina! This is brilliant but I’m really just in it for the satisfying noise it makes. [ RoCogMan Lab ] Fast and accurate physics simulation is an essential component of robot learning, where robots can explore failure scenarios that are difficult to produce in the real world and learn from unlimited on-policy data. Yet, it remains challenging to incorporate RGB-color perception into the sim-to-real pipeline that matches the real world in its richness and realism. In this work, we train a robot dog in simulation for visual parkour. We propose a way to use generative models to synthesize diverse and physically accurate image sequences of the scene from the robot’s ego-centric perspective. We present demonstrations of zero-shot transfer to the RGB-only observations of the real world on a robot equipped with a low-cost, off-the-shelf color camera. [ MIT CSAIL ] WalkON Suit F1 is a powered exoskeleton designed to walk and balance independently, offering enhanced mobility and independence. Users with paraplegia can easily transfer into the suit directly from their wheelchair, ensuring exceptional usability for people with disabilities. [ Angel Robotics ] In order to promote the development of the global embodied AI industry, the Unitree G1 robot operation data set is open sourced, adapted to a variety of open source solutions, and continuously updated. [ Unitree Robotics ] Spot encounters all kinds of obstacles and environmental changes, but it still needs to safely complete its mission without getting stuck, falling, or breaking anything. While there are challenges and obstacles that we can anticipate and plan for—like stairs or forklifts—there are many more that are difficult to predict. To help tackle these edge cases, we used AI foundation models to give Spot a better semantic understanding of the world. [ Boston Dynamics ] Wing drone deliveries of NHS blood samples are now underway in London between Guy’s and St Thomas’ hospitals. [ Wing ] As robotics engineers, we love the authentic sounds of robotics—the metal clinking and feet contacting the ground. That’s why we value unedited, raw footage of robots in action. Although unpolished, these candid captures let us witness the evolution of robotics technology without filters, which is truly exciting. [ UCR ] Eight minutes of chill mode thanks to Kuka’s robot DJs, which make up the supergroup the Kjays. A KR3 AGILUS at the drums, loops its beats and sets the beat. The KR CYBERTECH nano is our nimble DJ with rhythm in his blood. In addition, a KR AGILUS performs as a light artist and enchants with soft and expansive movements. In addition there is an LBR Med, which - mounted on the ceiling - keeps an eye on the unusual robot party. [ Kuka Robotics Corp. ] Am I the only one disappointed that this isn’t actually a little mini Ascento? [ Ascento Robotics ] This demo showcases our robot performing autonomous table wiping powered by Deep Predictive Learning developed by Ogata Lab at Waseda University. Through several dozen human teleoperation demonstrations, the robot has learned natural wiping motions. [ Tokyo Robotics ] What’s green, bidirectional, and now driving autonomously in San Francisco and the Las Vegas Strip? The Zoox robotaxi! Give us a wave if you see us on the road! [ Zoox ] Northrop Grumman has been pioneering capabilities in the undersea domain for more than 50 years. Now, we are creating a new class of uncrewed underwater vehicles (UUV) with Manta Ray. Taking its name from the massive “winged” fish, Manta Ray will operate long-duration, long-range missions in ocean environments where humans can’t go. [ Northrop Grumman ] I was at ICRA 2024 and I didn’t see most of the stuff in this video. [ ICRA 2024 ] A fleet of marble-sculpting robots is carving out the future of the art world. It’s a move some artists see as cheating, but others are embracing the change. [ CBS ]

Nvidia, Oracle, Google, Dell and 13 other companies reported how long it takes their computers to train the key neural networks in use today. Among those results were the first glimpse of Nvidia’s next generation GPU, the B200, and Google’s upcoming accelerator, called Trillium. The B200 posted a doubling of performance on some tests versus today’s workhorse Nvidia chip, the H100. And Trillium delivered nearly a four-fold boost over the chip Google tested in 2023. The benchmark tests, called MLPerf v4.1, consist of six tasks: recommendation, the pre-training of the large language models (LLM) GPT-3 and BERT-large, the fine tuning of the Llama 2 70B large language model, object detection, graph node classification, and image generation. Training GPT-3 is such a mammoth task that it’d be impractical to do the whole thing just to deliver a benchmark. Instead, the test is to train it to a point that experts have determined means it is likely to reach the goal if you kept going. For Llama 2 70B, the goal is not to train the LLM from scratch, but to take an already trained model and fine-tune it so it’s specialized in a particular expertise—in this case, government documents. Graph node classification is a type of machine learning used in fraud detection and drug discovery. As what’s important in AI has evolved, mostly toward using generative AI, the set of tests has changed. This latest version of MLPerf marks a complete changeover in what’s being tested since the benchmark effort began. “At this point all of the original benchmarks have been phased out,” says David Kanter, who leads the benchmark effort at MLCommons. In the previous round it was taking mere seconds to perform some of the benchmarks. Performance of the best machine learning systems on various benchmarks has outpaced what would be expected if gains were solely from Moore’s Law [blue line]. Solid line represent current benchmarks. Dashed lines represent benchmarks that have now been retired, because they are no longer industrially relevant.MLCommons According to MLPerf’s calculations, AI training on the new suite of benchmarks is improving at about twice the rate one would expect from Moore’s Law. As the years have gone on, results have plateaued more quickly than they did at the start of MLPerf’s reign. Kanter attributes this mostly to the fact that companies have figured out how to do the benchmark tests on very large systems. Over time, Nvidia, Google, and others have developed software and network technology that allows for near linear scaling—doubling the processors cuts training time roughly in half. https://public.flourish.studio/visualisation/20196...” width=”100%” alt=”scatter visualization” /> First Nvidia Blackwell training results This round marked the first training tests for Nvidia’s next GPU architecture, called Blackwell. For the GPT-3 training and LLM fine-tuning, the Blackwell (B200) roughly doubled the performance of the H100 on a per-GPU basis. The gains were a little less robust but still substantial for recommender systems and image generation—64 percent and 62 percent, respectively. The Blackwell architecture, embodied in the Nvidia B200 GPU, continues an ongoing trend toward using less and less precise numbers to speed up AI. For certain parts of transformer neural networks such as ChatGPT, Llama2, and Stable Diffusion, the Nvidia H100 and H200 use 8-bit floating point numbers. The B200 brings that down to just 4 bits. Google debuts 6th gen hardware Google showed the first results for its 6th generation of TPU, called Trillium—which it unveiled only last month—and a second round of results for its 5th generation variant, the Cloud TPU v5p. In the 2023 edition, the search giant entered a different variant of the 5th generation TPU, v5e, designed more for efficiency than performance. Versus the latter, Trillium delivers as much as a 3.8-fold performance boost on the GPT-3 training task. But versus everyone’s arch-rival Nvidia, things weren’t as rosy. A system made up of 6,144 TPU v5ps reached the GPT-3 training checkpoint in 11.77 minutes, placing a distant second to an 11,616-Nvidia H100 system, which accomplished the task in about 3.44 minutes. That top TPU system was only about 25 seconds faster than an H100 computer half its size. A Dell Technologies computer fine-tuned the Llama 2 70B large language model using about 75 cents worth of electricity. In the closest head-to-head comparison between v5p and Trillium, with each system made up of 2048 TPUs, the upcoming Trillium shaved a solid 2 minutes off of the GPT-3 training time, nearly an 8 percent improvement on v5p’s 29.6 minutes. Another difference between the Trillium and v5p entries is that Trillium is paired with AMD Epyc CPUs instead of the v5p’s Intel Xeons. Google also trained the image generator, Stable Diffusion, with the Cloud TPU v5p. At 2.6 billion parameters, Stable Diffusion is a light enough lift that MLPerf contestants are asked to train it to convergence instead of just to a checkpoint, as with GPT-3. A 1024 TPU system ranked second, finishing the job in 2 minutes 26 seconds, about a minute behind the same size system made up of Nvidia H100s. https://public.flourish.studio/visualisation/20251...” target=”_blank”>https://public.flourish.studio/visualisation/20251...” width=”100%” alt=”chart visualization” /> Training power is still opaque The steep energy cost of training neural networks has long been a source of concern. MLPerf is only beginning to measure this. Dell Technologies was the sole entrant in the energy category, with an eight-server system containing 64 Nvidia H100 GPUs and 16 Intel Xeon Platinum CPUs. The only measurement made was in the LLM fine-tuning task (Llama2 70B). The system consumed 16.4 megajoules during its 5-minute run, for an average power of 5.4 kilowatts. That means about 75 cents of electricity at the average cost in the United States. While it doesn’t say much on its own, the result does potentially provide a ballpark for the power consumption of similar systems. Oracle, for example, reported a close performance result—4 minutes 45 seconds—using the same number and types of CPUs and GPUs.

