{"id":2664,"date":"2019-11-21T13:41:43","date_gmt":"2019-11-21T13:41:43","guid":{"rendered":"https:\/\/www.tk.etf.unsa.ba\/?page_id=2664"},"modified":"2019-11-22T18:11:57","modified_gmt":"2019-11-22T18:11:57","slug":"ieee-news","status":"publish","type":"page","link":"https:\/\/www.tk.etf.unsa.ba\/bs\/ieee-news\/","title":{"rendered":"IEEE Novosti"},"content":{"rendered":"<div class=\"feedzy-eab7ca945e149d6467784ec0971ca762 feedzy-rss\"><div class=\"rss_header\"><h2><a href=\"https:\/\/spectrum.ieee.org\/\" class=\"rss_title\" rel=\"noopener\">IEEE Spectrum<\/a> <span class=\"rss_description\"> IEEE Spectrum<\/span><\/h2><\/div><ul><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/sustainability-robotics-barbara-mazzolai\" target=\"_blank\" rel=\" noopener\" title=\"Barbara Mazzolai Wants to Build a New Field of Robotics\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/photo-of-a-woman-standing-in-front-of-greenery-holding-a-device-shaped-like-a-small-octopus-arm.png?id=67787635&amp;width=980\" title=\"Barbara Mazzolai Wants to Build a New Field of Robotics\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/sustainability-robotics-barbara-mazzolai\" target=\"_blank\" rel=\" noopener\">Barbara Mazzolai Wants to Build a New Field of Robotics<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Edd Gent<\/a> on 22. Septembra 2026. at 14:00 <\/small><p>Throughout her career, roboticist Barbara Mazzolai has turned to nature for inspiration. Now she wants to ensure the technology she builds gives back to the environment, too.After starting her career as a biologist, a chance opportunity saw Mazzolai switch streams to engineering and become an early pioneer of bioinspired robotics. Building on her knowledge of biology\u2019s ability to solve a diverse set of problems, she has developed robots based on octopuses, plant roots, and even seeds. \u201cI\u2019ve always been fascinated by living organisms, [and] by the extraordinary variety of solutions in nature, selected by the evolutionary process,\u201d she says.Barbara MazzolaiEmployer: Italian Institute of TechnologyOccupation: Associate director for robotics; director of the Bioinspired Soft Robotics LaboratoryEducation: Master\u2019s degree in biology, University of Pisa; master\u2019s degree in eco-management and audit schemes, Scuola Superiore Sant\u2019Anna; Ph.D. in microsystems engineering, University of Rome Tor VergataBut Mazzolai, now the associate director for robotics at the Italian Institute of Technology, in Genoa, also believes engineering needs to reckon with its own impact on the natural world. That\u2019s why she is advocating for a new field of research she calls \u201csustainability robotics.\u201dIn a manifesto published in Nature Machine Intelligence in July, she and her collaborators outline a vision for a new approach to designing robots that\u2019s meant to improve the relationship between nature, humanity, and technology.\u201cWe need to reduce the footprint of our technology,\u201d she says. \u201cIt\u2019s really about thinking in a different way to open new possibilities for robotics [and] for society.\u201d In this new mode of thinking, Mazzolai considers sustainability a core component of the design.A child of natureMazzolai traces her fascination with the living world back to her childhood growing up on Italy\u2019s Tuscan coast, close to the port city Livorno. Her father was a public-health inspector and a professional mycologist, and the family spent a lot of time exploring forests and learning about the local fungi and plants.After toying with the prospect of pursuing art, her other major passion, Mazzolai ultimately decided to enroll at the University of Pisa in 1987 to study biology. She was particularly drawn to marine biology, but shortly before graduating with a master\u2019s degree in 1995, she secured a research position at the Italian National Research Council\u2019s Institute of Biophysics studying the cycles of heavy metals like mercury through both living and nonliving parts of the environment.This involved collecting and analyzing samples from water, soil, vegetables, and even humans to understand the impact these metals have on health and the environment. She balanced this work with studying environmental management at the Scuola Superiore Sant\u2019Anna, in Pisa, graduating with a master\u2019s degree in 1998.During that time, however, she learned that the university was recruiting biologists to help design new devices for environmental monitoring. She applied for and got the job in 1999 and began working as a research assistant under renowned bioroboticist Paolo Dario, first developing sensors and then robots meant to monitor air, water, and soil.Even before entering a doctoral program, Mazzolai was promoted to assistant professor in 2004 and shortly afterward made her first foray into bioinspired robotics. In collaboration with colleagues at Sant\u2019Anna, she helped design a soft robot inspired by the octopus. \u201cWe proposed it as a paradigm for launching this idea of soft robotics: demonstrating that [robots] can be soft, but at the same time apply strong force to the environment, like the animal does,\u201d she says.Back to schoolIn 2007 Mazzolai enrolled in a Ph.D. in microsystems engineering at Tor Vergata University of Rome, which she balanced with her role at Sant\u2019Anna. She was already relying heavily on microfabrication techniques to develop sensors for her robots, and she was keen to push that part of the field forward.While robots frequently feature sensors designed for perception, such as tactile or proprioceptive sensors, these systems typically focus on understanding the robot\u2019s position in its environment, she says. \u201cBut there are few robots that integrate physical or chemical sensors to really understand the environment they move in,\u201d she adds.\u201cI\u2019ve always been fascinated by living organisms, [and] by the extraordinary variety of solutions in nature.\u201dMazzolai was appointed as a team leader at the Center for Micro-BioRobotics of the Italian Institute of Technology in 2009, where she continued her work on the emerging field of bioinspired robotics. Two years later, she completed her Ph.D. and was promoted to director of the center.Planting the seedsAround this time Mazzolai says she became interested in using plants as a model for new kinds of robots, expanding bioinspiration beyond just animals. In particular, she was captivated by the ability of roots to efficiently explore the underground environment, and she imagined machines with the same deftness could have applications in both environmental modeling and precision agriculture.  While many bioinspired robots mimic animals, plants also serve as a muse for Mazzolai. This tendril-like bot can coil around other structures like a vine. Italian Institute of TechnologyWhen she first proposed the idea, colleagues were somewhat skeptical of robots based on seemingly static organisms. But in reality, she says, plants move nonstop through a process known as indeterminate growth. \u201cThey really grow for their entire life,\u201d she says. \u201cThey adapt their morphology, their behavior to the external environment; they repair, they sense, they communicate.\u201dTrying to mimic a system that operates on such different principles to conventional robotics required some serious thinking, however. Mazzolai says that working in bioinspired robotics sometimes requires you to have \u201ctwo separate brains\u201d\u2014one of a biologist and one of an engineer.The process often involves deep study of the target organism to learn the underlying principles that shape how it operates before trying to engineer a robot capable of mimicking them. \u201cIt\u2019s not a copy of natural organisms,\u201d says Mazzolai, because a living organism is both difficult to replicate and has different goals.In the case of plant roots, what makes them so efficient at exploring the soil is that they reduce friction by growing only at the very fine tip of the structure, while the thicker base of the root remains static. This significantly reduces the amount of energy required to push through the earth compared to that of a more conventional drill, which must push the entire structure from above.To realize this principle in a robot, her team developed a miniaturized 3D printer that sits at the machine\u2019s tip and feeds thermoplastic filament through a heated nozzle to build a snakelike body behind it. This allows the robot to push through the soil efficiently. The tip also contains sensors that allow it to avoid obstacles and detect nearby nutrients or water.Making robotics sustainableAfter spending so much of her career borrowing from nature, Mazzolai is now eager to return the favor. Many modern technologies, including plastics and car batteries, have been developed with little thought about how they will affect the environment at the end of their life cycles, she says.She wants to ensure that robotics doesn\u2019t follow the same path. This is the inspiration for what she and collaborators now call sustainability robotics. The approach has three central pillars: ensuring that robots have minimal impact on the environment; that they\u2019re available to people from across the world and all socioeconomic backgrounds; and that they\u2019re \u201csymbiotic,\u201d providing benefits to both humans and nature.More concretely, Mazzolai would like to incorporate the concept of a life cycle into the design of robots, so that at the end of their useful life these machines can be reused, recycled, or even biodegraded.While that might sound ambitious, she\u2019s confident that all the ingredients to make it a reality are in place. And it\u2019s a vision that she is certain will inspire future roboticists. \u201cThere are younger people who want to really work in this field because this is the future, their future,\u201d she says. Facing the threat of ongoing environmental damage, \u201cthey want to develop something that can help.\u201d<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/fanless-liquid-cooled-ai-servers-coolit\" target=\"_blank\" rel=\" noopener\" title=\"The Future Is Fanless: 100% Heat Capture for Liquid Cooled AI Servers\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/close-up-of-copper-liquid-cooling-plates-and-heat-pipes-inside-an-electronic-device.jpg?id=67771117&amp;width=980\" title=\"The Future Is Fanless: 100% Heat Capture for Liquid Cooled AI Servers\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/fanless-liquid-cooled-ai-servers-coolit\" target=\"_blank\" rel=\" noopener\">The Future Is Fanless: 100% Heat Capture for Liquid Cooled AI Servers<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Coolit, an Ecolab Company<\/a> on 22. Septembra 2026. at 12:22 <\/small><p>This article is brought to you by CoolIT, an Ecolab Company.Beyond 250 kW a server rack can no longer be cooled by a hybrid approach of liquid and air. At this density a 70\/30 liquid-air split leaves 75 kW of air load. The air cooling system needed to move it brings cost and complexity few operators will accept. The answer is near-total heat capture. Liquid takes effectively all the heat, air falls below 1 percent of the load, allowing the server to run fanless.CoolIT builds these loops today from modular coldplate blocks proven across six generations of fanless designs. Processor thermal design power (TDP) keeps climbing generation over generation. This rising heat load is now cascading into the memory, networking, storage, and power components that once ran comfortably on air.The heat escaped the chipFor years the story stayed simple. Cool the processor and let air handle the rest. That balance has shifted. As TDP climbs, heat spreads outward from the processor and cascades into the components around it. Memory, networking, storage, and power now run hot enough to demand liquid of their own. Engineers designing the next generation of AI servers face a board where heat capture rises with every launch.Beyond 250 kW per rack, air cooling becomes the bottleneck. Near-total liquid heat capture enables fanless AI server designs built for the next generation of computing.New parts, new rulesUnlike processors, which are cooled as flat rectangular packages, these peripherals come in a wide range of shapes, sizes, and mounting requirements, each with its own thermal limits. Some run cooler than the processor case temperature, others run hotter, which leaves them sensitive to a design tuned only for CPUs and GPUs. Operators need purpose-built solutions here, matched to the part rather than stretched across the board.CoolIT engineers meet this with a deep toolkit. Conductive plates, vapor chambers, heat pipes, and thermal transfer plates move heat from components closer to the liquid path. Riding coldplates enable pluggable components. Each solution stays true to the component it serves.  CoolIT Customer Showcase: How GWDG Cools HPC &amp; AI Systems with CoolIT\u2019s Direct Liquid Cooling CoolIT One loop, one serverCooling the parts is one challenge. Uniting them is the real work. Full heat capture means folding every one of these solutions into a single server loop that distributes coolant effectively and remains easy to install. Connection reliability, coolant routing, and the time it takes to assemble the loop at rack integration determine whether a design thrives in production or stalls on the bench. CoolIT builds these loops from proven modular blocks, so operators gain performance and deployment speed within the same solution.Density forces the decisionRack power continues to climb toward 1 MW, and the case for liquid grows stronger at every step. A 70\/30 split of liquid to air holds comfortably at lower density. Past roughly 250 kW it stops working. The 30 percent left to air becomes a 75 kW load inside a single rack, and moving that much heat demands a parallel air system whose cost and footprint few operators will accept. Adding density only widens the gap.As rack power continues to climb toward 1 MW, CoolIT\u2019s modeling places full heat capture as the standard server design for flagship rack-scale products through 2028.The simpler, more efficient answer is to capture the heat in liquid and drop air to less than 1 percent of the total load. True 100 percent remains almost impossible to reach in the strictest sense, so the honest and achievable target is near-total capture. That distinction matters to engineers who value precision, and the direction stays clear either way. Full heat capture moves from a premium option to a mainstream requirement as density rises, and CoolIT\u2019s modeling places it as the standard server design for flagship rack-scale products through 2028.CoolIT delivers itCoolIT scales heat capture all the way to 100 percent using modular coldplate building blocks proven across six generations of fanless server designs. Engineering teams are already working on designs for the maximum density racks coming next. As the cascade spreads and racks grow denser, near-total heat capture becomes the design that keeps AI running.Talk to CoolIT about building a server loop engineered for total heat capture.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/hermes-shortwave-radio-digital-data\" target=\"_blank\" rel=\" noopener\" title=\"This Digital Radio Gets Messages to the World\u2019s Remotest Locations\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/mans-face-framed-by-abstract-tech-graphics-antenna-towers-and-green-palm-leaves.png?id=67783301&amp;width=980\" title=\"This Digital Radio Gets Messages to the World\u2019s Remotest Locations\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/hermes-shortwave-radio-digital-data\" target=\"_blank\" rel=\" noopener\">This Digital Radio Gets Messages to the World\u2019s Remotest Locations<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Margo Anderson<\/a> on 21. Septembra 2026. at 13:00 <\/small><p>Shortwave radios offer a way to connect one location on Earth to practically anywhere else with minimal infrastructure. But these radios come with some drawbacks\u2014a significant one being that, unlike satellite communications, their transmission rates for digital data are typically measured in just hundreds of bits per second. Peter BloomPeter Bloom is the founder of Rhizomatica, a nonprofit that works with remote, indigenous, and off-grid communities around the world to build shortwave and cellular-communication infrastructure. Peter Bloom is the founder of Rhizomatica, a Philadelphia-based nonprofit that has open-sourced a digital shortwave-radio set called the High-frequency Emergency and Rural Multimedia Exchange System, or HERMES. The set operates in the high-frequency (HF) band from 3 to 30 megahertz, as does Mercury, its digital modem. Rhizomatica staff travel around the globe to remote locations in countries like Bangladesh, Brazil, and Ecuador. Wherever they go, they use HERMES to help connect locals to the rest of the world.Bloom spoke with IEEE Spectrum about how HERMES brings better data rates and encryption to shortwave radios.How does HERMES connect remote locations?Peter Bloom: We use the ionosphere as our satellite\u2014or mirror\u2014which helps us move information, voice, and data over really long distances. We\u2019re using small radios that put out about 20 watts of power, and we can pretty reliably do 400- to 600-kilometer links between two radios. We\u2019re talking about places that are not easy to reach, where it\u2019s not simple to put terrestrial infrastructure. What can HERMES send that a basic voice radio can\u2019t?Bloom: HERMES is a software stack\u2014it\u2019s a set of different programs that all work together in order to be able to send data over HF. HERMES allows you to send pretty much any file. Depending on what the file is, whether it\u2019s a photo or an email or a voice memo, it just sends it as a file. It\u2019s like a data pipeline over HF. Why does sending files and data matter more than just voice?Bloom: In emergency situations, people send their latitude and longitude over HF to say, \u201cHey, I\u2019m here at this place.\u201d People need to be able to send data over HF if there\u2019s a manifest, a parts list, telemedicine\u2014here\u2019s what we have, here\u2019s what we need. Instead of trying to read that out over the air, it\u2019s much easier to just send the file. Same with a photo\u2014if we need evidence that an area was logged illegally, we can just have someone send that over HF, rather than spending days getting down the river to get the photo where it needs to go.Why did you build in encryption that amateur-radio regulations in many countries don\u2019t allow?Bloom: Encryption [regulations] for ham radio operators are different in each country. So it\u2019s all optional\u2014you turn it on, you turn it off. The reason we built the encryption is that some of the partners we work with are in very sensitive areas and don\u2019t want to be sending out information that can be easily captured and used against them.How has HERMES made an impact?Bloom: We\u2019ve been working with artisanal fishers in Bangladesh on a pilot project. There\u2019s 10 or 11 boats that have HERMES systems on them. Pretty soon after we installed those, one of the boats had a mechanical issue in the Bay of Bengal, 100 or 200 kilometers offshore. They were able to send their GPS position and an SOS that they were having trouble. They were able to coordinate the rescue of the crew and the boat. So that was a really cool moment of HERMES in action that we\u2019re super happy about.This article appears in the October 2026 print issue as \u201cPeter Bloom.\u201d<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/content.knowledgehub.wiley.com\/76-faster-replication-same-infrastructure\/\" target=\"_blank\" rel=\" noopener\" title=\"Parallel Reads and Write Optimization for Large-Scale Data Replication\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/assets.rbl.ms\/67794446\/origin.png\" title=\"Parallel Reads and Write Optimization for Large-Scale Data Replication\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/content.knowledgehub.wiley.com\/76-faster-replication-same-infrastructure\/\" target=\"_blank\" rel=\" noopener\">Parallel Reads and Write Optimization for Large-Scale Data Replication<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/content.knowledgehub.wiley.com\" target=\"_blank\" title=\"content.knowledgehub.wiley.com\">Mike Spector<\/a> on 18. Septembra 2026. at 18:29 <\/small><p>This White Paper gives data engineers and architects a practical overview of how parallel partitioned reads, write-path optimization, and cloud-native bulk loading reduce large-table replication times, and why replication speed has become a business concern as data volumes grow.Download this free whitepaper now!<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/tech-talent-into-leadership-legacy\" target=\"_blank\" rel=\" noopener\" title=\"Turning Tech Talent Into Leadership Legacy\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/a-team-of-seven-people-having-a-work-meeting-in-an-open-concept-office-space.jpg?id=67787397&amp;width=980\" title=\"Turning Tech Talent Into Leadership Legacy\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/tech-talent-into-leadership-legacy\" target=\"_blank\" rel=\" noopener\">Turning Tech Talent Into Leadership Legacy<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Prachi Jain<\/a> on 18. Septembra 2026. at 18:00 <\/small><p>Transitioning from years of working in a senior technical or executive role to a leadership position is one of the most challenging phases of a STEM career. It requires moving away from making decisions on your own to mentoring others, making strategic decisions for the organization, and collaborating with coworkers from different generations.The shift in mindset is known as \u201clegacy leadership,\u201d a philosophy whereby success is no longer measured by personal achievements but by how effectively a senior leader empowers others.To help seasoned professionals and senior experts navigate the transition, the inaugural IEEE International Leadership Conference (ILC) will provide attendees practical advice on cultivating collaborations and guiding emerging talent.\u201cEarly in our STEM careers, we measure success by what we have achieved,\u201d says Jeewika Ranaweera, cochair of the IEEE ILC program committee. \u201cLater, we should measure success by what we enable, how many people we mentor, how much knowledge we transfer, and how many doors we open for the next generation.\u201dThe ILC is scheduled for 3 and 4 October in Budapest. Registration is open.Letting go of the \u201cexpert\u201d identityFor decades, seasoned technologists have been valued primarily for their technical expertise. Shifting from that identity can feel uncomfortable, but legacy leadership requires measuring success by different standards. They include a leader\u2019s influence on the staff, the ability to uphold the company\u2019s mission, and empowering others to lead and succeed.The transition requires leaders to find purpose outside their corporate titles, shifting their focus to the long-term sustainability of their teams, their organization, and the broader technical community.\u201cA professional legacy is not measured only by what we have achieved but also by sharing our knowledge, experience, and opportunities with others,\u201d says Sudhanshu S. Jamuar, another program committee cochair. \u201cThe real transition from expert to a legacy builder happens when we stop asking, \u2018What more can I accomplish myself?\u2019 and start asking, \u2018How many others can I enable to accomplish more?\u2019\u201dNeeli Rashmi Prasad, IEEE ILC treasurer and sponsorship cochair, adds that the transition transforms a lifetime of technical work into a platform for future innovation.\u201cThe true value of experience is not in how much knowledge we accumulate but in how intentionally we transfer it,\u201d Prasad says. \u201cWhen we partner with, mentor, and create space for others to lead, our expertise becomes a foundation for progress far beyond our own careers.\u201dMoving beyond advice-givingTrue knowledge transfer requires coaching rather than advising, and mastering the art of active listening. To build deep trust with early-career colleagues and ensure a seamless transfer of leadership to the next generation, senior leaders must avoid offering unsolicited or outdated anecdotes. Effective mentorship is a collaborative loop in which senior experts contribute hard-won industry wisdom while remaining curious and learning from the fresh, cutting-edge perspectives of talented coworkers.\u201cKnowledge transfer is most powerful when it is a two-way bridge: experience flows from one generation to the next, while new ideas and perspectives flow back,\u201d says Sudeendra Koushik, president of the IEEE Technology and Engineering Management Society and an ILC keynote speaker. \u201cThis approach transforms mentorship from simply passing on information into one where the next generation can question, experiment, innovate, and ultimately surpass what came before them.\u201dSeasoned professionals should encourage independent, disruptive thinkers rather than carbon copies of themselves, Prasad says.\u201cLegacy leadership is about building continuity,\u201d she says. \u201cWe should not simply prepare the next generation to follow the paths we created; we should give them the confidence, knowledge, and networks to challenge those paths, create new ones, and take technology further than we imagined.\u201dDesigning your next chapterLeadership does not need to stop when one\u2019s job ends; it can evolve. Seasoned, retired professionals can continue contributing meaningful service through pathways that align with their personal passions. They include:Advisory boards: steering corporate, technical, or community organizations.Civic engagement: applying engineering methodologies to solve community challenges.Volunteerism: mentoring the next generation of grassroots innovators through professional networks including IEEE.Those pathways offer experienced professionals an opportunity to redefine success, not in terms of position, authority, or personal achievement but in terms of sustained impact.\u201cRetirement from a job should never mean retirement from purpose,\u201d Koushik says. \u201cOur experience becomes even more valuable when we use it in service of the profession, society, and the generation that follows.\u201dAt the same time, the collaborative continuum relies on a proactive younger generation. Emerging leaders need to take responsibility for building their professional connections, Prasad says, advising: \u201cBe bold, stay curious, and build your network early!\u201dA space for continuityThe conference is designed to combine a drive to cultivate emerging innovators with a deep reservoir of industry stewardship and strategic perspective. Rather than a single classroom session, the conference will feature a dedicated career-readiness and mentorship track where attendees can explore practical frameworks for building strategic professional networks, connecting with peer advocates, and establishing reciprocal knowledge-sharing opportunities across generations.By participating, seasoned professionals can ensure their decades of expertise continue to yield dividends for generations to come.\u201cOur professional legacy is not the technology we build, the titles we earn, or the awards we receive,\u201d Ranaweera says. \u201cIt is the knowledge we share, the lives we influence, and the future we help others create.\u201dYou can view the agenda, read speaker biographies, and secure your seat at the ILC online.