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Cheryl Conrad no longer seethes with the frustration that threatened to overwhelm her in 2006. As described in IEEE Spectrum, Cheryl’s husband, Tom, has a rare genetic disease that causes ammonia to accumulate in his blood. At an emergency room visit two decades ago, Cheryl told the doctors Tom needed an immediate dose of lactulose to avoid going into a coma, but they refused to medicate him until his primary doctor confirmed his medical condition hours later. Making the situation more vexing was that Tom had been treated at that facility for the same problem a few months earlier, and no one could locate his medical records. After Tom’s recovery, Cheryl vowed to always have immediate access to them. Today, Cheryl says, “Happily, I’m not involved anymore in lugging Tom’s medical records everywhere.” Tom’s two primary medical facilities use the same electronic health record (EHR) system, allowing doctors at both facilities to access his medical information quickly. In 2004, President George W. Bush set an ambitious goal for U.S. health care providers to transition to EHRs by 2014. Electronic health records, he declared, would transform health care by ensuring that a person’s complete medical information was available “at the time and place of care, no matter where it originates.” President George W. Bush looks at an electronic medical record system during a visit to the Cleveland Clinic on 27 January 2005. Brooks Kraft/Corbis/Getty Images Over the next four years, a bipartisan Congress approved more than US $150 million in funding aimed at setting up electronic health record demonstration projects and creating the administrative infrastructure needed. Then, in 2009, during efforts to mitigate the financial crisis, newly elected President Barack Obama signed the $787 billion economic stimulus bill. Part of it contained the Health Information Technology for Economic and Clinical Health Act, also known as the HITECH Act, which budgeted $49 billion to promote health information technology and EHRs in the United States. As a result, Tom, like most Americans, now has an electronic health record. However, many millions of Americans now have multiple electronic health records. On average, patients in the United States visit 19 different kinds of doctors throughout their lives. Further, many specialists have unique EHR systems that do not automatically communicate medical data between each other, so you must update your medical information for each one. Nevertheless, Tom now has immediate access to all his medical treatment and test information, something not readily available 20 years ago. Tom’s situation underlines the paradox of how far the United States has come since 2004 and how far it still must go to achieve President Bush’s vision of a complete, secure, easily accessible, and seamlessly interoperable lifetime EHR. As of 2021, nearly 80 percent of physicians and almost all nonfederal acute-care hospitals deployed an electronic health record system. For many patients in the United States today, instead of fragmented, paper medical record silos, they have a plethora of fragmented, electronic medical record silos. And thousands of health care providers are burdened with costly, poorly designed, and insecure EHR systems that have exacerbated clinician burnout, led to hundreds of millions of medical records lost in data breaches, and created new sources of medical errors. EHR’s baseline standardization does help centralize a very fragmented health care system, but in the rush to get EHR systems adopted, key technological and security challenges were overlooked and underappreciated. Subsequently, problems were introduced due to the sheer complexity of the systems being deployed. These still-unresolved issues are now potentially coupled with the unknown consequences of bolting on immature AI-driven technologies. Unless more thought and care are taken now in how to proceed as a fully integrated health care system, we could unintentionally put the entire U.S. health care system in a worse place than when President Bush first declared his EHR goal in 2004. IT to Correct Health Care Inefficiencies Is a Global Project Putting government pressure on the health care industry to adopt EHR systems through various financial incentives made sense by the early 2000s. Health care in the United States was in deep trouble. Spending increased from $74.1 billion in 1970 to more than $1.4 trillion by 2000, 2.3 times as fast as the U.S. gross domestic product. Health care costs grew at three times the rate of inflation from 1990 to 2000 alone, surpassing 13 percent of GDP. Two major studies conducted by the Institute of Medicine in 2000 and 2001, titled To Err Is Human and Crossing the Quality Chasm, found that health care was deteriorating in terms of accessibility, quality, and safety. Inferior quality and needless medical treatments, including overuse or duplication of diagnostic tests, underuse of effective medical practices, misuse of drug therapies, and poor communication between health care providers emerged as particularly frustrating problems. Administrative waste and unnecessary expenditures were substantial cost drivers, from billing to resolving insurance claims to managing patients’ cases. Health care’s administrative side was characterized as a “ monstrosity,” showing huge transaction costs associated with an estimated 30 billion communications conducted by mail, fax, or telephone annually at that time. Both health care experts and policymakers agreed that reductions in health care delivery and its costs were possible only by deploying health information technology such as electronic prescribing and EHR. Early adopters of EHR systems like the Mayo Clinic, Cleveland Clinic, and the U.S. Department of Veterans Affairs proved the case. Governments across the European Union and the United Kingdom reached the same conclusion. There has been a consistent push, especially in more economically advanced countries, to adopt EHR systems over the past two decades. For example, the E.U. has set a goal of providing 100 percent of its citizens across 27 countries access to electronic health records by 2030. Several countries are well on their way to this achievement, including Belgium, Denmark, Estonia, Lithuania, and Poland. Outside the E.U., countries such as Israel and Singapore also have very advanced systems, and after a rocky start, Australia’s My Health Record system seems to have found its footing. The United Kingdom was hoping to be a global leader in adopting interoperable health information systems, but a disastrous implementation of its National Programme for IT ended in 2011 after nine years and more than £10 billion. Canada, China, India, and Japan also have EHR system initiatives in place at varying levels of maturity. However, it will likely be years before they achieve the same capabilities found in leading digital-health countries. EHRs Need a Systems-Engineering Approach When it comes to embracing automation, the health care industry has historically moved at a snail’s pace, and when it does move, money goes to IT automation first. Market forces alone were unlikely to speed up EHR adoption. Even in the early 2000s, health care experts and government officials were confident that digitalization could reduce total health spending by 10 percent while improving patient care. In a highly influential 2005 study, the RAND Corp. estimated that adopting EHR systems in hospitals and physician offices would cost $98 billion and $17 billion, respectively. The report also estimated that these entities would save at least $77 billion a year after moving to digital records. A highly cited paper in HealthAffairs from 2005 also claimed that small physician practices could recoup their EHR system investments in 2.5 years and profit handsomely thereafter. Moreover, RAND claimed that a fully automated health care system could save the United States $346 billion per year. When Michael O. Leavitt, then the Secretary of Health and Human Services, looked at the projected savings, he saw them as “a key part of saving Medicare.” As baby boomers began retiring en masse in the early 2010s, cutting health care costs was also a political imperative since Medicare funding was projected to run out by 2020. Some doubted the EHR revolution’s health care improvement and cost reduction claims or that it could be achieved within 20 years. The Congressional Budget Office argued that the RAND report overstated the potential costs and benefits of EHR systems and ignored peer-reviewed studies that contradicted it. The CBO also pointed out that RAND assumed EHR systems would be widely adopted and effectively used, which implies that effective tools already existed, though very few commercially available systems were. There was also skepticism about whether replicating the benefits for early adopters of EHR systems—who spent decades perfecting their systems—was possible once the five-year period of governmental EHR adoption incentives ended. Even former House Speaker Newt Gingrich, a strong advocate for electronic health record systems, warned that health care was “30 times more difficult to fix than national defense.” The extent of the problem was one reason the 2005 National Academy of Sciences report, Building a Better Delivery System: A New Engineering / Health Care Partnership, forcefully and repeatedly called for innovative systems-engineering approaches to be developed and applied across the entire health care delivery process. The scale, complexity, and extremely short time frame for attempting to transform the totality of the health care environment demanded a robust “system of systems” engineering approach. This was especially true because of the potential human impacts of automation on health care professionals and patients. Researchers warned that ignoring the interplay of computer-mediated work and existing sociotechnical conditions in health care practices would result in unexpected, unintentional, and undesirable consequences. Additionally, without standard mechanisms for making EHR systems interoperable, many potential benefits would not materialize. As David Brailer, the first National Health Information Technology Coordinator, stated, “Unless interoperability is achieved…potential clinical and economic benefits won’t be realized, and we will not move closer to badly needed health care reform in the U.S.” HITECH’s Broken Promises and Unforeseen Consequences A few years later, policymakers in the Obama administration thought it was unrealistic to prioritize interoperability. They feared that defining interoperability standards too early would lock the health industry into outdated information-sharing approaches. Further, no existing health care business model supported interoperability, and a strong business model actively discouraged providers from sharing information. If patient information could easily shift to another provider, for example, what incentive does the provider have to readily share it? Instead, policymakers decided to have EHR systems adopted as widely and quickly as possible during the five years of HITECH incentives. Tackling interoperability would come later. The government’s unofficial operational mantra was that EHR systems needed to become operational before they could become interoperable. “Researchers have found that doctors spend between 3.5 and 6 hours a day (4.5 hours on average) filling out their digital health records.” Existing EHR system vendors, making $2 billion annually at the time, viewed the HITECH incentive program as a once-in-a-lifetime opportunity to increase market share and revenue streams. Like fresh chum to hungry sharks, the subsidy money attracted a host of new EHR technology entrants eager for a piece of the action. The resulting feeding frenzy pitted an IT-naïve health care industry rushing to adopt EHR systems against a horde of vendors willing to promise (almost) anything to make a sale. A few years into the HITECH program, a 2013 report by RAND wryly observed the market distortion caused by what amounted to an EHR adoption mandate: “We found that (EHR system) usability represents a relatively new, unique, and vexing challenge to physician professional satisfaction. Few other service industries are exposed to universal and substantial incentives to adopt such a specific, highly regulated form of technology, which has, as our findings suggest, not yet matured.” In addition to forcing health care providers to choose quickly among a host of immature EHR solutions, the HITECH program completely undercut the warnings raised about the need for systems engineering or considering the impact of automation on very human-centered aspects of health care delivery by professionals. Sadly, the lack of attention to these concerns affects current EHR systems. Today, studies like that conducted by Stanford Medicine indicate that nearly 70 percent of health care professionals express some level of satisfaction with their electronic health record system and that more than 60 percent think EHR systems have improved patient care. Electronic prescribing has also been seen as a general success, with the risk of medication errors and adverse drug events reduced. However, professional satisfaction with EHRs runs shallow. The poor usability of EHR systems surfaced early in the HITECH program and continues as a main driver for physician dissatisfaction. The Stanford Medicine study, for example, also reported that 54 percent of physicians polled felt their EHR systems detracted from their professional satisfaction, and 59 percent felt it required a complete overhaul. “What we’ve essentially done is created 24/7/365 access to clinicians with no economic model for that: The doctors don’t get paid.” —Robert Wachter, chair of the department of medicine at the University of California, San Francisco Poor EHR system usability results in laborious and low-value data entry, obstacles to face-to-face patient communication, and information overload, where clinicians have to wade through an excess of irrelevant data when treating a patient. A 2019 study in Mayo Clinic Proceedings comparing EHR system usability to other IT products like Google Search, Microsoft Word, and Amazon placed EHR products in the bottom 10 percent. Electronic health record systems were supposed to increase provider productivity, but for many clinicians, their EHRs are productivity vampires instead. Researchers have found that doctors spend between 3.5 and 6 hours a day (4.5 hours on average) filling out their patient’s digital health records, with an Annals of Internal Medicine study reporting that doctors in outpatient settings spend only 27 percent of their work time face-to-face with their patients. In those visits, patients often complain that their doctors spend too much time staring at their computers. They are not likely wrong, as nearly 70 percent of doctors in 2018 felt that EHRs took valuable time away from their patients. To address this issue, health care providers employ more than 100,000 medical scribes today—or about one for every 10 U.S. physicians—to record documentation during office visits, but this only highlights the unacceptable usability problem. Furthermore, physicians are spending more time dealing with their EHRs because the government, health care managers, and insurance companies are requesting more patient information regarding billing, quality measures, and compliance data. Patient notes are twice as long as they were 10 years ago. This is not surprising, as EHR systems so far have not complemented clinician work as much as directed it. “A phenomenon of the productivity vampire is that the goalposts get moved,” explains University of Michigan professor emeritus John Leslie King, who coined the phrase “productivity vampire.” King, a student of system–human interactions, continues, “With the ability to better track health care activities, more government and insurance companies are going to ask for that information in order for providers to get paid.” Robert Wachter, chair of the department of medicine at the University of California, San Francisco, and author of The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age, believes that EHRs “became an enabler of corporate control and outside entity control.” “It became a way that entities that cared about what the doctor was doing could now look to see in real time what the doctor was doing, and then influence what the doctor was doing and even constrain it,” Wachter says. Federal law mandates that patients have access to their medical information contained in EHR systems—which is great, says Wachter, but this also adds to clinician workloads, as patients now feel free to pepper their physicians with emails and messages about the information. “What we’ve essentially done is created 24/7/365 access to clinicians with no economic model for that: The doctors don’t get paid,” Wachter says. His doctors’ biggest complaints are that their EHR system has overloaded email inboxes with patient inquiries. Some doctors report that their in-boxes have become the equivalent of a second set of patients. It is not so much a problem with the electronic information system design per se, notes Wachter, but with EHR systems that “meet the payment system and the workflow system in ways that we really did not think about.” EHRs also promised to reduce stress among health care professionals. Numerous studies have found, however, that EHR systems worsen clinician burnout, with Stanford Medicine finding that 71 percent of physicians felt the systems contributed to burnout. Half of U.S. physicians are experiencing burnout, with 63 percent reporting at least one manifestation in 2022. The average physician works 53 hours weekly (19 hours more than the general population) and spends over 4 hours daily on documentation. Clinical burnout is lowest among clinicians with highly usable EHR systems or in specialties with the least interaction with their EHR systems, such as surgeons and radiologists. Physicians who make, on average, 4,000 EHR system clicks per shift, like emergency room doctors, report the highest levels of burnout. Aggravating the situation, notes Wachter, was “that decision support is so rudimentary…which means that the doctors feel like they’re spending all this time entering data in the machine, (but) getting relatively little useful intelligence out of it.” Poorly designed information systems can also compromise patient safety. Evidence suggests that EHR systems with unacceptable usability contribute to low-quality patient care and reduce the likelihood of catching medical errors. According to a study funded by the U.S. Agency for Healthcare Research and Quality, EHR system issues were involved in the majority of malpractice claims over a six-and-a-half-year period of study ending in 2021. Sadly, the situation has not changed today. Interoperability, Cybersecurity Bite Back EHR system interoperability closely follows poor EHR system usability as a driver of health care provider dissatisfaction. Recent data from the Assistant Secretary for Technology Policy / Office of the National Coordinator for Health Information Technology indicates that 70 percent of hospitals sometimes exchange patient data, though only 43 percent claim they regularly do. System-affiliated hospitals share the most information, while independent and small hospitals share the least. Exchanging information using the same EHR system helps. Wachter observes that interoperability among similar EHR systems is straightforward, but across different EHR systems, he says, “it is still relatively weak.” However, even if two hospitals use the same EHR vendor, communicating patient data can be difficult if each hospital’s system is customized. Studies indicate that patient mismatch rates can be as high as 50 percent, even in practices using the same EHR vendor. This often leads to duplicate patient records that lack vital patient information, which can result in avoidable patient injuries and deaths. The ability to share information associated with a unique patient identifier (UPI), like other countries that use advanced EHRs, including Estonia, Israel, and Singapore, makes health information interoperability easier, says Christina Grimes, digital health strategist for the Healthcare Information and Management Systems Society (HIMSS). But in the United States, “Congress has forbidden it since 1998” and steadfastly resists allowing for UPIs, she notes. Using a single-payer health insurance system, like most other countries with advanced EHR systems, would also make sharing patient information easier, decrease time spent on EHRs, and reduce clinician burnout, but that is also a nonstarter in the United States for the foreseeable future. Interoperability is even more challenging because an average hospital uses 10 different EHR vendors internally to support more than a dozen different health care functions, and an average health system has 16 different EHR vendors when affiliated providers are included. Grimes notes that only a small percentage of health care systems use fully integrated EHR systems that cover all functions. EHR systems adoption also promised to bend the national health care cost curve, but these costs continue to rise at the national level. The United States spent an estimated $4.8 trillion on health care in 2023, or 17.6 percent of GDP. While there seems to be general agreement that EHRs can help with cost savings, no rigorous quantitative studies at the national level show the tens of billions of dollars of promised savings that RAND loudly proclaimed in 2005. However, studies have shown that health care providers, especially those in rural areas, have had difficulty saving money by using EHR systems. A recent study, for example, points out that rural hospitals do not benefit as much from EHR systems as urban hospitals in terms of reducing operating costs. With 700 rural hospitals at risk of closing due to severe financial pressures, investing in EHR systems has not proved to be the financial panacea they thought it would be. Cybersecurity is a major cost not included in the 2005 RAND study. Even though there were warnings that cybersecurity was being given short shrift, vendors, providers, and policymakers paid scant attention to the cybersecurity implications of EHR systems, especially the multitude of new cyberthreat access points that would be created and potentially exploited. Tom Leary, senior vice president and head of government relations at HIMSS, points out the painfully obvious fact that “security was an afterthought. You have to make sure that security by design is involved from the beginning, so we’re still paying for the decision not to invest in security.” From 2009 to 2023, a total of 5,887 health care breaches of 500 records or more have been reported to the U.S. Department of Health and Human Services Office for Civil Rights resulting in some 520 million health care records being exposed. Health care breaches have also led to widespread disruption to medical care in various hospital systems, sometimes for over a month. In 2024, the average cost of a health care data breach was $9.97 million. The cost of these breaches will soon surpass the $27 billion ($44.5 billion in 2024 dollars) provided under HITECH to adopt EHRs. 2025 may see the first major revision since 2013 to the Health Insurance Portability and Accountability Act (HIPAA) Security Rule outlining how electronic protected health information will need to be cybersecured. The proposed rule will likely force health care providers and their EHR vendors to make cybersecurity investment a much higher priority. $100 Billion Spent on Health Care IT: Was the Juice Worth the (Mega) Squeeze? The U.S. health care industry has spent more than $100 billion on information technology, but few providers are fully meeting President Bush’s vision of a nation of seamlessly interoperable and secure digital health records. Many past government policymakers now admit they failed to understand the complex business dynamics, technical scale, complexity, or time needed to create a nationwide system of usable, interoperable EHR systems. The entire process lacked systems-engineering thinking. As Seema Verma, former administrator of the Centers for Medicare and Medicaid Services, told Fortune, “We didn’t think about how all these systems connect with one another. That was the real missing piece.” Over the past eight years, successive administrations and congresses have taken actions to try to rectify these early oversights. In 2016, the 21st Century Cures Act was passed, which kept EHR system vendors and providers from blocking the sharing of patient data, and spurred them to start working in earnest to create a trusted health information exchange. The Cures Act mandated standardized application programming interfaces (APIs) to promote interoperability. In 2022, the Trusted Exchange Framework and Common Agreement (TEFCA) was published, which aims to facilitate technical principles for securely exchanging health information. “The EHR venture has proved troublesome thus far. The trouble is far from over.” —John Leslie King, University of Michigan professor emeritus In late 2023, the first Qualified Health Information Networks (QHINs) were approved to begin supporting the exchange of data governed by TEFCA, and in 2024, updates were made to the APIs to make information interoperability easier. These seven QHINs allow thousands of health providers to more easily exchange information. Combined with the emerging consolidation among hospital systems around three EHR vendors—Epic Systems Corp., Oracle Health, and Meditech—this should improve interoperability in the next decade. These changes, says HIMSS’s Tom Leary, will help give “all patients access to their data in whatever format they want with limited barriers. The health care environment is starting to become patient-centric now. So, as a patient, I should soon be able to go out to any of my healthcare providers to really get that information.” HIMSS’s Christina Grimes adds that the patient-centric change is the continuing consolidation of EHR system portals. “Patients really want one portal to interact with instead of the number they have today,” she says. In 2024, the Assistant Secretary for Technology Policy / Office of the National Coordinator for Health IT, the U.S. government department responsible for overseeing electronic health systems’ adoption and standards, was reorganized to focus more on cybersecurity and advanced technology like AI. In addition to the proposed HIPAA security requirements, Congress is also considering new laws to mandate better cybersecurity. There is hope that AI can help overcome EHR system usability issues, especially clinician burnout and interoperability issues like patient matching. Wachter states that the new AI scribes are showing real promise. “The way it works is that I can now have a conversation with my patient and look the patient in the eye. I’m actually focusing on them and not my keyboard. And then a note, formatted correctly, just magically appears. Almost ironically, this new set of AI technologies may well solve some of the problems that the last technology created.” Whether these technologies live up to the hype remains to be seen. More concerning is whether AI will exacerbate the rampant feeling among providers that they have become tools of their tools and not masters of them. As EHR systems become more usable, interoperable, and patient-friendly, the underlying foundations of medical care can be finally addressed. High-quality evidence backs only about 10 percent of the care patients receive today. One of the great potentials of digitizing health records is to discover what treatments work best and why and then distribute that information to the health care community. While this is an active research area, more research and funding are needed. Twenty years ago, Tom Conrad, who himself was a senior computer scientist, told me he was skeptical that having more information necessarily meant that better medical decisions would automatically be made. He pointed out that when doctors’ earnings are related to the number of patients they see, there is a trade-off between the better care that EHR provides and the sheer amount of time required to review a more complete medical record. Today, the trade-off is not in the patients’ or doctors’ favor. Whether it can ever be balanced is one of the great unknowns. Obviously, no one wants to go back to paper records. However, as John Leslie King says, “The way forward involves multiple moving targets due to advances in technology, care, and administration. Most EHR vendors are moving as fast as they can.” However, it would be foolish to think it will be smooth sailing from here on, King says: “The EHR venture has proved troublesome thus far. The trouble is far from over.”