At 8:30 p.m. on 16 May 1916, John J. Carty banged his gavel at the Engineering Societies Building in New York City to call to order a meeting of the American Institute of Electrical Engineers. This was no ordinary gathering. The AIEE had decided to conduct a live national meeting connecting more than 5,000 attendees in eight cities across four time zones. More than a century before Zoom made virtual meetings a pedestrian experience, telephone lines linked auditoriums from coast to coast. AIEE members and guests in Atlanta, Boston, Chicago, Denver, New York, Philadelphia, Salt Lake City, and San Francisco had telephone receivers at their seats so they could listen in. The AIEE, a predecessor to the IEEE, orchestrated this event to commemorate recent achievements in communications, transportation, light, and power. The meeting was a triumph of engineering, covered in newspapers in many of the host cities. The Atlanta Constitution heralded it as “a feat never before accomplished in the history of the world.” According to the Philadelphia Evening Ledger, the telephone connections involved traversed about 6,500 kilometers (about 4,000 miles) across 20 states, held up by more than 150,000 poles running through 5,000 switches. It’s worth noting that the first transcontinental phone call had been achieved only a year earlier. Carty, president of the AIEE, led the meeting from New York, while section chairmen directed the proceedings in the other cities. First up: roll call. Each city read off the number of members and guests in attendance—from 40 in Denver, the newest section of the institute, to 1,100 at AIEE headquarters in New York. In all, more than 5,100 members attended. Due to limited seating in New York and Philadelphia, members were allowed only a single admission ticket, and ladies were explicitly not invited. (Boo.) In Atlanta, Boston, and Chicago, members received two tickets each, and in San Francisco members received three; women were allowed to attend in all of these cities. (The AIEE didn’t admit its first woman until 1922, and only as an associate member; Edith Clarke was the first woman to publish a paper in an AIEE journal, in 1926.) These six cities were the only ones officially participating in the meeting. But because the telephone lines ran directly through both Denver and Salt Lake City, AIEE sections in those cities opted to listen in, although they were kept muted; during the meeting, they sent telegrams to headquarters with their attendance and greetings. In a modern-day Zoom call, these notes would have been posted in the chat. The first virtual meeting had breakout sessions Once everyone had checked in and confirmed that they all could hear, Carty read a telegram from U.S. President Woodrow Wilson, congratulating the members on this unique meeting: “a most interesting evidence of the inventive genius and engineering ability represented by the Institute.” Alexander Graham Bell then gave a few words in greeting and remarked that he was glad to see how far the telephone had gone beyond his initial idea. Theodore Vail, first president of AT&T and one of the men who was instrumental in establishing telephone service as a public utility, offered his own congratulations. Charles Le Maistre, a British engineer who happened to be in New York to attend the AIEE Standards Committee, spoke on behalf of his country’s engineering societies. Finally, Thomas Watson, who as Bell’s assistant was the first person to hear words spoken over a telephone, welcomed all of the electrical engineers scattered across the country. At precisely 9:00 p.m., the telephone portion of the meeting was suspended for 30 minutes so that each city could have its own local address by an invited guest. Let’s call them breakout sessions. These speakers reflected on the work and accomplishments of engineers. Overall, they conveyed an unrelentingly positive attitude toward engineering progress, with a few nuances. In Boston, Lawrence Lowell, president of Harvard University, said the discovery and harnessing of electricity was the greatest single advancement in human history. However, he admonished engineers for failing to foresee the subordination of the individual to the factory system. In Philadelphia, Edgar Smith, provost of the University of Pennsylvania, noted that World War I was limiting the availability of certain materials and supplies, and he urged more investment in developing the United States’ natural resources. Charles Ferris, dean of engineering at the University of Tennessee, praised the development of long-distance power distribution and the positive effects it had on rural life, but worried about the use of fossil fuels. His chief concern was running out of coal, gas, and oil, not their negative impacts on the environment. More than a century before Zoom made virtual meetings a pedestrian experience, telephone lines linked auditoriums from coast to coast for the AIEE’s national meeting. On the West Coast, Ray Wilbur, president of Stanford, argued for the value of dissatisfaction, struggle, and unrest on campus as spurs to growth and innovation. I suspect many university presidents then and now would disagree, but student protests remain a force for change. After the city breakout sessions, everyone reconnected by telephone, and the host cities took turns calling out their greetings, along with some engineering boasts. “Atlanta, located in the Piedmont section of the southern Appalachians, among their racing rivers and roaring falls, whose energy has been dragged forth and laid at her doors through high-tension transmission and in whose phenomenal development no factor has been more potent than the electrical engineers, sends greetings.” “Boston sends warmest greetings to her sister cities. The telephone was born here and here it first spoke, but its sound has gone out into all lands and its words unto the ends of the world.” “San Francisco hails its fellow members of the Institute…. California has by the pioneer spirit of domination created needs which the world has followed—the snow-crowned Sierras opened up the path of gold to the path of energy, which tonight makes it possible for us on the western rim of the continent of peace to be in instant touch with men who have harnessed rivers, bridled precipices, drawn from the ether that silent and unseen energy that has leveled distance and created force to move the world along lines of greater civilization by closer contacts.” That last sentence, my editor notes, is 86 words long, but we included it for its sheer exuberance. Maybe all tech meetings should have musical interludes The meeting then paused for a musical interlude. I find this idea delightfully weird, like the ballet dream sequence in the middle of the Broadway musical Oklahoma! Each city played a song of their choosing on a phonograph, to be transmitted through the telephone. From the south came strains of “Dixie,” countered by “Yankee Doodle” in New England. New York and San Francisco opted for two variations on the patriotic symbolism of Columbia: “Hail Columbia” and “Columbia the Gem of the Ocean,” respectively. Philadelphia offered up the “Star-Spangled Banner,” and although it wasn’t yet the national anthem, audience members in all auditoriums stood up while it played. For the record, the AIEE in those days took entertainment very seriously. Almost all of their conferences included a formal dinner dance, less-formal smokers, sporting competitions, and inspection field trips to local sites of engineering interest. There were even women’s committees to organize events specifically for the ladies. I suspect no one in attendance would have predicted that in the 21st century, people groan at the thought of another virtual meeting. After the music, Michael Pupin delivered an address on “The Engineering Profession,” a topic that was commonly discussed in the Proceedings of the AIEE in those days. Remember that electrical engineering was still a fairly new academic discipline, only a few decades old, and working engineers were looking to more established professions, such as medical doctors, to see how they might fit into society. Pupin had made a number of advancements in the efficiency of transmission over long-distance telephone, and in 1925 he served as the president of the AIEE. The meeting concluded with resolutions, amendments, acceptances, and seconding, following Robert’s Rules of Order. (IEEE meetings still adhere to the rules.) In the last resolution, the participants patted themselves on the back for hosting this first-of-its-kind meeting and acknowledging their own genius that made it possible. The Proceedings of the AIEE covered the meeting in great detail. Local press accounts offered less detail. I’ve found no evidence that they ever tried to replicate the meeting. They did try another experiment in which a member read the same paper at meetings in three different cities so that there could be a joint discussion about the contents. But it seems they returned to their normal schedule of annual and section meetings with technical paper sessions and discussion. And nowhere have I found answers to some of the basic questions that I, as a historian 100 years later, have about the 1916 event. First, how much did this meeting cost in long-distance fees and who paid for it? Second, what receivers did the audience members use and did they work? And finally, what did the members and guests think of this grand experiment? (My editor would also like to know why no one took a photo of the event.) But in the moment, rarely do people think about what later historians may want to know. And I suspect no one in attendance would have predicted that in the 21st century, people groan at the thought of another virtual meeting.

The IEEE Board of Directors shapes the future direction of IEEE and is committed to ensuring IEEE remains a strong and vibrant organization—serving the needs of its members and the engineering and technology community worldwide—while fulfilling the IEEE mission of advancing technology for the benefit of humanity. This article features IEEE Board of Directors members ChunChe “Lance” Fung, Eric Grigorian, and Christina Schober. IEEE Senior Member ChunChe “Lance” Fung Director, Region 10: Asia Pacific Joanna Mai Yie Leung Fung has worked in academia and provided industry consultancy services for more than 40 years. His research interests include applying artificial intelligence, machine learning, computational intelligence, and other techniques to solve practical problems. He has authored more than 400 publications in the disciplines of AI, computational intelligence, and related applications. Fung currently works on the ethical applications and social impacts of AI. A member of the IEEE Systems, Man, and Cybernetics Society, Fung has been an active IEEE volunteer for more than 30 years. As a member and chair of the IEEE Technical Program Integrity and Conference Quality committees, he oversaw the quality of technical programs presented at IEEE conferences. Fung also chaired the Region 10 Educational Activities Committee. He was instrumental in translating educational materials to local languages for the IEEE Reaching Locals project. As chair of the IEEE New Initiatives Committee, he established and promoted the US $1 Million Challenge Call for New Initiatives, which supports potential IEEE programs, services, or products that will significantly benefit members, the public, the technical community, or customers and could have a lasting impact on IEEE or its business processes. Fung has left an indelible mark as a dedicated educator at Singapore Polytechnic, Curtin University, and Murdoch University. He was appointed in 2015 as professor emeritus at Murdoch, and he takes pride in training the next generation of volunteers, leaders, teachers, and researchers in the Western Australian community. Fung received the IEEE Third Millennium Medal and the IEEE Region 10 Outstanding Volunteer Award. IEEE Senior Member Eric Grigorian Director, Region 3: Southern U.S. & Jamaica Sean McNeil/GTRI Grigorian has extensive experience leading international cross-domain teams that support the commercial and defense industries. His current research focuses on implementing model-based systems engineering, creating models that depict system behavior, interfaces, and architecture. His work has led to streamlined processes, reduced costs, and faster design and implementation of capabilities due to efficient modeling and verification. Grigorian holds two U.S. utility patents. Grigorian has been an active volunteer with IEEE since his time as a student member at the University of Alabama in Huntsville (UAH). He saw it as an excellent way to network and get to know people. He found his personality was suited for working within the organization and building leadership skills. During the past 43 years as an IEEE member, he has been affiliated with the IEEE Aerospace and Electronic Systems (AESS), IEEE Computer, and IEEE Communications societies. As Grigorian’s career has evolved, his involvement with IEEE has also increased. He has been the IEEE Huntsville Section student activities chair, as well as vice chair, and chair. He also was the section’s AESS chair. He served as IEEE SoutheastCon chair in 2008 and 2019, and served on the IEEE Region 3 executive committee as area chair and conference committee chair, enhancing IEEE members’ benefits, engagement, and career advancement. He has significantly contributed to initiatives within IEEE, including promoting preuniversity science, technology, engineering, and mathematics efforts in Alabama. Grigorian’s professional achievements have been recognized with numerous awards from employers and local technical chapters, including with the 2020 UAH Alumni of Achievement Award for the College of Engineering and the 2006 IEEE Region 3 Outstanding Engineer of the Year Award. He is a member of the IEEE–Eta Kappa Nu honor society. IEEE Life Senior Member Christina Schober Director, Division V Katie Fears/Brio Art Schober is an innovative engineer with a diverse design and manufacturing engineering background. With more than 40 years of experience, her career has spanned research, design, and manufacturing sensors for space, commercial, and military aircraft navigation and tactical guidance systems. She was responsible for the successful transition from design to production for groundbreaking programs including an integrated flight management system, the Stinger missile’s roll frequency sensor, and the designing of three phases of the DARPA atomic clock. She holds 17 U.S. patents and 24 other patents in the aerospace and navigation fields. Schober started her career in the 1980s, at a time when female engineers were not widely accepted. The prevailing attitude required her to “stay tough,” she says, and she credits IEEE for giving her technical and professional support. Because of her experiences, she became dedicated to making diversity and inclusion systemic in IEEE. Schober has held many leadership roles, including IEEE Division VIII Director, IEEE Sensors Council president, and IEEE Standards Sensors Council secretary. In addition to her membership in the IEEE Photonics Society, she is active with the IEEE Computer Society, IEEE Sensors Council, IEEE Standards Association, and IEEE Women in Engineering. She is also active in her local community, serving as an invited speaker on STEM for the public school system and was a volunteer at youth shelters. Schober has received numerous awards including the IEEE Sensors Council Lifetime Contribution Award and the IEEE Twin Cities Section’s Young Engineer of the Year Award. She is an IEEE Computer Society Gold Core member, a member of the IEEE–Eta Kappa Nu honor society and received the IEEE Third Millennium Medal.

In physical books, yellowing pages are usually a sign of age. But brand-new users of Amazon’s Kindle Colorsofts, the tech giant’s first color e-reader, are already noticing yellow hues appearing at the bottoms of their displays. Since the complaints began to trickle in, Amazon has reportedly suspended shipments and announced that it is working to fix the issue. (As of publication of this article, the US $280 Kindle had an average 2.6 star rating on Amazon.) It’s not yet clear what is causing the discoloration. But while the issue is new—and unexpected—the technology is not, says Jason Heikenfeld, an IEEE Fellow and engineering professor at the University of Cincinnati. The Kindle Colorsoft, which became available on 30 October, uses “a very old approach,” says Heikenfeld, who previously worked to develop the ultimate e-paper technology. “It was the first approach everybody tried.” Amazon’s e-reader uses reflective display technology developed by E Ink, a company that started in the 1990s as an MIT Media Lab spin-off before developing its now-dominant electronic paper displays. E Ink is used in Kindles, as well as top e-readers from Kobo, reMarkable, Onyx, and more. E Ink first introduced Kaleido—the basis of the Colorsoft’s display—five years ago, though the road to full-color e-paper started well before. How E-Readers Work Monochromatic Kindles work by applying voltages to electrodes in the screen that bring black or white pigment to the top of each pixel. Those pixels then reflect ambient light, creating a paperlike display. To create a full-color display, companies like E Ink added an array of filters just above the ink. This approach didn’t work well at first because the filters lost too much light, making the displays dark and low resolution. But with a few adjustments, Kaleido was ready for consumer products in 2019. (Other approaches—like adding colored pigments to the ink—have been developed, but these come with their own drawbacks, including a higher price tag.) Given this design, it initially seemed to Heikenfeld that the issue would have stemmed from the software, which determines the voltages applied to each electrode. This aligned with reports from some users that the issue appeared after a software update. But industry analyst Ming-Chi Kuo suggested in a post on X that the issue is due to the e-reader’s hardware. Amazon switched the optically clear adhesive (OCA) used in the Colorsoft to a material that may not be so optically clear. In its announcement of the Colorsoft, the company boasted “custom formulated coatings” that would enhance the color display as one of the new e-reader’s innovations. In terms of resolving the issue, Kuo’s post also stated that “While component suppliers have developed several hardware solutions, Amazon seems to be leaning toward a software-based fix.” Heikenfeld is not sure how a software fix would work, apart from blacking out the bottom of the screen. Amazon did not reply to IEEE Spectrum’s request for comment. In an email to IEEE Spectrum, E Ink stated, “While we cannot comment on any individual partner or product, we are committed to supporting our partners in understanding and addressing any issues that arise.” The Future of E-Readers It took a long time for color Kindles to arrive, and the future of reflective e-reader displays isn’t likely to improve much, according to Heikenfeld. “I used to work a lot in this field, and it just really slowed down at some point, because it’s a tough nut to crack,” Heikenfeld says. There are inherent limitations and inefficiencies to working with filter-based color displays that rely on ambient light, and there’s no Moore’s Law for these displays. Instead, their improvement is asymptotic—and we may already be close to the limit. Meanwhile, displays that emit light, like LCD and OLED, continue to improve. “An iPad does a pretty damn good job with battery life now,” says Heikenfeld. At the same time, he believes there will always be a place for reflective displays, which remain a more natural experience for our eyes. “We live in a world of reflective color,” Heikenfeld says. This is story was updated on 12 November 2024 to correct that Jason Heikenfeld is an IEEE Fellow.