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/physical-ai-robot-cybersecurity-vicone\" target=\"_blank\" rel=\" noopener\" title=\"Rethinking Robot Safety in the Age of AI\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/humanoid-robots-and-people-walking-through-a-modern-city-street-with-glass-buildings.jpg?id=67745861&amp;width=980\" title=\"Rethinking Robot Safety in the Age of AI\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/physical-ai-robot-cybersecurity-vicone\" target=\"_blank\" rel=\" noopener\">Rethinking Robot Safety in the Age of AI<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">VicOne<\/a> on 16. Septembra 2026. at 16:51 <\/small><p>This article is brought to you by VicOne.Robot safety has traditionally asked: Can a machine remain safe when something goes wrong? Physical AI raises a harder question: Can a machine remain safe when an attacker changes what it sees, decides, or does even when nothing appears to have failed?As AI and robotics continue to advance at an unprecedented pace, modern robots perceive through multimodal sensors, interpret context using AI models, and translate those interpretations into physical action. As they move into dynamic environments, their safety increasingly depends on the integrity of the data guiding their decisions.That dependence creates risks that conventional safety assessments may not fully capture. Recent research has demonstrated that manipulating what a robot sees, hears, or interprets can influence its behavior without requiring direct control.Such manipulation can occur anywhere across its complex sensing and decision-making system \u2014 a layered attack surface encompassing training pipelines, system infrastructure, and runtime perception.Layer One: Corrupting intelligence at its sourceIn 2017, BadNets demonstrated that a model could behave normally under most conditions, yet fail in the presence of a specific hidden trigger. In one example, a subtle pattern caused a stop sign to be misclassified as a speed limit sign without affecting the model\u2019s behavior on other inputs.What began as a classification vulnerability has since evolved into action manipulation.At NeurIPS 2025, researchers introduced BadVLA a backdoor attack targeting Vision-Language-Action (VLA) models that allow robots to see, interpret instructions, and produce coordinated physical movement. Rather than altering a single label, the attack caused conditional deviations in the robot\u2019s action trajectory when a trigger was present. Without the trigger, the model largely preserved normal task performance, while the backdoor remained effective under task transfers and model fine-tuning.A related study in 2025, GoBA, showed that ordinary objects such as a coffee mug could serve as a reliable trigger. The researchers reported a 97 percent attack success rate without degrading performance on clean inputs.A critical safety question today is whether Physical AI models remain within their task and safety boundaries under adversarial conditions.These studies expose a blind spot in model validation: A model may pass testing yet produce corrupted behavior when a hidden trigger appears in operation.So a critical safety question today is whether Physical AI models remain within their task and safety boundaries under adversarial conditions. Simulation tools such as NVIDIA Isaac Sim, when paired with VicOne Radeis, can test the effects of manipulated inputs before deployment.  VicOne LAB R7 demonstrates Radeis, a Physical AI safety validator for NVIDIA Isaac Sim that tests how adversarial visual inputs affect robot behavior before deployment.VicOneLayer Two: System vulnerabilities as gateways to AI controlEven a securely trained model can be subverted if the surrounding system stack is vulnerable.In September 2025, researchers disclosed UniPwn, a Bluetooth exploit chain affecting quadruped and humanoid robots from a major manufacturer. Hardcoded cryptographic keys allowed traffic decryption, authentication checks were bypassed, and command injection enabled root-level execution. The exploit is also described as \u201cwormable.\u201d A compromised robot could scan nearby units and potentially affect an entire fleet.  VicOne Lab R7\u2019s demo shows how chaining three wireless exploits can trigger uncontrolled robot behavior within 60 seconds, resulting in operational disruption.VicOneMiddleware creates another exposure point. Vulnerabilities in ROS 2 and DDS-based systems can enable arbitrary code execution or abuse unauthenticated topics to deliver malicious commands. With sufficient access, an attacker could override motor commands or replace AI model weights without directly attacking the model architecture.In this case, the components may still function as designed. What has changed is the trustworthiness of the commands flowing through the system. Vulnerability management can help teams identify known risks before deployment, while continuous monitoring can surface emerging threats.Layer Three: Manipulating perception and reasoning at runtimeAt runtime, manipulating inputs that shape perception or reasoning may require neither firmware modification nor a network breach.In 2024, RoboPAIR demonstrated how carefully structured prompts could redirect LLM-controlled robots into unsafe trajectories. BadRobot exposed a deeper architectural weakness: in several cases, a robot verbally refused a dangerous command while its motion controller executed the action anyway.Vision-based manipulation is equally powerful. VLAttack showed that an adversarial patch within the camera\u2019s view could reduce a VLA model\u2019s task success rate to zero. FreezeVLA showed that a single adversarial image could freeze a robot\u2019s decision-making loop, making it unresponsive to subsequent instructions.Runtime assurance must therefore look beyond whether individual components remain available and assess whether cyber events are beginning to affect physical behavior.In each case, the camera may still work, the model may still run, and the controller may still respond. Yet the resulting behavior can be unsafe because the robot is acting on manipulated perception or reasoning.Runtime assurance must therefore look beyond whether individual components remain available and assess whether cyber events are beginning to affect physical behavior. Security event correlation, behavioral-impact assessment, and policy-bounded response supported by edge AI, can help contain the affected path without unnecessarily stopping the entire robot fleet.From point-in-time safety to lifecycle assuranceThe risks across these three layers reveal the missing layer in robot safety assurance: cybersecurity. Functional safety addresses failures and unexpected operating conditions; cybersecurity extends that assurance to deliberate manipulation, including attacks that may leave the underlying system apparently functional.This requires assurance across the robot\u2019s lifecycle. During design, teams need to understand which cyber risks could invalidate assumptions behind intended behavior. Before deployment, they should test whether realistic attacks can cause a robot to deviate from its task or safety boundaries. In operation, monitoring should identify whether cyber events are beginning to affect behavior, contain the affected path, and preserve safe operation where possible.  VicOne\u2019s lifecycle approach combines AI model and vulnerability scanning, simulation-based validation, and continuous monitoring to help secure robots from development through operation.VicOneWhile cybersecurity does not replace functional safety, it helps ensure that Physical AI remains within acceptable boundaries even when what it sees, decides, or does is under attack.For a deeper look at the cybersecurity risks and defense strategies shaping autonomous robotics, download our whitepaper \u201cSecuring the Rise of AI Robots: Cyber Risks, Real-World Threats, and Defense Strategies.\u201d<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/content.knowledgehub.wiley.com\/single-phase-direct-liquid-cooling-is-proven-for-the-next-decade-of-ultra-dense-compute\/\" target=\"_blank\" rel=\" noopener\" title=\"Single-Phase Direct Liquid Cooling Is Proven for the Next Decade of Ultra-Dense Compute\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/assets.rbl.ms\/67781549\/origin.png\" title=\"Single-Phase Direct Liquid Cooling Is Proven for the Next Decade of Ultra-Dense Compute\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/content.knowledgehub.wiley.com\/single-phase-direct-liquid-cooling-is-proven-for-the-next-decade-of-ultra-dense-compute\/\" target=\"_blank\" rel=\" noopener\">Single-Phase Direct Liquid Cooling Is Proven for the Next Decade of Ultra-Dense Compute<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/content.knowledgehub.wiley.com\" target=\"_blank\" title=\"content.knowledgehub.wiley.com\">Coolit, an Ecolab Company<\/a> on 16. Septembra 2026. at 13:24 <\/small><p>Learn how single-phase direct liquid cooling manages the rising heat of AI and high-performance computing, and how it compares with two-phase and immersion approaches.Download this free whitepaper now!<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/inference-hardware-revolution\" target=\"_blank\" rel=\" noopener\" title=\"The AI Inference Revolution Is Here\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/silhouetted-hand-holding-a-glowing-computer-chip-against-a-blue-background.jpg?id=67740879&amp;width=980\" title=\"The AI Inference Revolution Is Here\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/inference-hardware-revolution\" target=\"_blank\" rel=\" noopener\">The AI Inference Revolution Is Here<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Matthew S. Smith<\/a> on 15. Septembra 2026. at 13:00 <\/small><p>Since about 2020, AI has largely focused on training bigger and better models. Large language models (LLMs) ballooned from millions of parameters to trillions. This proved effective: The largest version of OpenAI\u2019s GPT-3, released in 2020, correctly answered just 43.9 percent of questions on a popular knowledge-and-reasoning benchmark. Just four years later, GPT-4o reached a score of 88.7 percent on the same exam, effectively matching those of human experts.Advanced AI labs are still training ever larger models, but that training has somewhat receded to the background of the AI conversation. In 2026, inference\u2014the use of trained models to produce code, write essays, or make images of ourselves as elves\u2014has come to the forefront.\u201cIt\u2019s like training is yesterday\u2019s news,\u201d says Matt Kimball, principal data-center analyst at Moor Insights &amp; Strategy. \u201cAll that any chief information officer wants to talk about is inference.\u201d Nvidia CEO Jensen Huang, speaking at the company\u2019s GTC 2026 conference, touted this change as the \u201cinflection point of inference.\u201dPart of what\u2019s caused the shift is very simple: LLMs are becoming useful, so people are using them. On top of that, many models on the market today are reasoning models. In response to a user\u2019s query, they run inference not just once but multiple times, reprompting themselves in a process called chain of thought. Reasoning models generate longer outputs, and models with high reasoning effort can produce up to 20 times as much text as those with low or no effort. Adding even more to the world\u2019s inference workload, the rise of agentic AI has resulted in inference running not just as a real-time response to a user\u2019s query but also around the clock, working autonomously toward a user-defined goal.  Amazon\u2019s Trainium chip was originally designed for AI training. However, Amazon Web Services chose to break up AI inference into two parts, with Trainium running the more computationally complex portion and Cerebras\u2019s wafer-scale engine taking on the more memory-intensive portion.AmazonThe resulting explosion in inference demand has led to unexpected alliances among tech giants. OpenAI and Amazon have deployed chips the size of a dinner plate designed by Cerebras, despite Amazon having its own Trainium chips. Nvidia bought key talent and intellectual property from AI-inference startup Groq in a controversial deal worth US $20 billion. And Anthropic is paying LLM competitor SpaceXAI over a billion dollars per month to lease spare compute.Although they might seem similar, AI training and AI inference are computationally different. These big moves from tech giants signal that in order to support the inference demand, we\u2019re going to need a very different mix of hardware than experts may have expected even a couple of years ago.How does AI inference differ from AI training?An untrained LLM is like a jumble of Scrabble tiles on a table. Instead of single letters, though, the tiles show fragments of words, called tokens. Everything you\u2019d need to write almost anything is present, but nothing makes sense.Training a model organizes this jumble using a guessing game played at scale. The model is shown real text with the next token hidden and asked to predict what comes next. After each guess, the correct token is revealed and then compared to the prediction, and the difference is used to calculate the model\u2019s accuracy. The game is played not with a single sentence but over billions of passages.While a real game of Scrabble can be played over a bag of chips and a few drinks, AI training is computationally intense. The model updates its parameters through backpropagation, a process that repeatedly calculates how each of a model\u2019s billions or trillions of parameters should shift to make the next prediction better. This is why tech giants are building larger data centers than ever before.Eventually the model\u2019s creator decides further training isn\u2019t worth the cost, and the guessing game stops. Backpropagation ends, the parameters are frozen, and the LLM becomes a pretrained model. Fine-tuning\u2014a short training run on smaller, more specialized data\u2014adds final tweaks, and the model is deployed.Next comes inference. This is the process of using the deployed model, which, now that it\u2019s been trained, has learned to spit out Scrabble tiles\u2014tokens\u2014in a sensible order.You might think that AI inference is less computationally demanding because the backpropagation calculations used to update parameters are eliminated. But Sudeep Bhoja, founder and CTO of the inference-hardware company d-Matrix, explains that inference adds new challenges.The models are \u201cautoregressive\u201d in nature. That is, the next output depends on the previous one. \u201cSo to generate the next token, you have to read all of the weights and all of the [context] from the previous token,\u201d explains Bhoja. The context includes all of your prompts, all of the LLM\u2019s replies, and all of the files you upload. It\u2019s a lot of data and a lot of processing.An LLM generates its reply in two phases: prefill and decode. Prefill is the model reading a prompt. It processes every token at once, computing how each token relates to all the others. This operation is called attention, and it\u2019s a defining characteristic of the transformer architecture behind modern LLMs. It allows them to respond to a word in its sentence, paragraph, and larger context rather than on its own. Think of it like arranging Scrabble tiles before you place them in a game. Many players move tiles around to imagine how they connect. Self-attention plays a similar role, though instead of moving physical tiles, each token sends a query to the others and receives a score indicating the token\u2019s relevance.These queries result in two types of vectors: the keys and values. They are typically placed in a store called the KV cache. This isn\u2019t strictly required, as a model could instead recompute these vectors with each new token it generates. But nearly all LLMs use a KV cache to reduce how much computing they do. The KV cache is stored in memory and becomes a scratchpad to which the LLM can return to understand a conversation, and though it starts small, it can swell to dozens of gigabytes.Prefill is a problem that can be easily divided up and worked on in parallel. This is why GPUs became the dominant AI accelerator as LLMs surged in popularity. Graphics rasterization (computing the color of every pixel on a screen) is also massively parallel, so GPU architectures were a natural fit.Next comes decode. Here, the model generates its reply one token at a time. At each step it takes the most recent token, weighs it against everything in the KV cache, uses that information to predict the next token, and adds the new token\u2019s key and value to the cache. Then it repeats in sequence, token by token.This is where the autoregressive nature of the model works against inference speed. Predicting each token requires reading the entire model from memory, and that model consists of possibly tens to hundreds of gigabytes of parameters (the numbers representing what the model learned in training). Crucially, this is in addition to the memory required to store the KV cache.As a result, the movement of all this data through memory often requires more bandwidth than inference hardware has available. So at least some of the computing parts of a GPU sit idle as it waits for data. Researchers found that Nvidia H100 GPUs running open-source LLMs sit idle 50 to 80 percent of the time.Memory\u2019s role in inferencingShahriar \u201cSha\u201d Rabii, former head of silicon engineering at Meta and cofounder of the AI startup Majestic Labs, says idled processors are why many companies that are trying to improve AI-inference performance are laser-focused on memory. \u201cWith the GPU-based approach, you end up greatly over-provisioning compute and starved on memory. That\u2019s driving the big [memory] scale out,\u201d he says.Bhoja\u2019s d-Matrix and Rabii\u2019s Majestic Labs both focus on this memory bottleneck. However, their companies imagine different solutions.d-Matrix\u2019s second-generation AI accelerator, Raptor, aims to improve inference performance by minimizing the distance between compute and memory. The GPUs in most current AI-inference deployments do this by placing high-bandwidth memory (HBM) around the perimeter of the GPU. Each HBM is a stack of DRAM dies linked together and connected to a superfast interface to the GPU. This is great for training, but for inference, the amount of memory you can stack this way and the bandwidth it can provide leave something to be desired.d-Matrix\u2019s stacked-die architectured-Matrix\u2019s Raptor removes that bottleneck by stacking an AI accelerator on a DRAM die. Instead of stacking memory, d-Matrix stacks memory and compute. Bhoja says this reduces the distance that data must travel to \u201cmicrometers instead of millimeters.\u201d Like building a skyscraper, going vertical makes it possible to do more inside the same physical footprint.Majestic takes the opposite approach. Instead of trying to minimize the length that data must travel between compute and memory, the company is focused on improving the memory interface to accommodate longer wire traces while keeping bandwidth high. Longer wires allow Majestic to connect memory stacks that aren\u2019t directly next to the GPU, removing the space limitation of HBM.\u201cA memory interface has a very short physical distance it can operate over. In the case of HBM, it\u2019s up to 2 or 3 millimeters. You have this shoreline around the periphery, which is the only place where you can put HBM,\u201d says Rabii.Majestic claims its memory interface can transmit bits as far as about a meter. That\u2019s achieved with a proprietary copper link and a memory-aggregator chip that coordinates data. \u201cThe aggregator is the endpoint for the high-speed interface and a way to fan out to many, many commodity DRAM chips,\u201d says Rabii. Because of this, Majestic can support up to 128 terabytes of DRAM memory in a single server rack\u2014a significant increase over Nvidia\u2019s GB300 NVL72 rack, which has about 20 TB of HBM3E.Majestic Labs\u2019 memory-aggregation architecture d-Matrix and Majestic have one thing in common: Instead of HBM, they both use off-the-shelf DRAM. This is the most common type of computer memory in the world; it\u2019s in everything from smartphones to cars. Memory analyst Jim Handy says HBM costs two to three times as much as DRAM. d-Matrix and Majestic chose DRAM in part because of this price advantage. However, the proponents of HBM, which include memory giants like Samsung and SK Hynix, aren\u2019t sitting idle.HBM4, the latest version of HBM memory, is now in production and will be used by Nvidia\u2019s Vera Rubin GPU, which is expected to ship in the second half of 2026. Hoshik Kim, head of memory-systems research at SK Hynix, says HBM4 \u201cwill decisively break the memory bottlenecks constraining AI inference today\u201d by doubling HBM\u2019s maximum memory bandwidth and increasing the amount of HBM memory per stack.Combining chips for faster inferenceThe big players\u2014Nvidia and Amazon\u2014are going for an all-chips-on-deck approach. Nvidia\u2019s GPUs and Amazon\u2019s Trainium training accelerators are still great for part of the inference workload: the prefill stage, where all the context keys and values are calculated. But to accelerate decode, the part where new tokens are generated, they are looking to new, memory-centric architectures from smaller players.In Nvidia\u2019s case, the smaller player was Groq (not to be confused with Grok, the family of LLMs trained by SpaceXAI). Nvidia purchased intellectual property and hired talent from Groq at the end of 2025, and just three months later at the Nvidia\u2019s GTC 2026 conference, Jensen Huang unveiled the Nvidia Groq 3 language-processing unit (LPU). Groq\u2019s architecture relies on memory\u2014in its case, SRAM\u2014built directly into the chip\u2019s architecture.Unless you\u2019re a chip architect, or a hardcore PC gamer, you probably never give SRAM a thought. SRAM has the benefit of being tightly integrated into a compute chip\u2019s architecture\u2014it\u2019s on the same piece of silicon as the processor\u2014and has the drawback of being less dense and more expensive than DRAM. Most chips include only a few dozen megabytes of SRAM. AI inference, however, has ignited new interest in SRAM as a means of bringing the model weights stored in memory closer to compute.Ian Buck, vice-president and general manager of hyperscale and high-performance computing at Nvidia, says the LPU has a much different set of priorities than the company\u2019s GPUs. The LPU has far less raw computing power than a standard GPU, but it gains 500 megabytes of on-die SRAM connected directly to its floating-point math units. \u201cThe benefit is the memory bandwidth. The LPU has seven times the memory bandwidth of the GPU,\u201d he says.Between the Rubin GPU and the Groq LPU, prefill and decode can both be accelerated to get the best of both worlds, the theory goes. \u201cWe do all the attention math and context processing on the Vera Rubin [GPU] rack,\u201d explains Buck. \u201cFor all the expert calculations\u2026the matrix multiplications, we do that part on the LPU.\u201d The company packs 256 LPUs into the Groq 3 LPX, a system the size of a data-center rack.Nvidia\u2019s two-chip approach to inferenceAmazon Web Services (AWS), for its part, struck a deal with Cerebras, to pair the Trainium accelerator with Cerebras\u2019s Wafer-Scale Engine 3 (WSE-3). Cerebras takes a similar approach to Groq, though at a much larger scale. WSE-3 turns an entire silicon wafer into a single chip that contains over 4 trillion transistors. The design doesn\u2019t connect to external memory but instead etches 44 gigabytes of SRAM into each wafer. \u201cWe store the [model] weights on the SRAM,\u201d says James Wang, formerly director of product marketing at Cerebras who has since moved to SpaceXAI. \u201cSo that\u2019s easily 40 to up to 80 billion parameters that we can support on one chip.\u201dAmazon plans to use AWS Trainium chips for prefill, and Cerebras for decode. But Cerebras\u2019s chips can also go it alone in inference. WSE-3 was deployed by OpenAI to power GPT-5.3-Codex-Spark, a variant of the company\u2019s coding mode, outputting over 1,000 tokens per second. For comparison, OpenAI\u2019s standard GPT-5.4 deployment outputs 50 to 125 tokens per second.Amazon Web Services\u2019 two-chip inference strategy\u00a0 Cerebras can also tackle prefill without moving the workload to different specialized chips. For this, it networks together multiple WSE-3 chips to form a single pool of memory. Cerebras has demonstrated it can serve models with up to 1T parameters, such as Moonshot AI\u2019s Kimi 2.6, though Wang says \u201cthe architecture has no innate limitation in terms of how many parameters it will do.\u201dDespite these differences in strategy, Nvidia and AWS seem to agree that the future of AI inference will be solved by a systems approach that pools different kinds of chips together to tackle the largest LLMs. Or, as Buck says: \u201cTo do modern AI inference, you need all the chips.\u201dLearning to do more with less (bits)Nvidia became the world\u2019s most valuable tech company because it designed the world\u2019s most desired GPUs. But not all of the attention is focused on improving AI-inference hardware. AI researchers are also learning how to optimize LLM software and hardware in tandem to make the best use of the memory and compute components.Most computers store numbers in a 32-bit or 64-bit format. These determine how many bits are available to represent a single number. If too few bits are available, the number can\u2019t be stored without losing information. The quality of an LLM benefits from more-precise number formats, but this creates a problem for inference performance. More-precise numbers aren\u2019t free. The bits that describe them take up more space in memory and require more silicon and energy to compute.Gilles Backhus, cofounder of the AI-accelerator company Tensordyne, says this creates a tension between model size and number precision. \u201cWould you prefer a model that is size x but runs in 8-bit, or would you prefer a model that is twice the size but runs in 4-bit?\u201d The size of each model will be roughly the same in terms of memory and compute, \u201cbut the 4-bit approach gives you twice as many synapses, if you will. And people are figuring out that [the 4-bit approach] is worth it.\u201dThe process of converting an LLM from a more-precise number format to a less-precise format is called quantization, and it\u2019s been in use for several years. However, researchers are finding new ways to quantize models down while retaining a large majority of the model\u2019s quality.Nvidia recently created a new 4-bit number format, NVFP4, for this purpose. AMD, Intel, and Qualcomm have instead rallied around a competing 4-bit number format called MXFP4 that Nvidia also contributed to developing. \u201cIt\u2019s the black art of AI,\u201d says Buck, of Nvidia. When Nvidia quantized DeepSeek-R1 from FP8 to NVFP4, scores on seven major benchmarks degraded by less than one percent while performance improved by three times, the company says.Quantization is likely just the tip of the spear, as AI researchers and startups are investigating a diversity of opportunities for optimization, some of which could dramatically change the silicon found in AI-inference hardware.  