In Mira Daher’s home country of Lebanon, the national grid provides power for only a few hours a day. The country’s state-owned energy provider, Electricity of Lebanon (EDL), has long struggled to meet demand, and a crippling economic crisis that began in 2019 has worsened the situation. Most residents now rely on privately owned diesel-powered generators for the bulk of their energy needs. But in recent years, the rapidly falling cost of solar panels has given Lebanese businesses and families a compelling alternative, and the country has seen a boom in private solar-power installations. Total installed solar capacity jumped nearly eightfold between 2020 and 2022 to more than 870 megawatts, primarily as a result of off-grid rooftop installations. Daher, head of tendering at the renewable-energy company Earth Technologies, in Antelias, Lebanon, has played an important part in this ongoing revolution. She is in charge of bidding for new solar projects, drawing up designs, and ensuring that they are correctly implemented on-site. “I enjoy the variety and the challenge of managing diverse projects, each with its own unique requirements and technical hurdles,” she says. “And knowing that my efforts also contribute to a sustainable future for Lebanon fills me with pride and motivates me a lot.” An Early Mentor Daher grew up in the southern Lebanese city of Saida (also called Sidon) where her father worked as an electrical engineer in the construction sector. His work helped to inspire her interest in technology at a young age, she says. When she was applying for university, he encouraged her to study electrical engineering too. “My first mentor was my father,” says Daher. “He increased my curiosity and passion for technology and engineering, and when I watched him work and solve complex problems, that motivated me to follow in his footsteps.” In 2016, she enrolled at Beirut Arab University to study electrical and electronics engineering. When she graduated in 2019, Daher says, the country’s solar boom was just taking off, which prompted her to pursue a master’s degree in power and energy, with a specialization in solar power, at the American University of Beirut. “My thesis concentrated on the energy situation in Lebanon and explored potential solutions to increase the reliance on renewable resources,” she says. “Five or six years ago, solar systems had high costs. But today the cost [has] decreased a lot because of new technologies, and because there is a lot of production of solar panels in China.” Entering the Workforce After graduating in 2021, Daher started a job as a solar-energy engineer at the Beirut-based solar-power company Mashriq Energy, where she was responsible for developing designs and bids for new solar installations, similar to her current role. It was a steep learning curve, Daher says, because she had to quickly pick up business skills, including financial modeling and contract negotiations. She also learned to deal with the practicalities of building large solar developments, such as site constraints and regulations. In 2022, she joined Earth Technologies as a solar project design engineer. Various organizations, including Lebanese government and nongovernmental agencies such as the United Nations, request bids for solar power installations they want to build—a process known as tendering. Daher’s principal responsibility is to prepare and submit bids for these projects, but she also supervises their implementation. Daher’s role requires her to maintain a broad base of knowledge about the solar projects she oversees.Mira Daher “I oversee the entire project cycle, from identifying and managing tenders to designing, pricing, and implementing solar projects across residential, industrial, commercial, and utility sectors,” she says. The first step in the process is to visit the proposed installation site to determine where solar panels should be positioned based on the landscape and local weather conditions. Once this is done, Daher and her team come up with a design for the plant. This involves figuring out what kinds of solar panels, inverters, and batteries will fit the budget and how to wire all the components together. The team runs simulations of the proposed plant to ensure that the design meets the client’s needs. Daher is then responsible for negotiating with the client to make sure that the proposal fulfills their technical and budgetary requirements. Once the client has approved the design, other teams oversee construction of the plant, though Daher says she makes occasional site visits to ensure the design is being implemented correctly. Daher’s role requires her to have a solid understanding of all the components that go into a solar plant, from the different brands of power electronics to the civil engineering required to build supporting structures for solar panels. “You have to know everything about the project,” she says. Solar Power for Development Earth Technologies operates across the Middle East and Africa, but Daher says most of the solar installations she works on are in Lebanon. Some of the most interesting have been development-focused projects funded by the U.N. Daher led the U.N.-funded installation of solar panels at nine hospitals, as well as a project that uses solar power to pump water to people in remote parts of the country. More recently, she has started work on a solar and battery installation for street lighting in the town of Bourj Hammoud, which will allow shops to stay open later and help to boost the local economy. The projects she has overseen generally cost around US $700,000 to $800,000. But securing funding for renewable projects is an ongoing challenge in Lebanon, says Daher, given the uncertain economic situation. More recently, the country was also rocked by the conflict between Israel and the Lebanon-based paramilitary group Hezbollah. This resulted in widespread bombing of Beirut, the capital, and the country’s southern regions last October and November. “The two months of conflict were incredibly challenging,” says Daher. “The environment was unsafe and filled with uncertainty, leaving us constantly anxious about what the future held.” Safety concerns forced her to relocate from her home in Beirut to a village called Ain El Jdideh. This meant she had to drive about an hour and a half on unsafe roads to get to work. Several of the major projects she was working on were also halted as they were in the areas that bore the brunt of the conflict. One U.N.-funded project she worked on in Ansar, in southern Lebanon, was knocked offline when an adjacent building was destroyed. “Despite these hardships, we persevered, and I am grateful that the war has ended, allowing us to regain some stability and security,” says Daher. A Challenging But Fulfilling Career Despite these difficulties, Daher remains optimistic about the future of renewable energy in Lebanon, and she says it can be a deeply rewarding career. Breaking into the industry requires a strong educational foundation, though, so she recommends first pursuing a degree focused on power systems and renewable technologies. The energy sector is a male-dominated field, says Daher, which can make it difficult for women to find their footing. “I’ve often encountered biases, stereotypes that can make it more difficult to be taken seriously, or to have my voice heard,” she adds. “Overcoming these obstacles requires resilience, confidence, and a commitment to demonstrating my expertise and capabilities.” It also requires a commitment to continual learning, due to the continued advances being made in solar-power technology. “It’s very important to stay up to date,” she says. “This field is always evolving. Every day, you can see a lot of new technologies.”

Your weekly selection of awesome robot videos 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. RoboSoft 2025: 23–26 April 2025, LAUSANNE, SWITZERLAND ICUAS 2025: 14–17 May 2025, CHARLOTTE, NC ICRA 2025: 19–23 May 2025, ATLANTA, GA London Humanoids Summit: 29–30 May 2025, LONDON IEEE RCAR 2025: 1–6 June 2025, TOYAMA, JAPAN 2025 Energy Drone & Robotics Summit: 16–18 June 2025, HOUSTON, TX RSS 2025: 21–25 June 2025, LOS ANGELES ETH Robotics Summer School: 21–27 June 2025, GENEVA IAS 2025: 30 June–4 July 2025, GENOA, ITALY ICRES 2025: 3–4 July 2025, PORTO, PORTUGAL IEEE World Haptics: 8–11 July 2025, SUWON, KOREA IFAC Symposium on Robotics: 15–18 July 2025, PARIS RoboCup 2025: 15–21 July 2025, BAHIA, BRAZIL RO-MAN 2025: 25–29 August 2025, EINDHOVEN, NETHERLANDS Enjoy today’s videos! This robot can walk, without electronics, and only with the addition of a cartridge of compressed gas, right off the 3D-printer. It can also be printed in one go, from one material. Researchers from the University of California San Diego and BASF, describe how they developed the robot in an advanced online publication in the journal Advanced Intelligent Systems. They used the simplest technology available: a desktop 3D-printer and an off-the-shelf printing material. This design approach is not only robust, it is also cheap—each robot costs about $20 to manufacture. And details! [ Paper ] via [ University of California San Diego ] Why do you want a humanoid robot to walk like a human? So that it doesn’t look weird, I guess, but it’s hard to imagine that a system that doesn’t have the same arrangement of joints and muscles that we do will move optimally by just trying to mimic us. [ Figure ] I don’t know how it manages it, but this little soft robotic worm somehow moves with an incredible amount of personality. Soft actuators are critical for enabling soft robots, medical devices, and haptic systems. Many soft actuators, however, require power to hold a configuration and rely on hard circuitry for control, limiting their potential applications. In this work, the first soft electromagnetic system is demonstrated for externally-controlled bistable actuation or self-regulated astable oscillation. [ Paper ] via [ Georgia Tech ] Thanks, Ellen! A 180-degree pelvis rotation would put the “break” in “breakdancing” if this were a human doing it. [ Boston Dynamics ] My colleagues were impressed by this cooking robot, but that may be because journalists are always impressed by free food. [ Posha ] This is our latest work about a hybrid aerial-terrestrial quadruped robot called SPIDAR, which shows unique and complex locomotion styles in both aerial and terrestrial domains including thrust-assisted crawling motion. This work has been presented in the International Symposium of Robotics Research (ISRR) 2024. [ Paper ] via [ Dragon Lab ] Thanks, Moju! This fresh, newly captured video from Unitree’s testing grounds showcases the breakneck speed of humanoid intelligence advancement. Every day brings something thrilling! [ Unitree ] There should be more robots that you can ride around on. [ AgileX Robotics ] There should be more robots that wear hats at work. [ Ugo ] iRobot, who pioneered giant docks for robot vacuums, is now moving away from giant docks for robot vacuums. [ iRobot ] There’s a famous experiment where if you put a dead fish in current, it starts swimming, just because of its biomechanical design. Somehow, you can do the same thing with an unactuated quadruped robot on a treadmill. [ Delft University of Technology ] Mush! Narrowly! [ Hybrid Robotics ] It’s freaking me out a little bit that this couple is apparently wandering around a huge mall that is populated only by robots and zero other humans. [ MagicLab ] I’m trying, I really am, but the yellow is just not working for me. [ Kepler ] By having Stretch take on the physically demanding task of unloading trailers stacked floor to ceiling with boxes, Gap Inc has reduced injuries, lowered turnover, and watched employees get excited about automation intended to keep them safe. [ Boston Dynamics ] Since arriving at Mars in 2012, NASA’s Curiosity rover has been ingesting samples of Martian rock, soil, and air to better understand the past and present habitability of the Red Planet. Of particular interest to its search are organic molecules: the building blocks of life. Now, Curiosity’s onboard chemistry lab has detected long-chain hydrocarbons in a mudstone called “Cumberland,” the largest organics yet discovered on Mars. [ NASA ] This University of Toronto Robotics Institute Seminar is from Sergey Levine at UC Berkeley, on Robotics Foundation Models. General-purpose pretrained models have transformed natural language processing, computer vision, and other fields. In principle, such approaches should be ideal in robotics: since gathering large amounts of data for any given robotic platform and application is likely to be difficult, general pretrained models that provide broad capabilities present an ideal recipe to enable robotic learning at scale for real-world applications.From the perspective of general AI research, such approaches also offer a promising and intriguing approach to some of the grandest AI challenges: if large-scale training on embodied experience can provide diverse physical capabilities, this would shed light not only on the practical questions around designing broadly capable robots, but the foundations of situated problem-solving, physical understanding, and decision making. However, realizing this potential requires handling a number of challenging obstacles. What data shall we use to train robotic foundation models? What will be the training objective? How should alignment or post-training be done? In this talk, I will discuss how we can approach some of these challenges. [ University of Toronto ]

The U.S. government recommends that everyone have a disaster kit that includes a weather radio. These radios tune to a nationwide network run by the National Oceanic and Atmospheric Administration (NOAA) and the Federal Communications Commission that provides alerts about hazardous weather and other major emergencies. Such broadcasts can be a lifeline when other communication systems go out. But what if you could step it up and get not just audio information but also images, charts, and written reports, even while completely off the grid? Turns out you can, thanks to modern geosynchronous weather satellites, and it’s never been easier than with KrakenRF’s new Discovery Dish system. This system involves buying a US $115 70-centimeter-diameter parabolic antenna, and then one of a number of $109 swappable feeds that cover different frequency bands. To try out the system, I got one feed suitable for picking up L-band satellite transmissions, and another tuned for detecting the radio emissions from galactic hydrogen clouds. The parabolic antenna comes as three metal petals plus some ancillary bits and pieces for holding the feed and mounting the dish on a support. Everything is held together with nuts and bolts, so it can be dissembled and reassembled, and the petals are light and stack together nicely—you could pack them in a suitcase if you ever wanted to travel and sample a different sky. In addition to KrakenRF’s dish and feed, you’ll also need a software-defined radio (SDR) receiver and a computer with software to decode the signals coming from the feed. Many SDRs can be used, but you’ll need one that comes with what’s known as a bias tee built in, or you’ll need to add a bias tee yourself. The bias tee supplies power to the low-noise amplifiers used in KrakenRF’s feeds. I used the recommended $34 RTL-SDR Blog V3 (which comes as a USB dongle), with my MacBook, but you can use a PC or Raspberry Pi as a host computer as well. The Discovery Dish is formed by three petals [top center] that bolt together with other mounting gear [top left and right]. Feeds are mounted on a pole and adjusted to be level with the dish’s focus [bottom]. Different feeds allow different applications, such as 1680 megahertz for receiving L-band satellite transmissions or 1420 MHz for radio astronomy. A software-defined radio receiver decodes signals.James Provost After I inserted the L-band feed into the dish, it was time to look for a satellite. Following KrakenRF’s guide, I used Carl Reinemann’s Web app to print out a list of azimuths and elevations for geosynchronous weather satellites based on my location. Then I headed up to the roof of my New York City apartment building with the mast from my portable ham radio antenna to provide a mount. And then I headed straight back down again when I realized that it was too blustery for a temporary mount. The dish is perforated with holes to reduce air resistance, but there was still a real risk of the wind toppling the portable mast and sweeping it over the side of the building. A couple of days later, I returned to calmer conditions, and with my iPhone employed as a compass and inclinometer, I pointed the dish at the coordinates for the GOES-East weather satellite. This satellite hangs over the equator at a longitude of 75 degrees west, close to that of New York City. A second satellite, GOES-West, sits at 135 degrees west, over the Pacific Ocean. These GOES satellites are fourth-generation spacecraft in a long line of invaluable weather satellites that have been operated by NOAA and the U.S. National Weather Service for 50 years. The first of the current generation, known as GOES-R, launched in 2016 and features a number of upgrades. For radio enthusiasts, the most significant of the upgrades are its downlink broadcast capabilities. The current GOES satellites transmit images taken in multiple wavelengths and scales. A false-color full-disk image [above] is captured in an infrared band that detects moisture and ash; the image at top shows the eastern United States in an approximation of what you would see with the naked eye. Stephen Cass/NOAA The GOES-R satellites transmit data at 400 kilobits per second, versus a maximum of 128 Kb/s for previous generations, allowing more information to be included, such as images from other weather satellites around the globe. The satellites also merge satellite-image data and emergency and weather information into a single link that can be simultaneously picked up by one receiver, instead of needing two as previously. For fine dish-pointing adjustments, I was guided by watching the signal in the frequency spectrum analyzer built into SatDump, an open-software package designed for decoding satellite transmissions picked up by SDR receivers. I groaned when no matter how I adjusted the dish, I could barely get the signal above the noise. But much to my surprise, I nonetheless started seeing an image of the Earth begin to form on the display. The original GOES-R design specified that receiving ground dishes would have to be at least one meter in diameter, but the folks at KrakenRF have built their feeds around a new ultralow-noise amplifier that can make the weaker signal gathered by their smaller dish usable. Soon I had pictures of the Earth in multiple wavelengths, both raw and in false color, with and without the superimposed outlines of states and countries, plus a wide assortment of other charts plotting rainfall and wind speeds for different areas. The GOES satellites also broadcast information uploaded from the U.S. National Weather Service, such as this chart of marine wind speeds.Stephen Cass/National Weather Service My next test was to do a spot of radio astronomy, swapping out the L-band feed for the galactic hydrogen emission feed. Getting results was a much longer process: First I had to point the dish at a bit of the sky where I knew the Milky Way wasn’t to obtain baseline data (done with the help of the Stellarium astronomy site). Then I pointed the dish straight up and waited for the rotation of the Earth to bring the Milky Way into view. Pulling the signal out of the noise is a slow process—you have to integrate 5 minutes of data from the receiver—but eventually a nice curve formed that indicated I was still safely within the embrace of the spiral arms of our home galaxy. Much more sophisticated radio astronomy can be done, especially if you mount the dish on a scanning platform to generate 2D maps. But I swapped back the L-band feed just to marvel at how our planet looks from 36,000 kilometers away!