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Waiting for each part of a 3D-printed project to finish, taking it out of the printer, and then installing it on location can be tedious for multi-part projects. What if there was a way for your printer to print its creation exactly where you needed it? That’s the promise of MobiPrint, a new 3D printing robot that can move around a room, printing designs directly onto the floor. MobiPrint, designed by Daniel Campos Zamora at the University of Washington, consists of a modified off-the-shelf 3D printer atop a home vacuum robot. First it autonomously maps its space—be it a room, a hallway, or an entire floor of a house. Users can then choose from a prebuilt library or upload their own design to be printed anywhere in the mapped area. The robot then traverses the room and prints the design. It’s “a new system that combines robotics and 3D printing that could actually go and print in the real world,” Campos Zamora says. He presented MobiPrint on 15 October at the ACM Symposium on User Interface Software and Technology. Campos Zamora and his team started with a Roborock S5 vacuum robot and installed firmware that allowed it to communicate with the open source program Valetudo. Valetudo disconnects personal robots from their manufacturer’s cloud, connecting them to a local server instead. Data collected by the robot, such as environmental mapping, movement tracking, and path planning, can all be observed locally, enabling users to see the robot’s LIDAR-created map. Campos Zamora built a layer of software that connects the robot’s perception of its environment to the 3D printer’s print commands. The printer, a modified Prusa Mini+, can print on carpet, hardwood, and vinyl, with maximum printing dimensions of 180 by 180 by 65 millimeters. The robot has printed pet food bowls, signage, and accessibility markers as sample objects. MakeabilityLab/YouTube Currently, MobiPrint can only “park and print.” The robot base cannot move during printing to make large objects, like a mobility ramp. Printing designs larger than the robot is one of Campos Zamora’s goals in the future. To learn more about the team’s vision for MobiPrint, Campos Zamora answered a few questions from IEEE Spectrum. What was the inspiration for creating your mobile 3D printer? Daniel Campos Zamora: My lab is focused on building systems with an eye towards accessibility. One of the things that really inspired this project was looking at the tactile surface indicators that help blind and low vision users find their way around a space. And so we were like, what if we made something that could automatically go and deploy these things? Especially in indoor environments, which are generally a little trickier and change more frequently over time. We had to step back and build this entirely different thing, using the environment as a design element. We asked: how do you integrate the real world environment into the design process, and then what kind of things can you print out in the world? That’s how this printer was born. What were some surprising moments in your design process? Campos Zamora: When I was testing the robot on different surfaces, I was not expecting the 3D printed designs to stick extremely well to the carpet. It stuck way too well. Like, you know, just completely bonded down there. I think there’s also just a lot of joy in seeing this printer move. When I was doing a demonstration of it at this conference last week, it almost seemed like the robot had a personality. A vacuum robot can seem to have a personality, but this printer can actually make objects in my environment, so I feel a different relationship to the machine. Where do you hope to take MobiPrint in the future? Campos Zamora: There’s several directions I think we could go. Instead of controlling the robot remotely, we could have it follow someone around and print accessibility markers along a path they walk. Or we could integrate an AI system that recommends objects be printed in different locations. I also want to explore having the robot remove and recycle the objects it prints.

Finished chips coming in from the foundry are subject to a battery of tests. For those destined for critical systems in cars, those tests are particularly extensive and can add 5 to 10 percent to the cost of a chip. But do you really need to do every single test? Engineers at NXP have developed a machine-learning algorithm that learns the patterns of test results and figures out the subset of tests that are really needed and those that they could safely do without. The NXP engineers described the process at the IEEE International Test Conference in San Diego last week. NXP makes a wide variety of chips with complex circuitry and advanced chip-making technology, including inverters for EV motors, audio chips for consumer electronics, and key-fob transponders to secure your car. These chips are tested with different signals at different voltages and at different temperatures in a test process called continue-on-fail. In that process, chips are tested in groups and are all subjected to the complete battery, even if some parts fail some of the tests along the way. Chips were subject to between 41 and 164 tests, and the algorithm was able to recommend removing 42 to 74 percent of those tests. “We have to ensure stringent quality requirements in the field, so we have to do a lot of testing,” says Mehul Shroff, an NXP Fellow who led the research. But with much of the actual production and packaging of chips outsourced to other companies, testing is one of the few knobs most chip companies can turn to control costs. “What we were trying to do here is come up with a way to reduce test cost in a way that was statistically rigorous and gave us good results without compromising field quality.” A Test Recommender System Shroff says the problem has certain similarities to the machine learning-based recommender systems used in e-commerce. “We took the concept from the retail world, where a data analyst can look at receipts and see what items people are buying together,” he says. “Instead of a transaction receipt, we have a unique part identifier and instead of the items that a consumer would purchase, we have a list of failing tests.” The NXP algorithm then discovered which tests fail together. Of course, what’s at stake for whether a purchaser of bread will want to buy butter is quite different from whether a test of an automotive part at a particular temperature means other tests don’t need to be done. “We need to have 100 percent or near 100 percent certainty,” Shroff says. “We operate in a different space with respect to statistical rigor compared to the retail world, but it’s borrowing the same concept.” As rigorous as the results are, Shroff says that they shouldn’t be relied upon on their own. You have to “make sure it makes sense from engineering perspective and that you can understand it in technical terms,” he says. “Only then, remove the test.” Shroff and his colleagues analyzed data obtained from testing seven microcontrollers and applications processors built using advanced chipmaking processes. Depending on which chip was involved, they were subject to between 41 and 164 tests, and the algorithm was able to recommend removing 42 to 74 percent of those tests. Extending the analysis to data from other types of chips led to an even wider range of opportunities to trim testing. The algorithm is a pilot project for now, and the NXP team is looking to expand it to a broader set of parts, reduce the computational overhead, and make it easier to use. “Any novel solution that helps in test-time savings without any quality hit is valuable,” says Sriharsha Vinjamury, a principal engineer at Arm. “Reducing test time is essential, as it reduces costs.” He suggests that the NXP algorithm could be integrated with a system that adjust the order of tests, so that failures could be spotted earlier. This post was updated on 13 November 2024.

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. Humanoids 2024: 22–24 November 2024, NANCY, FRANCE Enjoy today’s videos! Just when I thought quadrupeds couldn’t impress me anymore... [ Unitree Robotics ] Researchers at Meta FAIR are releasing several new research artifacts that advance robotics and support our goal of reaching advanced machine intelligence (AMI). These include Meta Sparsh, the first general-purpose encoder for vision-based tactile sensing that works across many tactile sensors and many tasks; Meta Digit 360, an artificial fingertip-based tactile sensor that delivers detailed touch data with human-level precision and touch-sensing; and Meta Digit Plexus, a standardized platform for robotic sensor connections and interactions that enables seamless data collection, control and analysis over a single cable. [ Meta ] The first bimanual Torso created at Clone includes an actuated elbow, cervical spine (neck), and anthropomorphic shoulders with the sternoclavicular, acromioclavicular, scapulothoracic and glenohumeral joints. The valve matrix fits compactly inside the ribcage. Bimanual manipulation training is in progress. [ Clone Inc. ] Equipped with a new behavior architecture, Nadia navigates and traverses many types of doors autonomously. Nadia also demonstrates robustness to failed grasps and door opening attempts by automatically retrying and continuing. We present the robot with pull and push doors, four types of opening mechanisms, and even spring-loaded door closers. A deep neural network and door plane estimator allow Nadia to identify and track the doors. [ Paper preprint by authors from Florida Institute for Human and Machine Cognition ] Thanks, Duncan! In this study, we integrate the musculoskeletal humanoid Musashi with the wire-driven robot CubiX, capable of connecting to the environment, to form CubiXMusashi. This combination addresses the shortcomings of traditional musculoskeletal humanoids and enables movements beyond the capabilities of other humanoids. CubiXMusashi connects to the environment with wires and drives by winding them, successfully achieving movements such as pull-up, rising from a lying pose, and mid-air kicking, which are difficult for Musashi alone. [ CubiXMusashi, JSK Robotics Laboratory, University of Tokyo ] Thanks, Shintaro! An old boardwalk seems like a nightmare for any robot with flat feet. [ Agility Robotics ] This paper presents a novel learning-based control framework that uses keyframing to incorporate high-level objectives in natural locomotion for legged robots. These high-level objectives are specified as a variable number of partial or complete pose targets that are spaced arbitrarily in time. Our proposed framework utilizes a multi-critic reinforcement learning algorithm to effectively handle the mixture of dense and sparse rewards. In the experiments, the multi-critic method significantly reduces the effort of hyperparameter tuning compared to the standard single-critic alternative. Moreover, the proposed transformer-based architecture enables robots to anticipate future goals, which results in quantitative improvements in their ability to reach their targets. [ Disney Research paper ] Human-like walking where that human is the stompiest human to ever human its way through Humanville. [ Engineai ] We present the first static-obstacle avoidance method for quadrotors using just an onboard, monocular event camera. Quadrotors are capable of fast and agile flight in cluttered environments when piloted manually, but vision-based autonomous flight in unknown environments is difficult in part due to the sensor limitations of traditional onboard cameras. Event cameras, however, promise nearly zero motion blur and high dynamic range, but produce a large volume of events under significant ego-motion and further lack a continuous-time sensor model in simulation, making direct sim-to-real transfer not possible. [ Paper University of Pennsylvania and University of Zurich ] Cross-embodiment imitation learning enables policies trained on specific embodiments to transfer across different robots, unlocking the potential for large-scale imitation learning that is both cost-effective and highly reusable. This paper presents LEGATO, a cross-embodiment imitation learning framework for visuomotor skill transfer across varied kinematic morphologies. We introduce a handheld gripper that unifies action and observation spaces, allowing tasks to be defined consistently across robots. [ LEGATO ] The 2024 Xi’an Marathon has kicked off! STAR1, the general-purpose humanoid robot from Robot Era, joins runners in this ancient yet modern city for an exciting start! [ Robot Era ] In robotics, there are valuable lessons for students and mentors alike. Watch how the CyberKnights, a FIRST robotics team champion sponsored by RTX, with the encouragement of their RTX mentor, faced challenges after a poor performance and scrapped its robot to build a new one in just nine days. [ CyberKnights ] In this special video, PAL Robotics takes you behind the scenes of our 20th-anniversary celebration, a memorable gathering with industry leaders and visionaries from across robotics and technology. From inspiring speeches to milestone highlights, the event was a testament to our journey and the incredible partnerships that have shaped our path. [ PAL Robotics ] Thanks, Rugilė!