Tensordyne\u2019s unique approach to AI inference combines a logarithmic number format with bespoke hardware in the company\u2019s Napier chip. TensordyneTensordyne is expected to accelerate AI inference with a logarithmic number system that leans on a property of logarithms: The log of A times B equals the log of A plus the log of B. So, storing numbers as their exponents lets the chip add where it would otherwise multiply. That matters in silicon because multiplier circuits draw more power and use more die area than adders do. Tensordyne says its rack-scale hardware, called Napier, can produce up to 1,300 tokens per second per user, and can do so while using less than a tenth as much power as comparable Nvidia hardware.Etched, a startup based in San Jose, Calif., is even designing AI accelerators that translate the transformer architecture used by LLMs directly into silicon. Rather than building general-purpose GPUs, the company is wiring up the connections needed for efficient transformer calculations into its chip, making the chip much less flexible but more efficient for the tasks most performed by current LLMs. The company says its first AI accelerator, Sohu, can run Meta\u2019s Llama 70B model at a stunning 500,000 tokens per second, though this approach also means it won\u2019t be able to run LLMs that move away from a typical transformer architecture.Whether these ideas will prove fruitful remains to be seen. Etched just shipped their first rack in August. Tensordyne believes its first hardware will be available in 2027. Even so, these startups show how the demand for inference performance is fueling unconventional ideas.Inference is everyone\u2019s gameThe sheer variety of approaches to AI-inference acceleration\u2014stacking compute on memory, extending interfaces from millimeters to meters, using an entire silicon wafer for SRAM, squeezing models into 4 bits\u2014raises a question: Which is going to win, and which is going to lose?But that\u2019s likely not the right question, experts say. The demand for AI is currently insatiable, and while fears of an AI bubble stalk the industry, it has yet to hamper growth.On the contrary, Kimball of Moor Insights &amp; Strategy thinks inference could drive intense demand for AI hardware in the long term, because it\u2019s not obvious where that demand will end. \u201cYou could add a million agents into your organization,\u201d he says. \u201cThese things work 24 hours a day; they don\u2019t go home at five at night like we do.\u201dIf AI inference remains as desirable as Kimball expects, the evolution is likely to follow the same trajectory as the CPU. The CPU didn\u2019t improve along a single axis but instead across multiple fronts simultaneously. Once transistor scaling slowed, chip and system architecture innovations of all kinds proliferated. The list of individual innovations that led to today\u2019s ubiquitous, powerful personal compute could fill dozens of books.A few decades from now, the history of AI inference innovation will show similar depth.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/elon-musk-tesla\" target=\"_blank\" rel=\" noopener\" title=\"The Mind-bending Joyrides That Gave Rise to Tesla\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/a-collage-shows-four-men-and-two-sports-cars-one-red-and-one-yellow.jpg?id=67759786&amp;width=980\" title=\"The Mind-bending Joyrides That Gave Rise to Tesla\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/elon-musk-tesla\" target=\"_blank\" rel=\" noopener\">The Mind-bending Joyrides That Gave Rise to Tesla<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Charles J. Murray<\/a> on 15. Septembra 2026. at 12:13 <\/small><p>In 2003, Martin Eberhard, a cofounder of Tesla Motors, decided it was time to start wooing investors. To do that, however, he needed an electric car. So he talked to Alan Cocconi and asked if he could borrow the tZero, the revolutionary and blazingly fast electric roadster that Cocconi had built at AC Propulsion.Eberhard\u2019s idea was to drive the tZero up and down Sand Hill Road in the heart of Silicon Valley and do demonstrations for curious entrepreneurs and VCs. Eberhard was joined by Tom Gage, Cocconi\u2019s partner at AC Propulsion, on many of the visits. Like Tesla, AC Propulsion was also seeking investors, but to build a considerably more utilitarian EV.Adapted with permission from The EV Guys: How Caltech Engineers Reinvented the Electric Car, by Charles J. Murray, published by Purdue University Press.In December 2003, Eberhard also proposed a demo at Buck\u2019s of Woodside, a popular restaurant frequented by tech entrepreneurs. At 5 o\u2019clock on any evening, Buck\u2019s probably had more VCs per square foot than any building in the country. Eberhard\u2019s plan was to \u201cshow off what a real electric sports car can do,\u201d he wrote in an email to Buck\u2019s owner, Jamis MacNiven. MacNiven happily obliged.In some ways, the tZero was a hit. When a VC would ride shotgun in the car with Eberhard at the wheel, Eberhard would implore them to touch the dashboard. As they reached forward, he\u2019d punch the accelerator. As the car accelerated and the g forces piled up, the VC was literally unable to touch the dashboard. That was how powerful the tZero\u2019s acceleration was, Eberhard would say.Many of the VCs were astounded. Some even questioned whether the car was really electric. Many owned Ferraris or Lamborghinis. They knew sports cars\u2014but this? They could never have imagined it was possible to do this with an electric drivetrain.  On December 13, 2003, Martin Eberhard brought AC Propulsion\u2019s tZero electric roadster [yellow] to Buck\u2019s of Woodside, a popular hangout for entrepreneurs and venture capitalists. Next to the tZero is a Scion xB, which AC Propulsion\u2019s principals thought they could turn into a mass-market electric vehicle.Martin EberhardStill, the demo at Buck\u2019s garnered little investor interest\u2014with one exception. Google cofounders Sergey Brin and Larry Page were both at Buck\u2019s that day, and they told Gage they knew an individual whose funds were liquid, as he\u2019d recently sold his stake in a startup. What\u2019s more, this individual liked fast cars.The man\u2019s name was Elon Musk.Elon Musk, Meet the tZeroIn 2004, it wasn\u2019t apparent to anyone that Elon Musk had a future in the auto industry. He was notable for cofounding PayPal, which he then sold to eBay in 2002 for a whopping US $1.5 billion. He had already launched Space Exploration Technologies Corp., or SpaceX, with the stated goal of paving the way to a sustainable colony on Mars. Musk did love fast cars. He owned a million-dollar silver McLaren F1, one of only 64 road-going F1s in the world, as well as a 400-horsepower BMW M5 sports car and a 1967 XK-E Series 1 Jaguar roadster. But he\u2019d never expressed an interest in building cars or starting an auto company, at least not publicly.  Elon Musk was photographed in 2008 at Tesla\u2019s headquarters, then in San Carlos, Calif., around the time when Tesla\u2019s Roadster was being delivered to its first customers.Patrick Tehan\/MediaNews Group\/Bay Area News\/Getty ImagesStill, Musk\u2019s affinity for fast cars made it almost impossible for him to ignore an email from Gage on 21 January 2004. \u201cSergey Brin and JB Straubel both suggested you might be interested in driving our tZero electric sports car,\u201d Gage wrote. (Straubel was the young Stanford engineering graduate who would later serve as Tesla\u2019s chief technology officer.) \u201cThe tZero goes quite well,\u201d the email continued. \u201cWe ran it against a Viper last Monday and it won four of five sprints on a 1\/8th of a mile track. I lost one because I was carrying a 300-pound cameraman. Do you have time for me to bring it by?\u201dMusk quickly replied. \u201cSure, I would really enjoy seeing it. Don\u2019t think it could beat my McLaren (yet) though I\u2019m in town Feb 2nd through 4th.\u201d \u201cHmm, a McLaren, boy that would be a feather in my cap,\u201d Gage wrote back. \u201cI can have it there on Feb. 4.\u201d  Two of the principals behind AC Propulsion were businessman Tom Gage, left, and engineering genius Alan Cocconi.Left: Tom Gage; Right: Alec BrooksThe emails marked the beginning of Musk\u2019s involvement in electric cars and in the auto industry. Gage drove the car to SpaceX headquarters, a warehouse in El Segundo, Calif., about 30 kilometers southwest of Los Angeles. In Musk\u2019s cubicle in the \u201coffice\u201d portion of the warehouse, Gage made his pitch.There was a void in the market, he said. GM had abandoned the EV1. Toyota, Honda, Ford, and Chrysler were shutting down their electric car programs. California\u2019s zero-emission vehicle (ZEV) rules, which mandated the sale of increasing numbers of vehicles with no tailpipe emissions, had been plundered. But electric vehicle technology, he said, was getting a bad rap. Here was the tZero, an electric car that could take off like a jet. The tZero proved that the technology was readily available. He and Cocconi wanted to use that technology to make an electric car that was useful and practical: the eBox, an electrified Toyota Scion.  After building the tZero roadster, AC Propulsion\u2019s principals pinned their hopes on an electrified version of the Toyota Scion they called the \u201ceBox.\u201d It did not appeal to Elon Musk.Jeff Chiu\/APFor Musk, Gage\u2019s introduction of the eBox was unexpected. He was meeting with Gage because he was interested in the tZero. It was the car\u2019s performance that appealed to Musk.He wasn\u2019t interested in the eBox. He then drove the tZero and offered to buy it.  The lithium-ion version of the tZero electric roadster could get 515 kilometers on a charge and go from zero to 97 km\/hr (60 miles per hour) in 3.6 seconds. Only three tZeros were built and only two survive. Scott SorbeGage told him it wasn\u2019t for sale. Undeterred, Musk offered a quarter million dollars if AC Propulsion would squeeze its lithium-ion battery pack into his Porsche. Gage declined again. AC Propulsion needed money to electrify the Toyota Scion, Gage said.Musk shook his head. The idea seemed incredible to him. \u201cWho wants to take an ugly $20,000 car and buy it for $65,000?\u201d he asked incredulously, as he later recalled during an interview with Vanity Fair magazine. \u201cI wouldn\u2019t want to drive it. My wife certainly wouldn\u2019t want to drive it.\u201dMany years later, Musk would tell his biographer Walter Isaacson, \u201cNobody is going to pay anything near that for something that looks like crap.\u201d Musk believed that the way to start a car company was to build high-priced cars first and then let the technology trickle down to the mainstream. It was a classic Silicon Valley approach.  Alan Cocconi, the engineering whiz behind AC Propulsion, stands next to the company\u2019s legendary tZero electric roadster in a picture taken in the early 2000s. The small yellow wheeled pod on the other side of the car is a trailer with a small gasoline engine that, when connected to the tZero, turned it into a hybrid gas-electric vehicle.Martin EberhardIn Musk\u2019s mind, it was all very obvious. He liked fast cars. He liked the tZero and believed it \u201ccould change the world.\u201d He couldn\u2019t even imagine why Gage was sitting here trying to sell him on the idea of the eBox. \u201cGage and Cocconi were sort of madcap inventors,\u201d he told Isaacson. \u201cCommon sense was not their strong suit.\u201dGage concluded that he wasn\u2019t going to convince Musk to invest in AC Propulsion. \u201cWell, if you want to do a sports car, then you should talk to Martin Eberhard,\u201d Gage said. A few weeks later, Gage sent an email to Eberhard introducing him to Musk. \u201cElon Musk heads up SpaceX, is a car enthusiast,\u201d Gage wrote. \u201cHe would be interested in hearing about your activities at Tesla Motors.\u201dElon Musk, Meet Tesla MotorsAs it happens, Eberhard and Marc Tarpenning, Eberhard\u2019s partner and cofounder at Tesla, had considered contacting Musk even before Gage\u2019s email arrived. They\u2019d known of Musk and appreciated the way he thought. A few years earlier, they saw him speak at a Mars Society conference at Stanford University. Musk had talked about the rather improbable idea of sending mice to Mars. The presentation gave them a window into Musk\u2019s unconventional approach to high-tech entrepreneurism and to life in general.Eberhard and Musk agreed to meet, and then Eberhard emailed Gage. \u201cAny chance of my borrowing the car for next week?\u201d he wrote. Gage, of course, complied.  Martin Eberhard posed next to an electric motor at Tesla\u2019s San Carlos, Calif., headquarters in 2006. Paul Sakuma\/APBy this time, Tesla was nine months old. It still had just three employees\u2014Eberhard, Tarpenning, and Ian Wright, a New Zealand-born engineer and neighbor of Eberhard\u2019s. The founders were arranging to pay the licensing fee on AC Propulsion\u2019s drivetrain technology. And they were making arrangements to build their first cars using the chassis of the Lotus Elise two-seat roadster. They estimated they needed $6.5 million to go further. And that\u2019s where Musk came in.  The original Tesla Roadster prototype, or \u201cMule,\u201d was built inside the chassis of a 2002 Lotus Elise. Dylan Stewart\/Image of Sport\/Sipa\/AlamyEberhard and Wright flew to Los Angeles on a Friday and met Musk in his cubicle at SpaceX. The meeting was supposed to last a half hour, but Musk\u2019s questions came virtually nonstop, and as the meeting progressed, he repeatedly shouted to his assistant to cancel his next meeting.Over the following weekend, Musk called Tarpenning to get his input about their financial model. \u201cI just remember responding, responding, and responding,\u201d Tarpenning said, according to a 2015 book by Ashlee Vance.The Tesla founders were all impressed with Musk. He was unlike any of the VCs they\u2019d met with in the previous months. He was technically astute. He\u2019d earned a bachelor\u2019s degree in physics from the University of Pennsylvania, and in his two-day stint as a Ph.D. student at Stanford, he\u2019d intended to do a dissertation on solid-state capacitors for use in electric cars.Moreover, he wasn\u2019t averse to risk\u2014at least not intelligent risk. He loved technical challenges, and he loved proving that the impossible was possible. \u201cYou\u2019re presenting an electric car company to this person on the other side of the table, and he\u2019s doing something even crazier,\u201d Tarpenning said later. \u201cHe\u2019s building rocket ships.\u201dOn the Monday after their first meeting at SpaceX, Eberhard and Tarpenning flew back to Los Angeles. Musk agreed to invest $6.35 million. He would become the biggest shareholder as well as chairman of the company.Now, Tesla Motors was really in business. All it needed was someone to design and build a groundbreaking electric car.\u201cAll Electric Cars Have Sucked\u201dNo one at AC Propulsion believed that Tesla Motors had even a remote chance of success. The whole idea\u2014building and selling electric vehicles and competing against the giants in Detroit, Japan, and Germany\u2014seemed impossible. Even Toyota, which was having so much success with the hybrid Prius, was not planning to build pure electric cars.The prospect of starting any kind of auto company was unbelievably daunting. Automotive startups had been the undoing of many ambitious entrepreneurs, including Henry Kaiser, Preston Tucker, and John DeLorean. Such endeavors required mountains of money, connections, and expertise. There were unseen obstacles around every corner. And the people who\u2019d launched Tesla, as smart as they were, were almost certainly unprepared for what lay ahead.Years later, Musk would contend that their struggles were caused by the fact that Tesla had been founded on \u201ctwo false premises.\u201d The first was that Tesla\u2019s founders believed they could simply convert an existing gasoline sports car to electric. The second was that they could use the existing AC Propulsion technology with little or no modification. \u201cThat turned out to be, in retrospect, staggeringly dumb,\u201d Musk said.  In April or May of 2004, Tesla engineers worked on an early test vehicle, or \u201cmule,\u201d of the Roadster. The group included (clockwise from upper left) mechanical engineer Gene Berdechevsky, in the pink shirt, electrical engineer Phil Cole, and mechanical engineer Dave Lyons, in the dark blue shirt.Martin EberhardHe also later concluded that their early path almost doomed them. \u201cIt ended up being much worse than if we had designed the car from scratch,\u201d he said. But Tesla decided it could not go back and start over. It could only deal with the problem at hand.Eberhard and Tarpenning were terrified of going into production with the existing analog motor controller and drivetrain electronics, which were unreliable and jittery. If those problems weren\u2019t fixed, they knew their new vehicle would fail, and so would the company.Another looming issue was the safety of the Tesla lithium-ion battery pack. To test it, Eberhard brought the engineering team to his home, where they dug a pit in his backyard. They took a brick of cells, covered it with a sheet of Plexiglass, and then remotely heated one of the cells with an electrical wire. As they expected, the heated cell burst into flame, setting neighboring cells on fire. The cells went off one at a time\u2014pop, pop, pop. \u201cWe had a conflagration,\u201d Eberhard said. \u201cOne cell caught fire, and it blasted right through the pack.\u201dBuyers of sports cars were known to be forgiving. In their quest for performance, they could put up with poor reliability. But the fire hazard was another matter, and one that had the potential to take down the company. Eberhard took the news right to Tesla\u2019s board of directors. \u201cIt was my first big oh-shit moment to my board,\u201d he said. \u201cI told them we\u2019ll have a day-to-day schedule stop until we figure this out.\u201dWorking with friends from his Stanford days, Straubel began developing a new pack in his garage. The team acquired 7,000 lithium-ion cells from LG Chem, then constructed battery bricks, each with 69 cells, and tested them with different kinds of liquid-cooling systems. By October 2004, they\u2019d finished a prototype pack and used a crane to lower it into the back of a Lotus Elise sportscar.A few months later\u2014in January 2005\u2014the team had completed a working prototype of that first car. At the end of the month, they showed it off at a board meeting, and Musk took it for a spin. Impressed by its performance, he invested $9 million more, and Tesla completed a $13 million round of funding. Now the vehicle had a name\u2014Roadster\u2014and a tentative production schedule. The plan was to begin delivering it to customers in early 2006.Tesla\u2019s struggles with the Roadster were not apparent to the outside world, especially to those invited to the reveal of the Roadster at the Santa Monica Airport in July 2006. By then, the yellow test car, or \u201cmule,\u201d had evolved into two prototypes: a red car and a black car, both of which would be available for drives at the event.  Tesla unveiled the Roadster, its first vehicle, at the Santa Monica, Calif., airport on 19 July, 2006.Glenn Koenig\/Los Angeles Times\/Getty ImagesThe prototypes were more advanced than the mule, with more of a production-type design. But Musk and the team didn\u2019t know what to expect at the reveal. The company was just coming out of stealth mode, and at that point, Tesla had received no media coverage. It had no customers, no deposits, and no sales team.Still, Musk planned a huge party for the unveiling\u2014an \u201cawesome event,\u201d in his words, staged inside the airport\u2019s Barker Hangar. He told his personal assistant to invite 350 guests, including Michael Eisner of Disney, movie producer Richard Donner, actor Ed Begley Jr., California Governor Arnold Schwarzenegger, and many other luminaries. All were told to bring their checkbooks in case they wanted to write a $100,000 check to put a deposit on an electric Roadster. Meanwhile, a Roadster prototype zipped around a makeshift road inside the hangar, out the door, down a runway, and back inside again.Musk took center stage, telling the audience that they were witnessing the start of a new era in automotive technology, according to CNET\u2019s coverage of the event. \u201cUntil today, all electric cars have sucked,\u201d he told the audience. \u201cElectric cars play into the strength of Silicon Valley. A lot of the things inside the car are conventional automobile technology. The magic is the battery technology and the software and the controllers.\u201dTesla Hooks Arnold Schwarzenegger, George ClooneyMusk\u2019s message was perfect for such an event, especially for the dozens of reporters who were there to publicize the reemergence of the electric car. They adored the Roadster. It was small, powerful, electric, and above all, cool. It was anti-Detroit\u2014a new kind of car that burned no gasoline and was born in Silicon Valley instead of an antiquated factory in Michigan.The night was also a financial success for Tesla, with twenty $100,000 checks gathered from prospective buyers. And the momentum continued. A few days after the event, Joe Francis, creator of the adult entertainment franchise Girls Gone Wild, sent an armored truck to Tesla\u2019s San Carlos office to drop off $100,000 in cash. A few days after that, Schwarzenegger put his money down, as did actor George Clooney. Within two weeks, Tesla had presold 127 Roadsters.  California Governor Arnold Schwarzenegger was among the first buyers of the Tesla Roadster on the day the car was officially unveiled, 19 July, 2006.Glenn Koenig\/Los Angeles Times\/Getty ImagesMeanwhile, though, the company\u2019s manufacturing woes continued. The mechanical problems weren\u2019t even the biggest issue. The biggest issue was the supply chain. This was ironic, because some in the media admired Tesla for its global approach. They liked the fact that the battery pack, the motor, the chassis, and the assembly had an international flavor. It was a world car, they thought.But for Tesla, it was a nightmare. The battery pack was being assembled in Thailand by a manufacturer of barbecue grills. The facility was 3 hours from Bangkok, literally in a jungle where the heat was almost unbearable, and the factory building consisted of a truss roof held up by some steel columns. There were no walls because no one there wanted to work indoors.And because the pack assembler was inexperienced, Tesla engineers were repeatedly flying to and from Thailand to direct the effort. They would find animal droppings on the battery packs, which were sitting out in the open air all day and all night.For the umpteenth time, Musk wondered if the company would be able to survive. \u201cWe\u2019re doomed if we don\u2019t in-source the battery pack,\u201d he told one of Tesla\u2019s manufacturing engineers, \u201cbecause we have a supplier in Thailand who is great at making barbecues but not great at battery packs. And the supply chain is so long that it takes six months from when the cells are built to when the battery pack is done and in a car. So that means the capital cost is gigantic because we have to pay for all that inventory and process. And inevitably, there are mistakes in the design or fabrication of the battery pack, and then we have six months\u2019 worth of battery packs that don\u2019t work.\u201dNever mind that this chaotic approach was central to their plan. Tesla had never been envisioned as an old-fashioned, Detroit-style, vertically integrated manufacturer. From the beginning, it had been a Silicon Valley\u2013type enterprise that would rely on others for the bulk of its manufacturing. Only now, as the fledgling company sent batteries and motors and assembled cars back and forth across two oceans, was its plan beginning to appear untenable. \u201cWe had this misguided idea that everything must be cheaper and better if built in Asia,\u201d Straubel later said.Tesla\u2019s Chances of Success: 10 PercentFrom the beginning, Musk had never been optimistic about Tesla\u2019s chance of success. He repeatedly said he thought it was approximately 10 percent. \u201cIn 2004, the idea of starting a car company was extremely stupid,\u201d he said. \u201cThe idea of starting an electric car company was stupid squared.\u201d As he watched Tesla struggle with its supply chain, his earlier words were starting to look prescient.The only chance, the engineering team concluded, was to bring the manufacturing of all of the subsystems, such as the battery packs, motors, and inverters, in-house. They disassembled their overseas operations and moved them to California, starting with battery pack manufacturing. Assembly stations were loaded into huge shipping containers, transported back to one of the company\u2019s new facilities on Bing Street in San Carlos, and then reassembled there. It took five and a half months. They also redesigned the battery packs and developed machines for automating their assembly.  In 2008, with mass production of the Tesla Roadster just getting underway, Elon Musk gave an interview at the company\u2019s headquarters, then in San Carlos in northern California. At the time, Tesla was merely a startup in a precarious position, bleeding cash and grappling with many manufacturing problems.Ryan Anson\/Bloomberg\/Getty ImagesMusk concluded that the key to success was not the design of the car itself but rather the manufacturing. Henry Ford had reached the same conclusion a hundred years earlier. It was \u201cthe realization of how important it is to build the machine that builds the machine,\u201d Musk said at the Tesla Annual Shareholder Meeting in 2016. \u201cAnd how much harder it is to build the manufacturing system that builds the product, than it is to create the product in the first place. You can create a demo version of a product\u2026with a small team in maybe three to six months. But to build the machine that builds the machine takes at least a hundred to a thousand times more resources and difficulty.\u201dGradually, Tesla\u2019s idea of letting others do its manufacturing slipped away. Packs were built in San Carlos, and then installed in the Lotus Elise chassis there instead of in England.\u201cWe had control now,\u201d said manufacturing engineer Jason Mendez. \u201cWe had all the engineers there. We didn\u2019t have batteries on the water, not from Thailand to England and not from England to here.\u201dMusk began to talk about a new vision. He called it the gigafactory. Raw materials would enter at one end, and a car would exit at the other end. This was the ultimate in vertical integration, and it sounded a lot like Henry Ford\u2019s vision for the River Rouge plant in Dearborn, Mich., in 1917.Tesla Motors was becoming an auto manufacturer.