Marko Delimar has been a proponent of empowering the next generation of engineers, scientists, and technologists since he was an undergraduate engineering student at the University of Zagreb, in Croatia. The IEEE senior member now mentors undergraduate and graduate students at his alma mater, where he is a professor of electrical engineering and computing. IEEE has played a key role in his quest to provide students with the support they need, he says. Marko Delimar Employer: University of Zagreb, in Croatia Title: Professor of electrical engineering and computing Member grade: Senior member Alma mater: University of Zagreb Throughout his 30 years of volunteering, Delimar has worked to build a community for students. He founded the University of Zagreb’s IEEE student branch and later became its chair. He went on to become the branch’s counselor and a member of the IEEE Croatia Section’s student activities committee. He has held numerous IEEE leadership positions, and he served on the organization’s Board of Directors. To engage more student members and help connect student branches worldwide, he helped found the IEEEXtreme programming competition, an annual, 24-hour virtual contest in which teams compete to solve computer coding problems. He is continuing his mission as the 2025 president of the IEEE Foundation, focusing on how the organization’s charitable partner can help students and young professionals prosper. Thanks to donations, the Foundation is able to fund scholarships, research and travel grants, and fellowships in partnership with IEEE societies and sections. “Supporting IEEE programs is something that I’m very proud of,” Delimar says. His goals as president, he says, are to increase awareness of the Foundation’s donor-supported programs and to persuade more people to support its causes. Supporting the next generation of engineers After learning about IEEE from several of his professors who were members, Delimar joined the organization in 1994 during his second year at the University of Zagreb. But without a student branch at the school, there was no local community for student members. That year he successfully petitioned IEEE to establish the first student branch in Croatia. He served as its chair until he graduated with his EE bachelor’s degree in 1996. Through his involvement, he was simultaneously “learning about the organization and volunteering,” he says, adding that it helped him better understand IEEE. After graduating, he joined his alma mater as a teaching assistant and researcher. He was later hired as a faculty member. He also conducted research in power engineering under his former professor, IEEE Senior Member Zdravko Hebel, who is known for his work on the Croatian power transmission network. Delimar continued to volunteer, serving as chair of student activities for the IEEE Croatia Section until 2001. He also was the counselor for the student branch. “For me, IEEE Foundation Day highlights how the IEEE Foundation is more than a charitable organization—it is the heart of IEEE’s philanthropic efforts, where generosity meets impact.” Within four years of his guidance, “the branch was collaborating with other branches in not only Region 8 [Europe, Middle East, and Africa] but also around the world,” he says. He decided it was time to spread his wings, and he began volunteering for the region. His first position was as the 2005–2006 vice chair of the IEEE Region 8 student activities committee, which is responsible for student programs and benefits. At the time, IEEE was having trouble retaining student members, he says. “Students would graduate and not renew their membership,” he says. “There was also an issue with some of the student branches, as they were not communicating well or collaborating with each other.” Delimar and IEEE Member Ricardo Varela, who was also on the committee, brainstormed how to better engage students and increase their participation. The two men wanted to create an event that would allow students across the world to participate at the same time. “It sounded like a very crazy idea,” he says, “because it’s nighttime for one half of the world and daytime for the other half. You can’t even hold a meeting at the same time everywhere, let alone an activity.” To overcome the time-zone issue, Delimar and Varela devised a 24-hour competition on programming, which was popular among engineering students at the time, Delimar says. Having the contest take place over 24 hours ensured all participants were on equal footing, he says. Forty teams participated in the first IEEE Xtreme competition, which was held in October 2006. It has since grown in popularity. Last year nearly 8,800 teams from 75 countries participated. Although he’s not involved in the contest anymore, Delimar says he’s proud of its success. In 2007 he became vice chair of the IEEE Region 8 membership activities committee, which plans events for members. He was then elected as the 2010–2011 Region 8 director, and in 2013 he became IEEE secretary. Both are Board-level positions. “Being a part of the IEEE Board of Directors gave me the opportunity to learn about and serve on several interesting committees that were trying to reach particular goals at the time, such as increasing member engagement, improving training for new IEEE officers, and refining IEEE’s ability to quickly adapt to the fast-changing environment,” Delimar says. His time on the board inspired him to advocate for the formation of an ad hoc committee on European public policy activities. He served as its chair, and in 2018 it became a permanent committee. Renamed the IEEE European Public Policy Committee, it supports members of the European Union and European Free Trade Association in developing technology-related policies. Delimar was its chair until 2020. “IEEE has been able to provide a united, unbiased voice of what is good for technology and what is good for Europe,” he says. “It has been very well received by the European Commission.” In 2016 Pedro Ray, the 2010 IEEE president, asked Delimar to be a volunteer for the IEEE Foundation, and he joined the board the next year. “It’s been a very rewarding experience,” he says. Leading the IEEE Foundation Delimar says that his main goal as president is to increase awareness among IEEE members of the Foundation and its programs. “The Foundation supports more than 250 funds and programs, and I want to strengthen its connections and partnerships across IEEE,” he says. To accomplish that goal, the Foundation has been raising its visibility. In 2023 it celebrated its 50th anniversary with a reception in New York City. Other celebratory activities were held that year at the IEEE Vision, Innovation, and Challenges Summit and Honors Ceremony and the IEEE Power & Energy Society General Meeting. Last year the Foundation established 16 February as IEEE Foundation Day. The annual celebration marks the day in 1973 when the philanthropic organization was launched. The inaugural event was designed to reflect the Foundation’s vision of being the heart of IEEE’s charitable giving. This year’s celebration focused on students and young professionals, highlighting beneficiaries of scholarships, grants, and fellowships and the impact they have had on the recipients. “For me, IEEE Foundation Day highlights how the IEEE Foundation is more than a charitable organization—it is the heart of IEEE’s philanthropic efforts, where generosity meets impact,” Delimar says. “Our donor-supported programs—like scholarships, travel grants, awards, research grants, and competitions—are more than financial support for our students and young professionals; they are catalysts for making dreams come true.” He says he wants to engage members who aren’t typically donors and thus expand the Foundation’s reach. “I want to enable people with different professional journeys, economic backgrounds, cultures, and geography to be able to participate as donors for the IEEE Foundation,” he says. “Every donor—whether they are a student, young professional, or IEEE life member—is important.” Visit the IEEE Foundation website to discover upcoming events, learn ways to make a gift, and see how the organization’s charitable efforts are making an impact.

IEEE Spectrum is rebooting our careers newsletter! In partnership with tech career development company Taro, every issue will be bringing you deeper insight into how to pursue your goals and navigate professional challenges. Sign up now to get insider tips, expert advice, and practical strategies delivered to your inbox for free. One of my close friends is a hiring manager at Google. She recently posted about an open position on her team and was immediately overwhelmed with applications. We’re talking about thousands of applicants within days. What surprised me most, however, was the horrendous quality of the average submission. Most applicants were obviously unqualified or had concocted entirely fake profiles. The use of generative AI to automatically fill out (and, in some cases, even submit) applications is harmful to everyone; employers are unable to filter through the noise, and legitimate candidates have a harder time getting noticed—much less advancing to an interview. This problem exists even for companies that don’t have the magnetism of the Google brand. Recruiting is a numbers game with slim odds. As AI becomes increasingly mainstream, the job search can feel downright impossible. So how can job seekers stand out among the deluge of candidates? When there are hundreds or thousands of applicants, the best way to distinguish yourself is by leveraging your network. With AI, anyone with a computer can trivially apply to thousands of jobs. On the other hand, people are restricted by Dunbar’s number—the idea that humans can maintain stable social relationships with only about 150 people. Being one of those 150 people is harder, but it also carries more weight than a soulless job application. A referral from a trusted connection immediately elevates you as a promising candidate. Your goal, therefore, is to aggressively pursue opportunities based on people you’ve worked with. A strong referral has two benefits: Your profile gets increased visibility. You “borrow” the credibility of the person who gave a vote of confidence. So how do you get one of these coveted referrals? Start with groups you’re part of that have a well-defined admission criteria. Most commonly, this will be your university or workplace. Engage with university alumni working in interesting roles, or reconnect with an ex-colleague to see what they’re up to. Good luck out there!—Rahul ICYMI: Despite 2024 Layoffs, Tech Jobs Expected to Take Off In 2024, the technology sector saw additional cuts after massive layoffs in 2022 and 2023. Despite these numbers, however, engineers seem to be doing just fine. U.S. employment for electrical engineers is expected to grow 9 percent from 2023 to 2033, compared with 4 percent for all occupations. And the World Economic Forum’s Future of Jobs Report 2025, revealed that technology-related roles are the fastest-growing categories globally, with the most rapid growth in demand anticipated for big data specialists, financial-technology engineers, AI specialists, and software developers. Read more at https://spectrum.ieee.org/tech-jobs ICYMI: Electric Vehicles Made These Engineers Expendable When veteran Wall Street Journal reporter Mike Colias began writing about the automotive industry in 2010, the internal-combustion engine still served as the beating heart of legacy carmakers. Since then, the hard pivot to electric vehicles has sidelined engine design and upended a century of internal order at these companies. Colias has observed the transformation, and the recent detour back to plug-in hybrids, from a front-row seat in Detroit. Read an excerpt from his new book Inevitable: Inside the Messy, Unstoppable Transition to Electric Vehicles, which tells the tale of one power-train engineer at Ford whose internal-combustion-engine expertise slowly became expendable.

In my career, I’ve often been the only woman in a room full of men, a situation all too common in tech-related fields. From the start of my engineering journey, I was among a handful of women in my graduate program. This trend continued when I began my first software engineering job as the only woman on my team, and eight years later, I remain a minority in the tech world. Women currently constitute about 35 percent of the technical workforce. This statistic highlights the ongoing challenge of gender disparity in tech, where women have historically been underrepresented. And the gap becomes even more pronounced in leadership roles, with women making up only about 25 percent of CEOs in the technology sector, according to one report. While the gender gap remains, many women have successful careers in tech. With these five actionable tips, women can take charge and own their space in the engineering world. Negotiate Your Compensation with Confidence Women engineers in the United States earn about 10 percent less than their male counterparts in similar roles. When I started my career, I believed that as long as I worked on solving the problems I enjoyed, my compensation didn’t really matter. But over time, I realized this mind-set is flawed. Compensation isn’t just about money. It reflects your value and contributions to a company. Salary negotiation can feel daunting, especially if you feel uncertain about what’s fair or struggle to articulate your worth. But this is your chance to own your value. The first step in negotiating your salary is learning your market value. Websites like Glassdoor and Levels.fyi provide salary insights based on data from employees in similar roles. They’re excellent starting points to get a sense of base salaries, bonuses, stock options, and other compensation factors. Your network can provide another valuable source of information. Ask trusted friends or colleagues working at similar companies what someone with your experience would typically be paid. Phrasing the question as a hypothetical avoids putting anyone on the spot to disclose their salary, while still giving you helpful information. Once you clearly understand your market worth, you are in a solid position to negotiate your compensation, whether switching jobs or discussing a raise with your current employer. If you are in the midst of a job switch and have received an offer, you’ve already proven your value to the new company. The hiring team has invested significant time and resources in the interview process and is eager to bring you onboard. They’re often willing to negotiate at this point, but it’s essential to know when you have leverage. One of the best times to negotiate is when you have more than one offer or are approaching the final rounds of interviews with other companies. This creates a healthy competition for your skills. There are also ways to prepare if you’re approaching a compensation cycle in your current company. Start early: Begin conversations with your boss three to five months before you expect changes, as compensation is often finalized months ahead. You should also know your company’s policies; some employers may increase salary, while others focus on benefits or long-term career growth. Compensation isn’t just salary—stock and bonuses also matter. When you’re ready, talk to your manager confidently and with the right data. A good manager will appreciate your asking for what you deserve. Postdoctoral researcher Caitlin McCowan adjusts a customized scanning tunneling microscope. Craig Fritz Don’t Attribute Your Success to Gender When organizations are committed to advancing diversity, studies suggest that the public tends to perceive women’s promotions as driven less by their intelligence and effort, and more by their gender. If those perceptions also prevail within their company, women might be told they have an unfair advantage. These types of remarks can make it easy to start questioning, for example, whether you have truly earned a promotion. If you’re encountering such attitudes, you can instead embrace your accomplishments and take ownership of the work that led to them. Keeping a “brag list” to record your achievements and strengths can serve as a reminder of your true capabilities. This list can include concrete results or milestones you’ve reached through effort and skill, as well as personal qualities that contribute to your success. Creating a brag list isn’t about feeding your ego. It’s about reminding yourself of the hard work you’ve put in and acknowledging that you belong because of your talents. Nandu Koripally [front] and Lulu Yao work with a structural supercapacitor developed by engineers at the University of California, San Diego.David Baillot/UC San Diego Jacobs School of Engineering Open Doors That Are Closed to You If a door isn’t open to you, it doesn’t mean you don’t belong on the other side. I’m often surprised by how much women miss out on simply because we don’t ask about the opportunities that interest us. If you think you’re capable of an opportunity, clearly express interest to your manager. When you approach them, explain the type of opportunity you seek, such as leading a project or transitioning to a new role. Then highlight your strengths by connecting your request to your previous successes. This shows that you’ve delivered in the past and are ready for the next step. Identifying where you can add value will also make your request more compelling. If you see a gap or area for improvement, frame your request around how you can help the team or organization. If the opportunity isn’t immediately available, ask for feedback on your readiness and how to prepare for future opportunities. This shows you’re eager to learn and improve. Follow up regularly to express your continued interest as well. Paula Kirya, a mechanical engineering graduate student at the University of California, San Diego, studies light-manipulating micro- and nanostructures on Morpho butterfly wings to assess the level of fibrosis in cancer biopsy samples.David Baillot/UC San Diego Jacobs School of Engineering Practice Authentic Leadership As women, we often possess leadership traits that differ from what’s expected of men. These are shaped by societal expectations, cultural influences, and workplace dynamics that historically defined leadership in a particular way. The traits that are often associated with women can be viewed negatively in leadership due to gender biases. I’ve worked with many highly empathetic women who would make excellent leaders. However, they may be perceived as weak because traditional leadership norms prioritize assertiveness and authority over emotional intelligence. Traits like fostering relationships shouldn’t be seen as weaknesses simply because they don’t fit traditional leadership molds. Rather, these qualities can bring a different approach to leadership. It can be helpful to create a Venn diagram highlighting your strengths, improvement areas, and the overlap between them. This process may reveal characteristics that aren’t necessarily flaws but, when harnessed effectively, can become strengths. By recognizing these traits and being intentional in their application, you can transform them into key advantages in your leadership style. You don’t need to conform to a specific image of what leadership should look like. Instead, practice authentic leadership by providing guidance in a way that’s true to you. See Yourself as a Leader My final piece of advice is a simple one: Leadership isn’t just for other people—it’s for you, too. When my manager first asked if I would like to take on a new role as the team lead, I was thrilled. But when I went home, self-doubt and anxiety clouded my excitement. The image I had of a technical leader was that of a man, and I couldn’t envision myself in that role at first. Over time, however, I changed that mental image. Start visualizing yourself as a leader in your organization, regardless of your current position. Leadership is less about title and more about mind-set. Take initiative, lead by example, and make decisions that contribute to your team’s success. When you start thinking and acting like a leader, others will also begin to see you that way. Navigating the tech industry as a woman can be challenging, but it’s important to recognize and embrace your value. By confidently negotiating compensation, attributing your success to your skills, asking about new opportunities, embracing authentic leadership, and seeing yourself as a leader, you can carve out your space in the engineering world. These strategies will not only empower you, but contribute to a more inclusive and diverse tech industry.