In 6G telecom research today, a crucial portion of wireless spectrum has been neglected: the Frequency Range 3, or FR3, band. The shortcoming is partly due to a lack of viable software and hardware platforms for studying this region of spectrum, ranging from approximately 6 to 24 gigahertz. But a new, open-source wireless research kit is changing that equation. And research conducted using that kit, presented last week at a leading industry conference, offers proof of viability of this spectrum band for future 6G networks. In fact, it’s also arguably signaling a moment of telecom industry re-evaluation. The high-bandwidth 6G future, according to these folks, may not be entirely centered around difficult millimeter wave-based technologies. Instead, 6G may leave plenty of room for higher-bandwidth microwave spectrum tech that is ultimately more familiar and accessible. The FR3 band is a region of microwave spectrum just shy of millimeter-wave frequencies (30 to 300 GHz). FR3 is also already very popular today for satellite Internet and military communications. For future 5G and 6G networks to share the FR3 band with incumbent players would require telecom networks nimble enough to perform regular, rapid-response spectrum-hopping. Yet spectrum-hopping might still be an easier problem to solve than those posed by the inherent physical shortcomings of some portions of millimeter-wave spectrum—shortcomings that include limited range, poor penetration, line-of-sight operations, higher power requirements, and susceptibility to weather. Pi-Radio’s New Face Earlier this year, the Brooklyn, N.Y.-based startup Pi-Radio—a spinoff from New York University’s Tandon School of Engineering—released a wireless spectrum hardware and software kit for telecom research and development. Pi-Radio’s FR-3 is a software-defined radio system developed for the FR3 band specifically, says company co-founder Sundeep Rangan. “Software-defined radio is basically a programmable platform to experiment and build any type of wireless technology,” says Rangan, who is also the associate director of NYU Wireless. “In the early stages when developing systems, all researchers need these.” For instance, the Pi-Radio team presented one new research finding that infers direction to an FR3 antenna from measurements taken by a mobile Pi-Radio receiver—presented at the IEEE Signal Processing Society‘s Asilomar Conference on Signals, Systems and Computers in Pacific Grove, Calif. on 30 October. According to Pi-Radio co-founder Marco Mezzavilla, who’s also an associate professor at the Polytechnic University of Milan, the early-stage FR3 research that the team presented at Asilomar will enable researchers “to capture [signal] propagation in these frequencies and will allow us to characterize it, understand it, and model it... And this is the first stepping stone towards designing future wireless systems at these frequencies.” There’s a good reason researchers have recently rediscovered FR3, says Paolo Testolina, postdoctoral research fellow at Northeastern University’s Institute for the Wireless Internet of Things unaffiliated with the current research effort. “The current scarcity of spectrum for communications is driving operators and researchers to look in this band, where they believe it is possible to coexist with the current incumbents,” he says. “Spectrum sharing will be key in this band.” Rangan notes that the work on which Pi-Radio was built has been published earlier this year both on the more foundational aspects of building networks in the FR3 band as well as the specific implementation of Pi-Radio’s unique, frequency-hopping research platform for future wireless networks. (Both papers were published in IEEE journals.) “If you have frequency hopping, that means you can get systems that are resilient to blockage,” Rangan says. “But even, potentially, if it was attacked or compromised in any other way, this could actually open up a new type of dimension that we typically haven’t had in the cellular infrastructure.” The frequency-hopping that FR3 requires for wireless communications, in other words, could introduce a layer of hack-proofing that might potentially strengthen the overall network. Complement, Not Replacement The Pi-Radio team stresses, however, that FR3 would not supplant or supersede other new segments of wireless spectrum. There are, for instance, millimeter wave 5G deployments already underway today that will no doubt expand in scope and performance into the 6G future. That said, the ways that FR3 expand future 5G and 6G spectrum usage is an entirely unwritten chapter: Whether FR3 as a wireless spectrum band fizzles, or takes off, or finds a comfortable place somewhere in between depends in part on how it’s researched and developed now, the Pi-Radio team says. “We’re at this tipping point where researchers and academics actually are empowered by the combination of this cutting-edge hardware with open-source software,” Mezzavilla says. “And that will enable the testing of new features for communications in these new frequency bands.” (Mezzavilla credits the National Telecommunications and Information Administration for recognizing the potential of FR3, and for funding the group’s research.) By contrast, millimeter-wave 5G and 6G research has to date been bolstered, the team says, by the presence of a wide range of millimeter-wave software-defined radio (SDR) systems and other research platforms. “Companies like Qualcomm, Samsung, Nokia, they actually had excellent millimeter wave development platforms,” Rangan says. “But they were in-house. And the effort it took to build one—an SDR at a university lab—was sort of insurmountable.” So releasing an inexpensive open-source SDR in the FR3 band, Mezzavilla says, could jump start a whole new wave of 6G research. “This is just the starting point,” Mezzavilla says. “From now on we’re going to build new features—new reference signals, new radio resource control signals, near-field operations... We’re ready to ship these yellow boxes to other academics around the world to test new features and test them quickly, before 6G is even remotely near us.” This story was updated on 7 November 2024 to include detail about funding from the National Telecommunications and Information Administration.

Azerbaijan next week will garner much of the attention of the climate tech world, and not just because it will host COP29, the United Nation’s giant annual climate change conference. The country is promoting a grand, multi-nation plan to generate renewable electricity in the Caucasus region and send it thousands of kilometers west, under the Black Sea, and into energy–hungry Europe. The transcontinental connection would start with wind, solar, and hydropower generated in Azerbaijan and Georgia, and off-shore wind power generated in the Caspian Sea. Long-distance lines would carry up to 1.5 gigawatts of clean electricity to Anaklia, Georgia, at the east end of the Black Sea. An undersea cable would move the electricity across the Black Sea and deliver it to Constanta, Romania, where it could be distributed further into Europe. The scheme’s proponents say this Caspian-Black Sea energy corridor will help decrease global carbon emissions, provide dependable power to Europe, modernize developing economies at Europe’s periphery, and stabilize a region shaken by war. Organizers hope to build the undersea cable within the next six years at an estimated cost of €3.5 billion (US $3.8 billion). To accomplish this, the governments of the involved countries must quickly circumvent a series of technical, financial, and political obstacles. “It’s a huge project,” says Zviad Gachechiladze, a director at Georgian State Electrosystem, the agency that operates the country’s electrical grid, and one of the architects of the Caucasus green-energy corridor. “To put it in operation [by 2030]—that’s quite ambitious, even optimistic,” he says. Black Sea Cable to Link Caucasus and Europe The technical lynchpin of the plan falls on the successful construction of a high voltage direct current (HVDC) submarine cable in the Black Sea. It’s a formidable task, considering that it would stretch across nearly 1,200 kilometers of water, most of which is over 2 km deep, and, since Russia’s invasion of Ukraine, littered with floating mines. By contrast, the longest existing submarine power cable—the North Sea Link—carries 1.4 GW across 720 km between England and Norway, at depths of up to 700 meters. As ambitious as Azerbaijan’s plans sound, longer undersea connections have been proposed. The Australia-Asia PowerLink project aims to produce 6 GW at a vast solar farm in Northern Australia and send about a third of it to Singapore via a 4,300-km undersea cable. The Morocco-U.K. Power Project would send 3.6 GW over 3,800 km from Morocco to England. A similar attempt by Desertec to send electricity from North Africa to Europe ultimately failed. Building such cables involves laying and stitching together lengths of heavy submarine power cables from specialized ships—the expertise for which lies with just two companies in the world. In an assessment of the Black Sea project’s feasibility, the Milan-based consulting and engineering firm CESI determined that the undersea cable could indeed be built, and estimated that it could carry up to 1.5 GW—enough to supply over 2 million European households. But to fill that pipe, countries in the Caucasus region would have to generate much more green electricity. For Georgia, that will mostly come from hydropower, which already generates over 80 percent of the nation’s electricity. “We are a hydro country. We have a lot of untapped hydro potential,” says Gachechiladze. Azerbaijan and Georgia Plan Green Energy Corridor Generating hydropower can also generate opposition, because of the way dams alter rivers and landscapes. “There were some cases when investors were not able to construct power plants because of opposition of locals or green parties” in Georgia, says Salome Janelidze, a board member at the Energy Training Center, a Georgian government agency that promotes and educates around the country’s energy sector. “It was definitely a problem and it has not been totally solved,” says Janelidze. But “to me it seems it is doable,” she says. “You can procure and construct if you work closely with the local population and see them as allies rather than adversaries.” For Azerbaijan, most of the electricity would be generated by wind and solar farms funded by foreign investment. Masdar, the renewable-energy developer of the United Arab Emirates government, has been investing heavily in wind power in the country. In June, the company broke ground on a trio of wind and solar projects with 1 GW capacity. It intends to develop up to 9 GW more in Azerbaijan by 2030. ACWA Power, a Saudi power-generation company, plans to complete a 240-MW solar plant in the Absheron and Khizi districts of Azerbaijan next year and has struck a deal with the Azerbaijani Ministry of Energy to install up to 2.5 GW of offshore and onshore wind. CESI is currently running a second study to gauge the practicality of the full breadth of the proposed energy corridor—from the Caspian Sea to Europe—with a transmission capacity of 4 to 6 GW. But that beefier interconnection will likely remain out of reach in the near term. “By 2030, we can’t claim our region will provide 4 GW or 6 GW,” says Gachechiladze. “1.3 is realistic.” COP29: Azerbaijan’s Renewable Energy Push Signs of political support have surfaced. In September, Azerbaijan, Georgia, Romania, and Hungary created a joint venture, based in Romania, to shepherd the project. Those four countries in 2022 inked a memorandum of understanding with the European Union to develop the energy corridor. The involved countries are in the process of applying for the cable to be selected as an EU “project of mutual interest,” making it an infrastructure priority for connecting the union with its neighbors. If selected, “the project could qualify for 50 percent grant financing,” says Gachechiladze. “It’s a huge budget. It will improve drastically the financial condition of the project.” The commissioner responsible for EU enlargement policy projected that the union would pay an estimated €2.3 billion ($2.5 billion) toward building the cable. Whether next week’s COP29, held in Baku, Azerbaijan, will help move the plan forward remains to be seen. In preparation for the conference, advocates of the energy corridor have been taking international journalists on tours of the country’s energy infrastructure. Looming over the project are the security issues threaten to thwart it. Shipping routes in the Black Sea have become less dependable and safe since Russia’s invasion of Ukraine. To the south, tensions between Armenia and Azerbaijan remain after the recent war and ethnic violence. In order to improve relations, many advocates of the energy corridor would like to include Armenia. “The cable project is in the interests of Georgia, it’s in the interests of Armenia, it’s in the interests of Azerbaijan,” says Agha Bayramov, an energy geopolitics researcher at the University of Groningen, in the Netherlands. “It might increase the chance of them living peacefully together. Maybe they’ll say, ‘We’re responsible for European energy. Let’s put our egos aside.’”

EPICS in IEEE, a service learning program for university students supported by IEEE Educational Activities, offers students opportunities to engage with engineering professionals and mentors, local organizations, and technological innovation to address community-based issues. The following two environmentally focused projects demonstrate the value of teamwork and direct involvement with project stakeholders. One uses smart biodigesters to better manage waste in Colombia’s rural areas. The other is focused on helping Turkish olive farmers protect their trees from climate change effects by providing them with a warning system that can identify growing problems. No time to waste in rural Colombia Proper waste management is critical to a community’s living conditions. In rural La Vega, Colombia, the lack of an effective system has led to contaminated soil and water, an especially concerning issue because the town’s economy relies heavily on agriculture. The Smart Biodigesters for a Better Environment in Rural Areas project brought students together to devise a solution. Vivian Estefanía Beltrán, a Ph.D. student at the Universidad del Rosario in Bogotá, addressed the problem by building a low-cost anaerobic digester that uses an instrumentation system to break down microorganisms into biodegradable material. It reduces the amount of solid waste, and the digesters can produce biogas, which can be used to generate electricity. “Anaerobic digestion is a natural biological process that converts organic matter into two valuable products: biogas and nutrient-rich soil amendments in the form of digestate,” Beltrán says. “As a by-product of our digester’s operation, digestate is organic matter that can’t be transferred into biogas but can be used as a soil amendment for our farmers’ crops, such as coffee. “While it may sound easy, the process is influenced by a lot of variables. The support we’ve received from EPICS in IEEE is important because it enables us to measure these variables, such as pH levels, temperature of the reactor, and biogas composition [methane and hydrogen sulfide]. The system allows us to make informed decisions that enhance the safety, quality, and efficiency of the process for the benefit of the community.” The project was a collaborative effort among Universidad del Rosario students, a team of engineering students from Escuela Tecnológica Instituto Técnico Central, Professor Carlos Felipe Vergara, and members of Junta de Acción Comunal (Vereda La Granja), which aims to help residents improve their community. “It’s been a great experience to see how individuals pursuing different fields of study—from engineering to electronics and computer science—can all work and learn together on a project that will have a direct positive impact on a community.” —Vivian Estefanía Beltrán Beltrán worked closely with eight undergraduate students and three instructors—Maria Fernanda Gómez, Andrés Pérez Gordillo (the instrumentation group leader), and Carlos Felipe Vergara-Ramirez—as well as IEEE Graduate Student Member Nicolás Castiblanco (the instrumentation group coordinator). The team constructed and installed their anaerobic digester system in an experimental station in La Vega, a town located roughly 53 kilometers northwest of Bogotá. “This digester is an important innovation for the residents of La Vega, as it will hopefully offer a productive way to utilize the residual biomass they produce to improve quality of life and boost the economy,” Beltrán says. Soon, she adds, the system will be expanded to incorporate high-tech sensors that automatically monitor biogas production and the digestion process. “For our students and team members, it’s been a great experience to see how individuals pursuing different fields of study—from engineering to electronics and computer science—can all work and learn together on a project that will have a direct positive impact on a community. It enables all of us to apply our classroom skills to reality,” she says. “The funding we’ve received from EPICS in IEEE has been crucial to designing, proving, and installing the system.” The project also aims to support the development of a circular economy, which reuses materials to enhance the community’s sustainability and self-sufficiency. Protecting olive groves in Türkiye Türkiye is one of the world’s leading producers of olives, but the industry has been challenged in recent years by unprecedented floods, droughts, and other destructive forces of nature resulting from climate change. To help farmers in the western part of the country monitor the health of their olive trees, a team of students from Istanbul Technical University developed an early-warning system to identify irregularities including abnormal growth. “Almost no olives were produced last year using traditional methods, due to climate conditions and unusual weather patterns,” says Tayfun Akgül, project leader of the Smart Monitoring of Fruit Trees in Western Türkiye initiative. “Our system will give farmers feedback from each tree so that actions can be taken in advance to improve the yield,” says Akgül, an IEEE senior member and a professor in the university’s electronics and communication engineering department. “We’re developing deep-learning techniques to detect changes in olive trees and their fruit so that farmers and landowners can take all necessary measures to avoid a low or damaged harvest,” says project coordinator Melike Girgin, a Ph.D. student at the university and an IEEE graduate student member. Using drones outfitted with 360-degree optical and thermal cameras, the team collects optical, thermal, and hyperspectral imaging data through aerial methods. The information is fed into a cloud-based, open-source database system. Akgül leads the project and teaches the team skills including signal and image processing and data collection. He says regular communication with community-based stakeholders has been critical to the project’s success. “There are several farmers in the village who have helped us direct our drone activities to the right locations,” he says. “Their involvement in the project has been instrumental in helping us refine our process for greater effectiveness. “For students, classroom instruction is straightforward, then they take an exam at the end. But through our EPICS project, students are continuously interacting with farmers in a hands-on, practical way and can see the results of their efforts in real time.” Looking ahead, the team is excited about expanding the project to encompass other fruits besides olives. The team also intends to apply for a travel grant from IEEE in hopes of presenting its work at a conference. “We’re so grateful to EPICS in IEEE for this opportunity,” Girgin says. “Our project and some of the technology we required wouldn’t have been possible without the funding we received.” A purpose-driven partnership The IEEE Standards Association sponsored both of the proactive environmental projects. “Technical projects play a crucial role in advancing innovation and ensuring interoperability across various industries,” says Munir Mohammed, IEEE SA senior manager of product development and market engagement. “These projects not only align with our technical standards but also drive technological progress, enhance global collaboration, and ultimately improve the quality of life for communities worldwide.” For more information on the program or to participate in service-learning projects, visit EPICS in IEEE. On 7 November, this article was updated from an earlier version.