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/countries-seek-to-curb-social-media-addiction-for-kids\" target=\"_blank\" rel=\" noopener\" title=\"Countries Seek to Curb Social Media Addiction for Kids\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/students-in-red-uniforms-sit-outside-looking-at-smartphones.jpg?id=67749623&amp;width=980\" title=\"Countries Seek to Curb Social Media Addiction for Kids\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/countries-seek-to-curb-social-media-addiction-for-kids\" target=\"_blank\" rel=\" noopener\">Countries Seek to Curb Social Media Addiction for Kids<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">San Murugesan<\/a> on 14. Septembra 2026. at 18:00 <\/small><p>Social media plays a significant, multifaceted role in adolescents\u2019 development, influencing how they communicate, learn, socialize, and express themselves.The benefits, however, are accompanied by risks that can undermine youngsters\u2019 character as well as their cognitive and social development.The potential problems include excessive screen time, social media addiction, cyberbullying, misinformation, radicalization, privacy violations, exposure to inappropriate content, sextortion, and doomscrolling.A recent study published in Nature: Human Behaviour found that adolescents who begin using social media at an early age tend to have significantly lower academic performance. A Mashable article highlights additional issues including effects on mental health, self-harm, addiction to social media, compulsive, repetitive checking, and exposure to pornography and violent material.Protecting minors has largely fallen to parents, schools, and self-regulation by some social media providers.But that approach has proven ineffective and inadequate, so some governments and policymakers have stepped in and placed responsibility on social media providers.Australia\u2019s nationwide banAustralia was the first country to legislate a nationwide social media ban on children younger than 16\u2014which I wrote about in January for Communications of the ACM.Enacted in December, the ban initially applied to 10 platforms: Facebook, Instagram, Kick, Reddit, Snapchat, Threads, TikTok, Twitch, X, and YouTube. It excluded messaging, gaming, and nonsocial platforms including Discord, GitHub, Roblox, WhatsApp, YouTube Kids, and educational tools.The law places the responsibility for enforcement on the platform providers through age-assurance mechanisms, requiring the platforms to take \u201creasonable steps\u201d to prevent those 15 or younger from creating or holding accounts.It does not, however, apply to content consumption. Children can view publicly available posts and videos without logging in; they cannot comment or post, according to the law.The legislation mandates that the user\u2019s age be verified with tools such as government-issued identification, biometric or facial age-estimation tools, behavioral or inference algorithms, and self-declaration with optional checks.Penalties for noncompliance can reach US $35.6 million.The 10 platforms subsequently removed nearly 5 million accounts of young users.Although the ban received widespread support, human rights organizations and digital freedom advisory groups have argued that it limits young people\u2019s freedom of expression and access to useful information. They say the ban might contribute to social isolation and the loss of support networks, particularly among marginalized youth.Promising early outcomesThe Australian ban is producing positive outcomes, according to a recent Time magazine article, \u201cWhat the World Should Learn From Australia\u2019s Social Media Law.\u201dEarly findings indicate it has reduced account ownership and social media use among young children. A YouGov survey of Australians found that 61 percent of parents of children age 16 and younger reported positive changes including more face-to-face interaction, greater presence and engagement, and improved parent-child relationships. Three in five Australians surveyed called the ban effective.The ban has encouraged social media platforms to reconsider their features. Snapchat is moving toward a friends-only experience for 13- to 15-year-olds, for example.The law is stimulating the development of purpose-built online spaces for children younger than 16 that can support their developmental needs, offering alternatives to mainstream social media.The longer-term impact could be more significant if \u201cno social media account before age 16\u201d becomes an accepted norm, making it easier for parents and schools to support delayed social media use.Implementation strugglesDespite the early encouraging outcomes, one study found that online platforms struggle to implement age checks. Many under-16 users in Australia have continued to access platforms with little difficulty, the study said. They children have found workarounds to subvert restrictions, such as using a free VPN to bypass age checks\u2014some of which have questionable data-collection practices.Seven in 10 children retained their existing accounts on restricted platforms, the study found. Other teens created new accounts using incorrect age information. Some were incentivized to seek unregulated offshore platforms not subject to Australia\u2019s law.The workarounds prompted Australia to double the maximum fine and warn of court action against tech giants for noncompliance.Emphasis on age verificationA number of other countries are implementing or considering social media restrictions. They include Brazil, Canada, France, Greece, Indonesia, Norway, Poland, Thailand, T\u00fcrkiye, and the United Kingdom. The European Union is contemplating its own restrictions. The countries\u2019 mandates for age verification or age assurance shift the policy focus from whether to verify age to how to do so effectively while protecting user privacy.An article on think tank New America\u2019s website, \u201cAge Assurance and Verification,\u201d describes some methods: Age gating and screening. Users self-attest their age by checking a box or inputting a birth date.Age estimation. Several techniques are available, including profiling the user\u2019s online activity and scanning the user\u2019s face.Age verification. One way is providing a government-issued identification document. Other approaches include digital identity systems, digital wallets, and third-party verification.Reliable age verification is technically challenging and raises privacy concerns, as outlined in \u201cThe Age-Verification Trap,\u201d written by Cinderpoint consultant Waydell D. Carvalho and published in February in IEEE Spectrum. Carvallo says platforms need to balance age verification with protecting users\u2019 personal information.IEEE\u2019s contributionsIEEE is working on initiatives to provide a safer online environment for children. To help developers build age-appropriate social media platforms and websites, the IEEE Standards Association (IEEE SA) has published two guidelines.The IEEE Standard for Online Age Verification (IEEE 2089.1-2024) provides a framework for designing, specifying, evaluating, and deploying verification systems. The standard includes requirements for privacy protection, data security, and information management specific to the age-assurance process. It also provides procedures for verifying a user\u2019s age or age range with a high degree of accuracy.Based on the 5Rights Foundation\u2019s Principles for Children, the other standard (IEEE 2089-2021) provides practical steps to qualify online products and services for children. It requires systems to present information in an age-appropriate way and to uphold the rights established for youngsters in the U.N. Convention on the Rights of the Child.IEEE SA also offers an online age-verification-certification program, which assesses systems for compliance with the IEEE 2089.1 standard. The program certifies that organizations implement robust processes before granting access to age-restricted products and services, prioritizing children\u2019s safety, privacy, autonomy, and rights.As outlined in The Institute article \u201cIEEE Makes Strides to Improve Online Safety for Kids,\u201d certification is based on six key indicators: accuracy, frequency of assurance, counter-fraud measures, authenticity, frequency of authenticity checks, and birth date confidence.Indonesia used key provisions from the two IEEE guidelines to inform its child-protection regulation, which was signed into law last year.IEEE\u2019s ethically aligned design framework prioritizes human well-being, transparency, accountability, privacy, and protecting vulnerable populations including children.Calls for platform reformsAlthough social media bans would be globally significant policy responses, deeper structural issues remain largely unaddressed. Platform architecture and features contribute to social media harm.The focus needs to shift from constraints on account provisioning and content moderation to safer platform design.Meta in August agreed to pay $17.1 billion to settle a lawsuit brought by U.S. states. The suit said Meta designed its social media to be addictive to children, and the company concealed internal research showing Instagram\u2019s addictive effects on teenagers. As part of the settlement, Meta agreed to implement child-safety measures such as setting daily time limits and disabling Facebook and Instagram push notifications during school hours.The company still faces other lawsuits that could have far-reaching implications, pressuring other tech companies to design safer social media platforms.Architecture-driven features such as infinite scrolling, algorithmic recommendations, addictive platform design, data-driven engagement, and personalized advertising to minors are other contributing factors to social media addiction.IEEE Senior Member Katina Michael, professor at the University of Sydney business school and founding editor in chief of IEEE Transactions on Technology and Society, shared her perspective: \u201cSocial media bans may offer a short-term response to growing concerns, but they are not a long-term solution,\u201d she says. \u201cIEEE 2089-2021 advocates for socio-technical systems that are designed to promote human well-being, safety, and flourishing. Rather than relying on prohibition alone, we should focus on better design, building digital platforms that embed ethics, accountability, transparency, and human values from the outset.\u201dCollective responsibilityProtecting children online would require a combination of policy measures, improved platform design, digital literacy, parental involvement, and cultural change.Building a safe, secure, and inclusive digital ecosystem that supports adolescents\u2019 cognitive, social, and emotional development would require collaboration among technology companies, platform providers, content creators, parents, educators, policymakers, and young people themselves.Professional organizations such as IEEE can continue contributing through standards development, education, certification while promoting trustworthy and responsible digital technologies.This article was updated on 16 September 2026.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/webinars.on24.com\/wileyevents\/ResponsibleAI\" target=\"_blank\" rel=\" noopener\" title=\"Responsible AI for Higher Education\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/assets.rbl.ms\/67770920\/origin.png\" title=\"Responsible AI for Higher Education\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/webinars.on24.com\/wileyevents\/ResponsibleAI\" target=\"_blank\" rel=\" noopener\">Responsible AI for Higher Education<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/webinars.on24.com\" target=\"_blank\" title=\"webinars.on24.com\">IBM SkillsBuild<\/a> on 14. Septembra 2026. at 14:18 <\/small><p>This interactive webinar will introduce the different types of AI, address the concerns with AI, share how we IBM are approaching Responsible AI, and offer guidance to students about what they can do - as individuals, and members of their IEEE chapters. Participants will also have the opportunity to to apply the Responsible AI approach to a particular use case - IBM Bob, a software development life cycle agent, and Q&amp;A. This will be an interactive session, so have phones ready to engage! Register now for this free webinar!<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/ieee-to-reward-sections\" target=\"_blank\" rel=\" noopener\" title=\"IEEE to Reward Sections for High Voter Turnout in Annual Election\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/illustration-of-a-ballot-box-inside-a-dollar-bill-symbolizing-money-in-elections.jpg?id=67749579&amp;width=980\" title=\"IEEE to Reward Sections for High Voter Turnout in Annual Election\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/ieee-to-reward-sections\" target=\"_blank\" rel=\" noopener\">IEEE to Reward Sections for High Voter Turnout in Annual Election<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Elizabeth Fuscaldo<\/a> on 11. Septembra 2026. at 18:00 <\/small><p>For this year\u2019s IEEE annual election, the IEEE Tellers Committee will recognize the Sections with the highest voter turnout (based on total eligible voters) in each Region with an incentive reward after the election results are accepted by the IEEE Board of Directors.The incentive initiative is managed by the Tellers Committee, which retains full authority over all rules, operations, and decisions regarding the program.Incentive guidelines The Section sizes and their respective reward amounts are:Large sections consist of more than 1,501 eligible voting members. The top-performing large section in each IEEE region will be rewarded with US $1,000.Medium sections consist of 501 to 1,500 eligible voting members. Each region\u2019s top-performing medium section will receive $600.Small sections consist of 500 or fewer eligible voting members. The top-performing small section in each region will receive $400.To learn more about the incentive program, visit the IEEE annual election website. If you haven\u2019t voted in the 2026 elections, you can learn about the candidates and vote here. Send questions to: elections@ieee.org.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/codeveloper-of-ethernet-predecessor-dies\" target=\"_blank\" rel=\" noopener\" title=\"Codeveloper of Ethernet Predecessor Dies at 91\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/black-and-white-photograph-of-a-young-asian-man-in-a-suit-and-tie.jpg?id=67750969&amp;width=980\" title=\"Codeveloper of Ethernet Predecessor Dies at 91\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/codeveloper-of-ethernet-predecessor-dies\" target=\"_blank\" rel=\" noopener\">Codeveloper of Ethernet Predecessor Dies at 91<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Amanda Davis<\/a> on 10. Septembra 2026. at 20:00 <\/small><p>Franklin \u201cFrank\u201d KuoCodeveloper of ALOHAnetFellow, 91; died 14 AprilKuo helped develop ALOHAnet, a pioneering computer system at the University of Hawaii at M\u0101noa, in Honolulu. The system went online in 1971 and represented the first public demonstration of a wireless packet data network. It was an inspiration for Robert Metcalfe\u2019s development of Ethernet a couple of years later. In 2020 ALOHAnet was designated as an IEEE Milestone.Kuo earned bachelor\u2019s, master\u2019s, and doctoral degrees in electrical engineering from the University of Illinois, Urbana-Champaign. After earning his Ph.D. in 1960, he joined Bell Labs in Murray Hill, N.J., where he conducted research in computer communications.After six years at the company, Kuo left to become a professor of electrical engineering at the University of Hawaii. From 1968 to 1971 he and one of his colleagues, IEEE Life Fellow Norman Abramson, developed ALOHAnet. The network connected computers on Hawaiian islands using ultrahigh-frequency radio, transmitting information over radio waves instead of cables.ALOHAnet became the foundation for modern networks. Kuo pioneered the concept of a random-access protocol, or sharing a single channel without central coordination\u2014which led to the packet-switching principles that underpin modern Wi-Fi and mobile networks.Kuo authored or coauthored several books including Computer Communication Networks. Published in 1972, it was one of the earliest textbooks on the subject.He served as director of the university\u2019s Cosine committee, a project funded by the U.S. National Science Foundation to develop computer engineering courses.He took a sabbatical from 1975 to 1977 to work at the U.S. Pentagon as director of information systems in the defense secretary\u2019s office. He oversaw computer communications applications used in command, control, and intelligence programs.During the 1980s and \u201990s, he helped develop China\u2019s Internet. In 1982 he joined SRI International (formerly the Stanford Research Institute), in Menlo Park, Calif., as a researcher. He also was a consulting professor in Stanford\u2019s electrical engineering department and taught computer networking at Shanghai Jiao Tong University.As a UNESCO lecturer in Beijing in 1994, he helped Peking University, Tsinghua University, and the Chinese Academy of Sciences connect to the Internet. He also worked with Tsinghua University to develop CERNET, the country\u2019s first nationwide education and research computer network, which was managed by the Chinese Ministry of Education. For his work, he received an honorary degree from Shanghai Jiao Tong University.In the mid-1990s, Kuo helped found General Wireless Communications, a developer of mobile phone messaging services and games that was renamed Mtone Wireless.Muhammad Rezaul KarimBell Labs researcherLife senior member, 86; died 18 MayKarim was a distinguished member of the technical staff at Bell Labs in Murray Hill, N.J. His work was instrumental in the development of modern cellular communications technology.He joined Bell Labs in 1972 and worked in its mobile telecommunications laboratory as part of the team tasked with creating one of the earliest cellular networks.In 1975 Illinois Bell Telephone petitioned the U.S. Federal Communications Commission to develop and test a cellular system. The FCC, which now regulates radio, TV, telephone, Internet, satellite, and wireless services, authorized the project in March 1977. Karim and his team helped develop key elements of the technology, including the Bell Labs logic that controlled the cellular system, turning the concept into a working one. They also built radio receivers, transmitters, control systems, and cell-site equipment used in the first trial of the cellular system.The following year, Bell Labs and Illinois Bell deployed the Advanced Mobile Phone Service system across Chicago, with its switching office located in Oak Park, Ill. The initial test used approximately 100 mobile phones to work through hardware, software, and system-design problems.A subsequent test in 1979 involved 2,500 mobile users, providing a demonstration of the cellular technology in practice.The trials in Illinois helped establish the technical foundation for the commercial cellular networks that followed.Later in his career, Karim worked on the asynchronous transfer mode (ATM) technique, a high-speed networking technology crucial to the transition from traditional telephone networks to broadband and digital ones.In 2000 he published ATM Networks: Application, Systems, and Design, a textbook that served as a guide for designing and implementing ATM-based services.Karim received a bachelor\u2019s degree in electrical engineering from the Bangladesh University of Engineering and Technology, in Dhaka. He then earned a master\u2019s degree in EE from the University of Manchester, England, and a Ph.D. in EE from Stevens Institute of Technology, in Hoboken, N.J.Harry BosticFormer IEEE Region 4 directorLife senior member, 86; died 18 MarchBostic was an active IEEE volunteer who served as the 1998\u20131999 director of IEEE Region 4. In 2007 he received a lifetime achievement award from the IEEE Central Indiana Section for \u201coutstanding commitment and dedicated service as regional advisor to the volunteers and members of Region 4 and the Institute.\u201dHe was an engineer for 30 years at U.S. Navy\u2019s avionics facility, a research, development, and manufacturing concern in Indianapolis. He worked on flight control, navigation, and weapons systems there. (The facility closed in 1996.)Edwin C. Jones Jr.ProfessorLife Fellow, 91; died 10 MarchJones was widely recognized for his contributions to engineering education, curriculum development, and accreditation through decades of service to IEEE, ABET, and the American Society for Engineering Education.He earned a bachelor\u2019s degree in electrical engineering in 1955 from West Virginia University in Morgantown. The following year he earned a diploma of membership (equivalent to a master\u2019s degree) from Imperial College, London. He went on to serve in the U.S. Army Signal Corps for two years. After his service ended, he studied engineering education at the University of Illinois, Urbana-Champaign, earning a Ph.D. in 1961. Jones then joined the university\u2019s faculty.The following year, he left Illinois to join Iowa State University, in Ames, as an assistant professor. He was promoted to professor in 1995. Two years later he became associate chair of the electrical and computer engineering department and served in that position until 2001, when he retired and was named professor emeritus. In recognition of his commitment to students, Iowa State established a scholarship in his honor.In 2006 he accepted a part-time position as an adjunct professor in Minnesota at the University of St. Thomas, in St. Paul. He advised graduate students and helped develop the university\u2019s systems engineering program.An active IEEE volunteer, he served as 1975\u20131976 president of the IEEE Education Society. He was a member of the IEEE Educational Activities Board, helping strengthen the relationship among engineering education, professional practice, and accreditation organizations. He received an IEEE Centennial Medal in 1984 and the EAB Meritorious Achievement Award in Accreditation Activities in 1986. The IEEE Education Society later named its Meritorious Service Award in his honor.Jones was elected a Fellow of ABET in 1986. During his years of service as a program evaluator and leader, he helped advance the quality of engineering education and accreditation programs. ABET recognized him with its Grinter Distinguished Service Award, its highest honor.Alexander Robert SpitzerClinical neurology researcherLife senior member, 70; died 27 FebruarySpitzer was a neurologist for 40 years at the Wayne State University School of Medicine, in Detroit, where he also was a director of the electromyography laboratory at Harper University Hospital. The lab studied patients\u2019 brain and spinal cord activity in response to sensory stimuli. The evaluations assessed nerve pathway integrity to help diagnose multiple sclerosis, spinal cord injuries, and other conditions.After earning his medical degree from the Einstein College of Medicine, in New York City, Spitzer completed a fellowship at the U.S. National Institutes of Health, in Bethesda, Md. He then joined Wayne State as a clinical neurology researcher. His pioneering research in applying neural network analysis to electromyography and clinical neurophysiology resulted in peer-reviewed publications, grants, and several U.S. patents.He mentored generations of neurologists in electrodiagnostic medicine, a medical specialty that uses nerve-conduction and electromyography tests to evaluate and diagnose muscle and nerve disorders.In 2020 he founded Mackinac Neurology, a telemedicine-based practice that treated pa\u00adtients virtually during the COVID-19 pandemic.A longtime IEEE volunteer, he held numerous roles on the IEEE Regional Activities Board, now known as the Member and Geographic Activities Board. He was a member of the IEEE Ethics and Member Conduct and Nominations and Appointments committees, as well as the IEEE Educational Activities and IEEE-USA boards. He served as 1977\u20131979 director of the IEEE Central Indiana Section.Donald Leo DietmeyerProfessorLife Fellow, 93; died 13 FebruaryDietmeyer was a professor of electrical and computer engineering for 40 years at the University of Wisconsin-Madison.He developed a lifelong interest in radio and electronics at high school in Wausau, Wisc., and earned a Ph.D. in electrical engineering in 1959 from the University of Wisconsin. He\u2019d joined the university\u2019s electrical engineering faculty as a professor in 1958 while pursuing his doctorate.Dietmeyer\u2019s research focused on computer-aided design in the areas of switching theory, hardware description languages, and the decomposition of Boolean functions. His research contributed to the development of automation tools for integrated circuit design.He worked with Jim Duley, a former student, to pioneer the use of the digital system design language. He wrote the textbook Logic Design of Digital Systems, published in 1978.In the early 1980s, Dietmeyer worked with researchers to develop ConLan, a language-construction method that combined hardware description languages in one underlying framework.He served as associate dean of the University of Wisconsin\u2019s electrical and computer engineering department from 1983 to 1995. In 1998 he retired and was named professor emeritus.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/how-to-survive-a-layoff\" target=\"_blank\" rel=\" noopener\" title=\"An Engineer\u2019s Guide to Surviving a Layoff\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/an-illustration-of-stylized-people-wearing-business-casual-clothing.webp?id=65257424&amp;width=980\" title=\"An Engineer\u2019s Guide to Surviving a Layoff\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/how-to-survive-a-layoff\" target=\"_blank\" rel=\" noopener\">An Engineer\u2019s Guide to Surviving a Layoff<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Brian Jenney<\/a> on 9. Septembra 2026. at 15:03 <\/small><p>This article is crossposted from IEEE Spectrum\u2019s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free!I lost a cushy engineering management position at the same time I purchased a business, with a mortgage, three kids, and every other financial obligation of being an adult. It was one of the most stressful stretches of my life. If something similar has happened to you, I feel your pain.You lose more than the incomeFor many of us, our work became our identity, so we don\u2019t just think, \u201cI don\u2019t have a job anymore.\u201d We start thinking, \u201cI\u2019m not an engineer anymore.\u201dAn apple tree in winter is still an apple tree. A car parked in a driveway is still a car. You\u2019re still an engineer, the same way you were still one every evening you clocked out, and the same way you\u2019ll be one at the next job. Your skills took years to build and won\u2019t evaporate because a company stopped paying for them. This feeling can be particularly rough if part of your identity was tied to a recognizable employer. However, this can also be a chance to catch up on what you\u2019ve been putting off: working out, being present with your kids, writing, whatever hobby got shelved for a deadline three years ago.Once you\u2019ve caught your breath, here are some tips for when the actual work starts:1. Don\u2019t sprint on day oneI started applying for jobs the next morning after the layoff. It felt productive, but it gave me no time to settle my nervous system or consider what I actually wanted next. I took the first offer that came along, at a company I already knew wasn\u2019t right, and quit after exactly 30 days. Give yourself a few days before deciding anything. Plans built out of desperation rarely work out well.2. Audit your spendingGo through every subscription and recurring charge and cut what isn\u2019t essential. Every dollar you stop bleeding buys you patience instead of forcing a bad offer out of fear.3. Build a list wider than LinkedInStart with former colleagues and vendors, or businesses with a relationship to your previous employer. They already know you or your company, which gives you the halo effect: Some of the trust from your employer carries over to you automatically.LinkedIn is table stakes and is the most popular place to find work, but that doesn\u2019t mean your search should stop there. Try Facebook, Instagram, and other social media channels too, where plenty of people who aren\u2019t on LinkedIn might have leads for open roles. I found my first job years ago from a Facebook post, and both roles I landed after being laid off came from startup job boards and recruiters I found entirely outside LinkedIn. Your mileage may vary, but LinkedIn isn\u2019t the only game in town.Don\u2019t skip your inner circle either: Text your family and friends. Good leads rarely come from someone you know directly, but they do come from someone that person knows. Work this list daily and track who you\u2019ve contacted.4. Eight hours is a long timeWith no work to fill your day, you may default to treating the search like an eight-hour shift. You can\u2019t apply productively for 8 hours straight, and that\u2019s why people burn out. Use a focused morning block for the list in step 3, then spend the rest of your day on activities you\u2019ve been putting off.5. Catalog your wins before you study interview questionsCataloging your wins is a higher-leverage move than drilling practice questions this early. Can you explain the most impactful project you worked on in the last year? Probably not.Most people skip this step, then default to generic answers when a recruiter asks about their last role. Write down specific stories showing leadership, technical ability, and grace under pressure. They\u2019ll come up once you\u2019re in interviews, and cataloging them rebuilds your confidence along the way too. Save the deep prep for once an interview is on the calendar. Ask yourself daily whether today\u2019s work is generating interest in you, or leading you to someone who might hire you. If not, consider skipping it.One last thingA layoff rarely reflects your skills. It\u2019s usually a company protecting revenue\u2014nothing more personal than that. Knowing that doesn\u2019t make it easier, but hopefully this gets you back on your feet faster.