EPICS in IEEE, an Educational Activities program, celebrated its 15th anniversary last year. The Engineering Projects in Community Service initiative provides nonprofit organizations with technology to improve and deliver services to their community while broadening undergraduate EE students’ hands-on experience with engineering-related topics. The program reached new heights last year by distributing more than US $226,000 to 39 projects, engaging more than 900 students and 1,400 volunteers and IEEE members including proposal reviewers, mentors, and project support participants. The standout year caps Stephanie Gillespie’s three-year term as chair of the EPICS in IEEE committee. An IEEE member, she is an associate dean at the University of New Haven engineering college, in Connecticut. Under her leadership, the EPICS program streamlined processes for collecting data and boosted storytelling efforts to illustrate the impact of its activities. Proposal applications nearly doubled, and approved projects increased by 44 percent. “During my three years as committee chair, I’ve loved seeing so many students worldwide have the opportunity to engage with their community and truly consider the needs of their project stakeholders,” Gillespie says. “There are so many ways in which those positive experiences can trickle through their professional and personal lives. “New leadership will bring new ideas, and I’m excited for the future of EPICS in IEEE and the continued growth of this outstanding program.” Pedro Wightman is this year’s committee chair. He is an IEEE senior member and an associate professor in the engineering school at the Universidad del Rosario, in Bogotá. Wightman, who has been an active committee member, helped lead an EPICS project himself. “EPICS in IEEE represents a bridge between engineering theory and community needs,” Wightman says. “It brings these two realities together for humanitarian purposes and helps polish an engineering student’s academic experience so that they’re not only technology experts but humans first. “I hope the program continues to grow and strengthen, because there’s so much need out there but also so many young engineers around the world willing to put their energy and knowledge toward improving the well-being of people.” The following projects, all funded last year, showcase the enthusiasm and dedication of students to create and deploy solutions to help people. Augmented reality system for distance learning A lack of access to trained teachers and quality educational programs threatens students’ development, especially in rural areas with poor infrastructure and limited resources. A team from the Mehran University of Engineering and Technology (MUET) in Jamshoro, Pakistan, addressed such challenges with its Augmented Reality 3D System for Interactive Learning in Rural Elementary Schools project. It reached hundreds of pupils in underserved areas with a virtual, sustainable, and hands-on learning platform that engages students and teachers alike. The team received a$4,115 grant to design and deploy their prototype. “Many rural areas lack access to qualified educators and modern teaching resources,” says Sameer Qazi, an IEEE student member and a MUET senior. “Our project seeks to bridge that gap by providing an interactive and immersive learning system that delivers an innovative educational experience with a focus on science, art, and history.” The team from Mehran University of Engineering and Technology, in Jamshoro, Pakistan, assembles its augmented-reality prototype of its distance learning system. Mehran University of Engineering and Technology EPICS in IEEE project team The project involved nine students and two faculty supervisors from the university’s electronic engineering department. Each member contributed to one of the three project modules: content development, power management, and the display system. Qazi was project lead for the display system module. The team partnered with the Fast Rural Development Program, an organization dedicated to transforming underprivileged areas and driving sustainable development in rural parts of Pakistan’s Sindh province. The partnership helped ensure the team’s solution was aligned with community needs and could be more seamlessly integrated into the target schools’ curriculum. “I hope the program continues to grow and strengthen, because there’s so much need out there but also so many young engineers around the world willing to put their energy and knowledge toward improving the well-being of people.” —Pedro Wightman “From funding to mentorship, EPICS in IEEE provided us with essential support that allowed us to bring this project from concept to reality,” Qazi says. “I encourage anyone considering an EPICS project to seize the opportunity. It’s a chance to make a tangible impact on society while growing as an engineer and leader.” Solar-powered greenhouse A creative team of students from BridgeValley Community and Technical College in South Charleston, W.Va., used its $3,200 grant to establish their Solar PV–Powered Smart Sustainable Greenhouse project. It is designed to optimize plant growth by maintaining ideal conditions and conserving energy through remote monitoring. The team used Arduino and other technologies, and it partnered with Café Appalachia, an eco-friendly nonprofit with its own farms. The group developed a greenhouse system featuring solar-powered lithium-ion batteries, smart irrigation, fan control, and IoT integration for real-time data monitoring. Other components included tracking charge controllers, soil-moisture sensors, solenoid valves, flow meters, temperature and humidity sensors, a 356-millimeter fan, and a Wi-Fi module with data logging capabilities. “We chose to work with Café Appalachia because they had an interest in sustainable greenhouse practices and offered space for the system,” says team lead Joshua “Youngil” Kim, an IEEE member. “Their dedication to giving back to their community and addressing climate change aligned with our goals.” Kim, a former BridgeValley educator, now is an assistant professor of electrical engineering at Charleston Southern University, in South Carolina. The team expressed gratitude for the funding from the IEEE Antennas and Propagation Society, an EPICS in IEEE partner. “EPICS’ support of this greenhouse project has been instrumental in allowing us to contribute to our community and offer educational opportunities to our students,” Kim says. “Thanks to EPICS in IEEE’s generosity and commitment to fostering educational growth, it’s been a valuable experience for everyone involved.” To learn more about EPICS projects or to submit a project proposal, join the mailing list and receive monthly updates.

A long-awaited, emerging computer network component may finally be having its moment. At Nvidia’s GTC event last week in San Jose, the company announced that it will produce an optical network switch designed to drastically cut the power consumption of AI data centers. The system—called a co-packaged optics, or CPO, switch—can route tens of terabits per second from computers in one rack to computers in another. At the same time, startup Micas Networks, announced that it is in volume production with a CPO switch based on Broadcom’s technology. In data centers today, network switches in a rack of computers consist of specialized chips electrically linked to optical transceivers that plug into the system. (Connections within a rack are electrical, but several startups hope to change this.) The pluggable transceivers combine lasers, optical circuits, digital signal processors, and other electronics. They make an electrical link to the switch and translate data between electronic bits on the switch side and photons that fly through the data center along optical fibers. Co-packaged optics is an effort to boost bandwidth and reduce power consumption by moving the optical/electrical data conversion as close as possible to the switch chip. This simplifies the setup and saves power by reducing the number of separate components needed and the distance electronic signals must travel. Advanced packaging technology allows chipmakers to surround the network chip with several silicon optical-transceiver chiplets. Optical fibers attach directly to the package. So all the components are integrated into a single package except for the lasers, which remain external because they are made using nonsilicon materials and technologies. (Even so, CPOs require only one laser for every eight data links in Nvidia’s hardware.) “An AI supercomputer with 400,000 GPUs is actually a 24-megawatt laser.” —Ian Buck, Nvidia As attractive a technology as that seems, its economics have kept it from deployment. “We’ve been waiting for CPO forever,” says Clint Schow, a co-packaged optics expert and IEEE Fellow at the University of California, Santa Barbara, who has been researching the technology for 20 years. Speaking of Nvidia’s endorsement of technology, he said the company “wouldn’t do it unless the time was here when [GPU-heavy data centers] can’t afford to spend the power.” The engineering involved is so complex, Schow doesn’t think it’s worthwhile unless “doing things the old way is broken.” And indeed, Nvidia pointed to power consumption in upcoming AI data centers as a motivation. Pluggable optics consume “a staggering 10 percent of the total GPU compute power” in an AI data center, says Ian Buck, Nvidia’s vice president of hyperscale and high-performance computing. In a 400,000-GPU factory, that would translate to 40 megawatts, and more than half of that goes just to powering the lasers in a pluggable optics transceiver. “An AI supercomputer with 400,000 GPUs is actually a 24-megawatt laser,” he says. Optical Modulators One fundamental difference between Broadcom’s scheme and Nvidia’s is the optical modulator technology that encodes electronic bits onto beams of light. In silicon photonics there are two main types of modulators—Mach-Zehnder, which Broadcom uses and is the basis for pluggable optics, and microring resonator, which Nvidia chose. In the former, light traveling through a waveguide is split into two parallel arms. Each arm can then be modulated by an applied electric field, which changes the phase of the light passing through. The arms then rejoin to form a single waveguide. Depending on whether the two signals are now in phase or out of phase, they will cancel each other out or combine. And so electronic bits can be encoded onto the light. Microring modulators are far more compact. Instead of splitting the light along two parallel paths, a ring-shaped waveguide hangs off the side of the light’s main path. If the light is of a wavelength that can form a standing wave in the ring, it will be siphoned off, filtering that wavelength out of the main waveguide. Exactly which wavelength resonates with the ring depends on the structure’s refractive index, which can be electronically manipulated. However, the microring’s compactness comes with a cost. Microring modulators are sensitive to temperature, so each one requires a built-in heating circuit, which must be carefully controlled and consumes power. On the other hand, Mach-Zehnder devices are considerably larger, leading to more lost light and some design issues, says Schow. That Nvidia managed to commercialize a microring-based silicon photonics engine is “an amazing engineering feat,” says Schow. Nvidia CPO Switches According to Nvidia, adopting the CPO switches in a new AI data center would lead to one-fourth the number of lasers, boost power efficiency for trafficking data 3.5-fold, improve the on-time reliability of signals traveling from one computer to another by 63 times, make networks tenfold more resilient to disruptions, and allow customers to deploy new data-center hardware 30 percent faster. “By integrating silicon photonics directly into switches, Nvidia is shattering the old limitation of hyperscale and enterprise networks and opening the gate to million-GPU AI factories,” said Nvidia CEO Jensen Huang. - YouTube youtu.be The company plans two classes of switch, Spectrum-X and Quantum-X. Quantum-X, which the company says will be available later this year, is based on InfiniBand network technology, a network scheme more oriented to high-performance computing. It delivers 800 gigabits per second from each of 144 ports, and its two CPO chips are liquid-cooled instead of air-cooled, as are an increasing fraction of new AI data centers. The network ASIC includes Nvidia’s SHARP FP8 technology, which allows CPUs and GPUs to offload certain tasks to the network chip. Spectrum-X is an Ethernet-based switch that can deliver a total bandwidth of about 100 terabits per second from a total of either 128 or 512 ports and 400 Tb/s from 512 or 2,048 ports. Hardware makers are expected to have Spectrum-X switches ready in 2026. Nvidia has been working on the fundamental photonics technology for years. But it took collaboration with 11 partners—including TSMC, Corning, and Foxconn—to get the switch to a commercial state. Ashkan Seyedi, director of optical interconnect products at Nvidia, stressed how important it was that the technologies these partners brought to the table were co-optimized to satisfy AI data-center needs rather than simply assembled from those partners’ existing technologies. “The innovations and the power savings enabled by CPO are intimately tied to your packaging scheme, your packaging partners, your packaging flow,” Seyedi says. “The novelty is not just in the optical components directly, it’s in how they are packaged in a high-yield, testable way that you can manage at good cost.” Testing is particularly important, because the system is an integration of so many expensive components. For example, there are 18 silicon photonics chiplets in each of the two CPOs in the Quantum-X system. And each of those must connect to two lasers and 16 optical fibers. Seyedi says the team had to develop several new test procedures to get it right and trace where errors were creeping in. Micas Networks Switches Micas Networks is already in production with a switch based on Broadcom’s CPO technology.Micas Network Broadcom chose the more established Mach-Zehnder modulators for its Bailly CPO switch, in part because it is a more standardized technology, potentially making it easier to integrate with existing pluggable transceiver infrastructure, explains Robert Hannah, senior manager of product marketing in Broadcom’s optical systems division. Micas’s system uses a single CPO component, which is made up of Broadcom’s Tomahawk 5 Ethernet switch chip surrounded by eight 6.4-Tb/s silicon photonics optical engines. The air-cooled hardware is in full production now, putting it ahead of Nvidia’s CPO switches. Hannah calls Nvidia’s involvement an endorsement of Micas’s and Broadcom’s timing. “Several years ago, we made the decision to skate to where the puck was going to be,” says Mitch Galbraith, Micas’s chief operations officer. With data-center operators scrambling to power their infrastructure, the CPO’s time seems to have come, he says. The new switch promises a 40 percent power savings versus systems populated with standard pluggable transceivers. However, Charlie Hou, vice president of corporate strategy at Micas, says CPO’s higher reliability is just as important. “Link flap,” the term for transient failure of pluggable optical links, is one of the culprits responsible for lengthening AI training runs that are already very long, he says. CPO is expected to have less link flap because there are fewer components in the signal’s path, among other reasons. CPOs in the Future The big power savings that data centers are looking to get from CPOs are mostly a one-time benefit, Schow suggests. After that, “I think it’s just going to be the new normal.” However, improvements to the electronics’ other features will let CPO makers keep boosting bandwidth—for a time at least. Schow doubts that individual silicon modulators—which run at 200 Gb/s in Nvidia’s photonic engines—will be able to go past much more than 400 Gb/s. However, other materials, such as lithium niobate and indium phosphide, should be able to exceed that. The trick will be affordably integrating them with silicon components, something Santa Barbara–based OpenLight is working on, among other groups. In the meantime, pluggable optics are not standing still. This week, Broadcom unveiled a new digital signal processor that could lead to a more than 20 percent power reduction for 1.6 Tb/s transceivers, due in part to a more-advanced silicon process. And startups such as Avicena, Ayar Labs, and Lightmatter are working to bring optical interconnects all the way to the GPU itself. The first two have developed chiplets meant to go inside the same package as a GPU or other processor. Lightmatter is going a step farther, making the silicon photonics engine the packaging substrate upon which future chips are 3D-stacked.

There is an inherent tension in the dissemination of research. On one hand, science thrives on openness and communication. On the other, ensuring high-quality scientific work requires peer reviews that are often lengthy and closed. In 1991, physicist Paul Ginsparg created the arXiv repository to alleviate some of that tension. The idea is that researchers have a place to upload their preprint manuscripts before they are published in a journal. The preprints are free to all but have not undergone peer review (there is some screening). However, arXiv doesn’t facilitate open, two-way discussion. Now, two Stanford students have developed an extension of arXiv that creates a centralized public square, of sorts, for researchers to discuss preprints. IEEE Spectrum spoke with one of the two, Rehaan Ahmad, about the project. ​Rehaan Ahmad Rehaan Ahmad is the cofounder of alphaXiv, which he began as an undergraduate project while at Stanford University, alongside fellow student Raj Palleti. How does alphaXiv work? Rehaan Ahmad: You can change the “arXiv” in the URL to “alphaXiv,” and it opens up the paper and there’s comments and discussion. You can highlight sections and leave in-line comments. There’s also a more general home page where you can see what papers other people are reading through the site. It ends up being a nice way to filter for what papers are interesting and what aren’t. What motivated you to create the site? Ahmad: My cocreator Raj Palleti and I were undergrads at Stanford doing research in robotics and reinforcement learning. We figured a lot of people would have questions on papers, like us. So I put together a little mock-up two or three years ago. It was just sitting on my computer for a while. And then a year afterward I showed it to Raj, and he said we need to make this a public site. We thought of it as a version of Stack Overflow for papers. How difficult was it to build? Ahmad: Surprisingly difficult! Our background is in research, and one of the harder lessons for this project is that writing research code versus actual code that works are two different things. For research code, you write something once, you put it on GitHub, no one will use it—and if they do, it’s their problem to figure out. But here, the site has been around for a year and a half, and only recently have a lot of the bugs been kind of hashed out. The project started out on a single AWS server, and anytime someone would post about it, it would go viral, and the server would go down. How do you hope alphaXiv will be used? Ahmad: I see alphaXiv as just connecting the world of research in a way that’s more productive than Twitter [now X]. People find mistakes in papers here; people will read their opinions. I have been seeing more productive discussions with the authors. Your advisors include Udacity cofounder Sebastian Thrun and Meta’s chief AI scientist, Yann LeCun. How have your advisors contributed? Ahmad: After the first few months of operating alphaXiv, we circulated a lot within the computer-science community. But after discussing the platform with [University of Maryland physics professor] Victor Galitski, we realized having his voice and opinion to guide decisions that were relevant to the physics community would be incredibly important. Those interested in computer-science papers are usually more interested in the trending/likes/filtering aspect of our site, whereas those interested in physics are usually more discussion-oriented. This article appears in the April 2025 issue as “5 Questions for Rehaan Ahmad.”

This sponsored article is brought to you by Master Bond. Master Bond EP112 is an ultra-low-viscosity, electrically insulating, two-component heat curable epoxy system designed for demanding applications requiring optical clarity and resistance to chemicals commonly used in silicon processing. This article introduces a two-part case study involving a microelectronics fabrication, showcasing EP112’s role in bonding a silicon wafer to a glass substrate. Part 1: The START Process and EP112’s Role In the first part of this case study, researchers at Lawrence Livermore National Laboratory (LLNL) developed an innovative Silicon-on-Insulator (SOI) process called START (Silicon Transfer to Arbitrary Substrate). This method enables the transformation of standard bulk silicon wafers with completed circuits into SOI-like configurations without significantly increasing manufacturing costs. By using conventional fabrication techniques, the START process combines the benefits of bulk silicon electronics with those of SOI technology while maintaining cost efficiency. A critical step in this process involved bonding a silicon wafer to a glass support substrate. EP112 was selected as the adhesive of choice due to its ultra-low viscosity, strong bonding capabilities, and high chemical resistance. The bonded structure ultimately contributed to the successful development of a prototype liquid crystal display (LCD), demonstrating EP112’s effectiveness in microelectronics fabrication. Part 2: CMOS Wafer Thinning for SEU Resistance In the second part of this study, LLNL researchers applied EP112 in a novel wafer-thinning process to enhance the reliability of CMOS-based integrated circuits (ICs). The objective was to reduce susceptibility to Single Event Upsets (SEUs) by significantly decreasing the charge collection volume within the silicon substrate. To achieve this, EP112 was used to bond two substrates together, ensuring a secure attachment throughout the wafer-thinning steps. The process involved a high-temperature alkaline etching step, where EP112’s superior chemical resistance played a crucial role in preventing de-bonding. By maintaining structural integrity under these harsh conditions, EP112 enabled the successful completion of the thinning process, further demonstrating its suitability for advanced semiconductor applications. To read more about the key parameters and requirements, and learn about the results, please download the full case study here.