Last week the organization tasked with running the the biggest chunk of U.S. CHIPS Act’s US $13 billion R&D program made some significant strides: The National Semiconductor Technology Center (NSTC) released a strategic plan and selected the sites of two of three planned facilities and released a new strategic plan. The locations of the two sites—a “design and collaboration” center in Sunnyvale, Calif., and a lab devoted to advancing the leading edge of chipmaking, in Albany, N.Y.—build on an existing ecosystem at each location, experts say. The location of the third planned center—a chip prototyping and packaging site that could be especially critical for speeding semiconductor startups—is still a matter of speculation. “The NSTC represents a once-in-a-generation opportunity for the U.S. to accelerate the pace of innovation in semiconductor technology,” Deirdre Hanford, CEO of Natcast, the nonprofit that runs the NSTC centers, said in a statement. According to the strategic plan, which covers 2025 to 2027, the NSTC is meant to accomplish three goals: extend U.S. technology leadership, reduce the time and cost to prototype, and build and sustain a semiconductor workforce development ecosystem. The three centers are meant to do a mix of all three. New York gets extreme ultraviolet lithography NSTC plans to direct $825 million into the Albany project. The site will be dedicated to extreme ultraviolet lithography, a technology that’s essential to making the most advanced logic chips. The Albany Nanotech Complex, which has already seen more than $25 billion in investments from the state and industry partners over two decades, will form the heart of the future NSTC center. It already has an EUV lithography machine on site and has begun an expansion to install a next-generation version, called high-NA EUV, which promises to produce even finer chip features. Working with a tool recently installed in Europe, IBM, a long-time tenant of the Albany research facility, reported record yields of copper interconnects built every 21 nanometers, a pitch several nanometers tighter than possible with ordinary EUV. “It’s fulfilling to see that this ecosystem can be taken to the national and global level through CHIPS Act funding,” said Mukesh Khare, general manager of IBM’s semiconductors division, speaking from the future site of the NSTC EUV center. “It’s the right time, and we have all the ingredients.” While only a few companies are capable of manufacturing cutting edge logic using EUV, the impact of the NSTC center will be much broader, Khare argues. It will extend down as far as early-stage startups with ideas or materials for improving the chipmaking process “An EUV R&D center doesn’t mean just one machine,” says Khare. “It needs so many machines around it… It’s a very large ecosystem.” Silicon Valley lands the design center The design center is tasked with conducting advanced research in chip design, electronic design automation (EDA), chip and system architectures, and hardware security. It will also host the NSTC’s design enablement gateway—a program that provides NSTC members with a secure, cloud-based access to design tools, reference processes and designs, and shared data sets, with the goal of reducing the time and cost of design. Additionally, it will house workforce development, member convening, and administration functions. Situating the design center in Silicon Valley, with its concentration of research universities, venture capital, and workforce, seems like the obvious choice to many experts. “I can’t think of a better place,” says Patrick Soheili, co-founder of interconnect technology startup Eliyan, which is based in Santa Clara, Calif. Abhijeet Chakraborty, vice president of engineering in the technology and product group at Silicon Valley-based Synopsys, a leading maker of EDA software, sees Silicon Valley’s expansive tech ecosystem as one of its main advantages in landing the NSTC’s design center. The region concentrates companies and researchers involved in the whole spectrum of the industry from semiconductor process technology to cloud software. Access to such a broad range of industries is increasingly important for chip design startups, he says. “To design a chip or component these days you need to go from concept to design to validation in an environment that takes care of the entire stack,” he says. It’s prohibitively expensive for a startup to do that alone, so one of Chakraborty’s hopes for the design center is that it will help startups access the design kits and other data needed to operate in this new environment. Packaging and prototyping still to come A third promised center for prototyping and packaging is still to come. “The big question is where does the packaging and prototyping go?” says Mark Granahan, cofounder and CEO of Pennsylvania-based power semiconductor startup Ideal Semiconductor. “To me that’s a great opportunity.” He points out that because there is so little packaging technology infrastructure in the United States, any ambitious state or region should have a shot at hosting such a center. One of the original intentions of the act, after all, was to expand the number of regions of the country that are involved in the semiconductor industry. But that hasn’t stopped some already tech-heavy regions from wanting it. “Oregon offers the strongest ecosystem for such a facility,” a spokesperson for Intel, whose technology development is done there. “The state is uniquely positioned to contribute to the success of the NSTC and help drive technological advancements in the U.S. semiconductor industry.” As NSTC makes progress, Granahan’s concern is that bureaucracy will expand with it and slow efforts to boost the U.S. chip industry. Already the layers of control are multiplying. The Chips Office at the National Institute of Standards and Technology executes the Act. The NSTC is administered by the nonprofit Natcast, which directs the EUV center, which is in a facility run by another nonprofit, NY CREATES. “We want these things to be agile and make local decisions.”

Research expeditions conducted at sea using a rotating gravity machine and microscope found that the Earth’s oceans may not be absorbing as much carbon as researchers have long thought. Oceans are believed to absorb roughly 26 percent of global carbon dioxide emissions by drawing down CO2 from the atmosphere and locking it away. In this system, CO2 enters the ocean, where phytoplankton and other organisms consume about 70 percent of it. When these organisms eventually die, their soft, small structures sink to the bottom of the ocean in what looks like an underwater snowfall. This “marine snow” pulls carbon away from the surface of the ocean and sequesters it in the depths for millennia, which enables the surface waters to draw down more CO2 from the air. It’s one of Earth’s best natural carbon-removal systems. It’s so effective at keeping atmospheric CO2 levels in check that many research groups are trying to enhance the process with geoengineering techniques. But the new study, published on 11 October in Science, found that the sinking particles don’t fall to the ocean floor as quickly as researchers thought. Using a custom gravity machine that simulated marine snow’s native environment, the study’s authors observed that the particles produce mucus tails that act like parachutes, putting the brakes on their descent—sometimes even bringing them to a standstill. The physical drag leaves carbon lingering in the upper hydrosphere, rather than being safely sequestered in deeper waters. Living organisms can then consume the marine snow particles and respire their carbon back into the sea. Ultimately, this impedes the rate at which the ocean draws down and sequesters additional CO2 from the air. The implications are grim: Scientists’ best estimates of how much CO2 the Earth’s oceans sequester could be way off. “We’re talking roughly hundreds of gigatonnes of discrepancy if you don’t include these marine snow tails,” says Manu Prakash, a bioengineer at Stanford University and one of the paper’s authors. The work was conducted by researchers at Stanford, Rutgers University in New Jersey, and Woods Hole Oceanographic Institution in Massachusetts. Oceans Absorb Less CO2 Than Expected Researchers for years have been developing numerical models to estimate marine carbon sequestration. Those models will need to be adjusted for the slower sinking speed of marine snow, Prakash says. The findings also have implications for startups in the fledgling marine carbon geoengineering field. These companies use techniques such as ocean alkalinity enhancement to augment the ocean’s ability to sequester carbon. Their success depends, in part, on using numerical models to prove to investors and the public that their techniques work. But their estimates are only as good as the models they use, and the scientific community’s confidence in them. “We’re talking roughly hundreds of gigatonnes of discrepancy if you don’t include these marine snow tails.” —Manu Prakash, Stanford University The Stanford researchers made the discovery on an expedition off the coast of Maine. There, they collected marine samples by hanging traps from their boat 80 meters deep. After pulling up a sample, the researchers quickly analyzed the contents while still on board the ship using their wheel-shaped machine and microscope. The researchers built a microscope with a spinning wheel that simulates marine snow falling through sea water over longer distances than would otherwise be practical.Prakash Lab/Stanford The device simulates the organisms’ vertical travel over long distances. Samples go into a wheel about the size of a vintage film reel. The wheel spins constantly, allowing suspended marine-snow particles to sink while a camera captures their every move. The apparatus adjusts for temperature, light, and pressure to emulate marine conditions. Computational tools assess flow around the sinking particles and custom software removes noise in the data from the ship’s vibrations. To accommodate for the tilt and roll of the ship, the researchers mounted the device on a two-axis gimbal. Slower Marine Snow Reduces Carbon Sequestration With this setup, the team observed that sinking marine snow generates an invisible halo-shaped comet tail made of viscoelastic transparent exopolymer—a mucus-like parachute. They discovered the invisible tail by adding small beads to the seawater sample in the wheel, and analyzing the way they flowed around the marine snow. “We found that the beads were stuck in something invisible trailing behind the sinking particles,” says Rahul Chajwa, a bioengineering postdoctoral fellow at Stanford. The tail introduces drag and buoyancy, doubling the amount of time marine snow spends in the upper 100 meters of the ocean, the researchers concluded. “This is the sedimentation law we should be following,” says Prakash, who hopes to get the results into climate models. The study will likely help models project carbon export—the process of transporting CO2 from the atmosphere to the deep ocean, says Lennart Bach, a marine biochemist at the University of Tasmania in Australia, who was not involved with the research. “The methodology they developed is very exciting and it’s great to see new methods coming into this research field,” he says. But Bach cautions against extrapolating the results too far. “I don’t think the study will change the numbers on carbon export as we know them right now,” because these numbers are derived from empirical methods that would have unknowingly included the effects of the mucus tail, he says. Marine snow may be slowed by “parachutes” of mucus while sinking, potentially lowering the rate at which the global ocean can sequester carbon in the depths.Prakash Lab/Stanford Prakash and his team came up with the idea for the microscope while conducting research on a human parasite that can travel dozens of meters. “We would make 5- to 10-meter-tall microscopes, and one day, while packing for a trip to Madagascar, I had this ‘aha’ moment,” says Prakash. “I was like: Why are we packing all these tubes? What if the two ends of these tubes were connected?” The group turned their linear tube into a closed circular channel—a hamster wheel approach to observing microscopic particles. Over five expeditions at sea, the team further refined the microscope’s design and fluid mechanics to accommodate marine samples, often tackling the engineering while on the boat and adjusting for flooding and high seas. In addition to the sedimentation physics of marine snow, the team also studies other plankton that may affect climate and carbon-cycle models. On a recent expedition off the coast of Northern California, the group discovered a cell with silica ballast that makes marine snow sink like a rock, Prakash says. The crafty gravity machine is one of Prakash’s many frugal inventions, which include an origami-inspired paper microscope, or “foldscope,” that can be attached to a smartphone, and a paper-and-string biomedical centrifuge dubbed a “paperfuge.”