\u2014BrianIEEE Global Careers Fair: September 23-24Looking for a job? For the first time, IEEE is taking its Career Fair worldwide. The inaugural IEEE Global Virtual Career Fair runs September 23 (5:00 PM EST) through September 24 (8:00 PM EST), following the sun across every region to connect engineering and technology professionals with employers around the world. Read more here. United States Invests in Industry Partnerships for Ph.D. TrainingWhile most engineering Ph.D. grads end up in jobs at commercial companies, academia and industry often operate in their own bubbles. Now, the U.S. National Science Foundation is investing in a program to integrate industry experience into STEM doctoral programs and help bridge the gap. Modeled after similar programs in other countries, students in the I-PhD will spend at least one year working on research at an industry site and receive a combination of funding from the company, NSF, and the university. Read more here.AI Efficiency Could Cost Us the Next Generation of ExpertsWhen systems engineer Richard Mitchell designed a digitally-controlled nuclear plant, he made a counterintuitive decision: including manual steps for the human operator that a machine could execute on its own. The strategy was meant to keep the operator sharp, and it\u2019s one that could help address one of the biggest issues facing the workforce today: What happens to human expertise when AI does the work that used to build it?Read more here.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/rivian-self-driving\" target=\"_blank\" rel=\" noopener\" title=\"Rivian\u2019s Gambit for Full Autonomy\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/two-men-in-dark-blue-shirts-watch-suvs-being-put-together-on-an-assembly-line.jpg?id=67724197&amp;width=980\" title=\"Rivian\u2019s Gambit for Full Autonomy\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/rivian-self-driving\" target=\"_blank\" rel=\" noopener\">Rivian\u2019s Gambit for Full Autonomy<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Lawrence Ulrich<\/a> on 8. Septembra 2026. at 13:00 <\/small><p>I\u2019m sitting in a Rivian R1S SUV as it drives itself down the leafy streets of Palo Alto, Calif., through areas crowded with touchstones of tech history. We cruise near the landmark HP Garage, the one-car workshop where Hewlett-Packard, and, arguably, Silicon Valley, was founded in 1939. I skirt Stanford University, where a team led by computer science professor Sebastian Thrun won a US $2 million DARPA Grand Challenge in 2005. The team\u2019s Volkswagen SUV, named Stanley, became the world\u2019s first vehicle to navigate a grueling 212-kilometer Mojave Desert course with no human intervention.This Rivian might look like any other electric SUV in this affluent town, with its concentration of tech bros, venture capital, and startups. But inside this boxy EV is something special: an Autonomy+ system that will allow owners to enter an address, sit back, and let the vehicle drive to any mapped destination in the U.S. and Canada. This point-to-point system is one of the most advanced semiautonomous-driving systems coming to market. It is also a precursor of the company\u2019s bid to make self-driving cars a reality, for robotaxis and\u2014eventually\u2014for everyday car buyers. After years of incremental advances and frustrating setbacks, self-driving has been swept up in the great AI resurgence, and is now a top priority for investors and global automakers, who envision vast new streams of profits.So here I am, 21 years after that DARPA challenge, riding shotgun in Stanley\u2019s vastly more advanced descendant. Rivian\u2019s Autonomy+ is intended to operate seamlessly on suburban streets like these, sensing and responding to traffic lights, crosswalks, and stop signs. That point-to-point system is set to debut on Rivian\u2019s all-new R2 SUV by roughly the end of this year, and via over-the-air updates for its newest R1S and R1T models. Rivian says it will charge $49.99 a month, or $2,500 up front, versus Tesla\u2019s $99 per month for its rival system, which is somewhat misleadingly called Full Self-Driving (Supervised), or FSD. Mercedes, meanwhile, plans to charge $3,950 for a three-year subscription for the forthcoming MB.Drive Assist Pro on its CLA-Class EV; that system still requires at least one hand on the steering wheel.  Video released by Rivian shows the company\u2019s R1 SUV being driven on a variety of urban and rural roads, according to the company. Rivian plans to introduce this self-driving system to compete with Tesla\u2019s offering before the end of 2026. Rivian Impressive as it is, Autonomy+ is only a Level 2+ system in the classification system established by the Society of Automotive Engineers. Level 2+ means that a human driver must pay full attention and be ready to retake control at any moment. Rivian, along with a horde of deep-pocketed rivals, is aggressively working toward more impressive (and potentially lucrative) levels of autonomy. At Level 3, drivers could \u201ccheck out\u201d behind the wheel for limited periods, to scroll through emails or watch a movie\u2014but not to sleep.The big race right now is to deliver Level 4 autonomy: A car you could (in theory) dispatch to pick up a pizza, and have it carted home on the heated, unoccupied driver\u2019s seat\u2014or in which you could spend the ride lounging alone in the back seat, enjoying a private slice while reading a newspaper.  At Rivian\u2019s software lab in Palo Alto, Calif., a technician evaluated code for the company\u2019s self-driving system.Jason Henry\/Bloomberg\/Getty ImagesRobotaxis currently roaming select cities in the U.S., China, and the Middle East have proved that driverless, Level 4 autonomy is possible. These cars operate in relatively tiny numbers in a couple of dozen cities, and within the specific constraints of commercial services. Now Rivian and its many rivals\u2014including Tesla, Toyota, Mercedes, Volkswagen, and China\u2019s BYD\u2014are racing to bring that level of self-guided mobility to the masses. Rivian\u2019s strategy combines a suite of cameras, radar, and lidar; a custom set of silicon chips, developed in-house, to process sensor data; and an AI autonomy model running on those chips. With $1.25 billion in backing from Uber, Rivian plans to graduate to a fleet of self-driving, Level 4 robotaxis starting in 2028. Those taxis, along with Rivian\u2019s consumer fleet, will be the literal training wheels for extending Level 4 ability to consumer vehicles.Meanwhile, millions of connected cars, as they cruise every nook and cranny of the globe, are already sending data to train automakers\u2019 systems. The race is on to funnel those data through fast-improving AI models with \u201cend to end\u201d capability: an AI architecture, powered by deep learning, that processes raw sensor data directly into physical vehicle commands. So equipped, engineers anticipate they\u2019ll be able to solve the tricky edge cases\u2014tangled urban streets, unique geographies, swarms of pedestrians, inclement weather\u2014that skeptics once deemed intractable.Rivian\u2019s Plan for Level 4 Self-Driving Despite the company\u2019s high media profile, including a spotlight on RJ Scaringe, its MIT-doctorate founder and chief executive, Rivian holds a relatively tiny slice of the U.S. passenger-vehicle market. It sold just 42,000 vehicles last year across its three models, the adventure-minded R1S SUV and R1T pickup, and the Electric Delivery Van. Tesla sold about 1.6 million units. Toyota, the world\u2019s largest automaker, sold more than 11 million.  The first generation of the Rivian Autonomy Processor, an AI processing chip developed in-house, was tested at Rivian\u2019s Palo Alto, Calif., lab in December, 2025. Jason Henry\/Bloomberg\/Getty ImagesRivian\u2019s underdog strategy is to leverage software and tech to make itself a serious player. Volkswagen, among the world\u2019s largest automakers, saw enough value there to invest up to $5.8 billion in a joint venture called Rivian and Volkswagen Group Technologies. The joint venture gives Rivian crucial capital for development. It gives Volkswagen access to Rivian\u2019s electrical architecture and to the software for the R2, new-generation Rivian SUV that went on sale in June.   Unlike traditional lidar units, which protrude like a layer cake from the roof of a vehicle, Rivian\u2019s unit on the new R2 SUV is housed in a small, sleek enclosure where the windshield meets the roof.Rivian (2);Jason Henry\/Bloomberg\/Getty Images\u201cRivian developed an architecture so important that VW is spending billions to buy it, as opposed to trying to re-create it themselves,\u201d says Bryan Reimer, a research scientist in MIT\u2019s Center for Transportation and Logistics.But the joint venture doesn\u2019t give VW access to Rivian\u2019s autonomous tech. In March, that R2 architecture underpinned Rivian\u2019s $1.25 billion deal to supply Uber with up to 50,000 robotaxis. The companies plan to initially deploy 10,000 taxis, beginning in San Francisco and Miami in 2028, before expanding across 25 cities in the U.S., Canada, and Europe.Rivian\u2019s vulnerabilities include struggles with reliability, along with expensive body repair costs that the company says it strove to reduce for its new R2. As impressive as Rivian\u2019s in-house tech may appear, the company has miles to go to catch up with Tesla, which recently announced it has 1.1 million active users of its FSD system. Toyota is also jumping into the game; its Woven by Toyota subsidiary has partnered with the Alphabet-owned Waymo to develop an autonomy platform for robotaxis and consumer cars. The lidar unit on a Waymo robotaxi protrudes noticeably from the roof of the vehicle.Andrej Sokolow\/picture alliance\/Getty ImagesUntil recently, most observers would have gone all-in on Tesla as the winner of the autonomous race. Elon Musk\u2019s company has begun operating a small test fleet of Model Y robotaxis in three Texas cities and in Florida. Tesla has also begun producing a dedicated autonomous vehicle, the Cybercab robotaxi. But in April, Musk pushed back his timeline for Level 4 autonomy for general consumers: \u201cI\u2019m just guessing here, but probably in the fourth quarter\u201d of 2026, he said. It was the latest in a series of deflating walkbacks from the man who once promised 1 million robotaxis on the road by 2020.Scaringe, during our drive of his company\u2019s make-or-break R2 SUV at a Utah state park, says that showroom Rivians will start adopting some of its robotaxis\u2019 Level 4 capabilities no later than 2030, perhaps beginning with self-parking functions.How Self-Driving Systems Are Learning From HumansLike most autonomous cars, Rivian\u2019s system fuses data from multiple sensors to create a robust picture of a fast-moving environment and its obstacles. Data is fed to a neural network\u2014what Rivian refers to as its \u201cLarge Driving Model,\u201d or LDM\u2014that churns through hundreds of trillions of operations per second to interpret and fuse data from cameras, radar, and lidar. That network is end to end, meaning that it processes multiple streams of raw sensor data (such as camera pixels) and outputs driving controls (for steering, braking, and acceleration) through a single data pipeline. More traditional systems coded distinct steps for data collection, feature extraction, prediction, and decision-making.That proprietary AI driver identifies features in images and point clouds, groups them into objects, and tracks them across frames, time-stamped to the millisecond to account for differing frame rates. The AI thus builds confidence over time, acting on object detections that persist across several frames, rather than, say, slamming the brakes due to a camera blip on a single frame. The virtual driver can then navigate safely even when sensors disagree, by favoring the persistent data. The output\u2014 commands for electric motors and other systems\u2014is backed by redundant hardware for by-wire systems such as steering and brakes.During my demo of Rivian\u2019s point-to-point Autonomy+ system, a company test driver sits behind the wheel. Nick Nguyen, the engineer who directs Rivian\u2019s products and programs related to autonomy, watches from the back seat. Compared to, say, a large language model that writes news or fiction, Nguyen says, an autonomous-driving AI is less subjective and easier to evaluate, so there\u2019s little room for error. \u201cWe want clich\u00e9. We want boring. Just safe, repeatable driving,\u201d he says.  The Rivian R2 SUV plans to offer a self-driving system by roughly year\u2019s end 2026. The R2 competes with the more urban-oriented Tesla Y.RivianFrom my brief drive, I\u2019d say suburban boredom is achieved in this Rivian R1S. Unlike some modes of Tesla\u2019s Full Self-Driving (Supervised), Rivian\u2019s system drives like a soccer dad, obeying speed limits to the digit, stopping gracefully at traffic lights, and easing over speed bumps like a driver delivering antiques. Yet this robo-driver isn\u2019t timid or tentative. For robotaxi companies in the U.S. and China, these types of routine trips are boosting optimism and investment to dizzying heights. Waymo claims 92 percent fewer fatal or serious-injury accidents than human drivers, based on 220 million miles of autonomous ride data. But the real challenge is how well the higher levels of autonomy will work when they reach consumer cars [see Sidebar, \u201cThe Growing Proof That Autonomous Cars Save Lives\u201d].Rivian\u2019s core LDM currently ingests cloud data from up to 125,000 cars for analysis and validation, which then fine-tunes the model through simulations. Onboard computing is smart enough to trigger recording only for unusual scenarios. Owners have to agree explicitly to data collection beforehand. Updated LDMs will be beamed back to customer cars via monthly over-the-air updates. part of that self-reinforcing data flywheel. It\u2019s part of what Scargine calls the \u201cdata flywheel,\u201d the self-improving AI loop that continuously refines the system. As is true for some of its rivals, Rivian no longer needs to fully rely on an onboard high-definition map or even a cellular link to pinpoint the car for navigational purposes.  That strategic shift reduces data demands, and ensures steady driving in urban canyons or tunnels with no connections. Instead, the Rivian recognizes and responds to its surroundings through recognition and repetition, just as a human would do  interpreting street signs, following lane markers, being alert to hazards. The Rivian R2 features 11 high-definition cameras and five radars. It will integrate a lidar unit early next year to lay the groundwork for future autonomy. That miniaturized lidar will integrate smoothly into the R2\u2019s existing roofline, an improvement over the bulky, drag-producing units seen on Waymo Jaguars, and older partially autonomous models. Vidya Rajagopalan, Rivian\u2019s senior vice-president of electrical engineering hardware, says lidar costs have fallen from above $10,000 to a few hundred dollars in under a decade.  Vidya Rajagopalan, Rivian\u2019s senior vice president of electrical engineering hardware, holds a RAP1 AI processor chip.Jason Henry\/Bloomberg\/Getty ImagesA mix of sensors plays up the strengths and diminishes the weaknesses of each, Rajagopalan says. Cameras capture color and texture and can distinguish between objects, but they struggle in darkness and low-contrast lighting. Lidar is unaffected by darkness or blinding sunlight, and senses shapes in three dimensions. This inherent 3D capability makes lidar more reliable for slowing or halting a car for random objects\u2014\u201ca tire in the road, or maybe a large dinosaur,\u201d Nguyen quips. Multiple cameras can further contribute 3D data, after a short delay for processing.Sensors with 360-degree vision can outperform human senses in key situations. Radar and lidar can spot nighttime pedestrians or animals hundreds of meters down the road, something no human can do. But lidar can be thrown off by dust, fog, and snow. Radar can \u201csee\u201d through rain or snow, but with relatively low spatial resolution.Why Rivian Ditched NvidiaTo handle the flood of sensor data, Rivian has taken on an ambitious challenge: designing its own custom autonomy chip in-house. The Rivian Autonomy Processor (RAP1) is a 5-nanometer processor that can execute 800 trillion operations per second (TOPS), three times as fast as the Nvidia Jetson Orin chip used in its earlier models. The chip will be built to Rivian\u2019s specs by Taiwan Semiconductor Manufacturing Co. , which also makes custom chips for Tesla.  Rivian\u2019s autonomy module contains two Rivian Autonomy Processors, each capable of 800 trillion operations per second.RivianNvidia\u2019s latest automotive system-on-a-chip, the Drive AGX Thor processor, is being adopted by the likes of BYD, Hyundai, Lucid, Mercedes, Nissan, Volvo, and Xiaomi, along with the Aurora and Waabi autonomous-trucking companies.On paper, a single AGX Thor chip is slightly faster in terms of TOPS, at 1,000 trillion operations per second. But Rivian combines a pair of chips in each autonomy module, giving it 1,600 TOPS and execution rates around 5 billion pixels of data per second, versus 3.5 billion for Nvidia\u2019s Thor.Rajagopalan says developing the chip and AI software simultaneously shaved a critical full year from development. Experts say it\u2019s the kind of fast-to-market speed that China has mastered and that legacy automakers are struggling to match. The in-house design allows Rivian to custom-tailor its software to the chip, and vice versa. Nvidia\u2019s general-purpose chip, designed to satisfy multiple customers with various needs, must devote computing power to onboard infotainment, displays, or other systems. Rivian\u2019s chip is designed to run autonomy and nothing but.During my visit to Rivian\u2019s Silicon Valley campus, Rivian engineers Prasun Raha and Mukund Chavan tutored me on the rapid pace of the company\u2019s autonomy evolution. A cluttered wallboard displays a first-gen architecture that Rivian debuted just five years ago. The initial R1S SUV and R1T pickup used nearly a score of electronic control units (ECUs), the \u201cblack boxes\u201d that traditionally control vehicle functions. For its latest R1 models, Rivian reduced the ECU count to seven. The zonal architecture organizes nearly every vehicle function into three zones, hugely consolidating the electronics and simplifying manufacturing. Rivian also leaned into an autonomy trend called \u201cearly fusion\u201d: mixing raw, time-and-space-aligned sensor data into a shared view before the neural network acts upon it. In late fusion, each sensor performs solo recognition before it\u2019s combined into a single picture.The self-driving revolution will really begin when the technology migrates from controlled taxi fleets to consumer cars, giving owners back the precious time they waste on commuting. Early fusion preserves the richest sensor data for maximum accuracy in self-driving. But it demands the enormous computing power the RAP1 can deliver. Raha says the approach helps the multimodal system degrade gracefully and continue to operate with certainty even if, say, a camera\u2019s lens gets covered with mud.Together, these elements make up Rivian\u2019s third-generation autonomy platform. Displayed on a test bench, a new Autonomy Compute Module pairs two RAP1 supercomputing chips. The module is eight times as powerful as before but 60 percent smaller, according to the company. Raha says the system was designed expressly to expand Rivians to Level 4 autonomy from today\u2019s Level 2+. RivLink, the automaker\u2019s interconnect technology, can bridge multiple RAP modules to scale processing power. \u201cIt lets us build this extensible system, with perhaps two more chips for Level 3 or four for Level 4, depending on how the model scales,\u201d Raha says.Rivian\u2019s Road Map to Full AutonomyRivian\u2019s next planned milestone toward self-driving will be Level 3 autonomy\u2014a hands-off and eyes-off system, but for highways only. (Remember, Tesla\u2019s current FSD is technically a Level 2 system: hands off but not eyes off.) On the freeway, Nguyen points out, drivers would be spared the drudgery of dealing with stop-and-go traffic, allowing them to boost productivity or just goof off.Some autonomy critics are leery of Level 3, envisioning a limbo zone in which drivers are lulled into a false sense of security when a car drives for long stretches with no human attention required. Ford and GM are among the automakers pivoting toward limited eyes-off functions.Rivian\u2019s senior vice-president of autonomy, James Philbin, sees Level 3 as an inevitable stepping-stone to Level 4. The company expects it will initially be limited to highways, not the cut-and-thrust of city traffic. If a driver fails to respond to alerts, the system will slow the vehicle, pull off on a shoulder, or call 911. Rivian has not announced a timeline for introducing limited Level 3 capability.Navigating a Tricky Liability Shift on the Way to Immense ProfitsReady or not, these much more autonomous systems are coming, a natural evolution of today\u2019s semiautonomous helpers. In developed markets, adoption of showroom cars with partial-to-full automation is projected to jump from 8 percent in 2024 to 28 percent by 2030, according to Morgan Stanley.\u201cOne in four cars sold globally may be equipped with smart-driving technology in five years, versus one in eight cars now,\u201d wrote Tim Hsiao, a Morgan Stanley analyst, in a note posted on the company\u2019s website.  Combining cameras, lidar, and radar gives a self-driving car a better view of people and objects in front of it, according to Rivian. The company expects to release a self-driving system before the end of 2026 that will compete with Tesla\u2019s, which uses cameras alone. Rivian MIT\u2019s Reimer believes the self-driving revolution will really begin when the technology migrates from controlled taxi fleets to consumer cars, giving owners back the precious time they waste on commuting. If owners could truly send their autonomous car to safely chauffeur children, keep aged parents mobile, or run errands\u2014while owners keep working or playing\u2014the automakers who first help make that happen will enjoy a massive competitive edge, he says. As automakers struggle to convert buyers to subscription models, Reimer believes that self-driving appears to be the one advance for which consumers might actually pay plenty.But the greatest impediment to that revolution has little to do with technology. Public skepticism over self-driving is rampant; and the fate of fully autonomous testing in New York City is uncertain. Even going from Level 2 to Level 3 might shift legal liability for some accidents from drivers to automakers. But with Tesla still fighting lawsuits over its rudimentary Autopilot systems, those questions aren\u2019t anywhere near settled.Experts worry that self-driving cars may become as politicized as EVs. Labor unions are pushing back, fearing job losses from taxis to trucking. A crazy quilt of state or local regulations has failed to create coherent industry guidelines. Publicized failures\u2014even ones that don\u2019t result in injuries, such as Waymos driving onto a flooded street or impeding emergency workers\u2014give the industry a black eye. Companies like Tesla and even Waymo, Reimer says, have too often relied on an arrogant \u201cTrust me\u201d approach, resisting regulation and oversight.Nevertheless, the momentum toward truly self-driving cars, and massive backing from automakers and AI-besotted investors, suggests their time has come. The rest of the journey will depend as much on social and regulatory issues as technical ones, and so Reimer has a bit of advice.\u201cDo it right, and share all your data,\u201d he says. \u201cEarn the right to scale\u2026. It\u2019s about establishing trust, and developing a framework in which we truly believe these systems can operate as a trusted part of our transportation network.\u201d This article was updated on 08 September 2026.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/are-self-driving-cars-safe\" target=\"_blank\" rel=\" noopener\" title=\"The Growing Proof That Autonomous Cars Save Lives\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/a-road-scene-shot-from-the-perspective-of-a-driver-shows-red-splotches-to-indicate-where-nearby-vehicles-were-detected-using-l.png?id=67724498&amp;width=980\" title=\"The Growing Proof That Autonomous Cars Save Lives\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/are-self-driving-cars-safe\" target=\"_blank\" rel=\" noopener\">The Growing Proof That Autonomous Cars Save Lives<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Lawrence Ulrich<\/a> on 8. Septembra 2026. at 12:59 <\/small><p>Plenty of people remain spooked by autonomous vehicles, or AVs. Some experts and policymakers have cautioned that AVs won\u2019t necessarily make roads safer. When it comes to partial or full autonomy, the picture isn\u2019t entirely clear, in part because there aren\u2019t enough self-driving cars to make meaningful apples-to-apples comparisons.Yet mounting research suggests that self-driving cars crash significantly less often than people, and with far fewer injuries. Evidence also shows that advanced driver assistance systems (ADAS) and other building blocks of autonomy\u2014some of which are already mandated on every new car\u2014are also reducing occupant and pedestrian injuries and deaths, along with insurance claims.On the ADAS front, the Insurance Institute for Highway Safety found that automatic emergency braking (AEB) systems that recognize people in front of the car cut pedestrian crashes by 27 percent. Those AEB systems are mandated for all light vehicles in the U.S. by 2029, and more than 90 percent of new models already comply under a voluntary automakers\u2019 agreement. A separate IIHS study found that automated braking greatly reduced rear-end crashes, by 50 percent, and their injuries by 56 percent. The Highway Loss Data Institute found that cars with AEB alone showed a 13 percent drop in property-damage claims. Cars that bundled ADAS features, including automatic braking for pedestrians, adaptive cruise control, and lane-departure warnings, saw claims reductions up to 39 percent.Move to Level 4 autonomy, and Waymo says its robotaxis have now given 20 million paid rides over 220 million miles, the equivalent of 250 lifetimes of driving. In March, Waymo\u2019s independent study showed 92 percent fewer fatal or serious-injury crashes, a 13-fold reduction versus human drivers in comparable city environments. That included 92 percent fewer pedestrian injuries, 83 percent fewer crashes with airbag deployments, and 82 percent fewer crashes with any injuries whatsoever. That included a 96 percent reduction in injury-causing crashes at intersections, among the deadliest environments for any automobile.How Does Limited Fair-Weather Data Compare to Traditional Crash Statistics?A key question is whether Waymo\u2019s robotaxis, currently limited to fair-weather operation in a handful of cities in the U.S., are directly comparable to humans driving a wider variety of roads in much more variable conditions.The IIHS is looking to dig deeper by cleaning up often-incomplete data. Researchers estimate roughly half of human crashes go unreported, and up to one-third of injury accidents, because drivers hope to avoid insurance price hikes. That potentially skews safety numbers in favor of human drivers. And while Waymo leads the industry in transparency, and robotaxi operators are required to report even the tiniest scrape to the National Highway Traffic Safety Administration (NHTSA), not every company voluntarily reports their total miles driven.Related: Rivian\u2019s Gambit for Full AutonomyThe IIHS\u2019s latest July study flatly stated that automated cars crash less often than people. But it also sought clarity by creating a more-reliable category of \u201cpolice-reportable crashes.