Analog/RF IC design has been traditionally considered an art – sometimes even a “black art” – because, contrary to digital IC design, analog/RF design combines complexity, non-linearity, conflicting design objectives and limited automation in EDA tooling. Analog/RF IC designers rely on a blend of technical expertise, intuition, accumulated experience, and creativity to meet the demanding targets of modern applications like high operating frequencies, low power, miniaturization, and shrinking design cycles. In this webinar you will learn about the revolutionary AI-driven electromagnetic-aware methodology that automates the optimization of the floor plan of analog and RF physical layouts. Join us to discover how Ansys AI solutions can add structure to the “madness” of analog/RF design. What Attendees will Learn: How the Ansys AI-driven electromagnetic-aware methodology blends with your existing custom IC design methodology and design flow How to define goals and constraints to identify the global optimum instead of settling for locally optimal solutions How the Ansys AI-driven optimization methodology will help you shave off several days or weeks from your design cycle Register now for this free webinar!

When Meta announced its plans for a vast new fiber optic network covering 50,000 kilometers and linking five continents last month, the company’s selling point was cutting-edge undersea cable tech. What went unsaid, however, was the geopolitical challenges the project might also face, along with potential insights it could reveal about Meta’s upcoming priorities. The company is hardly alone as a private player extending long fiber optic routes across oceans. Last year Google, for instance, announced a US $1 billion investment in undersea cables connecting the United States to Japan. Titans like Meta and Google investing heavily in undersea cables represents “a trend we’ve been tracking for over a decade,” says Lane Burdette, senior analyst at the Washington, D.C.–based firm TeleGeography. The challenge comes in piecing together technical details for each project, given the inevitably sketchy notes a company’s PR team provides. (Contacted by IEEE Spectrum, a Meta spokesperson declined to comment.) Meta’s new cable will be called Waterworth, after a pioneering Meta engineer who passed away last year. Waterworth hasn’t yet been added to TeleGeography’s comprehensive global submarine cable map, Burdette says, because no geographical routing plans for the fiber network have yet been announced. Once added, it would join 81 other currently planned cable routes that TeleGeography does track across the planet, alongside the world’s other 570 undersea fiber optic cables now in service. Meta’s Next 24-Fiber-Pair Undersea Line To help contextualize Meta’s news, says Howard Kidorf, managing partner at the Hoboken, N.J.–based analysis firm Pioneer Consulting, consider a point of reference: Laying cable from California to Singapore requires some 16,000 km of fiber. But going much beyond 16,000 km, he says, pushes the limits of cable tech today. “You lose capacity on each fiber pair as you go further,” he says. “So I could say 20,000 km, but then you’re running into an economic trade-off—losing total capacity.” Tiny fiber optic amplifiers are typically built into the housings of undersea cables today. And powering that network of amplifiers can represent a real bottleneck constraining the maximum length of any given cable. “It sounds like not a very challenging thing just to put more fibers in a cable,” Kidorf says. “But it’s also a bigger challenge to be able to put more optical amplifiers in.… And the biggest challenge on top of that is how do you power those optical amplifiers?” Every 50 to 80 km, an optical amplifier inside the cable must boost the optical signal, according to Kidorf. Meanwhile, each repeater typically consumes 50 to 100 watts. Do the math, and at minimum a California-to-Singapore line needs at least 10 kilowatts coursing through it just to keep the lights on. (Real-world figures, Kidorf says, come out closer to 15 to 18 kW.) “Unrepeatered cables can have over 100 fiber pairs across a single segment,” Burdette says. “But so far, the maximum fiber pairs used in a repeatered system is 24.” Waterworth will be using all 24 fiber pairs of that present-day capacity. Which puts it at the forefront of undersea cable tech today—although Waterworth isn’t the first undersea 24-fiber cable Meta has laid down. “Meta is expected to activate Anjana, the first 24-pair repeatered system, this year,” adds Burdette. “Anjana was supplied by NEC.” (Other 24-pair fiber cables with repeaters in them are also under development both by NEC and others, Burdette notes, although Meta now appears to be first in line to actually activate such a system.) Anjana is less than 8,000 km—connecting Myrtle Beach, S.C., to Santander, Spain. It will yield the social media behemoth 480 terabits per second of new bandwidth between the United States and Europe. Compared to the hypothetical California-to-Singapore cable, above, whose 16,000-km length would stretch existing fiber-tech capabilities to the extreme, Anjana isn’t setting any underwater distance records. On the other hand, Waterworth’s anticipated 50,000-km span—more than six times that of Anjana—would represent quite a leap forward. Perhaps that is why both Kidorf and Burdette wanted to clarify something about that 50,000 figure. “50,000 is a nice headline number,” Kidorf says. “It is a lot of cable. It’s roughly the output of a single cable factory for an entire year.... But this is not one cable that goes 50,000 kilometers. It’s a cable that lands in a number of places for regeneration.” “Waterworth is one project with multiple cable systems,” Burdette says. “This distinction can get kind of muddy as cable systems often have multiple segments that may even enter service at different times. So what makes something ‘one cable’ can come down to an issue of branding.” Where Will Waterworth Make Landfall? One outstanding Waterworth question, Kidorf says, concerns where and why the undersea cable will make landfall at its six or more landing points—according to Meta’s preliminary map (above). According to Kidorf, geopolitics and tech collide where international hotspots are concerned. Nobody wants their expensive cable being damaged, either intentionally or accidentally, in a conflict zone. “For example, connectivity to get from Asia to North America without going through the Red Sea is a major goal of everybody,” Kidorf says. Another goal, he adds, concerns avoiding the South China Sea. In other words, it might be charitable to imagine Meta’s Brazilian, South African, and Indian landing points as a play to bridge the digital divide. But it’s probably not coincidence, Kidorf says, that Waterworth’s projected route also neatly circumnavigates the globe while still avoiding both of those two geopolitical tinderboxes. What doesn’t yet make sense, he adds, is how Waterworth might “unlock AI innovation” (in the words of Meta’s press release) via these particular landing points. Because AI implies big data centers awaiting the cord coming out of the ocean. Yet at least two inferred Waterworth landing points (from the approximate circles on Meta’s map) currently lack major Meta data centers, he says. “Building data centers is a more significant investment in capital than building these cables are,” Kidorf says. “So not only do you need to build a data center, you have to find a way to power them. And India is a tough place to get 500 megawatts, which is what data centers are being built out as. Brazil also is not a data center capital.” More Waterworth details will clearly be needed, that is, not only to place Waterworth on TeleGeography’s map but also to determine how the cable’s networking potential will be used—as well as how truly cutting edge Waterworth’s tech specs may actually be. “They didn’t provide enough detail to really say whether it’s a technological marvel or not, because the issue is how far can you go before you have to hit land?” Kidorf says. And returning to solid ground, he says, is the ultimate technological constraint.

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. European Robotics Forum: 25–27 March 2025, STUTTGART, GERMANY RoboSoft 2025: 23–26 April 2025, LAUSANNE, SWITZERLAND ICUAS 2025: 14–17 May 2025, CHARLOTTE, N.C. ICRA 2025: 19–23 May 2025, ATLANTA London Humanoids Summit: 29–30 May 2025, LONDON IEEE RCAR 2025: 1–6 June 2025, TOYAMA, JAPAN 2025 Energy Drone & Robotics Summit: 16–18 June 2025, HOUSTON RSS 2025: 21–25 June 2025, LOS ANGELES ETH Robotics Summer School: 21–27 June 2025, GENEVA IAS 2025: 30 June–4 July 2025, GENOA, ITALY ICRES 2025: 3–4 July 2025, PORTO, PORTUGAL IEEE World Haptics: 8–11 July 2025, SUWON, KOREA IFAC Symposium on Robotics: 15–18 July 2025, PARIS RoboCup 2025: 15–21 July 2025, BAHIA, BRAZIL Enjoy today’s videos! Every time you see a humanoid demo in a warehouse or factory, ask yourself: Would a “superhumanoid” like this actually be a better answer? [ Dexterity ] The only reason that this is the second video in Video Friday this week, and not the first, is because you’ve almost certainly already seen it. This is a collaboration between the Robotics and AI Institute and Boston Dynamics, and RAI has its own video, which is slightly different: - YouTube [ Boston Dynamics ] via [ RAI ] Well this just looks a little bit like magic. [ University of Pennsylvania Sung Robotics Lab ] After hours of dance battles with professional choreographers (yes, real human dancers!), PM01 now nails every iconic move from Kung Fu Hustle. [ EngineAI ] Sanctuary AI has demonstrated industry-leading sim-to-real transfer of learned dexterous manipulation policies for our unique, high degree-of-freedom, high strength, and high speed hydraulic hands. [ Sanctuary AI ] This video is “introducing BotQ, Figure’s new high-volume manufacturing facility for humanoid robots,” but I just see some injection molding and finishing of a few plastic parts. [ Figure ] DEEP Robotics recently showcased its “One-Touch Navigation” feature, enhancing the intelligent control experience of its robotic dog. This feature offers two modes: map-based point selection and navigation and video-based point navigation, designed for open terrains and confined spaces respectively. By simply typing on a tablet screen or selecting a point in the video feed, the robotic dog can autonomously navigate to the target point, automatically planning its path and intelligently avoiding obstacles, significantly improving traversal efficiency. What’s in the bags, though? [ Deep Robotics ] This hurts my knees to watch, in a few different ways. [ Unitree ] Why the recent obsession with two legs when instead robots could have six? So much cuter! [ Jizai ] via [ RobotStart ] The world must know: who killed Mini-Duck? [ Pollen ] Seven hours of Digit robots at work at ProMat. And there are two more days of these livestreams if you need more! [ Agility ]

In partnership with Google, the Computer History Museum (CHM) has released the source code to AlexNet, the neural network that in 2012 kickstarted today’s prevailing approach to AI. The source code is available as open source on CHM’s GitHub page. What Is AlexNet? AlexNet is an artificial neural network created to recognize the contents of photographic images. It was developed in 2012 by then–University of Toronto graduate students Alex Krizhevsky and Ilya Sutskever and their faculty advisor, Geoffrey Hinton. The Origins of Deep Learning Hinton is regarded as one of the fathers of deep learning, the type of artificial intelligence that uses neural networks and is the foundation of today’s mainstream AI. Simple three-layer neural networks with only one layer of adaptive weights were first built in the late 1950s—most notably by Cornell researcher Frank Rosenblatt—but they were found to have limitations. [This explainer gives more details on how neural networks work.] In particular, researchers needed networks with more than one layer of adaptive weights, but there wasn’t a good way to train them. By the early 1970s, neural networks had been largely rejected by AI researchers. Frank Rosenblatt [left, shown with Charles W. Wightman] developed the first artificial neural network, the perceptron, in 1957.Division of Rare and Manuscript Collections/Cornell University Library In the 1980s, neural network research was revived outside the AI community by cognitive scientists at the University of California, San Diego, under the new name of “connectionism.” After finishing his Ph.D. at the University of Edinburgh in 1978, Hinton had become a postdoctoral fellow at UCSD, where he collaborated with David Rumelhart and Ronald Williams. The three rediscovered the backpropagation algorithm for training neural networks, and in 1986 they published two papers showing that it enabled neural networks to learn multiple layers of features for language and vision tasks. Backpropagation, which is foundational to deep learning today, uses the difference between the current output and the desired output of the network to adjust the weights in each layer, from the output layer backward to the input layer. In 1987, Hinton joined the University of Toronto. Away from the centers of traditional AI, Hinton’s work and those of his graduate students made Toronto a center of deep learning research over the coming decades. One postdoctoral student of Hinton’s was Yann LeCun, now chief scientist at Meta. While working in Toronto, LeCun showed that when backpropagation was used in “convolutional” neural networks, they became very good at recognizing handwritten numbers. ImageNet and GPUs Despite these advances, neural networks could not consistently outperform other types of machine learning algorithms. They needed two developments from outside of AI to pave the way. The first was the emergence of vastly larger amounts of data for training, made available through the Web. The second was enough computational power to perform this training, in the form of 3D graphics chips, known as GPUs. By 2012, the time was ripe for AlexNet. Fei-Fei Li’s ImageNet image dataset, completed in 2009, was pivotal in training AlexNet. Here, Li [right] talks with Tom Kalil at the Computer History Museum.Douglas Fairbairn/Computer History Museum The data needed to train AlexNet was found in ImageNet, a project started and led by Stanford professor Fei-Fei Li. Beginning in 2006, and against conventional wisdom, Li envisioned a dataset of images covering every noun in the English language. She and her graduate students began collecting images found on the Internet and classifying them using a taxonomy provided by WordNet, a database of words and their relationships to each other. Given the enormity of their task, Li and her collaborators ultimately crowdsourced the task of labeling images to gig workers, using Amazon’s Mechanical Turk platform. Completed in 2009, ImageNet was larger than any previous image dataset by several orders of magnitude. Li hoped its availability would spur new breakthroughs, and she started a competition in 2010 to encourage research teams to improve their image-recognition algorithms. But over the next two years, the best systems only made marginal improvements. The second condition necessary for the success of neural networks was economical access to vast amounts of computation. Neural-network training involves a lot of repeated matrix multiplications, preferably done in parallel, something that GPUs are designed to do. Nvidia, cofounded by CEO Jensen Huang, had led the way in the 2000s in making GPUs more generalizable and programmable for applications beyond 3D graphics, especially with the CUDA programming system released in 2007. Both ImageNet and CUDA were, like neural networks themselves, fairly niche developments that were waiting for the right circumstances to shine. In 2012, AlexNet brought together these elements—deep neural networks, big datasets, and GPUs—for the first time, with pathbreaking results. Each of these needed the other. How AlexNet Was Created By the late 2000s, Hinton’s grad students at the University of Toronto were beginning to use GPUs to train neural networks for both image and speech recognition. Their first successes came in speech recognition, but success in image recognition would point to deep learning as a possible general-purpose solution to AI. One student, Ilya Sutskever, believed that the performance of neural networks would scale with the amount of data available, and the arrival of ImageNet provided the opportunity. A milestone occurred in 2011, when DanNet, a convolutional neural network trained on GPUs created by Dan Cireşan and others at Jürgen Schmidhuber’s lab in Switzerland, won 4 image recognition contests. However, these results were on smaller datasets and weren’t able to move the field of computer vision. ImageNet, which was much larger and more comprehensive, was different. That same year, Sutskever convinced fellow Toronto grad student Alex Krizhevsky, who had a keen ability to wring maximum performance out of GPUs, to train a convolutional neural network for ImageNet, with Hinton serving as principal investigator. AlexNet used Nvidia GPUs running CUDA code trained on the ImageNet dataset. Nvidia CEO Jensen Huang was named a 2024 CHM Fellow for his contributions to computer graphics chips and AI.Douglas Fairbairn/Computer History Museum Krizhevsky had already written CUDA code for a convolutional neural network using Nvidia GPUs, called cuda-convnet, trained on the much smaller CIFAR-10 image dataset. He extended cuda-convnet with support for multiple GPUs and other features and retrained it on ImageNet. The training was done on a computer with two Nvidia cards in Krizhevsky’s bedroom at his parents’ house. Over the course of the next year, he constantly tweaked the network’s parameters and retrained it until it achieved performance superior to its competitors. The network would ultimately be named AlexNet, after Krizhevsky. Geoff Hinton summed up the AlexNet project this way: “Ilya thought we should do it, Alex made it work, and I got the Nobel prize.” Krizhevsky, Sutskever, and Hinton wrote a paper on AlexNet that was published in the fall of 2012 and presented by Krizhevsky at a computer-vision conference in Florence, Italy, in October. Veteran computer-vision researchers weren’t convinced, but LeCun, who was at the meeting, pronounced it a turning point for AI. He was right. Before AlexNet, almost none of the leading computer-vision papers used neural nets. After it, almost all of them would. AlexNet was just the beginning. In the next decade, neural networks would advance to synthesize believable human voices, beat champion Go players, and generate artwork, culminating with the release of ChatGPT in November 2022 by OpenAI, a company cofounded by Sutskever. Releasing the AlexNet Source Code In 2020, I reached out to Krizhevsky to ask about the possibility of allowing CHM to release the AlexNet source code, due to its historical significance. He connected me to Hinton, who was working at Google at the time. Google owned AlexNet, having acquired DNNresearch, the company owned by Hinton, Sutskever, and Krizhevsky. Hinton got the ball rolling by connecting CHM to the right team at Google. CHM worked with the Google team for five years to negotiate the release. The team also helped us identify the specific version of the AlexNet source code to release—there have been many versions of AlexNet over the years. There are other repositories of code called AlexNet on GitHub, but many of these are re-creations based on the famous paper, not the original code. CHM is proud to present the source code to the 2012 version of AlexNet, which transformed the field of artificial intelligence. You can access the source code on CHM’s GitHub page. This post originally appeared on the blog of the Computer History Museum. This article was updated on 25 March 2025. Acknowledgments Special thanks to Geoffrey Hinton for providing his quote and reviewing the text, to Cade Metz and Alex Krizhevsky for additional clarifications, and to David Bieber and the rest of the team at Google for their work in securing the source code release. References Fei-Fei Li, The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI. First edition, Flatiron Books, New York, 2023. Cade Metz, Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World. First edition, Penguin Random House, New York, 2022.