Like many innovators, Hagit Messer-Yaron had a life-changing idea while doing something mundane: Talking with a colleague over a cup of coffee. The IEEE Life Fellow, who in 2006 was head of Tel Aviv University’s Porter School of Environmental Studies, was at the school’s cafeteria with a meteorological researcher. He shared his struggles with finding high-resolution weather data for his climate models, which are used to forecast and track flash floods. Predicting floods is crucial for quickly evacuating residents in affected areas and protecting homes and businesses against damage. Hagit Messer-Yaron Employer Tel Aviv University Title Professor emerita Member grade Life Fellow Alma mater Tel Aviv UniversityHer colleague “said researchers in the field had limited measurements because the equipment meteorologists used to collect weather data—including radar satellites—is expensive to purchase and maintain, especially in developing countries,” Messer-Yaron says. Because of that, she says, high-resolution data about temperature, air quality, wind speed, and precipitation levels is often inconsistent—which is a problem when trying to produce accurate models and predictions. An expert in signal processing and cellular communication, Messer-Yaron came up with the idea of using existing wireless communication signals to collect weather data, as communication networks are spread across the globe. In 2006 she and her research team developed algorithms that process and analyze data collected by communication networks to monitor rainfall. They measure the difference in amplitude of the signals transmitted and received by the systems to extract data needed to predict flash floods. The method was first demonstrated in Israel. Messer-Yaron is working to integrate it into communication networks worldwide. For her work, she received this year’s IEEE Medal for Environmental and Safety Technologies for “contributions to sensing of the environment using wireless communication networks.” The award is sponsored by Toyota. “Receiving an IEEE medal, which is the highest-level award you can get within the organization, was really a surprise, and I was extremely happy to [receive] it,” she says. “I was proud that IEEE was able to evaluate and see the potential in our technology for public good and to reward it.” A passion for teaching Growing up in Israel, Messer-Yaron was interested in art, literature, and science. When it came time to choose a career, she found it difficult to decide, she says. Ultimately, she chose electrical engineering, figuring it would be easier to enjoy art and literature as hobbies. After completing her mandatory service in the Israel Defense Forces in 1973, she began her undergraduate studies at Tel Aviv University, where she found her passion: Signal processing. “Electrical engineering is a very broad topic,” she says. “As an undergrad, you learn all the parts that make up electrical engineering, including applied physics and applied mathematics. I really enjoyed applied mathematics and soon discovered signal processing. I found it quite amazing how, by using algorithms, you can direct signals to extract information.” She graduated with a bachelor’s degree in EE in 1977 and continued her education there, earning master’s and doctoral degrees in 1979 and 1984. She moved to the United States for a postdoctoral position at Yale. There she worked with IEEE Life Fellow Peter Schultheiss, who was known for his research in using sensor array systems in underwater acoustics. Inspired by Schultheiss’s passion for teaching, Messer-Yaron decided to pursue a career in academia. She was hired by Tel Aviv University as an electrical engineering professor in 1986. She was the first woman in Israel to become a full professor in the subject. “Being a faculty member at a public university is the best job you can do. I didn’t make a lot of money, but at the end of each day, I looked back at what I did [with pride].” For the next 14 years, she conducted research in statistical signal processing, time-delay estimation, and sensor array processing. Her passion for teaching took her around the world as a visiting professor at Yale, the New Jersey Institute of Technology, the Institut Polytechnique de Paris, and other schools. She collaborated with colleagues from the universities on research projects. In 1999 she was promoted to director of Tel Aviv University’s undergraduate electrical engineering program. A year later, she was offered an opportunity she couldn’t refuse: Serving as chief scientist for the Israeli Ministry of Science, Culture, and Sports. She took a sabbatical from teaching and for the next three years oversaw the country’s science policy. “I believe [working in the public sector] is part of our duty as faculty members, especially in public universities, because that makes you a public intellectual,” she says. “Working for the government gave me a broad view of many things that you don’t see as a professor, even in a large university.” When she returned to the university in 2004, Messer-Yaron was appointed as the director of the new school of environmental studies. She oversaw the allocation of research funding and spoke with researchers individually to better understand their needs. After having coffee with one researcher, she realized there was a need to develop better weather-monitoring technology. Hagit Messer-Yaron proudly displays her IEEE Medal for Environmental and Safety Technologies at this year’s IEEE Honors Ceremony. She is accompanied by IEEE President-Elect Kathleen Kramer and IEEE President Tom Couglin.Robb Cohen Using signal processing to monitor weather Because the planet is warming, the risk of flash floods is steadily increasing. Warmer air holds more water—which leads to heavier-than-usual rainfall and results in more flooding, according to the U.S. Environmental Protection Agency. Data about rainfall is typically collected by satellite radar and ground-based rain gauges. However, radar images don’t provide researchers with precise readings of what’s happening on the ground, according to an Ensia article. Rain gauges are accurate but provide data from small areas only. So Messer-Yaron set her sights on developing technology that connects to cellular networks close to the ground to provide more accurate measurements, she says. Using existing infrastructure eliminates the need to build new weather radars and weather stations. Communication systems automatically record the transmitted signal level and the received signal level, but rain can alter otherwise smooth wave patterns. By measuring the difference in the amplitude, meteorologists could extract the data necessary to track rainfall using the signal processing algorithms. In 2005 Messer-Yaron and her group successfully tested the technology. The following year, their “Environmental Monitoring by Wireless Communication Networks” paper was published in Science. The algorithm is being used in Israel in partnership with all three of the country’s major cellular service providers. Messer-Yaron acknowledges, however, that negotiating deals with cellular service companies in other countries has been difficult. To expand the technology’s use worldwide, Messer-Yaron launched a research network through the European Cooperation in Science and Technology (COST), called an opportunistic precipitation sensing network known as OPENSENSE. The group connects researchers, meteorologists, and other experts around the world to collaborate on integrating the technology in members’ communities. Monitoring the effects of climate change Since developing the technology, Messer-Yaron has held a number of jobs including president of the Open University of Israel and vice chair of the country’s Council for Higher Education, which accredits academic institutions. She is maintaining her link with Tel Aviv University today as a professor emerita. “Being a faculty member at a public university is the best job you can do,” she says. “I didn’t make a lot of money, but at the end of each day, I looked back at what I did [with pride]. Because of the academic freedom and the autonomy I had, I was able to do many things in addition to teaching, including research.” To continue her work in developing technology to monitor weather events, in 2016, she helped found ClimaCell, now Tomorrow.io, based in Boston. The startup aims to use wireless communication infrastructure and IoT devices to collect real-time weather data. Messer-Yaron served as its chief scientist until 2017. She continues to update the original algorithms with her students, most recently with machine learning capabilities to extract data from physical measurements of the signal level in communication networks. A global engineering community When Messer-Yaron was an undergraduate student, she joined IEEE at the suggestion of one of her professors. “I didn’t think much about the benefits of being a member until I became a graduate student,” she says. “I started attending conferences and publishing papers in IEEE journals, and the organization became my professional community.” She is an active volunteer and a member of the IEEE Signal Processing Society. From 1994 to 2010 she served on the society’s Signal Processing Theory and Methods technical committee. She was associate editor of IEEE Signal Processing Letters and IEEE Transactions on Signal Processing. She is a member of the editorial boards of the IEEE Journal of Selected Topics in Signal Processing and IEEE Transactions on Signal Processing. In the past 10 years, she’s been involved with other IEEE committees including the conduct review, ethics and member conduct, and global public policy bodies. “I don’t see my career or my professional life without the IEEE,” she says

Boston Dynamics is the master of dropping amazing robot videos with no warning, and last week, we got a surprise look at the new electric Atlas going “hands on” with a practical factory task. This video is notable because it’s the first real look we’ve had at the new Atlas doing something useful—or doing anything at all, really, as the introductory video from back in April (the first time we saw the robot) was less than a minute long. And the amount of progress that Boston Dynamics has made is immediately obvious, with the video showing a blend of autonomous perception, full body motion, and manipulation in a practical task. We sent over some quick questions as soon as we saw the video, and we’ve got some extra detail from Scott Kuindersma, senior director of Robotics Research at Boston Dynamics. If you haven’t seen this video yet, what kind of robotics person are you, and also here you go: Atlas is autonomously moving engine covers between supplier containers and a mobile sequencing dolly. The robot receives as input a list of bin locations to move parts between. Atlas uses a machine learning (ML) vision model to detect and localize the environment fixtures and individual bins [0:36]. The robot uses a specialized grasping policy and continuously estimates the state of manipulated objects to achieve the task. There are no prescribed or teleoperated movements; all motions are generated autonomously online. The robot is able to detect and react to changes in the environment (e.g., moving fixtures) and action failures (e.g., failure to insert the cover, tripping, environment collisions [1:24]) using a combination of vision, force, and proprioceptive sensors. Eagle-eyed viewers will have noticed that this task is very similar to what we saw hydraulic Atlas (Atlas classic?) working on just before it retired. We probably don’t need to read too much into the differences between how each robot performs that task, but it’s an interesting comparison to make. For more details, here’s our Q&A with Kuindersma: How many takes did this take? Kuindersma: We ran this sequence a couple times that day, but typically we’re always filming as we continue developing and testing Atlas. Today we’re able to run that engine cover demo with high reliability, and we’re working to expand the scope and duration of tasks like these. Is this a task that humans currently do? Kuindersma: Yes. What kind of world knowledge does Atlas have while doing this task? Kuindersma: The robot has access to a CAD model of the engine cover that is used for object pose prediction from RGB images. Fixtures are represented more abstractly using a learned keypoint prediction model. The robot builds a map of the workcell at startup which is updated on the fly when changes are detected (e.g., moving fixture). Does Atlas’s torso have a front or back in a meaningful way when it comes to how it operates? Kuindersma: Its head/torso/pelvis/legs do have “forward” and “backward” directions, but the robot is able to rotate all of these relative to one another. The robot always knows which way is which, but sometimes the humans watching lose track. Are the head and torso capable of unlimited rotation? Kuindersma: Yes, many of Atlas’s joints are continuous. How long did it take you folks to get used to the way Atlas moves? Kuindersma: Atlas’s motions still surprise and delight the team. OSHA recommends against squatting because it can lead to workplace injuries. How does Atlas feel about that? Kuindersma: As might be evident by some of Atlas’s other motions, the kinds of behaviors that might be injurious for humans might be perfectly fine for robots. Can you describe exactly what process Atlas goes through at 1:22? Kuindersma: The engine cover gets caught on the fabric bins and triggers a learned failure detector on the robot. Right now this transitions into a general-purpose recovery controller, which results in a somewhat jarring motion (we will improve this). After recovery, the robot retries the insertion using visual feedback to estimate the state of both the part and fixture. Were there other costume options you considered before going with the hot dog? Kuindersma: Yes, but marketing wants to save them for next year. How many important sensors does the hot dog costume occlude? Kuindersma: None. The robot is using cameras in the head, proprioceptive sensors, IMU, and force sensors in the wrists and feet. We did have to cut the costume at the top so the head could still spin around. Why are pickles always causing problems? Kuindersma: Because pickles are pesky, polarizing pests.