\u201d It then compared crash rates of human-driven cars against Waymo taxis in San Francisco, Phoenix, Los Angeles, and Austin. Waymo\u2019s Jaguar I-Pace taxis traveled about 50 million driverless miles over the study period, versus 222 billion human miles in the same cities.In a potential boost for public trust, the study generally supported Waymo\u2019s own findings. Waymo taxis were involved in 68 percent fewer crashes overall than human drivers: 76 percent lower in Phoenix, 71 percent in LA, and 35 percent in San Francisco. A 4 percent higher Waymo rate in Austin may reflect an extremely small sample size. Significantly, Waymo\u2019s injury crashes were still 81 percent lower on a per-mile basis.The industry and its supporters continue to press the safety advantages of autonomous vehicles that never get drunk, drowsy, or distracted. Yet for this fledgling AV industry, there are still no national performance or safety standards. A crazy quilt of state or local regulations can allow or prohibit their deployment. That balkanized approach makes it harder to compare crash rates, according to the IIHS, which is calling for better federal reporting standards.A posting on the IIHS website quotes the institute\u2019s director of statistical services, Eric Teoh: \u201cThose are encouraging signs for the future of driverless vehicles.\u201d Teoh, who was also the lead author of the institute\u2019s study, added that \u201cNow we need to get the data-collection system right, so that we can ensure that level of safety continues as these technologies become more prevalent.\u201dAmazon\u2019s Zoox Gets an Exemption for its RobotaxisOn July 30, in a move seen as fast-tracking the tech\u2019s deployment, NHTSA granted Zoox, a subsidiary of Amazon, the first-ever exemption from certain motor-vehicle safety standards. That will allow commercial operation of Zoox\u2019s toaster-shaped robotaxis, which have no steering wheel or pedals aboard. The agency determined that Zoox\u2019s purpose-built robotaxi \u201cwould provide an equivalent level of safety\u201d as a compliant vehicle, thereby satisfying the standard for an exemption.On that final day of the SAE\u2019s Automated Transportation Symposium, NHTSA also announced a partnership with SAE Industry Technologies to develop the nation\u2019s first performance and competency standards for AVs, via a three-year, $5 million \u201cA2SCEND\u201d consortium.Some doctors and health professionals are arguing that policymakers need to stop viewing self-driving cars as a tech moonshot but rather as a critical public-health intervention. Jonathan Slotkin, a neurosurgeon, makes a powerful case for the medical and societal benefits of AVs. Researchers at the Johns Hopkins Bloomberg School of Public Health say that highlighting the social value of AVs is critical to driving public trust and adoption.Consider that roughly 40,000 people in the U.S., including more than 7,000 pedestrians, are killed each year in roadway accidents. About 1.16 million people die in roadway crashes around the world, making them the leading cause of death for children and young adults between the ages of 5 and 29. Cutting that by even 50 percent\u2014let alone the 90 percent reductions suggested by some studies\u2014would save 580,000 lives a year. That social and economic gain would dwarf that of seat-belt adoption or anti\u2013drunk driving campaigns.This article was updated on 08 September 2026.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/ieee-workshop-xplore-africa\" target=\"_blank\" rel=\" noopener\" title=\"Workshops Educate African Researchers On How to Publish With IEEE\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/a-group-of-adults-dressed-in-ugandan-garb-looking-thoughtfully-in-the-distance-while-seated-in-a-university-classroom.jpg?id=67725356&amp;width=980\" title=\"Workshops Educate African Researchers On How to Publish With IEEE\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/ieee-workshop-xplore-africa\" target=\"_blank\" rel=\" noopener\">Workshops Educate African Researchers On How to Publish With IEEE<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Kathy Pretz<\/a> on 7. Septembra 2026. at 18:00 <\/small><p>Many researchers and students in Kenya, Rwanda, and Uganda struggle to access and publish scientific and technical articles because of financial barriers including publishing fees and subscriptions to research libraries. To help, IEEE has made its Xplore Digital Library more accessible by offering discounts on subscriptions and lowering fees to publish articles. But the number of papers published by technologists in the three nations still lags behind those from other developing countries.It might be that many researchers haven\u2019t received training in methodology, been instructed on how to write academic articles, or fully understand the process for publishing in scientific journals.Staff from the IEEE Publication and Information Products group and IEEE volunteers held educational workshops this year in the three countries. The sessions covered the publishing process, IEEE publication outlets, ways to ensure the integrity of research papers, and tips for making better use of IEEE Xplore.\u201cWe want to make sure those in this region are on par with other research communities and ensure they have the support and knowledge they need to make informed publishing decisions,\u201d says Kristopher Zakrzewski, the IEEE area manager for Europe, the Middle East, Africa, and parts of Central Asia. \u201cOur goal is to give them the tools they need to increase visibility and allow them to participate in global conversations in the technology space.\u201dWorkshops on the publishing processMore than 120 participants attended the workshops, which were held in February at the Novotel Nairobi Westlands hotel, the University of Rwanda, and Makerere University, in Kampala, Uganda.IEEE volunteers who are also authors showed attendees how to prepare, submit, and publish papers. They covered the peer-review process and the benefits of working with IEEE, which publishes about 30 percent of the world\u2019s technical literature on electrical engineering and computer science.IEEE Senior Member Nelson Ijumba presented at the session in Rwanda. Member Kennedy Ronoh led the Nairobi workshop. Sheila N. Mugala spoke to attendees in Kampala.\u201cThe great thing about these sessions,\u201d Zakrzewski says, \u201cis that each had a local author who presented tips and best practices to ensure that new and returning authors have the information they need to prepare their paper for submission, determine where best to publish their article, and find the right journal or conference that would be the best fit for their research.\u201dOne of the facilitators at the Uganda session was IEEE Senior Member Mayur Kumar Chhipa, head of engineering at the International Business, Science, and Technology University in Kampala and vice chair of the IEEE Uganda Section. The university has about 200 engineering students and about 50 researchers.More than 100 people attended Chhipa\u2019s session, where he shared practical guidance on conducting literature reviews and identifying high-impact research.\u201cResearchers in Uganda typically present their paper at an IEEE conference, and that\u2019s it,\u201d he says. \u201cWhat we\u2019re trying to do is encourage them to take the next step and get their paper published in an IEEE journal.\u201dHe encourages his students to submit a summary of their thesis to an IEEE conference, he says.\u201cOtherwise,\u201d he says, \u201ctheir thesis sits in the university\u2019s library or collects dust on a bookshelf.\u201cWhen you publish your research, the world knows you are a scholar who has done good work. Having a paper published at a conference or in a journal can help you get into a master\u2019s program globally.\u201dIEEE Xplore accessAttendees were given an overview of the features of their IEEE Xplore subscription. The digital library contains more than 7 million technical documents from industry-leading journals, conferences, ebooks, and eLearning courses, as well as partner content.IEEE provides access to the library to more than 50 universities in Kenya through a subscription agreement with the country\u2019s Library and Information Services Consortium, which includes university and public libraries and research institutions. Sixteen universities in Uganda and one institution in Rwanda receive discounted subscriptions.\u201cIt was really important to establish the direct correlation between having access to the technical literature and the publishing output from their university and the region as a whole,\u201d Zakrzewski says. Many publishing optionsThe workshops covered publishing options offered by IEEE. That includes both traditional and open access journals, with more than 200 periodicals in total.There are approximately 180 hybrid journals, which contain a mix of subscription-based and open-access articles, and 30 gold open access journals.Open access is a publishing model that makes scholarly research and literature freely available online to everyone. Instead of institutions paying for subscriptions, authors or funders typically pay an article processing charge (APC) of between US $2,160 and $2,800 to have their piece published. IEEE offers authors in Kenya a 50 percent discount off the APC rate, and authors from Rwanda and Uganda can publish in IEEE open access journals for free.The open access program provides authors with greater visibility for their research and enhances discoverability, Zakrzewski says, leading to an increased number of references and citations.Publishing with IEEE opens additional opportunities including scholarship awards, research assistant job offers, networking opportunities, and speaking engagements.\u201d \u2014IEEE Senior Member Mayur Kumar ChhipaIEEE Xplore contains more than 200,000 open access articles, he notes. More than 109,000 articles have been published in IEEE Access, a multidisciplinary open access megajournal.\u201cIEEE supports author choice,\u201d Zakrzewski says. \u201cWe really want to make sure that an author has the option to publish the research that will meet any consortium, funder, university, or coauthor requirements\u2014which is why we\u2019re focusing on growing our open access program to complement our traditional publishing program and offer more options to authors.\u201dThe sessions are having an impactParticipants at the Uganda session told Chhipa that they appreciated the IEEE Xplore Digital Library demonstrations and found the guidance on academic publishing valuable.\u201cMany attendees mentioned that the session helped them better understand how to search for relevant literature, evaluate the quality of research papers, and write stronger manuscripts for publication,\u201d he says.\u201cI have observed increased interest among students and faculty in using IEEE Xplore as their primary research resource,\u201d he adds. \u201cResearchers are also more aware of ethical publishing practices and are developing stronger research proposals and manuscripts.\u201cThe program contributes to building a stronger research culture by encouraging evidence-based research, international collaboration, and higher-quality publications, which will ultimately enhance the global visibility of research from Uganda and Africa.\u201dPublishing has its privilegesChhipa says getting your research published has many benefits, and IEEE staff and members agree.IEEE and several of its societies offer student grants to help cover the expense of traveling to conferences and presenting papers. The money typically covers airfare and a hotel room. Some grants also pay for conference registration fees, Chhipa says.Chhipa assists students at his university with writing and submitting research papers to IEEE journals and conferences. Students gain confidence when their paper gets accepted, he says. One who attended the recent IEEE session was informed that his paper was accepted by an IEEE conference\u2014which Chhipa says he was excited about.He encouraged that student to apply for a travel grant.\u201cMaybe he\u2019ll get it. Maybe he won\u2019t. But at least he learned how to write a paper, apply for a visa to attend the conference, and book an airplane ticket,\u201d Chhipa says. \u201cIt will help him grow personally and professionally.\u201dPresenting a paper at an IEEE conference can be life-changing, he says.\u201cIt opens additional opportunities,\u201d he says, \u201cincluding scholarship awards, research assistant job offers, networking opportunities, and speaking engagements. This is how publishing a research paper in the IEEE Xplore Digital Library can directly, positively impact the life of students and scholars from Africa, especially Uganda, Rwanda, and Kenya.\u201d<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/industrial-robot-cable-carrier-protection\" target=\"_blank\" rel=\" noopener\" title=\"Protecting Dynamic Industrial Robot Cable Carriers\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/industrial-robotic-arm-with-cable-management-system-and-flexible-energy-chains.jpg?id=67633840&amp;width=980\" title=\"Protecting Dynamic Industrial Robot Cable Carriers\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/industrial-robot-cable-carrier-protection\" target=\"_blank\" rel=\" noopener\">Protecting Dynamic Industrial Robot Cable Carriers<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Tsubaki Kabelschlepp<\/a> on 3. Septembra 2026. at 12:18 <\/small><p>This article is brought to you by Tsubaki KabelSchlepp.In modern automated manufacturing, six-axis articulated robots perform high-speed, multidirectional maneuvers under demanding operational cycles. However, as robot arms swivel, rotate, and extend, the electrical cables, fiber optics, and pneumatic hoses supplying them endure severe mechanical stress. Torsional twist, rapid acceleration, and repeated contact with machine structures often lead to premature conductor fatigue, insulation breakdown, and costly unplanned production halts.To overcome these multi-axis motion challenges, the Tsubaki KabelSchlepp Robotrax System provides a specialized three-dimensional cable carrier engineered specifically for complex robotic motion.Managing High Tensile Forces With Central Steel TechnologyConventional cable carriers often transfer operational movement stress directly onto internal electrical lines and hoses. The Robotrax system changes this dynamic through a central steel cable that runs through the core of every chain link.The Robotrax system\u2019s central steel cable absorbs the primary tensile loads and preserves conductor integrity, dramatically extending cable service life.When robot arms undergo rapid directional shifts and accelerations up to 10 g, this internal steel cable absorbs the primary tensile loads. By isolating electrical and fluid lines from pulling forces, the design preserves conductor integrity and dramatically extends cable service life. Mechanics can easily calibrate and adjust system tension using an integrated clamping piece, ensuring consistent mechanical support throughout long operational cycles.Spherical Link Design and Modular Cable RoutingThe foundation of the Robotrax system lies in its open, single-piece plastic links featuring spherical snap-on connections on both sides. This geometry allows the carrier to flex smoothly across three axes, providing radial rotation of up to \u00b1450 degrees per meter depending on the model size.To optimize internal organization, carrier links contain up to three distinct chambers. This physical separation prevents signal interference and mechanical abrasion between heavy power lines, sensitive data channels, and fluid hoses. For standard models (R040 through R100), technicians can press cables directly into the carrier without tools, drastically reducing installation and maintenance time. Larger configurations, such as the R140X, incorporate swiveling crossbars with snap locks alongside vertical and horizontal dividers for customized interior partitioning.\u200bROBOTRAX SystemSteel cable for transferring extremely high tensile forcesTension piece for locking the chain linksType with toolless opening swivel crossbars and divider module availableOpen design\u2013 Fast cable laying as the cables are simply pressed in\u2013 Easy checking of all cablesSpecial plastic for long service lifeProtective covers or heat shields made from different materials are available for different environmental conditionsQuick-release bracket for fixing and continuationStrain relief with LineFix clampsProtection against hard impacts, excessive abrasion and premature wear as well as limitation of the bending radius through protectorActive Retraction and Impact ProtectionLarge robot work envelopes and high-speed motion trajectories can cause loose cable carrier loops to swing and strike the robot body. To eliminate these destructive collisions, Tsubaki KabelSchlepp integrates the Pull Back Unit (PBU).The PBU serves as an active retraction mechanism that maintains optimal tension on the cable carrier throughout the entire motion cycle. By preventing excess slack and eliminating interfering contours, the PBU minimizes collision risks across complex movement paths. The unit requires zero maintenance on its retraction element and offers standard mounting configurations for leading industrial robot platforms, including KUKA, ABB, and FANUC.Tsubaki KabelSchlepp\u2019s Pull Back Unit maintains optimal tension on the cable carrier and minimizes collision risks across complex movement paths.Additionally, external protectors can be retrofitted onto individual chain links. These durable impact shields limit the minimum bending radius to prevent over-flexing while shielding the chain body from severe external abrasion. If wear occurs, technicians simply replace the modular protector rather than the entire cable carrier assembly.Built for Demanding Industrial EnvironmentsFrom automotive welding cells to high-speed machining centers, Robotrax systems adapt to severe working conditions through tailored protective accessories:Heat Shields: Aluminum-coated textile fiber covers protect against radiated heat, hot weld spatter, and flying sparks.Protective Covers: Coated polyester sleeves shield sensitive lines against aggressive cutting fluids, hydraulic oils, paint overspray, and abrasive dust.LineFix Strain Relief: Multi-layer clamping devices anchor cables securely at both ends to prevent axial displacement during intense motion.By combining central load absorption, multi-axis flexibility, and active retraction control, the Robotrax system offers plant engineers and system integrators a reliable path toward maximizing robot uptime and reducing total operational costs.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/forms-of-engineering-mentorship\" target=\"_blank\" rel=\" noopener\" title=\"Applying Different Forms of Mentorship\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/an-illustration-of-stylized-people-wearing-business-casual-clothing.webp?id=65257424&amp;width=980\" title=\"Applying Different Forms of Mentorship\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/forms-of-engineering-mentorship\" target=\"_blank\" rel=\" noopener\">Applying Different Forms of Mentorship<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Brian Jenney<\/a> on 2. Septembra 2026. at 19:45 <\/small><p>This article is crossposted from IEEE Spectrum\u2019s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free!Asking someone to be your mentor is weird. Walking up to someone and asking, \u201cWill you be my mentor?\u201d has always seemed to me like the adult version of a kid walking up to another kid at a party and asking, \u201cWill you be my friend?\u201dWhat you\u2019re really asking is: \u201cWill you commit some amount of unpaid time to guiding my career for an indefinite period?\u201dFramed that way, of course some people hesitate to say yes.But formal mentorship isn\u2019t the only way to benefit from the wisdom of those who came before. I\u2019ve never formally asked anyone to mentor me. And yet I\u2019ve had dozens of unofficial mentors.The Copy-Paste MethodOne way to learn from others is by copying what you observe. Sometimes this means reading books or blogs from engineers you respect and directly applying their ideas to your work.I\u2019ve also been fortunate to work alongside some extremely talented engineers, and I shamelessly copied the things they did well.When I meet one of these engineers, I try to figure out what they\u2019re doing differently: How do they approach a problem? What do they read? How do they communicate in meetings? What do they know that I don\u2019t?Then I steal whatever seems useful and apply it to my own career.Great artists steal. Engineers should too.Curiosity CompoundsStill, just observing has its limits. Asking questions can get you even farther. I\u2019ve asked managers how they approached difficult conversations, and I\u2019ve asked engineers what their process was for solving problems I thought were impossible. If someone seems unusually knowledgeable: \u201cWhat are you reading right now?\u201d If I respect someone\u2019s work: \u201cWhat\u2019s something you think I could do better?\u201dThese aren\u2019t profound questions. They don\u2019t need to be. You get one useful piece of information, apply it, and move on.And if you don\u2019t work around exceptional engineers, you can still do this. The only real requirement is curiosity. When you encounter something you don\u2019t understand, make it a rule to investigate instead of moving past it.You don\u2019t need one person willing to guide your career. You need a collection of people who know things you don\u2019t.Pay attention to them. Ask questions. And shamelessly copy the good parts.Ask me! If you have a career question you\u2019re struggling with, like an upcoming decision, a problem at work, an interview, whatever\u2014submit it here: https:\/\/docs.google.com\/forms\/d\/e\/1FAIpQLSdj_2BZIhrGF__7BCLH33zJ9NMv8C7Vsg9NNusASrYj7-9Idw\/viewform. You can include your name or remain anonymous.I\u2019ll be reading through them and answering some in future articles. Consider it mentorship without the awkward \u201cwill you be my mentor?\u201d conversation.\u2014BrianICYMI: The Institute June 2026 issueIEEE members have a wealth of experience and knowledge to draw from. In the most recent issue of The Institute, several members share their career advice for engineers, from engineers. You can also learn about other IEEE programs and courses. Read more here.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/canadarm-ieee-300th-milestone\" target=\"_blank\" rel=\" noopener\" title=\"NASA\u2019s Cargo-Moving Robotic Arm Named 300th IEEE Milestone\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/close-up-of-an-extended-robotic-arm-in-low-earth-orbit.jpg?id=67717218&amp;width=980\" title=\"NASA\u2019s Cargo-Moving Robotic Arm Named 300th IEEE Milestone\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/canadarm-ieee-300th-milestone\" target=\"_blank\" rel=\" noopener\">NASA\u2019s Cargo-Moving Robotic Arm Named 300th IEEE Milestone<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Joanna Goodrich<\/a> on 2. Septembra 2026. at 18:00 <\/small><p>In the 1960s NASA began developing a system of reusable space shuttles to make its work more efficient and to reduce costs. The shuttles could launch like rockets, maneuver in Earth\u2019s orbit, and land like airplanes. They also could carry large satellites to and from orbit.Like other types of transportation, machinery eventually breaks down, and parts need to be replaced or fixed. And the cargo being carried to and from Earth has to be moved to its final destination. To complete such tasks, Spar Aerospace (now part of MDA Space) of Brampton, Ont., Canada, and the National Research Council in Ottawa developed a robotic arm, the Shuttle Remote Manipulator System. The project was a joint venture between the U.S. and Canadian governments.Known as Canadarms, the robotic tools attached to shuttles\u2019 exteriors. They allowed astronauts to handle and transfer tools, satellites, and other payloads. Inspections of the shuttle and repairs could be completed using the robots.The system was first deployed in 1981 aboard Columbia\u2019s second flight. Canadarm was used for 30 years on five shuttles and on the International Space Station.The robotic arm was dedicated on 19 June as the 300th IEEE Milestone. The ceremony was held at MDA Space headquarters. The IEEE Toronto Section sponsored the nomination.\u201cIt is appropriate that the 300th Milestone is the Canadarm,\u201d says Michael Geselowitz, senior director of the IEEE History and Heritage group. \u201cThe technology spans aerospace, robotics, and computing fields of interest. It involves international cooperation between the United States and Canada, and it shows how IEEE and its members are at the cutting edge of many frontiers of science and technology.\u201dInternational collaboration for space explorationSeeking to collaborate with other countries on the reusable spacecraft, NASA invited Canada to participate in 1969. It took some time for the country\u2019s officials to determine what technology it could contribute. They learned of a robot that loaded and replaced spent fuel bundles in Canada\u2019s deuterium uranium nuclear reactors, according to the Milestone webpage. That robot, developed by DSMA-Atcon (also now part of MDA Space), inspired what would become the Canadarm.A proposal was submitted in 1974 to design and build the Shuttle Remote Manipulator System. The robotic arm would unload the contents of the space shuttle\u2019s payload bay. NASA approved the project, and development began in 1975.Canada had no space agency at the time, so the country\u2019s National Research Council coordinated the organizations that collaborated on the project. Spar Aerospace led the subcontractor team, which included DMSA-Atcon, CAE, and the Canadian subsidiary of RCA Corp. Engineers from the University of Toronto\u2019s Institute for Aerospace Studies contributed to the project.Building an arm for zero gravityNASA had strict requirements for the robot: The arm had to be lightweight and small enough to fit on the shuttle, as detailed in an article published by the University of Toronto. It also had to move forward and backward, up and down, left and right, and rotate along three perpendicular axes (known as six degrees of freedom).To achieve all that, engineer Peter Carlisle Hughes designed the robot with two shoulder joints, one elbow, and three rotating wrists.\u201cEach joint had six degrees of freedom, and the arm had six links so that it could grab anything from any angle and move it anywhere,\u201d Hughes said in the article. The IEEE life member worked at the Institute for Aerospace Studies.