Technology should benefit humanity. One of the most remarkable examples of technology’s potential to provide enduring benefits is the Itaipu Hydroelectric Dam, a massive binational energy project between Brazil and Paraguay. Built on the Paraná River, which forms part of the border between the two nations, Itaipu transformed a once-contested hydroelectric resource into a shared engine of economic progress. The power plant has held many records. For decades, it was the world’s largest hydroelectric facility; the dam spans the river’s 7.9-kilometer width and reaches a height of 196 meters. Itaipu was also the first hydropower plant to generate more than 100 terawatt hours of electricity in a year. To acknowledge Itaipu’s monumental engineering achievement, on 27 March the dam will be recognized as an IEEE Milestone during a ceremony in Hernandarias, Paraguay. The ceremony will commemorate the project’s impact on engineering and energy production. Itaipu’s massive scale By the late 1960s, Brazil and Paraguay recognized the Paraná River’s untapped hydroelectric potential, according to the Global Infrastructure Hub. Brazil, which was undergoing rapid industrialization, sought a stable, renewable energy source to reduce its dependence on fossil fuels. Meanwhile, Paraguay, lacking the financial resources to construct a gigawatt-scale hydroelectric facility independently, entered into a treaty with Brazil in 1973. The agreement granted both countries equal ownership of the dam and its power generation. Construction began in 1975 and was completed in 1984, costing US $19.6 billion. The scale of the project was staggering. Engineers excavated 50 million cubic meters of earth and rock, poured 12.3 million cubic meters of concrete, and used enough iron and steel to construct 380 Eiffel Towers. Itaipu was designed for continuous expansion. It initially launched with two 700-megawatt turbine units, providing 1.4 gigawatts of capacity. By 1991, the power plant reached its planned 12.6 GW capacity. In 2006 and 2007, it was expanded to 14 GW with the addition of two more units, for a total of 20. Although China’s 22.5-GW Three Gorges Dam, on the Yangtze River near the city of Yichang, surpassed Itaipu’s capacity in 2012, the South American dam remains one of the world’s most productive hydroelectric facilities. On average, Itaipu generates around 90 terawatt-hours of electricity annually. It set a record by generating 103.1 TWh in 2016 (surpassed in 2020 by Three Gorges’ 111.8-TWh output). To put 100 TWh into perspective, a power plant would need to burn approximately 50 million tonnes of coal to produce the same amount of energy, according to the U.S. Energy Information Administration. By harnessing 62,200 cubic meters of river water per second, Itaipu prevents the release of nearly 100 million tonnes of carbon dioxide each year. During its 40-year lifetime, the dam has generated more than 3,000 TWh of electricity, meeting nearly 90 percent of Paraguay’s energy needs and contributing roughly 10 percent of Brazil’s electricity supply. Itaipu’s legacy endures as a testament to the benefits of international cooperation and sustainable energy and to the power of engineering to shape the future. IEEE recognition for Itaipu The IEEE Milestone commemorative plaque, now displayed in the dam’s visitor center, highlights Itaipu’s role as a world leader in hydroelectric power generation. It reads: “Itaipu power plant construction began in 1975 as a joint Brazil-Paraguay venture. When power generation started in 1984, Itaipu set a world record for the single largest installed hydroelectric capacity (14 GW). For at least three decades, Itaipu produced more electricity annually than any other hydroelectric project. Linking power plants, substations, and transmission lines in both Brazil and Paraguay, Itaipu’s system provided reliable, affordable energy to consumers and industry.” Administered by the IEEE History Center and supported by donors, the Milestone program recognizes outstanding technical developments worldwide. The IEEE Paraguay Section sponsored the nomination. This article was updated on 21 March 2025.

The rapid development of AI is fueling a data center boom, unlocking billions of dollars in investments to build the infrastructure needed to support data- and energy-hungry models. Amazon, Microsoft, and Google are among the key players backing large-scale AI projects, betting that new data centers will create jobs. In the United States, the Trump administration announced in late January the US $500 billion Stargate Project, a partnership with OpenAI, Oracle, and SoftBank to build data centers nationwide. The project promises more than 100,000 new U.S. jobs over the next four years. Globally, the demand for data centers is projected to rise between 19 and 22 percent from 2023 to 2030, according to McKinsey & Co., with operators expanding their facilities into parts of Asia, South America, and the Middle East. This surge in construction has created a strong demand for certain electrical engineers, whose expertise in power systems and energy efficiency is essential for designing, building, and maintaining energy-intensive AI infrastructure. The data center industry contributed 4.7 million jobs to the U.S. economy in 2023—a 60 percent increase from 2017, according to a 2025 PwC report. The U.S. Bureau of Labor Statistics projects a 9 percent growth rate for electrical engineering jobs between 2023 and 2033, more than double the average for all occupations, with a median pay of $109,010 per year. But with the uptick in demand, data centers are struggling to find qualified talent. A 2023 Uptime Institute report found that 58 percent of global data center operators faced difficulties sourcing talent for open roles. Without enough skilled engineers to manage power-distribution systems, data centers risk increased downtime and potential grid instability, especially as AI-driven workloads demand more energy. Engineers Are Key to AI Infrastructure Big tech companies are increasingly searching for electrical engineers to scale their data infrastructure. As of mid-March, Amazon, Meta, and Google were looking to fill roles for electrical design engineers, research and development engineers, and mechatronics engineers to build robotic systems, with salaries reaching $281,000 a year, according to job listings. Oracle, which operates more than 150 data centers worldwide, has listed more than a dozen electrical engineering roles across its data facilities. Engineering firms are also seeing a spike in demand. Karina Hershberg, associate principal at PAE Consulting Engineers, says data center clients in need of her firm’s expertise have drastically increased over the past two years. Many clients require power systems on the scale of a “small town,” Hershberg says, and electrical engineers are expected to meet their demands within tight timelines of a year or two. The work involves sourcing power, optimizing energy distribution, designing emergency backup systems, and integrating cooling mechanisms to prevent overheating. As AI advances, engineers must also address new challenges in power stability and energy efficiency. “It’s all hands on deck,” Hershberg says. “AI is introducing a whole new set of challenges to power systems, and we need people who really understand the engineering and science behind it.” Sustainability efforts are further boosting demand for electrical engineers. As data centers explore solar, wind, and nuclear energy, engineers must design power-distribution systems that can efficiently transmit energy from production sites to facilities, according to Jim Kozlowski, chief sustainability officer and vice president of global data center operations at Ensono, an IT service management firm. Engineers specializing in renewable energy, he says, will find plenty of opportunities in the data center industry. “As an engineer coming into this world, you could focus on renewable energy and develop a great career there, because those opportunities are only going to grow,” Kozlowski says. Competitive Data Center Jobs The hiring landscape for electrical engineers in data centers is fiercely competitive. Grace Søhoel-Goldberg, who leads electrical engineering recruitment at LVI Associates, a global staffing firm for the infrastructure sector, has seen demand for electrical engineers soar as more companies enter the data center ring. What was once a small group of companies competing for niche talent, she says, has nearly tripled over the past few years. “It’s very cutthroat,” Søhoel-Goldberg says of the data center job market. Even with high demand, hiring the right people remains a challenge, she adds. Data center operators and engineering firms compete for the same limited pool of candidates, and large companies with better salaries and perks have the advantage, often poaching talent from smaller firms. Another barrier is employer expectations. Many companies only want to hire engineers who’ve been working at data centers for years, making it harder to hire qualified candidates from transferable backgrounds such as industrial engineering. Søhoel-Goldberg believes this creates an unnecessary talent bottleneck. A lack of awareness about data center careers further widens the talent gap. Electrical engineering roles in data centers require specialized knowledge in power infrastructure, typically learned in the construction industry. But based on conversations with students, PAE Consulting’s Hershberg says that university programs rarely highlight career paths in this sector. As a result, graduates are often steered toward software and high-tech fields, making it harder for them to transition into data centers later in their careers. “We’re just not seeing the talent pool at that graduation level,” Hershberg says. Closing the Talent Gap Some universities are stepping up to address the skills shortage. Southern Methodist University in Texas, for example, offers one of the few master’s programs in data center systems engineering designed for students interested in a data center career. The University of Wisconsin–Madison runs boot camps including a three-day course on data center design and operation, preparing students for roles such as electrical designers. Beyond universities, online platforms like Udemy offer data center fundamentals courses, and Amazon Web Services and Microsoft run skills training programs for both current employees and aspiring professionals to create a pipeline of entry-level technician talent. From a hiring perspective, Søhoel-Goldberg advises engineers without direct data center experience to gain transferable skills in uninterruptible power supply (UPS) systems by working in other sectors including health care, wastewater treatment, or manufacturing. She also recommends acquiring certifications such as Engineer-in-Training (EIT) and Professional Engineer (PE) licenses.

When you see a squirrel jump to a branch, you might think (and I myself thought, up until just now) that they’re doing what birds and primates would do to stick the landing: just grabbing the branch and hanging on. But it turns out that squirrels, being squirrels, don’t actually have prehensile hands or feet, meaning that they can’t grasp things with any significant amount of strength. Instead, they manage to land on branches using a “palmar” grasp, which isn’t really a grasp at all, in the sense that there’s not much grabbing going on. It’s more accurate to say that the squirrel is mostly landing on its palms and then balancing, which is very impressive. This kind of dynamic stability is a trait that squirrels share with one of our favorite robots: Salto. Salto is a jumper too, and it’s about as nonprehensile as it’s possible to get, having just one limb with basically no grip strength at all. The robot is great at bouncing around on the ground, but if it could move vertically, that’s an entire new mobility dimension that could lead to some potentially interesting applications, including environmental scouting, search and rescue, and disaster relief. In a paper published today in Science Robotics, roboticists have now taught Salto to leap from one branch to another like squirrels do, using a low torque gripper and relying on its balancing skills instead. Squirrel Landing Techniques in Robotics While we’re going to be mostly talking about robots here (because that’s what we do), there’s an entire paper by many of the same robotics researchers that was published in late February in the Journal of Experimental Biology about how squirrels land on branches this way. While you’d think that the researchers might have found some domesticated squirrels for this, they actually spent about a month bribing wild squirrels on the University of California, Berkeley, campus to bounce around some instrumented perches while high-speed cameras were rolling. Squirrels aim for perfectly balanced landings, which allow them to immediately jump again. They don’t always get it quite right, of course, and they’re excellent at recovering from branch landings where they go a little bit over or under where they want to be. The research showed how squirrels use their musculoskeletal system to adjust their body position, dynamically absorbing the impact of landing with their forelimbs and altering their mass distribution to turn near misses into successful perches. It’s these kinds of skills that Salto really needs to be able to usefully make jumps in the real world. When everything goes exactly the way it’s supposed to, jumping and perching is easy, but that almost never happens and the squirrel research shows how important it is to be able to adapt when things go wonky. It’s not like the little robot has a lot of degrees of freedom to work with—it’s got just one leg, just one foot, a couple of thrusters, and that spinning component which, believe it or not, functions as a tail. And yet, Salto manages to (sometimes!) make it work. Those balanced upright landings are super impressive, although we should mention that Salto only achieved that level of success with two out of 30 trials. It only actually fell off the perch five times, and the rest of the time, it did manage a landing but then didn’t quite balance and either overshot or undershot the branch. There are some mechanical reasons why this is particularly difficult for Salto—for example, having just one leg to use for both jumping and landing means that the robot’s leg has to be rotated mid-jump. This takes time, and causes Salto to jump more vertically than squirrels do, since squirrels jump with their back legs and land with their front legs. Based on these tests, the researchers identified four key features for balanced landings that apply to robots (and squirrels): Power and accuracy are important! It’s easier to land a shallower jump with a more horizontal trajectory. Being able to squish down close to the branch helps with balancing. Responsive actuation is also important! Of these, Salto is great at the first one, very much not great at the second one, and also not great at the third and fourth ones. So in some sense, it’s amazing that the roboticists have been able to get it to do this branch-to-branch jumping as well as they have. There’s plenty more to do, though. Squirrels aren’t the only arboreal jumpers out there, and there’s likely more to learn from other animals—Salto was originally inspired by the galago (also known as bush babies), although those are more difficult to find on the UC Berkeley campus. And while the researchers don’t mention it, the obvious extension to this work is to chain together multiple jumps, and eventually to combine branch jumping with the ground jumping and wall jumping that Salto can do already to really give those squirrels a jump for their nuts.

Yesterday, three members of Microsoft’s quantum team presented their work toward a topological quantum computer at the APS Global Summit in Anaheim, Calif. Last month, the team made waves announcing their first topological quantum chip, the Majorana 1. More quietly, Nokia Bell Labs has been working on its own version of a topological quantum computer, and the company claims that it demonstrated the key ingredients in 2023. Both efforts represent scientific achievements, but bulletproof evidence of a topological quantum bit is elusive. “I would say all quantum computing is early stages,” says Bertrand Halperin, emeritus professor of physics at Harvard, who is not involved in either effort. “But topological quantum computing is further behind. It could catch up; it’s taking a somewhat different path.” What’s a Topological Quantum Computer? Quantum computers run on qubits valued at 0, 1, or some superposition of the two, usually encoded through some local quantum property—say, whether an electron’s spin is up or down. This gives quantum computers different capabilities than their classical cousins, promising to easily crack certain types of problems that are out of reach of even the largest supercomputers. The issue is that these quantum superpositions are very fragile. Any noise in the environment, be it temperature fluctuations or small changes in electric or magnetic fields, can knock qubits out of superposition, causing errors. Topological quantum computing is a fundamentally different approach to building a qubit, one that in theory would be a much less fragile. The idea is that instead of using some local property to encode the qubit, you would use a global, topological property of a whole sea of electrons. Topology is a field of mathematics that deals with shapes: Two shapes are topologically identical if they can be transformed into each other without tearing new holes or connecting previously unconnected ends. For example, an infinite rope extending into space is topologically distinct from the same rope with a knot in it. Electrons can “twist” around each other to form something akin to a knot. This knot is more difficult to tie or untie, offering protection against noise. (This is an analogy—the qubits would not be literal knots. For a full technical explanation, see this “short” introduction.) The issue is that electrons don’t often naturally twist themselves into knots. Theorists have postulated for decades that such states could exist, but creating the right conditions for them to arise in practice has been elusive. It’s extremely difficult to make devices that could give rise to knotted electrons, and arguably even more difficult to prove that one has done so. Microsoft’s “Quantraversy” The Microsoft team’s approach to creating knotted electrons is to start with a semiconducting nanowire. Then, they layer a superconducting material on top of this nanowire. Both the semiconductor and superconductor layers have to be almost completely devoid of material defects, and held at millikelvin temperatures. In theory, this allows an electron from the semiconducting layer to use the superconductor to effectively spread out over the whole wire, forming something akin to a rope that can be tied into knots. This rope is called a Majorana zero mode. Definitively showing that they’ve created a Majorana zero mode has proven difficult for the Microsoft team. The team and their collaborators claimed they had achieved this milestone back in 2018, but some researchers were unconvinced by the evidence, saying imperfections in the device could have resulted in the same measurements. The paper was retracted. In 2023, Microsoft and collaborators published further evidence that they’ve created Majoranas, although some scientists have remained unconvinced, and say not enough data was shared to reproduce the results. Last month’s claim remains contentious. “We are very confident that our devices host Majorana zero modes,” says Chetan Nayak, the lead of the Microsoft effort. “There is no evidence of even the basic physics of Majoranas in these devices, let alone that you could build a qubit out of them,” says Henry Legg, lecturer at the University of St. Andrews, in Scotland, who has authored two preprints disputing Microsoft’s results. “We would probably all agree that further experiments and better data are necessary before the issue can be considered closed,” Harvard’s Halperin says. Whether or not the Microsoft team has created Majorana zero modes, making them is just the first step. The team also has to show they can be manipulated to actually do computations. Several types of operations are required to make the kind of knot that represents 0, untie it and tie it into a knot that represents 1, or create a quantum superposition of the two. The most recent paper demonstrated the team’s capability to do one of the necessary measurements. “It’s a big step,” says Jay Sau, professor of physics at the University of Maryland who has a consulting appointment with the Microsoft team. In an unusual move, Microsoft’s quantum team held a limited-access meeting at their headquarters at Station Q, and invited several researchers in the field. There, they revealed preliminary results demonstrating another such measurement. “There’s still quite a bit of work to do on that side,” says Michael Eggleston, data and devices leader at Nokia, who was present at the Station Q meeting. “There’s a lot of noise in that system. But I think they’re on a good path.” To sum up, the Microsoft team has not yet reached the milestone where the scientific community would agree that they’ve created a single topological qubit. “They have a concept chip which has eight lithographically fabricated qubits,” Eggleston says. “But they’re not functional qubits; that’s the fine print. It’s their concept of what they’re moving towards.” Nokia Bell Labs quantum-computing researchers Hasan Siddiquee [right] and Ian Crawley connect a dilution refrigerator sample loader for cooldown.Nokia Bell Labs Nokia’s Approach A team at Nokia Bell Labs is also pursuing the dream of topological quantum computers, although through a different physical implementation. The team, led by lifelong topological quantum-computing devotee Robert Willett, is sandwiching a thin sheet of gallium arsenide in between two other semiconducting slabs. They then cool the sandwich to millikelvin temperatures and subject it to a strong magnetic field. If the device properties are just right, this could give rise to a two-dimensional version of a global electronic state that can be knotted up. A qubit would require both the creation of this state, and the ability to controllably knot and unknot it. Robert Willett and his collaborators have also had trouble convincing the scientific community that what they had on their hands are really the highly coveted topological states. “We’re very confident that we have a topological state,” says Nokia’s Eggleston, who oversees the quantum computing effort. “I find it reasonably convincing,” Harvard’s Halperin says. “But not everyone would agree.” The Nokia team has not yet claimed the ability to do operations with the device. Eggleston says they are working on demonstrating these operations, and plan to have results in the second quarter of this year. Proving Topological Quantum States Proving the necessary topological ingredients beyond the shadow of a doubt remains elusive. Practically speaking, the most important thing is not whether the exotic topological state can be proven to be present, but whether researchers can build a qubit that is both controllable and much more robust against noise than approaches that are more mature. Nokia’s team claims that they can maintain error-free quantum states for days, although they cannot control them yet. Data revealed by Microsoft at the Station Q meeting shows their devices remain error-free for 5 microseconds, but they believe this can be improved. (For comparison, a traditional superconducting qubit in IBM’s quantum computer remains error-free for up to 400 ms). “There’s always going to be people who don’t necessarily agree or want more data,” Nokia’s Eggleston says, “and I think that’s the strength of the scientific community to always ask for more. Our feeling on this is you need to scale up complexity of devices.” “I think at some point you go to the regime where it’s a reasonably good qubit, whether it’s precisely topological or not, that becomes the point of the debate,” Maryland’s Sau says. “But at that point it’s more useful to ask how good or bad of a qubit it is.” Despite difficulties, topological quantum computing continues to be—at least theoretically—a very promising approach. “I look at these other qubit types that we see out there today. They’re really nice demonstrations. It’s great science. It’s really hard engineering. Unfortunately, it’s kind of like the vacuum tube back in the ’40s,” Eggleston says. “You build computers out of them because that’s all you have, and they’re really challenging to scale up. To me, topological qubits really offer the potential that the transistor did. Something small, something robust, something that’s scalable. And that’s what I think the future of quantum computing is.”