This is a sponsored article brought to you by BESydney. In July 2024, Sydney woman Katherine Bennell-Pegg made history as the first astronaut to graduate under the Australian flag and the first female astronaut in Australia. Her journey, marked by determination and discipline, showcases Australia’s growing prominence in space exploration and research. From her academic achievements at the University of Sydney (USYD) to her rigorous training at the European Space Agency (ESA), Bennell-Pegg’s success has paved a path forward for aspiring space and aerospace professionals in Australia and globally. A journey to the stars begins in Sydney Katherine Bennell-Pegg was born in Sydney, New South Wales, and grew up in the Northern Beaches area. Her fascination with space began at an early age. “I always dreamed of being an astronaut,” Bennell-Pegg shared in her “Insights from an Australian Astronaut” Space Forum Speech in July 2024. “When I was young, it was for the adventure, but after more than a decade working in space, it’s now because I know the role it plays in tackling real-world problems and developing new knowledge that can benefit our society, environment and science.” Sydney: A Hub for Space Innovation Sydney, the vibrant heart of the state of New South Wales (NSW), stands at the forefront of aerospace innovation in Australia. With its world-class research facilities, leading academic institutions and strategic geographic positioning, Sydney is not only Australia’s gateway to the Indo-Pacific but also a burgeoning hub for international aerospace endeavours. NSW is home to more than 40 per cent of Australia’s aerospace industry. Substantial investments from both the state and federal governments support this concentration of capabilities, underpinning Sydney’s role as a leader in aerospace. From advanced manufacturing and cybersecurity to quantum technologies and space exploration, this progressive city is truly thriving. Sydney’s appeal as a desirable location for hosting aerospace conferences and business events is bolstered by its comprehensive infrastructure, vibrant startup community and strategic position as a transport hub. Sydney’s track record of successfully hosting events highlights the city’s ability to organise impactful international gatherings, including: Australian Space Summit New Horizons Summit CubeSatPlus2024 - NEW SPACE: Unbounded Skies Sydney will also host the 76th International Astronautical Congress from 29 September to 3 October 2025 and the 34th Congress of the International Council for the Aeronautical Sciences (ICAS) to be held 13 to 17 September 2026. Both will take place at ICC Sydney, further solidifying Sydney’s status as a central hub for aerospace events. Would you like to know more about Sydney’s credentials in Aerospace? Download our Aerospace eBook or visit besydney.com.au Sydney proved to be the ideal location for Bennell-Pegg’s journey to begin. She studied at the University of Sydney, where she earned a Bachelor of Engineering (Honors) in Aeronautical Engineering (Space) and a Bachelor of Science (Advanced) in Physics. Sydney’s universities are at the forefront of aerospace education and research. Institutions such as the University of Sydney (USYD), the University of New South Wales (UNSW Sydney) and the University of Technology Sydney (UTS) attract students from around the world. UNSW Sydney, with its School of Aerospace, Mechanical, and Mechatronic Engineering, is renowned for its innovative research in space technology and satellite systems, while UTS provides cutting-edge programs in aerospace engineering and physics, emphasizing practical applications and industry partnerships. USYD excels in aeronautical engineering and space science, supported by advanced facilities and strong ties to major aerospace organisations. Together, these universities offer comprehensive programs that integrate theoretical knowledge with hands-on experience, preparing students for dynamic careers in the rapidly evolving aerospace and space sectors. Having excelled in her studies at USYD, Bennell-Pegg was awarded the Charles Kuller Graduation Prize for her top-placed undergraduate thesis. Subsequently, her quest for knowledge took her to Europe, where she earned two Master of Science degrees: one in Astronautics and Space Engineering from Cranfield University and another in Space Technology from Luleå University of Technology. Reflecting on her educational path, Bennell-Pegg stated, “With the encouragement of my parents, I researched what it would take to become an astronaut and worked hard at school, participating in everything from aerobatic flying lessons to amateur astronomy.” Inside the rigorous training regimen of an astronaut Bennell-Pegg’s professional career began with roles at Airbus UK, where she contributed to numerous space missions and concept studies, such as Martian in-situ resource utilisation and space debris removal. Her expertise led her to the Australian Space Agency, where she became the Director of Space Technology. In 2021, Bennell-Pegg was invited by the European Space Agency (ESA) to undertake Basic Astronaut Training at the European Astronaut Centre in Germany. When the ESA application opened in 2021, it was the first opening in 15 years. Bennell-Pegg jumped at the opportunity to apply alongside over 22,000 others from 22 countries. She endured six knock-out rounds, including medical, psychometrics, psychology and technical tests and made it to the group of 25 who passed. This historic invitation marked the first time an international astronaut candidate was offered training by the ESA. “The training was demanding, but it was also an incredible opportunity to learn from some of the best minds in the field and to be part of a team that is pushing the boundaries of human exploration.”—Katherine Bennell-Pegg Bennell-Pegg’s training regimen was intense, encompassing physical conditioning, complex simulations, and theoretical classes designed to prepare candidates for long-duration missions to the International Space Station (ISS) and beyond. This included: Studies in biology, astronomy, earth sciences, meteorology, materials, medical and fluids, both in theory and in labs. Radiation research – an area of expertise for Australia. This will increase as humans travel back to the Moon. Medical operations: Astronauts need to be able to perform medical procedures on themselves and others. Training for expeditions: This included honing team dynamics through behavioral training, ocean and winter survival training, rescue and firefighting. Sharing her thoughts on this transformative experience, Bennell-Pegg said, “The training was demanding, but it was also an incredible opportunity to learn from some of the best minds in the field and to be part of a team that is pushing the boundaries of human exploration.” In April 2024, Bennell-Pegg completed her training, graduating with her ESA classmates from “The Hoppers” group. Upon graduation, she became fully qualified for assignments on long-duration missions to the ISS, making her the first Australian female astronaut and the first person to train as an astronaut under the Australian flag. “I want to use this experience to open doors for Australian scientists and engineers to utilize space for their discoveries,” Bennell-Pegg said. “I hope to inspire the pursuit of STEM careers and show all Australians that they too can reach for the stars.” Elevating Australia’s role in space exploration Katherine Bennell-Pegg’s achievements represent a significant milestone. Her journey from the University of Sydney to the rigorous training programs at the European Astronaut Centre showcases the potential of Australian talent in the global space community. “Being the first astronaut trained under the Australian flag is an incredible honor,” Bennell-Pegg said. “I’m grateful for the support that has fueled me through intense training and opened doors for more Australians in space exploration. Whether I fly or not, there is much to accomplish here on Earth. I’m excited to leverage this experience to inspire future generations in STEM and elevate Australia’s presence in the global space community. Becoming an astronaut is just the beginning.” Bennell-Pegg’s dream to become an Australian astronaut is more than just a personal triumph; it is a win for anyone who aspires to a career in space or aerospace. Sydney, with its world-class educational institutions, advanced manufacturing facilities scheduled for the Western Sydney Aerotropolis and expanding opportunities in aerospace and defence, is an ideal starting point for anyone looking to make their mark in these sectors. Would you like to know more about Sydney’s credentials in Aerospace? Download our Aerospace eBook or visit besydney.com.au

Tactile controls are back in vogue. Apple added two new buttons to the iPhone 16, home appliances like stoves and washing machines are returning to knobs, and several car manufacturers are reintroducing buttons and dials to dashboards and steering wheels. With this “re-buttonization,” as The Wall Street Journal describes it, demand for Rachel Plotnick’s expertise has grown. Plotnick, an associate professor of cinema and media studies at Indiana University in Bloomington, is the leading expert on buttons and how people interact with them. She studies the relationship between technology and society with a focus on everyday or overlooked technologies, and wrote the 2018 book Power Button: A History of Pleasure, Panic, and the Politics of Pushing (The MIT Press). Now, companies are reaching out to her to help improve their tactile controls. Rachel Plotnick on... Researching the history of buttons The renaissance of physical controls Working with companies on “re-buttoning” You wrote a book a few years ago about the history of buttons. What inspired that book? Rachel Plotnick: Around 2009, I noticed there was a lot of discourse in the news about the death of the button. This was a couple years after the first iPhone had come out, and a lot of people were saying that, as touchscreens were becoming more popular, eventually we weren’t going to have any more physical buttons to push. This started to happen across a range of devices like the Microsoft Kinect, and after films like Minority Report had come out in the early 2000s, everyone thought we were moving to this kind of gesture or speech interface. I was fascinated by this idea that an entire interface could die, and that led me down this big wormhole, to try to understand how we came to be a society that pushed buttons everywhere we went. Rachel Plotnick studies the ways we use everyday technologies and how they shape our relationships with each other and the world.Rachel Plotnick The more that I looked around, the more that I saw not only were we pressing digital buttons on social media and to order things from Amazon, but also to start our coffee makers and go up and down in elevators and operate our televisions. The pervasiveness of the button as a technology pitted against this idea of buttons disappearing seemed like such an interesting dichotomy to me. And so I wanted to understand an origin story, if I could come up with it, of where buttons came from. What did you find in your research? Plotnick: One of the biggest observations I made was that a lot of fears and fantasies around pushing buttons were the same 100 years ago as they are today. I expected to see this society that wildly transformed and used buttons in such a different way, but I saw these persistent anxieties over time about control and who gets to push the button, and also these pleasures around button pushing that we can use for advertising and to make technology simpler. That pendulum swing between fantasy and fear, pleasure and panic, and how those themes persisted over more than a century was what really interested me. I liked seeing the connections between the past and the present. [Back to top] We’ve experienced the rise of touchscreens, but now we might be seeing another shift—a renaissance in buttons and physical controls. What’s prompting the trend? Plotnick: There was this kind of touchscreen mania, where all of a sudden everything became a touchscreen. Your car was a touchscreen, your refrigerator was a touchscreen. Over time, people became somewhat fatigued with that. That’s not to say touchscreens aren’t a really useful interface, I think they are. But on the other hand, people seem to have a hunger for physical buttons, both because you don’t always have to look at them—you can feel your way around for them when you don’t want to directly pay attention to them—but also because they offer a greater range of tactility and feedback. If you look at gamers playing video games, they want to push a lot of buttons on those controls. And if you look at DJs and digital musicians, they have endless amounts of buttons and joysticks and dials to make music. There seems to be this kind of richness of the tactile experience that’s afforded by pushing buttons. They’re not perfect for every situation, but I think increasingly, we’re realizing the merit that the interface offers. What else is motivating the re-buttoning of consumer devices? Plotnick: Maybe screen fatigue. We spend all our days and nights on these devices, scrolling or constantly flipping through pages and videos, and there’s something tiring about that. The button may be a way to almost de-technologize our everyday existence, to a certain extent. That’s not to say buttons don’t work with screens very nicely—they’re often partners. But in a way, it’s taking away the priority of vision as a sense, and recognizing that a screen isn’t always the best way to interact with something. When I’m driving, it’s actually unsafe for my car to be operated in that way. It’s hard to generalize and say, buttons are always easy and good, and touchscreens are difficult and bad, or vice versa. Buttons tend to offer you a really limited range of possibilities in terms of what you can do. Maybe that simplicity of limiting our field of choices offers more safety in certain situations. It also seems like there’s an accessibility issue when prioritizing vision in device interfaces, right? Plotnick: The blind community had to fight for years to make touchscreens more accessible. It’s always been funny to me that we call them touchscreens. We think about them as a touch modality, but a touchscreen prioritizes the visual. Over the last few years, we’re seeing Alexa and Siri and a lot of these other voice-activated systems that are making things a little bit more auditory as a way to deal with that. But the touchscreen is oriented around visuality. It sounds like, in general, having multiple interface options is the best way to move forward—not that touchscreens are going to become completely passé, just like the button never actually died. Plotnick: I think that’s accurate. We see paradigm shifts over time with technologies, but for the most part, we often recycle old ideas. It’s striking that if we look at the 1800s, people were sending messages via telegraph about what the future would look like if we all had this dashboard of buttons at our command where we could communicate with anyone and shop for anything. And that’s essentially what our smartphones became. We still have this dashboard menu approach. I think it means carefully considering what the right interface is for each situation. [Back to top] Several companies have reached out to you to learn from your expertise. What do they want to know? Plotnick: I think there is a hunger out there from companies designing buttons or consumer technologies to try to understand the history of how we used to do things, how we might bring that to bear on the present, and what the future looks like with these interfaces. I’ve had a number of interesting discussions with companies, including one that manufactures push-button interfaces. I had a conversation with them about medical devices like CT machines and X-ray machines, trying to imagine the easiest way to push a button in that situation, to save people time and improve the patient encounter. I’ve also talked to people about what will make someone use a defibrillator or not. Even though it’s really simple to go up to these automatic machines, if you see someone going into cardiac arrest in a mall or out on the street, a lot of people are terrified to actually push the button that would get this machine started. We had a really fascinating discussion about why someone wouldn’t push a button, and what would it take to get them to feel okay about doing that. In all of these cases, these are design questions, but they’re also social and cultural questions. I like the idea that people who are in the humanities studying these things from a long-term perspective can also speak to engineers trying to build these devices. So these companies also want to know about the history of buttons? Plotnick: I’ve had some fascinating conversations around history. We all want to learn what mistakes not to make and what worked well in the past. There’s often this narrative of progress, that things are only getting better with technology over time. But if we look at these lessons, I think we can see that sometimes things were simpler or better in a past moment, and sometimes they were harder. Often with new technologies, we think we’re completely reinventing the wheel. But maybe these concepts existed a long time ago, and we haven’t paid attention to that. There’s a lot to be learned from the past. [Back to top]