\u201cThis milestone is a reminder of the privilege we all have at MDA Space\u2014as engineers, designers, builders, operators\u2014to build technology that shapes history.\u201d \u2014Holly Johnson, MDA Space vice presidentThe arm was 50 meters long and weighed 400 kilograms. It was made of materials that could withstand outer space\u2019s harsh environment: titanium, stainless steel, and graphite epoxy. The arm was so lightweight that it couldn\u2019t support itself under Earth\u2019s gravity, so it lay on air bearings on the lab floor at Spar\u2019s Brampton headquarters.CAE engineers, including IEEE Life Member David A. Weston, designed the display and control panel as well as the hand controllers astronauts would use to monitor and operate the robot.Because the robotic arm was meant to work in zero gravity, a room that simulated a weightless environment was built to test it. A computer-based simulation facility was constructed in Spar\u2019s headquarters to evaluate its controllability using two simulation models, according to the University of Toronto. RIGID, an early computer simulation model, tested every part of the arm except for its flexible properties. ASAD, which stood for \u201call singing, all dancing,\u201d examined the arm\u2019s movements, ensuring the joints operated correctly. Both were created by Hughes and Spar engineer Andrew A. Goldenberg, who is now a professor emeritus at the University of Toronto.The facility was also used to train astronauts on how to use Canadarm.It took five years for the first Canadarm to be completed. In February 1981, it was presented to NASA at the Kennedy Space Center in Cape Canaveral, Fla., and deployed that November.Lift off into space  Astronaut Stephen Robinson is anchored to a foot restraint on the extended Canadarm2 attached to the International Space Station during an extravehicular activity he conducted in 2005.NASAThe Canadarm was attached to the outside of the shuttle. Astronauts were able to monitor the arm\u2019s movements through a live video feed provided by cameras installed on the wrist and elbow joints, according to the Milestone webpage. Using a hand controller and monitors located in the shuttle\u2019s flight deck, astronauts handled and transferred tools, satellites, and other payloads weighing up 266,000 kilograms using minimal electricity.NASA ordered four more systems. In 2001, Canadarm2 was attached to the International Space Station and used to help build the orbiting laboratory. It is a permanent part of the station, still completing maintenance tasks and moving supplies.During the course of the 30-year shuttle program, the arms performed successfully and achieved the flight\u2019s mission.The original Canadarm took its final flight in July 2011 aboard the Atlantis shuttle.Celebrating IEEE\u2019s 300th MilestoneThe IEEE Milestone dedication ceremony was held at MDA Space\u2019s headquarters in Toronto, where the division that developed the Canadarm was located. The event brought together IEEE leaders and many of the engineers who helped develop the robotic system. Jill Gostin, the 2026 IEEE president\u2011elect, gave the opening remarks at the ceremony. She emphasized that the Milestone was not only celebrating the technology but also \u201cthe engineers, builders, programmers, and visionaries who believed technology could expand human possibility and who dared to push the boundaries of what humanity could achieve beyond Earth.\u201dTo commemorate the achievement, Holly Johnson, vice president of MDA Robotics and Space Operations, and IEEE Life Senior Member David Michelson, chair of the IEEE Communications Society\u2019s Communications History Committee, unveiled a bronze plaque that honored the technology. Michelson was the Milestone\u2019s proposer.\u201cThis milestone is a reminder of the privilege we all have at MDA Space\u2014as engineers, designers, builders, operators\u2014to build technology that shapes history,\u201d Johnson said. \u201cThat same pioneering spirit that drove our team in those early days of space exploration now propels us into a new era as we work to build the infrastructure for the moon and beyond.\u201dThe plaque, which was placed at MDA Space headquarters, reads: In 1981 NASA first deployed a Shuttle Remote Manipulator System aboard the Space Shuttle. Developed by Spar Aerospace (now MDA Space) and the National Research Council of Canada, the Canadarm allowed astronauts to safely and reliably manipulate and transfer heavy payloads outside of the Shuttle, and to conduct inspections and repairs. This robotic system played a key role in the Shuttle and International Space Station programs, and revolutionized human spaceflight.Reviewed by the IEEE History Committee and approved by the IEEE Board of Directors, IEEE Milestones recognize outstanding technical developments around the world that are at least 25 years old. The Milestone program is administered by the IEEE History and Heritage group.To learn more about historical figures in engineering, IEEE Milestones, and IEEE History Center programs and events, check out The Institute\u2019s IEEE Tech History collection. IEEE Spectrum also covers aspects of tech history.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/ai-engineer-skills\" target=\"_blank\" rel=\" noopener\" title=\"AI Efficiency Could Cost Us the Next Generation of Experts\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/human-and-robotic-hands-share-a-caliper-over-technical-engineering-blueprints.png?id=67702640&amp;width=980\" title=\"AI Efficiency Could Cost Us the Next Generation of Experts\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/ai-engineer-skills\" target=\"_blank\" rel=\" noopener\">AI Efficiency Could Cost Us the Next Generation of Experts<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Richard Mitchell<\/a> on 2. Septembra 2026. at 13:00 <\/small><p>A little over a decade ago, I led the controls design for a first-of-its-kind full digital-control system for a U.S. nuclear plant. It was, on paper, a beautiful machine\u2014engineered to run itself the way a modern airliner does, with operators watching over a system that rarely needed them. And we made a decision that, to an efficiency-minded observer, looked backward: We deliberately left manual steps inside sequences the system could execute on its own.We were solving a specific problem. An operator who only ever supervises automation slowly stops being an operator. The hands go cold. The mental model of what the plant is actually doing gets fuzzy. Then comes the day the automation hands control back. It\u2019s always the worst day, because automation only quits when it\u2019s confused or in trouble. But by then, you have a person in the chair who hasn\u2019t truly operated the thing in years. The manual steps were there to keep the human current. It was inefficient by design, on purpose.That plant, as it happened, was never built. It was shelved amid the politics and economics that surround nuclear power in this country, for reasons that had nothing to do with the engineering. But the design instinct outlived the project, and I\u2019ve come to believe it\u2019s the most useful idea I can offer to the argument now consuming every boardroom: What happens to human expertise when AI does the work that used to build it?AI Is Disrupting the Engineering Career LadderThe data has gotten hard to wave away. A Harvard University working paper covering some 65 million workers at more than 280,000 U.S. firms found that after companies adopted generative AI, junior employment fell roughly 9 percent within six quarters relative to nonadopters, while senior employment kept right on growing. A Stanford analysis of ADP payroll records points the same way: The youngest workers in the most AI-exposed occupations lost ground after late 2022 while their more-experienced colleagues held theirs. The Stanford researchers found that the losses concentrate where AI automates the work; where it merely augments, junior employment holds steady or rises.The causal story is still contested, and honesty requires saying so. Researchers at the New York Fed attribute much of the rise in young-graduate unemployment not to AI but to remote work, arguing that firms are reluctant to hire inexperienced people whom they cannot train and mentor at a distance. But notice what the explanations share. Whether a model is absorbing the formative work or distance is severing the mentorship around it, both describe the same broken mechanism: the apprenticeship channel through which expertise passes from senior to junior. Either way, \u201centry-level\u201d has quietly come to mean \u201cthree years of experience required.\u201dStrip away the noise and you\u2019re left with one deceptively simple problem: You cannot become a senior engineer without first being a junior one. Expertise is not downloaded. It is earned through failed builds, dead-end debugging sessions, and the \u201cwhy on earth did that work\u201d moments that a capable AI will now happily spare the newcomer. Spare them enough of those and you produce a cohort that can supervise a model on paper but never developed the gut sense to know when the model is confidently, catastrophically wrong.Most of the commentary stops at the diagnosis, or reaches for policy solutions that treat the loss of junior jobs as an economic problem. Yet it\u2019s also an engineering problem, and safety-critical fields have already spent decades learning how to solve it.Aviation\u2019s Lessons About the Automation ParadoxMy own career started at the sharp end of automation. My first job out of school was verifying and validating the software in the digital jet-engine controller that decides, faster than any pilot could, how a fighter plane\u2019s engine responds. Even then, in the late 1980s, the central tension was visible: The machine outperforms the human in routine cases, but the human is all that stands between the aircraft and disaster in the cases the machine didn\u2019t anticipate. This tension is known as the automation paradox, in which increasingly capable automation gives human operators less practice, while leaving them only the most difficult situations.Aviation learned, repeatedly and expensively, what happens when human skills atrophy inside that gap. The canonical example is Air France flight 447, which fell into the Atlantic in 2009. The proximate cause was mundane. Iced-over airspeed sensors fed the autopilot bad data, and it did what it is designed to do: It disconnected and handed control of the airplane back to the crew. What followed was not a hardware failure. It was a competence failure. A recoverable situation became an unrecoverable one because the pilots, conditioned by thousands of hours of watching the automation fly, could not read a high-altitude aerodynamic stall and hand-fly their way out of it. The airplane was working. The training the automation had quietly eroded was not.The industry\u2019s response is instructive, and it\u2019s the same move we made in that nuclear control room. It did not rip out the autopilot. It built deliberate manual practice back in. In 2017 the FAA issued Safety Alert for Operators 17007, \u201cManual Flight Operations Proficiency,\u201d declaring that \u201cmanual flight is the foundation upon which other technical flying skills are built.\u201d The alert formally recognized skill decay as a hazard in its own right. Some airlines amended their procedures to encourage hand-flying both the initial climb and initial descent in benign conditions, knowingly trading a sliver of fuel efficiency to keep the crew\u2019s raw flying skills alive. That trade is the whole point. A perfectly optimized system that produces incompetent operators is not optimized at all. It has simply moved its failure mode somewhere the spreadsheet can\u2019t see it.Manual Gates Could Preserve Engineering SkillsPut the aviation lesson and the nuclear instinct side by side and they point to one design pattern we now need in AI-augmented work: the deliberate \u201cmanual gate.\u201dA manual gate is a point in a workflow where a human takes the controls, not because it is the fastest way to get the task done, and not only as a safety interlock, but specifically to exercise and preserve a skill that would otherwise decay. The distinguishing feature is that it is chosen. You decide, as a matter of design, which competencies your organization must keep alive in human beings because those are the ones you will need on the bad day. Then you engineer the friction required to keep them warm.Picture how this might work on a software team that leans on AI for most of its code. The team places a manual gate around the skill it can least afford to lose: debugging. When a defect surfaces in a critical module, the assigned engineer\u2014deliberately, often a junior one\u2014must first reproduce the failure, trace it to root cause, and write an automated test that captures the bug, all with the AI assistant switched off. Only after the engineer commits to a diagnosis does the model come back on, to propose the fix, generate alternatives, and sweep the code base for similar bugs. The engineer then compares their diagnosis against the model\u2019s. When the two disagree, that\u2019s the design working, surfacing the disagreement before the bad day instead of during it.This approach reframes the junior engineer entirely. The instinct today is to let AI do the entry-level work because it is faster and cheaper. But some of that work is not overhead to be eliminated. It is the training apparatus of your future senior staff, and you should protect it the way you\u2019d protect any other piece of critical infrastructure. It may not be efficient this quarter, but dismantling it quietly mortgages your capability a decade out.Why Companies Must Keep Training Junior EngineersNone of this is free, and pretending otherwise would insult the people who have to sign the budgets. A deliberate manual gate is, by construction, less efficient in the near term than full automation. Keeping juniors doing formative work and running the manual sequences costs something now to protect something later.That\u2019s a hard sell in a market that judges most leaders on quarterly results. A hired executive who carries \u201cunnecessary\u201d humans that AI could replace will hear about it from the board long before the payoff arrives. The math only works for someone insulated from that pressure: a founder with control, a private company, an institution with a genuinely long horizon, or a regulator willing to require workers to demonstrate their skills regularly, as pilots must. Which means the organizations most likely to preserve their own expertise are the ones structurally able to spend short-term margin on long-term capability; everyone else will need that outside push.So here is the argument, in one line: Deliberate inefficiency is not waste. In safety-critical engineering we have always known it as insurance, and we buy it on purpose. As AI takes over the work where expertise is forged, the smart move is not to resist the automation. It is to keep our hands on the controls by design\u2014so that when the automation fails, as it always eventually does, there is still someone in the chair who knows how to fly.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/president-ieee-note-september-2026\" target=\"_blank\" rel=\" noopener\" title=\"IEEE President\u2019s Note: Technology for Social Good\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/person-wearing-a-scarf-over-a-dark-sweater-with-a-blue-background.png?id=65004859&amp;width=980\" title=\"IEEE President\u2019s Note: Technology for Social Good\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/president-ieee-note-september-2026\" target=\"_blank\" rel=\" noopener\">IEEE President\u2019s Note: Technology for Social Good<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Mary Ellen Randall<\/a> on 1. Septembra 2026. at 18:00 <\/small><p>Across IEEE, our strength lies not only in the excellence of our individual communities but also in our ability to bring them together around shared problems that demand interdisciplinary solutions. Our mission as a public charity\u2014to advance technology for the benefit of humanity\u2014is becoming an increasingly powerful differentiator. It is more than a statement of principle; it is a strategic advantage. When engineers and technologists serve with purpose and lead with heart, they strengthen the future of our profession and demonstrate why IEEE is uniquely positioned to lead at the intersection of technology and societal impact.IEEE Humanitarian Technologies is a consortium of programs and initiatives\u2014supported by a global network of volunteers and technical professionals\u2014working together to apply technology to solve the world\u2019s most pressing problems. These include Empower a Billion Lives, EPICSinIEEE, MOVE, IEEE REACH, IEEE SIGHT, IEEE Smart Village, and IEEE Tech4Good. These programs embody our mission in action. They are not simply charitable activities; they are strategic assets that help IEEE lead globally, innovate boldly, and remain essential to technical professionals at every stage of their careers. While deeply human in purpose, humanitarian technologies are fundamentally engineering challenges, demanding the full depth of engineering rigor and realized through disciplined, deeply technical work.Cultivating Technical LeadersIEEE Humanitarian Technologies sits at the intersection of engineering excellence, societal need, and global opportunity. Its programs allow our members to show the world that engineering and technology are forces for good, capable of addressing urgent challenges with precision, creativity, and compassion. These programs do more than inspire; they strengthen the technical ecosystem that underpins IEEE\u2019s leadership.Bringing together experts from power and energy, communications, computing, robotics, biomedical engineering, and many other domains to address real-world problems, these interdisciplinary intersections are where breakthroughs emerge. When engineers and technologists collaborate with the right humanitarian frameworks across sectors and cultures, they illuminate new constraints, design pathways, and opportunities that traditional project environments rarely reveal. This is how humanitarian technologies help shape the future of engineering itself.These efforts also illustrate a broader opportunity for IEEE. By identifying critical challenges that can be addressed only through collaboration across disciplines, IEEE can mobilize the power of its global community toward solving problems around the world. In doing so, we strengthen both our impact on society and the value we provide to members, partners, and future generations.These programs also build the leadership capacity our profession needs. Engineers working in humanitarian contexts learn to navigate ambiguity, engage diverse stakeholders, manage constraints, and design for environments where failure has real human consequences. They develop systems thinking, ethical reasoning, and cross\u2011cultural fluency\u2014competencies increasingly essential in a world where technology and society are deeply intertwined. They also learn to transition from R&amp;D to implementation by engineering the support, manufacturing, and delivery systems that make solutions viable in specific countries, all while balancing competing requirements. In doing so, humanitarian programs equip professionals with the capabilities that define modern technical practice.Humanitarian technologies also help prepare the future technical workforce. Students and young professionals increasingly seek meaningful, high\u2011impact work. By engaging in purpose\u2011driven projects, they can discover their own capacity to grow, strengthen their technical skills, and become the leaders and problem\u2011solvers who will guide our profession forward.Purpose Inspires EngagementOur members feel this deeply. Engagement research shows that members increasingly cited \u201cgiving back to my profession and the world community\u201d as a reason for joining the organization and renewing their membership. Those with higher membership grades identify \u201cparticipation in humanitarian technology efforts\u201d as one of the most satisfying experiences IEEE offers. These are not just data points; they are also signals of what our community values and what it expects IEEE to champion.Younger generations amplify this even more. Millennials view IEEE through a global lens, prioritizing \u201chumanitarian impact\u201d and \u201clarge-scale collaboration.\u201d One millennial member shared that teaching robotics to children in under-resourced communities transformed them into a deeply engaged member. Gen Z members emphasize inclusivity, environmental responsibility, and purpose-driven engineering, recommending that IEEE offer humanitarian-based challenges and competitions to increase engagement.These findings reveal something powerful: Humanitarian programs are not only meaningful; they also are magnetic. They attract younger engineers, keep them engaged, and help them build a professional identity rooted in purpose and impact. They also create loyalty and develop the leadership pipeline IEEE needs for the decades ahead.These programs also strengthen our brand. Members across segments describe IEEE as an organization that works hard to make real changes in the world. That perception is not just flattering, it is strategic. It positions IEEE as a global leader in responsible innovation that can be trusted to guide technology for the public good, catalyzing innovation that benefits society at scale.As we look ahead, IEEE has an opportunity to become the world\u2019s leading convening force for developing interdisciplinary technology solutions to solve humanity\u2019s most important challenges. Our future relevance will be defined not only by the technologies we advance but also by the problems we choose to help solve.Read more powerful stories about how technology is improving lives across global initiatives in the 2025 IEEE Social Impact Report at ieee.org\/advancing-technology\/building-better-world\/social-impact-report.\u2014MARY ELLEN RANDALLIEEE president and CEOPlease share your thoughts with me: president@ieee.org.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/ruchira-shree-ham-radio\" target=\"_blank\" rel=\" noopener\" title=\"This Teen Helped Native American Students Earn Ham Radio Licenses\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/a-teenage-girl-smiling-as-she-stands-in-front-of-a-table-displaying-her-kelvin-water-dropper-project.jpg?id=67701388&amp;width=980\" title=\"This Teen Helped Native American Students Earn Ham Radio Licenses\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/ruchira-shree-ham-radio\" target=\"_blank\" rel=\" noopener\">This Teen Helped Native American Students Earn Ham Radio Licenses<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Liz Wegerer<\/a> on 31. Augusta 2026. at 18:00 <\/small><p>For many high school students, summer vacation is a time to unplug. For Ruchira Shree, a rising sophomore at West Windsor\u2013Plainsboro High School South, in New Jersey, the break allows her to ramp up her extracurricular pursuits.Much of her time is spent assisting with IEEE Princeton Central Jersey Section activities. She got involved with the PCJS because of her mother, IEEE Senior Member Shubha Bommalingaiahnapallya, a principal engineer at Intel.\u201cI started going to the IEEE meetings when I was little,\u201d Shree says. \u201cI used to go with my mom and just sit in the back of the room.\u201dThis summer she says she\u2019s focusing on improving her mathematics skills by attending the Program in Algorithmic and Combinatorial Thinking summer course on math and computer science. She wants to qualify for the American Invitational Mathematics Examination, an event for the top American Mathematics Competitions scorers. She earned a place on the AMC 8 honor roll\u2014a recognition awarded to the top 5 percent of participants in the national competition\u2014when she was in seventh grade.Shree\u2019s IEEE involvement and her advanced math skills caught the attention of an internship recruiter for the Alliance for Indigenous Math Circles, a group dedicated to expanding STEM opportunities for Native American students. The AIMC organizes and sponsors weeklong overnight camps. Interns assist with activities and teach some of the sessions. Shree met a recruiter at one of the section\u2019s events, and she interned at one of the camps last year.The IEEE-math camp connectionShree\u2019s involvement with the PCJS evolved naturally as she got older, she says, along the way preparing name badges and tackling similar assignments. She met Francis O\u2019Connell, an IEEE life senior member and founder of FXO, in Plainsboro, N.J. O\u2019Connell is the treasurer of the IEEE Integrated STEM in Education Conference (ISEC).He has been a mentor to Shree for the past two years, he says.At last year\u2019s ISEC, she assisted at the registration desk and met Harini Frederickson, an AIMC intern recruiter for New Jersey.Frederickson invited Shree, along with nine other students, to volunteer at an upcoming camp being held in Santa Fe, N.M.\u201cRuchira is a real go-getter,\u201d Frederickson says. \u201cWhen she has an idea, she follows through and doesn\u2019t get easily discouraged.\u201dThe AIMC was created to address an important need, says math teacher Donna Fernandez, codirector of the organization. U.S. Indigenous students have the lowest rate of pursuing STEM studies across all demographics, according to the U.S. National Science Foundation. Systemic barriers such as a lack of role models in STEM fields, socioeconomic inequities, and Eurocentric teaching frameworks are some of the reasons, Rechel Shrisunder and Dwight Figueiredo wrote in a chapter of Minorities: New Challenges and Horizons, a book edited by John R. Hermann.Indigenous people have a long tradition of mathematics, Fernandez says. She cites the Navajo code talkers from World War II as examples. The Navajo, along with 14 other Indigenous tribes, used their native languages to code and transmit critical messages for the U.S. military during the war.There was a student at camp whose grandfather was a code talker, Shree says.Navajo people also use math to build hogans: conical dwellings that require precise calculations to construct. Native communities have used math when building the structures for centuries, Fernandez says.Fernandez believes typical classroom math curricula overlook the importance of mathematics in Indigenous cultures. Combining STEM activities with cultural elements helps Indigenous students better understand their ancestors\u2019 role as mathematicians, she says.That, in turn, helps the students see themselves in those careers, she adds.The AIMC was built upon a program already in place: the Navajo Nation Math Circles, founded in 2012 by three university professors. Their goal was to provide the Navajo Nation\u2019s students with tools to overcome barriers to STEM education.To expand the math circle program, the AIMC was added to reach Indigenous students in the Four Corners area of Arizona, Colorado, New Mexico, and Utah.Since 2017, the organization has run two camps every year at the Navajo Preparatory School in Farmington, N.M. In 2025 one camp was moved to the Santa Fe Indian School.During each weeklong event, students and interns work in math circles. It\u2019s a cooperative way to solve problems creatively, organizers say. Students collaborate on STEM-focused projects and learn from Indigenous STEM professionals. Interns also get the opportunity to experience an off-site cultural event.The camps are free for students, thanks to sponsorships and donations. Teachers and interns cover their own travel expenses. Shree secured a US $1,500 sponsorship grant through the PCJS.Building relationships through STEM activitiesRelationships are an influential part of the week, Fernandez says: \u201cOne of the best things we see at the camp is that students return the following year and ask, \u2018Is so-and-so intern coming back this year?