This article is part of our exclusive career advice series in partnership with the IEEE Technology and Engineering Management Society. I’ve taught graduate-level communication courses to working professionals in high-tech disciplines for more than a decade. Although students who come to my programs are skilled in technical areas, expertise is only part of the equation for professional success. The ability to communicate complex ideas clearly and persuasively is critical to the success of any technical professional. Whether presenting to clients, colleagues, or management, take the time to develop and practice your presentation skills so you can make an impression. Here are five tips for mastering technical presentations. Know your audience One size does not fit all in presentations. Not all audience members want or need to know about specific technical aspects of your ideas, recommendations, or conclusions. Assuming that all stakeholders must receive the same information is a common error among technical professionals. To avoid this pitfall, the presenter needs to do three things: Assess the audience. Are they nonspecialists, technical peers, or management? The complexity of the message should be adjusted based on the answer. Not everyone is likely to be as enthusiastic about technical data as you are. Understand the goals. What does the audience need from your presentation? Updates? Information about the budget or staffing needs? Shape your presentation to help those in your audience reach their goals. Read the room. Don’t continue to power through your presentation if you notice eyes glazing over or people checking their phone. Gauge the audience as you speak. Use the cues to adapt, clarify, or simplify the information. Mastering presentations means understanding your audience. How you describe your work to your friend, manager, or CTO needs to be different because each person you’re addressing has different needs and expertise. Keep that in mind, as it can help shape your speech and terminology. The BLUF principle The bottom line up front (BLUF) principle is a must when presenting technical information. It is critical that, as a speaker, you define your key takeaways early so the audience knows what you are trying to convey. The principle has you begin the presentation with your recommendations or conclusions and then structure the rest of your content to support those elements. “By developing your presentation skills, you are ensuring that you can effectively share your expertise and ideas with others—which is essential for your success.” Perhaps the biggest challenge for technical presenters is to pare down the actual technical data. It can be difficult to do. But remember that, in most cases, nontechnical people don’t share your passion for the details. Including only the most relevant information in your presentation helps ensure you don’t get lost in the weeds. Save the data deep dives for supplementary materials or in support of audience questions that arise. The bottom line depends on the goal of the presentation. Are you delivering a status update on a project, describing a technical problem that needs a solution, requesting funding for equipment or tools, requesting a change in scope for a product specification, or soliciting approval for a key deliverable? Whatever the goal, your audience should be apprised of it right from the beginning. Become a storyteller The biggest mistake I see with technical professionals during presentations is failing to provide a human connection—the “so what” behind data. Although people want a firm base of solid technical reasoning, they also want to understand the real-world implications of what you are presenting. How can your proposed solution solve a problem or provide a positive outcome for an end user? A terrific way to supplement your presentation is to leverage visuals that align with your story to help the audience turn abstract numbers into relatable data. If you are wondering how to start incorporating storytelling into your presentations, use a simple distillation of a linear narrative arc to relay your story with the following elements: The challenge: How did you start on this research, project, or journey? Explain the problem you are trying to solve and discuss the catalyst. Perhaps you had a product failure, or a stakeholder faced a complex problem with no existing solution. Use the story to set the stage for your presentation. The process: Explain the complexities of the project or issue and the different ways you thought about it. Also discuss any challenges or roadblocks you encountered. The resolution: Explain how you solved the problem or completed the journey. What was the impact of your work? How does it relate to what the audience cares about? Storytelling can be a powerful tool for making a presentation come alive. As a bonus, it can help you think about your presentation’s organizational structure, which is a key element for audience understanding and retention. Be authentic Speakers who are genuine and relatable build more audience rapport and engagement. Among technical professionals, the overuse of jargon and overly technical language is a real risk. Make sure you match the tone and knowledge of your audience. The point of presenting is to clearly communicate, not dazzle the audience with your command of discipline-specific nomenclature. Some of the best presentations I have heard are those in which the speakers discuss a challenge they personally faced while trying to solve a problem. It requires the speaker to have some vulnerability and openness with the audience. That can feel uncomfortable to a technical expert, but it can help you build a rapport with your audience. Practice makes perfect Being able to communicate your ideas, processes, and solutions is critical for success. Delivering impactful presentations is a skill, and like any skill, it needs to be practiced. Athletes understand that repetition builds muscle memory and confidence. The same is true for presentation skills. Don’t strive for perfection. Take every opportunity you can to present to people. As you build your presentation skills, seek feedback from trusted colleagues and adjust accordingly. With each presentation you give, you can develop your competence and confidence. Soft skills are essential Too often, communication is referred to as a soft skill that is nice to have. But it should be considered essential for success, especially in technical fields where complicated ideas and concepts abound. By incorporating the five tips in this article, technical professionals can convey complex ideas more effectively and persuasively, leaving a lasting impact on their audience. To secure buy-in, support, or resources, your ideas must be communicated clearly and compellingly. As a technical professional, you understand the importance of continuously expanding your knowledge to stay current in your field, and communication skills must be part of the equation. By developing your presentation skills, you are ensuring that you can effectively share your expertise and ideas with others. Remember: Your ideas are only as good as your ability to communicate them to others.

Self-driving cars were supposed to be in our garages by now, according to the optimistic predictions of just a few years ago. But we may be nearing a few tipping points, with robotaxi adoption going up and consumers getting accustomed to more and more sophisticated driver-assistance systems in their vehicles. One company that’s pushing things forward is the Silicon Valley-based Helm.ai, which develops software for both driver-assistance systems and fully autonomous vehicles. The company provides foundation models for the intent prediction and path planning that self-driving cars need on the road, and also uses generative AI to create synthetic training data that prepares vehicles for the many, many things that can go wrong out there. IEEE Spectrum spoke with Vladislav Voroninski, founder and CEO of Helm.ai, about the company’s creation of synthetic data to train and validate self-driving car systems. How is Helm.ai using generative AI to help develop self-driving cars? Vladislav Voroninski: We’re using generative AI for the purposes of simulation. So given a certain amount of real data that you’ve observed, can you simulate novel situations based on that data? You want to create data that is as realistic as possible while actually offering something new. We can create data from any camera or sensor to increase variety in those data sets and address the corner cases for training and validation. I know you have VidGen to create video data and WorldGen to create other types of sensor data. Are different car companies still relying on different modalities? Voroninski: There’s definitely interest in multiple modalities from our customers. Not everyone is just trying to do everything with vision only. Cameras are relatively cheap, while lidar systems are more expensive. But we can actually train simulators that take the camera data and simulate what the lidar output would have looked like. That can be a way to save on costs. And even if it’s just video, there will be some cases that are incredibly rare or pretty much impossible to get or too dangerous to get while you’re doing real-time driving. And so we can use generative AI to create video data that is very, very high-quality and essentially indistinguishable from real data for those cases. That also is a way to save on data collection costs. How do you create these unusual edge cases? Do you say, “Now put a kangaroo in the road, now put a zebra on the road”? Voroninski: There’s a way to query these models to get them to produce unusual situations—it’s really just about incorporating ways to control the simulation models. That can be done with text or prompt images or various types of geometrical inputs. Those scenarios can be specified explicitly: If an automaker already has a laundry list of situations that they know can occur, they can query these foundation models to produce those situations. You can also do something even more scalable where there’s some process of exploration or randomization of what happens in the simulation, and that can be used to test your self-driving stack against various situations. And one nice thing about video data, which is definitely still the dominant modality for self-driving, you can train on video data that is not just coming from driving. So when it comes to those rare object categories, you can actually find them in a lot of different data sets. So if you have a video data set of animals in a zoo, is that going to help a driving system recognize the kangaroo in the road? Voroninski: For sure, that kind of data can be used to train perception systems to understand those different object categories. And it can also be used to simulate sensor data that incorporates those objects into a driving scenario. I mean, similarly, very few humans have seen a kangaroo on a road in real life. Or even maybe in a video. But it’s easy enough to conjure up in your mind, right? And if you do see it, you’ll be able to understand it pretty quickly. What’s nice about generative AI is that if [the model] is exposed to different concepts in different scenarios, it can combine those concepts in novel situations. It can observe it in other situations and then bring that understanding to driving. How do you do quality control for synthetic data? How do you assure your customers that it’s as good as the real thing? Voroninski: There are metrics you can capture that assess numerically the similarity of real data to synthetic data. One example is you take a collection of real data and you take a collection of synthetic data that’s meant to emulate it. And you can fit a probability distribution to both. And then you can compare numerically the distance between those probability distributions. Secondly, we can verify that the synthetic data is useful for solving certain problems. You can say, “We’re going to address this corner case. You can only use simulated data.” You can verify that using the simulated data actually does solve the problem and improve the accuracy on this task without ever training on real data. Are there naysayers who say that synthetic data will never be good enough to train these systems and teach them everything they need to know? Voroninski: The naysayers are typically not AI experts. If you look for where the puck is going, it’s pretty clear that simulation is going to have a huge impact on developing autonomous driving systems. Also, what’s good enough is a moving target, same as the definition of AI or AGI [artificial general intelligence]. Certain developments are made, and then people get used to them, “Oh, that’s no longer interesting. It’s all about this next thing.” But I think it’s pretty clear that AI-based simulation will continue to improve. If you explicitly want an AI system to model something, there’s not a bottleneck at this point. And then it’s just a question of how well it generalizes.

Attending an IEEE conference is an opportunity to learn about the latest advances in technology, meet some of the world’s leading researchers, and network with thought leaders and industry practitioners. Last year IEEE held nearly 2,300 conferences in 109 countries. The research from these cutting-edge events comprises more than 72 percent of the IEEE Xplore Digital Library. Not everyone can attend an international conference in person, though. To more broadly share some of the cutting-edge research that was conducted last year, IEEE Conferences, Events & Experiences, in collaboration with the IEEE China office, produced a new virtual series. Held in December, the IEEE Tech Frontiers event curated content from three leading gatherings: the IEEE Transmission and Distribution Conference and Exposition, the IEEE Photovoltaic Specialists Conference, and the IEEE Conference on Computer Vision and Pattern Recognition. Those conferences cover some of today’s most important technologies, such as power and energy, photovoltaic technology, and artificial intelligence, and they attract article submissions from all over the world. Conference organizers were able to highlight the hot topics and interesting elements of each event. Nearly 2,500 people attended the two-hour session, which was presented in English and Mandarin. “IEEE conferences are tech frontiers that provide scholars and engineers with a platform to exchange and learn about the latest developments so that we can have innovation and excellence,” Bin Zhao, president of the IEEE Electron Devices Society, said at the event. The IEEE Fellow served as the event’s champion and moderator. “We would like to help professionals build networks and connections,” Zhao said, “so that we have better capabilities to push our tech forward.” Conferences in China advance tech innovation IEEE Senior Member Yinghong Wen, chair of the IEEE China Council, kicked off the event. “IEEE China has continued to grow, and international events have played a key role in our growth,” Wen said. “International events are the best source from which we learn the latest tech breakthroughs and carry out international tech exchanges. IEEE conferences are regarded as the most premier academic gatherings for electronics, electrical engineering, computer science, and other relevant fields.” Weiqing Tang, CEO of the China Computer Federation, said the organization believes in the importance of cooperation with international organizations such as the IEEE Computer Society. The CCF has nearly 120,000 members and holds more than 1,600 events each year. The two organizations have partnered on several initiatives including member development, publications, and conferences. The collaboration supports common missions, Tang said. “The CCF steadfastly supports IEEE China in continuing to hold events,” he said, “and is willing to lend a helping hand so that more people are aware of these events.” “IEEE conferences are tech frontiers that provide scholars and engineers with a platform to exchange and learn about the latest developments so that we can have innovation and excellence.” —Bin Zhao, IEEE Electron Devices Society president Wen pointed out that the IEEE Transmission and Distribution Conference and Exposition has promoted technology exchanges and cooperation, allowing China’s power sector to move toward a more sustainable future. “That [yearly] event has served as an important foundation for China’s smart power grid and efficient power transmission,” she said. IEEE Fellow C.Y. Chung, a power grid specialist and the IEEE Power & Energy Society’s 2025 president-elect, provided an overview of the annual conference and some of the topics it covers, such as smart grid development and energy storage systems. More than 13,800 people from 78 countries attended last year’s conference, making it among IEEE’s largest, he said. “Power engineers are working very hard in decarbonizing our power system,” he said. “They are working on employing energy efficiency policies and programs to reduce energy usage, increasing the use of renewable energies such as wind and solar, and promoting economy-wide electrification.” The IEEE Photovoltaic Specialists Conference (PVSC), sponsored by the IEEE Electron Devices Society, covers developments in PV science and engineering, manufacturing, reliability, deployment, policy, and sustainability. The IEEE Tech Frontiers presenters (from top left): Bin Zhao, Yinghong Wen, Weiqing Tang, Fred Schindler, Tyler J. Grassman, C.Y. Chung, and Walter Scheirer.IEEE Conferences, Events & Experiences The PVSC has “promoted tech innovation in the Chinese PV sector, elevating IEEE’s international competitiveness,” Wen said. “This allows China to realize net-zero power transmission and contribute to the global response to climate change.” IEEE Senior Member Tyler J. Grassman, the 2025 PVSC conference chair, added that the event is a popular venue for announcing new world records set for solar cells and modules. Another highly attended event is the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), which is sponsored by the IEEE Computer Society. The 2024 conference attracted more than 12,000 attendees from 76 countries and regions—the largest to date, says IEEE Senior Member Walter Scheirer. He noted that many of the attendees were from China. Scheirer is chair of the IEEE Computer Society’s Technical Community on Pattern Analysis and Machine Intelligence. “It is really exciting to see this cross-Pacific engagement happening,” he said. “We want researchers from all over the globe who are working on computer vision to engage with us.” He discussed trends in the field, such as generative AI, which is a trending topic in computer vision and other areas. “There is also a lot of emphasis on AI technologies being used to create images and videos,” he said. “CVPR remains the primary place where this new work is rolling out.” The conference, Wen said, “stimulates a large number of innovative ideas and the commercialization of computer vision in China.” Visit the IEEE Tech Frontiers website to view the event on demand. It is available in English and Mandarin.

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. European Robotics Forum: 25–27 March 2025, STUTTGART, GERMANY RoboSoft 2025: 23–26 April 2025, LAUSANNE, SWITZERLAND ICUAS 2025: 14–17 May 2025, CHARLOTTE, NC ICRA 2025: 19–23 May 2025, ATLANTA, GA London Humanoids Summit: 29–30 May 2025, LONDON IEEE RCAR 2025: 1–6 June 2025, TOYAMA, JAPAN 2025 Energy Drone & Robotics Summit: 16–18 June 2025, HOUSTON, TX RSS 2025: 21–25 June 2025, LOS ANGELES ETH Robotics Summer School: 21–27 June 2025, GENEVA IAS 2025: 30 June–4 July 2025, GENOA, ITALY ICRES 2025: 3–4 July 2025, PORTO, PORTUGAL IEEE World Haptics: 8–11 July 2025, SUWON, KOREA IFAC Symposium on Robotics: 15–18 July 2025, PARIS RoboCup 2025: 15–21 July 2025, BAHIA, BRAZIL Enjoy today’s videos! In 2026, a JAXA spacecraft is heading to the Martian moon Phobos to chuck a little rover at it. [ DLR ] Happy International Women’s Day! UBTECH humanoid robots Walker S1 deliver flowers to incredible women and wish all women a day filled with love, joy and empowerment. [ UBTECH ] TRON 1 demonstrates Multi-Terrain Mobility as a versatile biped mobility platform, empowering innovators to push the boundaries of robotic locomotion, unlocking limitless possibilities in algorithm validation and advanced application development. [ LimX Dynamics ] This is indeed a very fluid running gait, and the flip is also impressive, but I’m wondering what sort of actual value these skills add, you know? Or even what kind of potential value they’re leading up to. [ EngineAI ] Designing trajectories for manipulation through contact is challenging as it requires reasoning of object & robot trajectories as well as complex contact sequences simultaneously. In this paper, we present a novel framework for simultaneously designing trajectories of robots, objects, and contacts efficiently for contact-rich manipulation. [ Paper ] via [ Mitsubishi Electric Research Laboratories ] Thanks, Yuki! Running robot, you say? I’m thinking it might actually be a power walking robot. [ MagicLab ] Wake up, Reachy! [ Pollen ] Robot vacuum docks have gotten large enough that we’re now all supposed to pretend that we’re happy they’ve become pieces of furniture. [ Roborock ] The SeaPerch underwater robot, a “do-it-yourself” maker project, is a popular educational tool for middle and high school students. Developed by MIT Sea Grant, the remotely operated vehicle (ROV) teaches hand fabrication processes, electronics techniques, and STEM concepts, while encouraging exploration of structures, electronics, and underwater dynamics. [ MIT Sea Grant ] I was at this RoboGames match! In 2010! And now I feel old! [ Hardcore Robotics ] Daniel Simu with a detailed breakdown of his circus acrobat partner robot. If you don’t want to watch the whole thing, make sure and check out 3:30. [ Daniel Simu ]