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. Humanoids 2024: 22–24 November 2024, NANCY, FRANCE Enjoy today’s videos! We’re hoping to get more on this from Boston Dynamics, but if you haven’t seen it yet, here’s electric Atlas doing something productive (and autonomous!). And why not do it in a hot dog costume for Halloween, too? [ Boston Dynamics ] Ooh, this is exciting! Aldebaran is getting ready to release a seventh generation of NAO! [ Aldebaran ] Okay I found this actually somewhat scary, but Happy Halloween from ANYbotics! [ ANYbotics ] Happy Halloween from the Clearpath! [ Clearpath Robotics Inc. ] Another genuinely freaky Happy Halloween, from Boston Dynamics! [ Boston Dynamics ] This “urban opera” by Compagnie La Machine took place last weekend in Toulouse, featuring some truly enormous fantastical robots. [ Compagnie La Machine ] Thanks, Thomas! Impressive dismount from Deep Robotics’ DR01. [ Deep Robotics ] Cobot juggling from Daniel Simu. [ Daniel Simu ] Adaptive-morphology multirotors exhibit superior versatility and task-specific performance compared to traditional multirotors owing to their functional morphological adaptability. However, a notable challenge lies in the contrasting requirements of locking each morphology for flight controllability and efficiency while permitting low-energy reconfiguration. A novel design approach is proposed for reconfigurable multirotors utilizing soft multistable composite laminate airframes. [ Environmental Robotics Lab paper ] This is a pitching demonstration of new Torobo. New Torobo is lighter than the older version, enabling faster motion such as throwing a ball. The new model will be available in Japan in March 2025 and overseas from October 2025 onward. [ Tokyo Robotics ] I’m not sure what makes this “the world’s best robotic hand for manipulation research,” but it seems solid enough. [ Robot Era ] And now, picking a micro cat. [ RoCogMan Lab ] When Arvato’s Louisville, Ky. staff wanted a robotics system that could unload freight with greater speed and safety, Boston Dynamics’ Stretch robot stood out. Stretch is a first of its kind mobile robot designed specifically to unload boxes from trailers and shipping containers, freeing up employees to focus on more meaningful tasks in the warehouse. Arvato acquired its first Stretch system this year and the robot’s impact was immediate. [ Boston Dynamics ] NASA’s Perseverance Mars rover used its Mastcam-Z camera to capture the silhouette of Phobos, one of the two Martian moons, as it passed in front of the Sun on Sept. 30, 2024, the 1,285th Martian day, or sol, of the mission. [ NASA ] Students from Howard University, Moorehouse College, and Berea College joined University of Michigan robotics students in online Robotics 102 courses for the fall ‘23 and winter ‘24 semesters. The class is part of the distributed teaching collaborative, a co-teaching initiative started in 2020 aimed at providing cutting edge robotics courses for students who would normally not have access to at their current university. [ University of Michigan Robotics ] Discover the groundbreaking projects and cutting-edge technology at the Robotics and Automation Summer School (RASS) hosted by Los Alamos National Laboratory. In this exclusive behind-the-scenes video, students from top universities work on advanced robotics in disciplines such as AI, automation, machine learning, and autonomous systems. [ Los Alamos National Laboratory ] This week’s Carnegie Mellon University Robotics Institute Seminar is from Princeton University’s Anirudha Majumdar, on “Robots That Know When They Don’t Know.” Foundation models from machine learning have enabled rapid advances in perception, planning, and natural language understanding for robots. However, current systems lack any rigorous assurances when required to generalize to novel scenarios. For example, perception systems can fail to identify or localize unfamiliar objects, and large language model (LLM)-based planners can hallucinate outputs that lead to unsafe outcomes when executed by robots. How can we rigorously quantify the uncertainty of machine learning components such that robots know when they don’t know and can act accordingly? [ Carnegie Mellon University Robotics Institute ]

Every invention begins with a problem—and the creative act of seeing a problem where others might just see unchangeable reality. For one 5-year-old, the problem was simple: She liked to have her tummy rubbed as she fell asleep. But her mom, exhausted from working two jobs, often fell asleep herself while putting her daughter to bed. “So [the girl] invented a teddy bear that would rub her belly for her,” explains Stephanie Couch, executive director of the Lemelson MIT Program. Its mission is to nurture the next generation of inventors and entrepreneurs. Anyone can learn to be an inventor, Couch says, given the right resources and encouragement. “Invention doesn’t come from some innate genius, it’s not something that only really special people get to do,” she says. Her program creates invention-themed curricula for U.S. classrooms, ranging from kindergarten to community college. This article is part of our special report, “Reinventing Invention: Stories from Innovation’s Edge.” We’re biased, but we hope that little girl grows up to be an engineer. By the time she comes of age, the act of invention may be something entirely new—reflecting the adoption of novel tools and the guiding forces of new social structures. Engineers, with their restless curiosity and determination to optimize the world around them, are continuously in the process of reinventing invention. In this special issue, we bring you stories of people who are in the thick of that reinvention today. IEEE Spectrum is marking 60 years of publication this year, and we’re celebrating by highlighting both the creative act and the grindingly hard engineering work required to turn an idea into something world changing. In these pages, we take you behind the scenes of some awe-inspiring projects to reveal how technology is being made—and remade—in our time. Inventors Are Everywhere Invention has long been a democratic process. The economist B. Zorina Khan of Bowdoin College has noted that the U.S. Patent and Trademark Office has always endeavored to allow essentially anyone to try their hand at invention. From the beginning, the patent examiners didn’t care who the applicants were—anyone with a novel and useful idea who could pay the filing fee was officially an inventor. This ethos continues today. It’s still possible for an individual to launch a tech startup from a garage or go on “Shark Tank” to score investors. The Swedish inventor Simone Giertz, for example, made a name for herself with YouTube videos showing off her hilariously bizarre contraptions, like an alarm clock with an arm that slapped her awake. The MIT innovation scholar Eric von Hippel has spotlighted today’s vital ecosystem of “user innovation,” in which inventors such as Giertz are motivated by their own needs and desires rather than ambitions of mass manufacturing. But that route to invention gets you only so far, and the limits of what an individual can achieve have become starker over time. To tackle some of the biggest problems facing humanity today, inventors need a deep-pocketed government sponsor or corporate largess to muster the equipment and collective human brainpower required. When we think about the challenges of scaling up, it’s helpful to remember Alexander Graham Bell and his collaborator Thomas Watson. “They invent this cool thing that allows them to talk between two rooms—so it’s a neat invention, but it’s basically a gadget,” says Eric Hintz, a historian of invention at the Smithsonian Institution. “To go from that to a transcontinental long-distance telephone system, they needed a lot more innovation on top of the original invention.” To scale their invention, Hintz says, Bell and his colleagues built the infrastructure that eventually evolved into Bell Labs, which became the standard-bearer for corporate R&D. In this issue, we see engineers grappling with challenges of scale in modern problems. Consider the semiconductor technology supported by the U.S. CHIPS and Science Act, a policy initiative aimed at bolstering domestic chip production. Beyond funding manufacturing, it also provides US $11 billion for R&D, including three national centers where companies can test and pilot new technologies. As one startup tells the tale, this infrastructure will drastically speed up the lab-to-fab process. And then there are atomic clocks, the epitome of precision timekeeping. When researchers decided to build a commercial version, they had to shift their perspective, taking a sprawling laboratory setup and reimagining it as a portable unit fit for mass production and the rigors of the real world. They had to stop optimizing for precision and instead choose the most robust laser, and the atom that would go along with it. These technology efforts benefit from infrastructure, brainpower, and cutting-edge new tools. One tool that may become ubiquitous across industries is artificial intelligence—and it’s a tool that could further expand access to the invention arena. What if you had a team of indefatigable assistants at your disposal, ready to scour the world’s technical literature for material that could spark an idea, or to iterate on a concept 100 times before breakfast? That’s the promise of today’s generative AI. The Swiss company Iprova is exploring whether its AI tools can automate “eureka” moments for its clients, corporations that are looking to beat their competitors to the next big idea. The serial entrepreneur Steve Blank similarly advises young startup founders to embrace AI’s potential to accelerate product development; he even imagines testing product ideas on digital twins of customers. Although it’s still early days, generative AI offers inventors tools that have never been available before. Measuring an Invention’s Impact If AI accelerates the discovery process, and many more patentable ideas come to light as a result, then what? As it is, more than a million patents are granted every year, and we struggle to identify the ones that will make a lasting impact. Bryan Kelly, an economist at the Yale School of Management, and his collaborators made an attempt to quantify the impact of patents by doing a technology-assisted deep dive into U.S. patent records dating back to 1840. Using natural language processing, they identified patents that introduced novel phrasing that was then repeated in subsequent patents—an indicator of radical breakthroughs. For example, Elias Howe Jr.’s 1846 patent for a sewing machine wasn’t closely related to anything that came before but quickly became the basis of future sewing-machine patents. Another foundational patent was the one awarded to an English bricklayer in 1824 for the invention of Portland cement, which is still the key ingredient in most of the world’s concrete. As Ted C. Fishman describes in his fascinating inquiry into the state of concrete today, this seemingly stable industry is in upheaval because of its heavy carbon emissions. The AI boom is fueling a construction boom in data centers, and all those buildings require billions of tons of concrete. Fishman takes readers into labs and startups where researchers are experimenting with climate-friendly formulations of cement and concrete. Who knows which of those experiments will result in a patent that echoes down the ages? Some engineers start their invention process by thinking about the impact they want to make on the world. The eminent Indian technologist Raghunath Anant Mashelkar, who has popularized the idea of “Gandhian engineering”, advises inventors to work backward from “what we want to achieve for the betterment of humanity,” and to create problem-solving technologies that are affordable, durable, and not only for the elite. Durability matters: Invention isn’t just about creating something brand new. It’s also about coming up with clever ways to keep an existing thing going. Such is the case with the Hubble Space Telescope. Originally designed to last 15 years, it’s been in orbit for twice that long and has actually gotten better with age, because engineers designed the satellite to be fixable and upgradable in space. For all the invention activity around the globe—the World Intellectual Property Organization says that 3.5 million applications for patents were filed in 2022—it may be harder to invent something useful than it used to be. Not because “everything that can be invented has been invented,” as in the apocryphal quote attributed to the unfortunate head of the U.S. patent office in 1889. Rather, because so much education and experience are required before an inventor can even understand all the dimensions of the door they’re trying to crack open, much less come up with a strategy for doing so. Ben Jones, an economist at Northwestern’s Kellogg School of Management, has shown that the average age of great technological innovators rose by about six years over the course of the 20th century. “Great innovation is less and less the provenance of the young,” Jones concluded. Consider designing something as complex as a nuclear fusion reactor, as Tom Clynes describes in “An Off-the-Shelf Stellarator.” Fusion researchers have spent decades trying to crack the code of commercially viable fusion—it’s more akin to a calling than a career. If they succeed, they will unlock essentially limitless clean energy with no greenhouse gas emissions or meltdown danger. That’s the dream that the physicists in a lab in Princeton, N.J., are chasing. But before they even started, they first had to gain an intimate understanding of all the wrong ways to build a fusion reactor. Once the team was ready to proceed, what they created was an experimental reactor that accelerates the design-build-test cycle. With new AI tools and unprecedented computational power, they’re now searching for the best ways to create the magnetic fields that will confine the plasma within the reactor. Already, two startups have spun out of the Princeton lab, both seeking a path to commercial fusion. The stellarator story and many other articles in this issue showcase how one innovation leads to the next, and how one invention can enable many more. The legendary Dean Kamen, best known for mechanical devices like the Segway and the prosthetic “Luke” arm, is now trying to push forward the squishy world of biological manufacturing. In an interview, Kamen explains how his nonprofit is working on the infrastructure—bioreactors, sensors, and controls—that will enable companies to explore the possibilities of growing replacement organs. You could say that he’s inventing the launchpad so others can invent the rockets. Sometimes everyone in a research field knows where the breakthrough is needed, but that doesn’t make it any easier to achieve. Case in point: the quest for a household humanoid robot that can perform domestic chores, switching effortlessly from frying an egg to folding laundry. Roboticists need better learning software that will enable their bots to navigate the uncertainties of the real world, and they also need cheaper and lighter actuators. Major advances in these two areas would unleash a torrent of creativity and may finally bring robot butlers into our homes. And maybe the future roboticists who make those breakthroughs will have cause to thank Marina Umaschi Bers, a technologist at Boston College who cocreated the ScratchJr programming language and the KIBO robotics kit to teach kids the basics of coding and robotics in entertaining ways. She sees engineering as a playground, a place for children to explore and create, to be goofy or grandiose. If today’s kindergartners learn to think of themselves as inventors, who knows what they’ll create tomorrow?

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

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

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