IEEE continues to raise its visibility as a trusted voice on mitigating the effects of climate change. Last year Saifur Rahman, the 2023 IEEE president, represented the organization in several sessions at the U.N. Climate Change Conference (COP29). Representatives from more than 200 countries attended the November event, held in Baku, Azerbaijan. Rahman, a power expert, is a professor of electrical and computer engineering at Virginia Tech. In Baku he discussed IEEE’s efforts including helping to develop technologies that help mitigate climate change, teaching sustainable technologies to young engineers in developing countries, and publishing unbiased information. Then in December, IEEE and the International Telecommunication Union held a symposium on achieving climate resilience. Rahman was the event’s general chair. In addition to high-level representatives from U.N. agencies, there were representatives from other IEEE groups including the Power & Energy Society, the Standards Association, Young Professionals, and the organization’s Europe office. The event was held at ITU’s headquarters in Geneva on 12 and 13 December. Participants included engineers, industry experts, policymakers, researchers, and standards development organizations. Discussions were held around four key areas: research, technology, and standards; policy, regulation and implementation; education and skills development; and finance, trade, and development. IEEE’s climate action activities at COP29 IEEE can serve humanity by promoting clean-tech solutions for climate sustainability, Rahman declared in his COP29 presentation. “Pragmatic and accessible technical solutions are urgently needed to address climate change,” he said. “As engineers and technologists, we are uniquely placed to provide technical solutions and offer a neutral space for discussion and action.” He highlighted several IEEE resources including the Climate Change website, which houses all the organization’s resources. The IEEE Xplore Digital Library’s climate change collection contains publications, conference proceedings, technical standards, and other research materials. The latest research and upcoming conferences are in the IEEE Technology Center for Climate. Rahman pointed out IEEE Standards Association Industry Connections programs on green hydrogen, marine carbon dioxide removal, and low-carbon building electrical technology. He reiterated six feasible solutions for decarbonization in industrialized and emerging economies that he first promoted at COP27 in 2022 to facilitate the global shift toward renewable energy. The solutions involve reducing electricity usage; making coal plants more efficient; using hydrogen, carbon capture, and storage technologies; promoting the use of renewables; installing new types of nuclear reactors; and encouraging cross-border power transfers. Rahman attended several COP29 side events: The Towards a Skills Pledge for Tripling Renewables session covered ways to increase electricity capacity by 2030. A skilled workforce is needed to achieve the goal, Rahman pointed out, so it will require an investment in education and training. He said he believes countries must look outside their borders to find experienced technologists to help design, install, and maintain renewable energy projects. IEEE can help enable knowledge transfer and workforce development, he noted. The Bridging Finance and Technology event focused on funding climate action. Money alone won’t solve the issue, however, Rahman said, without a viable technical plan. He said IEEE’s role is to help facilitate investments in carbon-reduction technologies by promoting energy-efficient systems and renewable energy projects. “Pragmatic and accessible technical solutions are urgently needed to address climate change. As engineers and technologists, we are uniquely placed to provide technical solutions and offer a neutral space for discussion and action.” —2023 IEEE President Saifur Rahman In the Developing Green Skills for Young Professionals session, he spoke about the need to train young engineers in developing countries on renewable energy and sustainable technologies. He pointed to the IEEE Young Professionals Climate and Sustainability Task Force, launched in 2023 to encourage the next generation to lead initiatives and develop potential solutions. An article published by The Institute discusses several of the task force’s recent activities. The Intergenerational Dialog for Shaping Future Climate Landscapes session covered managing long-term atmospheric carbon dioxide, building a global climate risk network, and aligning carbon pricing globally. Rahman again pointed to IEEE’s 30,000 young professionals from 190 countries. He encouraged the audience to utilize them to help spread the word about how technology can help address climate change. International Telecommunication Union Secretary-General Doreen Bogdan-Martin and 2023 IEEE President Saifur Rahman were panelists at a U.N. symposium discussing technologies that can help mitigate climate change. D. Woldu/ITU “IEEE has a very strong climate change program,” he said. “We write papers, but we [also] want to make sure people on the ground benefit from our work. IEEE has sections in over 140 countries. They include engineers, IT professionals, and even businesspeople. I’m pleading with you to use us for the benefit of the local community.” IEEE-ITU strategic opportunities In Rahman’s opening remarks, he stressed a bottom-up approach to technology that supports top-down policy frameworks, ensuring that IEEE’s more than 486,000 members—he calls its human technologies—can contribute to solutions. James E. Matthews III, president of the IEEE Standards Association, stressed that technical standards are the foundation for scalable climate solutions. He said the agility of organizations such as IEEE SA in developing guidelines for new technologies including artificial intelligence and green tech solutions has resulted in their rapid adoption. In discussing how the role of intellectual property, especially patents, can help achieve the U.N. Sustainable Development Goals, IEEE Fellow Claudio Canizares called for a shift from isolated and proprietary work to more collaborative solutions. The electrical and computer engineering professor at the University of Waterloo, in Ontario, Canada, explained how IEEE could play a crucial role. Another topic of discussion was the need to educate and train people on implementing climate-resistant technologies. Sneha Satish Hegde, an IEEE member, highlighted how the IEEE Young Professionals Climate and Sustainability Task Force provides mentorship and training. Hegde, a scientific researcher, is the task force’s partnership lead. The task force held a panel session during last year’s Climate Week NYC, which ran from 22 to 29 September to coincide with the U.N. Summit of the Future. Climate-change experts from organizations and government agencies around the world highlighted the intersection of technology, policy, and citizen engagement. How IEEE can assist A summary of the IEEE-ITU symposium outlines ways IEEE can continue to take a lead role in climate resilience by expanding partnerships, standardizing solutions, strengthening the repository of data, promoting circular economies, advancing sustainable practices, and bridging the digital divide. The summary concludes that by finding more partners, creating and strengthening standards, and fostering capacity-building, IEEE could catalyze systemic change, contributing to a more resilient future.

A version of this post originally appeared in Refactoring, a Substack offering advice for software engineers. Most of us have encountered a few software engineers who seem practically magician-like, a class apart from the rest of us in their ability to reason about complex mental models, leap to nonobvious yet elegant solutions, or emit waves of high-quality code at unreal velocity. I have run into many of these incredible beings over the course of my career. I think their existence is what explains the curious durability of the notion of a “10x engineer,” someone who is 10 times as productive or skilled as their peers. The idea—which has become a meme—is based on flimsy, shoddy research, and the claims people have made to defend it have often been risible (for example, 10x engineers have dark backgrounds, are rarely seen doing user-interface work, and are poor mentors and interviewers) or blatantly double down on stereotypes (“we look for young dudes in hoodies who remind us of Mark Zuckerberg”). But damn if it doesn’t resonate with experience. It just feels true. I don’t have a problem with the idea that there are engineers who are 10 times as productive as other engineers. The problems I do have are twofold. Measuring productivity is fraught and imperfect First, how are you measuring productivity? I have a problem with the implication that there is One True Metric of productivity that you can standardize and sort people by. Consider the magnitude of skills and experiences at play: Are you working on microprocessors, IoT, database internals, Web services, user experience, mobile apps—what? Are you using Golang, Python, Cobol, or Lisp? Which version, libraries, and frameworks? What other software must you have mastered? What adjacent skills, market segments, and product subject matter expertise are you drawing upon? Design, security, compliance, data visualization, marketing, finance? What stage of development? What scale of usage? Are you writing for a Mars rover, or shrink-wrapped software you can never change? Also, people and their skills and abilities are not static. At one point, I was a pretty good database reliability engineer. Maybe I was even a 10x database engineer then, but certainly not now. I haven’t debugged a query plan in years. “10x engineer” makes it sound like productivity is an immutable characteristic of a person. But someone who is a 10x engineer in a particular skill set is still going to have infinitely more areas where they are average (or below average). I know a lot of world-class engineers, but I’ve never met anyone who is 10 times better than everyone else across the board, in every situation. Engineers don’t own software, teams own software Second, and even more importantly: So what? Individual engineers don’t own software; engineering teams own software. It doesn’t matter how fast an individual engineer can write software. What matters is how fast the team can collectively write, test, review, ship, maintain, refactor, extend, architect, and revise the software that they own. Everyone uses the same software delivery pipeline. If it takes the slowest engineer at your company five hours to ship a single line of code, it’s going to take the fastest engineer at your company five hours to ship a single line of code. The time spent writing code is typically dwarfed by the time spent on every other part of the software development lifecycle. If you have services or software components that are owned by a single engineer, that person is a single point of failure. I’m not saying this should never happen. It’s quite normal at startups to have individuals owning software, because the biggest existential risk that you face is not moving fast enough and going out of business. But as you start to grow as a company, ownership needs to get handed over to a team. Individual engineers get sick, go on vacation, and leave the company, and the business has to be resilient to that. When a team owns the software, then the key job of any engineering leader is to craft a high-performing engineering team. If you must 10x something, build 10x engineering teams. The best engineering organizations are the ones where normal engineers can do great work When people talk about world-class engineering organizations, they often have in mind teams that are top-heavy with staff and principal engineers, or that recruit heavily from the ranks of former Big Tech employees and top universities. But I would argue that a truly great engineering org is one where you don’t have to be one of the “best” or most pedigreed engineers to have a lot of impact on the business. I think it’s actually the other way around. A truly great engineering organization is one where perfectly normal, workaday software engineers, with decent skills and an ordinary amount of expertise, can consistently move fast, ship code, respond to users, understand the systems they’ve built, and move the business forward a little bit more, day by day, week by week. Anyone can build an org where the most experienced, brilliant engineers in the world can create products and make progress. That’s not hard. And putting all the spotlight on individual ability has a way of letting your leaders off the hook from doing their jobs. It is a huge competitive advantage if you can build systems where less experienced engineers can convert their effort and energy into product and business momentum. And the only meaningful measure of productivity is whether or not you are moving the business materially forward. A truly great engineering org also happens to be one that mints world-class software engineers. But I’m getting ahead of myself here. Let’s talk about “normal” engineers A lot of technical people got really attached to our identities as smart kids. The software industry tends to reflect and reinforce this preoccupation at every turn, as seen in Netflix’s claim that “we look for the top 10 percent of global talent” or Coinbase’s desire to “hire the top 0.1 percent.” I would like to challenge us to set that baggage to the side and think about ourselves as normal people. It can be humbling to think of yourself as a normal person. But most of us are, and there is nothing wrong with that. Even those of us who are certified geniuses on certain criteria are likely quite normal in other ways—kinesthetic, emotional, spatial, musical, linguistic, and so on. Software engineering both selects for and develops certain types of intelligence, particularly around abstract reasoning, but nobody is born a great software engineer. Great engineers are made, not born. Build sociotechnical systems with “normal people” in mind When it comes to hiring talent and building teams, yes, absolutely, we should focus on identifying the ways people are exceptional. But when it comes to building sociotechnical systems for software delivery, we should focus on all the ways people are normal. Normal people have cognitive biases—confirmation bias, recency bias, hindsight bias. We work hard, we care, and we do our best; but we also forget things, get impatient, and zone out. Our eyes are inexorably drawn to the color red (unless we are colorblind). We develop habits and resist changing them. When we see the same text block repeatedly, we stop reading it. We are embodied beings who can get overwhelmed and fatigued. If an alert wakes us up at 3 a.m., we are much more likely to make mistakes while responding to that alert than if we tried to do the same thing at 3 p.m. Our emotional state can affect the quality of our work. When your systems are designed to be used by normal engineers, all that excess brilliance they have can get poured into the product itself, instead of wasting it on navigating the system. Great engineering orgs mint world-class engineers A great engineering organization is one where you don’t have to be one of the best engineers in the world to have a lot of impact. But—rather ironically—great engineering orgs mint world-class engineers like nobody’s business. The best engineering orgs are not the ones with the smartest, most experienced people in the world. They’re the ones where normal software engineers can consistently make progress, deliver value to users, and move the business forward. Places where engineers can have a large impact are a magnet for top performers. Nothing makes engineers happier than building things, solving problems, and making progress. If you’re lucky enough to have world-class engineers in your organization, good for you! Your role as a leader is to leverage their brilliance for the good of your customers and your other engineers, without coming to depend on their brilliance. After all, these people don’t belong to you. They may walk out the door at any moment, and that has to be okay. These people can be phenomenal assets, assuming they can be team players and keep their egos in check. That’s probably why so many tech companies seem to obsess over identifying and hiring them, especially in Silicon Valley. But companies attach too much importance to finding these people after they’ve already been minted, which ends up reinforcing and replicating all the prejudices and inequities of the world at large. Talent may be evenly distributed across populations, but opportunity is not. Don’t hire the “best” people. Hire the right people We place too much emphasis on individual agency and characteristics, and not enough on the systems that shape us and inform our behaviors. I believe a whole slew of issues (candidates self-selecting out of the interview process, diversity of applicants, and more) would be improved simply by shifting the focus of hiring away from this inordinate emphasis on hiring the best people and realigning around the more reasonable and accurate right people. It’s a competitive advantage to build an environment where people can be hired for their unique strengths, not their lack of weaknesses; where the emphasis is on composing teams; where inclusivity is a given both for ethical reasons and because it raises the bar for performance for everyone. Inclusive culture is what meritocracy depends on. This is the kind of place that engineering talent is drawn to like a moth to a flame. It feels good to move the business forward, sharpen your skills, and improve your craft. It’s the kind of place that people go when they want to become world-class engineers. And it tends to be the kind of place where world-class engineers want to stick around and train the next generation.

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Generative AI models are getting closer to taking action in the real world. Already, the big AI companies are introducing AI agents that can take care of web-based busywork for you, ordering your groceries or making your dinner reservation. Today, Google DeepMind announced two generative AI models designed to power tomorrow’s robots. The models are both built on Google Gemini, a multimodal foundation model that can process text, voice, and image data to answer questions, give advice, and generally help out. DeepMind calls the first of the new models, Gemini Robotics, an “advanced vision-language-action model,” meaning that it can take all those same inputs and then output instructions for a robot’s physical actions. The models are designed to work with any hardware system, but were mostly tested on the two-armed Aloha 2 system that DeepMind introduced last year. In a demonstration video, a voice says: “Pick up the basketball and slam dunk it” (at 2:27 in the video below). Then a robot arm carefully picks up a miniature basketball and drops it into a miniature net—and while it wasn’t a NBA-level dunk, it was enough to get the DeepMind researchers excited. Google DeepMind released this demo video showing off the capabilities of its Gemini Robotics foundation model to control robots. Gemini Robotics “This basketball example is one of my favorites,” said Kanishka Rao, the principal software engineer for the project, in a press briefing. He explains that the robot had “never, ever seen anything related to basketball,” but that its underlying foundation model had a general understanding of the game, knew what a basketball net looks like, and understood what the term “slam dunk” meant. The robot was therefore “able to connect those [concepts] to actually accomplish the task in the physical world,” says Rao. What are the advances of Gemini Robotics? Carolina Parada, head of robotics at Google DeepMind, said in the briefing that the new models improve over the company’s prior robots in three dimensions: generalization, adaptability, and dexterity. All of these advances are necessary, she said, to create “a new generation of helpful robots.” Generalization means that a robot can apply a concept that it has learned in one context to another situation, and the researchers looked at visual generalization (for example, does it get confused if the color of an object or background changed), instruction generalization (can it interpret commands that are worded in different ways), and action generalization (can it perform an action it had never done before). Parada also says that robots powered by Gemini can better adapt to changing instructions and circumstances. To demonstrate that point in a video, a researcher told a robot arm to put a bunch of plastic grapes into a clear Tupperware container, then proceeded to shift three containers around on the table in an approximation of a shyster’s shell game. The robot arm dutifully followed the clear container around until it could fulfill its directive. Google DeepMind says Gemini Robotics is better than previous models at adapting to changing instructions and circumstances. Google DeepMind As for dexterity, demo videos showed the robotic arms folding a piece of paper into an origami fox and performing other delicate tasks. However, it’s important to note that the impressive performance here is in the context of a narrow set of high-quality data that the robot was trained on for these specific tasks, so the level of dexterity that these tasks represent is not being generalized. What is embodied reasoning? The second model introduced today is Gemini Robotics-ER, with the ER standing for “embodied reasoning,” which is the sort of intuitive physical world understanding that humans develop with experience over time. We’re able to do clever things like look at an object we’ve never seen before and make an educated guess about the best way to interact with it, and this is what DeepMind seeks to emulate with Gemini Robotics-ER. Parada gave an example of Gemini Robotics-ER’s ability to identify an appropriate grasping point for picking up a coffee cup. The model correctly identifies the handle, because that’s where humans tend to grasp coffee mugs. However, this illustrates a potential weakness of relying on human-centric training data: for a robot, especially a robot that might be able to comfortably handle a mug of hot coffee, a thin handle might be a much less reliable grasping point than a more enveloping grasp of the mug itself. DeepMind’s Approach to Robotic Safety Vikas Sindhwani, DeepMind’s head of robotic safety for the project, says the team took a layered approach to safety. It starts with classic physical safety controls that manage things like collision avoidance and stability, but also includes “semantic safety” systems that evaluate both its instructions and the consequences of following them. These systems are most sophisticated in the Gemini Robotics-ER model, says Sindhwani, which is “trained to evaluate whether or not a potential action is safe to perform in a given scenario.” And because “safety is not a competitive endeavor,” Sindhwani says, DeepMind is releasing a new data set and what it calls the Asimov benchmark, which is intended to measure a model’s ability to understand common-sense rules of life. The benchmark contains both questions about visual scenes and text scenarios, asking models’ opinions on things like the desirability of mixing bleach and vinegar (a combination that make chlorine gas) and putting a soft toy on a hot stove. In the press briefing, Sindhwani said that the Gemini models had “strong performance” on that benchmark, and the technical report showed that the models got more than 80 percent of questions correct. DeepMind’s Robotic Partnerships Back in December, DeepMind and the humanoid robotics company Apptronik announced a partnership, and Parada says that the two companies are working together “to build the next generation of humanoid robots with Gemini at its core.” DeepMind is also making its models available to an elite group of “trusted testers”: Agile Robots, Agility Robotics, Boston Dynamics, and Enchanted Tools.

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.