\u2019 They remember the relationships they developed, especially the cultural exchanges they had.\u201cThose exchanges go both ways, benefiting the interns too.\u201dStudents spend mornings at camp working in math circles, then gather for a wrangle, during which each team defends its math circle answer and challenges other teams\u2019 solutions. Shree and the other interns are on hand to answer questions and observe the teams as they work through the math circle problems.\u201cMath problems typically have very binary answers,\u201d she says. \u201cBut in math circles, you focus more on talking through your answers to open-ended questions and learning from each other.\u201dStudents spend afternoons working on projects. In one, the students used household items to create a replica of the Batmobile, Shree says. The car was required to be self-propelled without an engine. Balloons were a popular alternative.Another activity focused on the Indigenous tradition of basket weaving. Students learned the cultural meaning behind traditional designs while understanding how geometry concepts influenced the finished product.  These Native American middle school students work on solving a mathematical pattern-matching game, one of the activities held at the summer camp.Ruchira ShreeRole models inspire students\u201cBecause there\u2019s a lack of Indigenous STEM role models, many Native American students don\u2019t see themselves in mathematics or science,\u201d Shree says.To bridge that gap, Fernandez ensures Indigenous role models are part of the camp. Some of the people who spoke with students during Shree\u2019s internship were Jessica Benally, a Ph.D. student in the learning sciences and human development program at the University of California, Berkeley, and engineers from the New Mexico Mathematics, Engineering, and Science Achievement program, which supports underrepresented preuniversity students.\u201cI believe the students were very inspired,\u201d Shree says, \u201cbecause they could see how they themselves could pursue STEM careers. They had people to look up to in the field who had come from backgrounds just like theirs.\u201dInterns in actionThe interns\u2019 primary responsibility was leading a two-hour, after-dinner Radio Weaves session. They taught students about a popular communication technology that doesn\u2019t require the Internet or cell towers.Ham radio, also known as amateur radio, is a communication method that uses designated frequencies. In the United States, anyone can listen to amateur radio transmissions; to legally transmit on the frequencies, though, a user needs a Federal Communications Commission license. The Radio Weaves project is designed to prepare students to pass the FCC technician license exam.To make that happen, the interns customized Gimkit, a learning game, loading it with radio-specific content that mirrored topics that could appear on the test.Each intern worked with two or three students to complete the Gimkit materials.Frederickson, who was on hand for the camp, says the aim was to send students home with something tangible that demonstrated their STEM accomplishments.Nearly all the students passed the exam on the first try, she says, and she worked with those who didn\u2019t to retake the test.All the students ultimately received their license, she says.Inspiration comes in several formsThe interns took an afternoon off to attend a Pueblo Feast Day, a celebration filled with music and dance that culminated in visits with nearby families, with whom they shared dinner.\u201cThe tradition is very generous and community-based,\u201d Shree says. \u201cIt represents that every home in the village will welcome any guest to have a meal.\u201dThe feast was the highlight of Shree\u2019s week, she says: \u201cI got to really experience Native American culture firsthand.\u201dThe students inspired her, she says.\u201cSeeing the joy on their faces when they passed the technician exam or when they got a math problem correct showed me how much joy they find in learning,\u201d she says. \u201cIt made me realize that I want to help provide more opportunities for them to learn and challenge themselves.\u201d\u201cBecause there\u2019s a lack of Indigenous STEM role models, many Native American students don\u2019t see themselves in mathematics or science.\u201d \u2014Ruchira ShreeThat realization spurred her idea for a new initiative. After she returned home, she founded Rukie Cookie to create \u201csafe, inclusive, and inspiring spaces where youths explore STEAM [and] build curiosity, strategic thinking, and innovation\u2014empowering them to become confident leaders and active contributors to a more just and equitable society,\u201d according to the project\u2019s website.Baking is one of Shree\u2019s hobbies, and she sees it as a way to fulfill a financial need she observed at camp.\u201cI noticed that at lunch breaks, they [camp students] used to play chess on the side, but they couldn\u2019t actually participate in tournaments because that requires a U.S. Chess Federation (USCF) membership fee, which they couldn\u2019t afford,\u201d she says. Shree bakes cookies and sells them at PCJS events. Proceeds go toward youth chess classes and USCF memberships for children in underrepresented communities.She has raised enough money to sponsor six USCF memberships, five of whom are camp attendees, she says.\u201cI hope that the students I have gotten a membership for will continue growing their passion for chess,\u201d she says, \u201cbut also that it will encourage them to challenge themselves with difficult problems.\u201dWhat\u2019s next?Shree planned to attend an AIMC camp this year, she says, but it was canceled due to resourcing issues. She says she intends to return next year with goals of adding a formal chess component to the schedule and continuing to help more students overcome financial hurdles to join the USCF.She\u2019s also writing a novel about Alzheimer\u2019s disease and identity loss, and she\u2019s conducting independent research on cognitive decline at the New Jersey Institute of Technology. Watching her great-grandmother struggle with the condition sparked her interest in the subject, she says.She is confident STEM will be part of her future, she says. Math and cognitive science are areas of interest she plans to study, but she\u2019s still undecided about a major. Her interest in Alzheimer\u2019s research and a desire to apply AI to health care will influence her decision, she says.She adds that she plans to join IEEE once she\u2019s eligible.This article was updated on  8 September 2026.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/europes-ai-drive\" target=\"_blank\" rel=\" noopener\" title=\"The E.U.\u2019s AI Drive Undermines Its \u200bOwn Chip Strategy\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/illustration-of-a-microchip-with-eu-flag-symbols.jpg?id=67681157&amp;width=980\" title=\"The E.U.\u2019s AI Drive Undermines Its \u200bOwn Chip Strategy\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/europes-ai-drive\" target=\"_blank\" rel=\" noopener\">The E.U.\u2019s AI Drive Undermines Its \u200bOwn Chip Strategy<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Joana Soares<\/a> on 31. Augusta 2026. at 14:00 <\/small><p>This story was originally published by Tech Policy Press.The European Union\u2019s push for technological sovereignty faces an uncomfortable contradiction.As the E.U. rolls out AI factories, gigafactories, and new data centers, it is creating a surge in demand for the advanced semiconductors that underpin artificial intelligence. Yet Europe produces fewer than 10 percent of the world\u2019s chips and remains heavily dependent on U.S. designers and Asian manufacturers for the most advanced processors.That tension sits at the heart of Chips Act 2.0, the European Commission\u2019s planned overhaul of its flagship semiconductor strategy.The original Chips Act, adopted in 2023, sought to raise Europe\u2019s share of global semiconductor production to 20 percent by 2030. But the European Court of Auditors has warned that target is unlikely to be met, while the Commission\u2019s own projections put Europe\u2019s market share at about 11.7 percent.The Commission now wants to correct what officials see as a major weakness in the first law: It focused on expanding supply without doing enough to stimulate demand. To address that gap, Chips Act 2.0 is expected to introduce demand-side measures, including public procurement tools, demand accelerators, and closer coordination between semiconductor producers and industrial users. The Commission\u2019s calculation is straightforward: Stronger domestic demand will encourage companies to invest in designing and manufacturing chips in Europe.But the strategy carries a paradox. The AI infrastructure that the Commission hopes will anchor a European semiconductor ecosystem will initially rely almost entirely on advanced processors designed by U.S. companies and manufactured in Asia.\u201cKey positions are held by a small number of firms, mostly outside Europe,\u201d Claire Godfrey, executive director of the Balanced Economy Project, told Tech Policy Press.AI factories create a demand trapThe European Commission\u2019s AI Continent action plan includes 19 AI factories, computing facilities that integrate energy sources, specialized chips, and other infrastructure for running AI models and applications, plans for up to five AI gigafactories (since upgraded to seven), and a proposal to at least triple the bloc\u2019s data-center capacity within five to seven years under the Cloud and AI Development Act. That expansion will require a large supply of advanced AI processors.The Center for European Policy Studies (CEPS) estimates that each planned AI factory site requires up to 25,000 advanced chips, while a gigafactory requires at least 100,000.Almost all of those processors are expected to come from Nvidia. The company supplies most of the graphics processing units deployed in Europe, while its proprietary CUDA software underpins much of the AI software ecosystem. CEPS warns this could create an \u201cNvidia dependency trap,\u201d where computing infrastructure is physically located in Europe but remains technologically dependent on a single U.S. supplier.Recent AI infrastructure projects in Europe illustrate the problem. Mistral has lined up 13,800 Nvidia GPUs for a data center near Paris. Deutsche Telekom\u2019s Munich Industrial AI Cloud is being built with nearly 10,000 Nvidia Blackwell GPUs. And Nscale says its deployment for Microsoft, in Sines, Portugal, will start with more than 12,600 Nvidia Blackwell Ultra GPUs before expanding to more than 66,000 in 2027.Europe still doesn\u2019t control the chip supply chainThe challenge extends well beyond Nvidia. Even if Europe succeeds in expanding semiconductor manufacturing, the global supply chain limits how much autonomy any single region can achieve.\u201cEurope depends on both the United States and Asia, but at different stages of the value chain,\u201d Toni Rold\u00e1n-Mon\u00e9s, economist and assistant professor of public policy at IE University, told Tech Policy Press.\u201cThe United States maintains a dominant position in areas such as chip design, intellectual property, and certain frontier equipment. Meanwhile, the manufacturing of the most advanced semiconductors is highly concentrated in Asia, especially in Taiwan and South Korea, while China plays a fundamental role in various materials, industrial processes, and critical minerals,\u201d said Rold\u00e1n.Europe\u2019s reliance on third countries is more evident in some parts of the chip value chain. In fabrication, Taiwan produces around 90 percent of the world\u2019s most advanced chips. In packaging, assembly, and testing, the E.U. holds just 4 percent of the market and remains highly dependent on Asia, according to Laith Altimime, President of SEMI Europe.\u201cThe objective is\u2026to avoid excessive dependence on a single country, company, or technology.\u201d \u2014Toni Rold\u00e1n-Mon\u00e9s\u201cNo top 20 assembly, test, and packaging company is headquartered in the E.U.,\u201d Godfrey said. \u201cThere is also the materials issue. China dominates several inputs used in key parts of the semiconductor and advanced electronics supply chain.\u201dEurope nevertheless retains important advantages.The region is home to ASML, the Dutch company that dominates the market for extreme ultraviolet lithography systems, and to Belgium\u2019s Imec, one of the world\u2019s leading semiconductor-research centers. Europe also remains a key supplier of specialist materials and power electronics.Those strengths, however, \u201cdo not translate into autonomy across the semiconductor value chain,\u201d Rold\u00e1n said.Sovereignty means resilience, not self-sufficiencyFew experts believe complete semiconductor self-sufficiency is achievable.Instead, the goal should be to reduce strategic vulnerabilities rather than eliminate international interdependence. \u201cIt is not conceivable that one country can rebuild the supply chain. Global collaboration is key,\u201d SEMI Europe\u2019s Altimime told Tech Policy Press. SEMI forecasts that by 2028 the Europe, Middle East, and Africa region will  only manufacture about 68 percent by volume of the non-memory semiconductor chips it demands.\u201cThe challenge is to reduce dependencies that could become geopolitical vulnerabilities,\u201d argues Rold\u00e1n. \u201cThe sensible approach is to strengthen critical parts of the value chain, diversify suppliers, protect sensitive data, and develop domestic capabilities in strategic sectors. That can coexist perfectly well with foreign suppliers: The objective is not to expel them, but to avoid excessive dependence on a single country, company, or technology.\u201dThat distinction is especially relevant for Europe\u2019s sovereignty ambitions. As Godfrey notes, \u201cEuropean firms are building around Nvidia hardware, CUDA, cloud infrastructure, and the software choices that come with them. That leaves Europe with two problems. It relies on Asian manufacturing and materials chokepoints. It is also at risk of trying to address that exposure by tying itself more closely to U.S.-controlled AI and cloud infrastructure. The Chips Act 2.0 needs to deal with both, or it will miss a large part of the problem.\u201dRold\u00e1n said Europe\u2019s greatest vulnerability is dependence on partners willing to use global supply chains for geopolitical leverage. Whether Chips Act 2.0 reduces that risk, experts say, will depend on whether it diversifies suppliers rather than just shifting dependence from Asian manufacturers to U.S. technology companies.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/voltaic-pile-first-battery\" target=\"_blank\" rel=\" noopener\" title=\"The First Battery Was Inspired By a Dead Frog\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/a-collage-of-historic-images-showing-two-men-in-18th-century-garb-with-background-illustrations-of-a-device-with-two-columns-and.jpg?id=67685016&amp;width=980\" title=\"The First Battery Was Inspired By a Dead Frog\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/voltaic-pile-first-battery\" target=\"_blank\" rel=\" noopener\">The First Battery Was Inspired By a Dead Frog<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Allison Marsh<\/a> on 31. Augusta 2026. at 12:00 <\/small><p>In a display case on the lower level of the Faraday Museum at the Royal Institution in London, there\u2019s an unassuming stack of gray metal discs and blotting paper. It\u2019s not at all obvious that this humble object is the starting point of today\u2019s multibillion-dollar global battery industry. The object\u2019s invention in 1799 grew out of a disagreement that Alessandro Volta\u2014the Italian physicist for whom the unit of measurement for electrical potential is named\u2014had with his friend Luigi Galvani over a dead frog.The Debate Over Animal Electricity Galvani was a well-respected Italian physician. In the 1770s, he began investigating the use of electricity to stimulate the muscles of dissected frogs. Armed with an electrostatic generator and an early type of capacitor called a Leyden jar, he was able to create a charge, store it, and then zap his animal specimens at will. He was intrigued when the frog legs twitched as if they were still alive. He spent the last three decades of the 18th century studying the phenomenon, and in 1791, he published De viribus electricitatis in motu musculari commentarius (Commentary on the Effect of Electricity on Muscular Motion).  Luigi Galvani spent decades investigating what he believed to be a natural electric force emanating from animals. Universal History Archive\/Getty ImagesGalvani saw the frog as embodying an \u201canimal electricity,\u201d an innate vital force that activated nerves and muscles, similar to what had been observed in (living) electric eels and torpedo rays. For Galvani, the frog was an electrical machine analogous to a Leyden jar. The brain was the source of the electrical charge; the nerves conducted the electrical fluid; and the muscles stored opposite charges. The illustrations in his 1791 book are fabulous\u2014frog legs spread all over his laboratory table!  Galvani was wrong in thinking that his frogs were electrical machines, but he was right that the muscle contractions were caused by electric signals.SSPL\/Getty ImagesAt first, Volta, chair of physics at the University of Pavia, concurred with his friend. But after beginning his own experiments, he concluded that Galvani was wrong and that the frog generated no electricity at all. He thought of the frog as nothing more than an electroscope, an instrument to indicate the presence of an electrical charge. Volta posited that the source of the charge Galvani observed came from two different metals in contact with the frog. He termed this \u201cmetallic electricity.\u201d   Alessandro Volta came to disagree with Galvani\u2019s theory of animal electricity.Apic\/Getty ImagesTo prove his point, Volta created an \u201cartificial electric organ.\u201d He stacked alternating discs of copper and zinc, separated by cardboard, blotting paper, or cloth soaked in brine or acid. When the top and bottom plates were connected, an electric current flowed through the stack. As opposed to a Leyden jar, which is essentially a capacitor that can store an electric charge and release it in a brief powerful discharge, his stack of discs generated its own electricity through a chemical reaction and delivered a sustained low-current output.Volta didn\u2019t publicly demonstrate or announce his artificial electric organ until after Galvani died in 1798. But when he finally did, in 1799, it immediately began upending science. Just six weeks after Volta wrote to the Royal Society about his invention, the English scientists William Nicholson and Anthony Carlisle used a voltaic pile to run a current through water to separate it into hydrogen and oxygen. They had discovered chemical electrolysis. Humphry Davy later used a large voltaic pile to isolate a number of elements, including potassium, sodium, calcium, strontium, and barium. Early piles petered out after a few hours. Users who stacked up more metal discs to make more powerful piles found the weight of the discs squeezed out the moisture in the paper or cloth.  Invented in 1799, Volta\u2019s \u201cartificial electric organ\u201d (later known as the voltaic pile) was the first battery. Volta presented this one to Michael Faraday in 1814.Royal Institution of Great Britain\/Science SourceOne of the most enthusiastic users of the voltaic pile was Galvani\u2019s nephew, Giovanni Aldini, who spent much of his career defending his uncle\u2019s ideas. Aldini created spectacles across Europe in which he used voltaic piles to shock the carcasses of livestock and, occasionally, the bodies of recently executed convicts. Vivid descriptions in the popular press, as well as Aldini\u2019s own writings, raised the question of whether electricity could bring the dead back to life. Mary Shelley provided her answer in her 1818 novel, Frankenstein; or, The Modern Prometheus. In an introduction to an 1831 edition, Shelley cites galvanism as one of her inspirations for the monster\u2019s reanimation process.Beyond Winners and Losers in Scientific DebatesScientists and historians share a common trait: They like stories with clear winners and losers. The narrative of competition helps drive a narrative of progress that makes it look like humanity is always moving forward. In the case of Galvani and Volta, Volta is usually depicted as the clear winner in the debate over animal versus metallic electricity. The Encyclopedia Britannica goes as far as to write that \u201cwith his announcement of the first electric battery in 1800, victory was assured for Volta.\u201dBut both science and history are more nuanced than that. In fact, Galvani and Volta were both partially right and partially wrong. There was no universal force of animal electricity, but Galvani was correct that electrical signals caused muscle contractions, which he discussed in his anonymous 1794 publication Dell\u2019uso e dell\u2019attivit\u00e0 dell\u2019arco conduttore nella contrazione dei muscoli (On the Use and Activity of the Conductive Arch in the Contraction of Muscles). Volta was right to push back on Galvani\u2019s animal electricity theory, but he was wrong that electrophysiological effects require two different types of metal, or any metal at all; the circuit in the voltaic pile was closed by the wet paper or cloth.It seems a little presumptuous for the Encyclopedia Britannica to declare Volta the winner and Galvani the loser. Volta definitely thought his friend was wrong, but he waited until after Galvani\u2019s death to make his views public. It\u2019s closer to the truth to say they were both genuinely curious to understand the nature of electricity. In the process, they unknowingly helped develop different fields of inquiry: electrophysiology for Galvani and electrochemistry and battery science for Volta.RELATED: Who Really Invented the Rechargeable Lithium-Ion Battery?Such an outcome is actually quite common in scientific disagreements. For example, Isaac Newton\u2019s dispute with Christiaan Huygens over the nature of light\u2014did light consist of particles, or corpuscles, as Newton termed them, or waves, as Huygens contested\u2014breaks down today into quantum optics and classical optics. Similarly, Louis Pasteur\u2019s and Justus von Liebig\u2019s debate over fermentation (microorganisms versus chemical decomposition) led to two complementary fields: microbiology and biochemistry.Maybe instead of looking for winners and losers, we would be better off expanding our horizons and considering the multiple paths of inquiry and discovery. Writing in 1816, toward the end of his career, Volta graciously acknowledged Galvani\u2019s pioneering work, saying \u201cit contains one of the most beautiful and surprising discoveries and the germ of many others.\u201d What new revelations are waiting to develop out of today\u2019s scientific debates?Part of a continuing series looking at historical artifacts that embrace the boundless potential of technology.An abridged version of this article appears in the September 2026 print issue as \u201cThe First Battery.\u201d ReferencesOn 20 March 1800, a year and three months after the death of Luigi Galvani, Alessandro Volta wrote a letter (in French) to Joseph Banks, president of the Royal Society, describing his invention of an artificial electric organ. It was read before the Society on 26 June and published in Philosophical Transactions on the last day of that year as \u201cOn the electricity excited by the mere contact of conducting substances of different kinds.\u201dThe Smithsonian Institution Libraries used their rare books in the online exhibit The Body Electric, which has more information on both Galvani and Aldini.The website of the Whipple Museum in Cambridge, England, has a number of pages devoted to frogs, including a very informative description of the role frogs played in Galvani\u2019s experiments and how those led to Volta\u2019s work.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/bimodal-nuclear-engine\" target=\"_blank\" rel=\" noopener\" title=\"Noodling on Nuclear Engines\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/hand-drawn-schematic-of-turbine-powered-heat-pipe-system-with-labeled-components.png?id=67675201&amp;width=980\" title=\"Noodling on Nuclear Engines\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/bimodal-nuclear-engine\" target=\"_blank\" rel=\" noopener\">Noodling on Nuclear Engines<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Harry Goldstein<\/a> on 29. Augusta 2026. at 15:35 <\/small><p>One day in 1982, Joseph \u201cRod\u201d Canion and two colleagues from Texas Instruments sat down at the House of Pies in Houston and used a placemat to sketch out what would become Compaq\u2019s portable PC. In 1996, Felix Zandman, founder of Vishay Intertechnology, dined at Husker Steak House in Columbus, Neb., grabbed a napkin and drafted a design for a power metal strip resistor, which became crucial to power-management components in industrial, automotive, and consumer applications. Now that wispy piece of paper resides at the Smithsonian\u2019s National Museum of American History. Perhaps most famously, Robert Metcalfe, working at Xerox PARC in 1973, roughed out some early designs for what would become Ethernet, though contrary to popular belief, no napkin was involved.Yet another napkin (see above) was pressed into service last year, when Kurt Polzin, chief engineer of the space nuclear propulsion project at NASA\u2019s Marshall Space Flight Center, met Robert \u00adSchleicher of General Atomics Electromagnetic Systems at a conference and started talking about nuclear rocket design.\u201cThey did the classic let\u2019s-sketch-out-an-idea-on-a-napkin,\u201d says IEEE Spectrum\u2019s Special Projects Editor and our in-house spaceflight expert Stephen Cass. \u201cThis rocket engine is still at the paper-planning stage, which, to be fair, is where most of NASA\u2019s humans-to-Mars planning has been for the last 60 years.\u201dMeanwhile, the world\u2019s richest person is pushing for humans to colonize Mars in time for him to escape our hospitable blue marble for a completely barren red planet. One big problem with this idea: It takes a long time in hostile space to get there.\u201cHere\u2019s our napkin. Noodle on this with us and tell us what you think.\u201d \u2014Stephen CassThere\u2019s an old saw, mostly used in the context of road safety, that speed kills. But when it comes to sending humans on interplanetary missions, the faster the better. As Cass pointed out, \u201cOnce you get outside the Van Allen belts, there\u2019s so much natural radioactivity\u2014that\u2019s the real killer.\u201d Nuclear electric propulsion could both minimize launch costs and the time fragile human bodies are subjected to microgravity and radiation. And that makes a synchronal bimodal nuclear engine that could both power and propel a spaceship an attractive alternative to a conventional rocket.Polzin and Schleicher think their nuclear engines could halve the time it takes to get to Mars. There may be ways to tweak the design to go even faster and further. But for now, the idea sits on the drawing board, awaiting feedback and refinement.\u201cThey\u2019re pulling together a lot of fairly mature technology,\u201d says Cass, who edited Polzin and Schleicher\u2019s article, \u201cA Reimagined Nuclear Rocket.\u201d \u201cElectric propulsion is mature. Nuclear thermal propulsion is not, but thanks to [previous efforts], we have a good idea how to do it. The new part is merging them together, and that of course throws up its own challenges.\u201dThanks to Cass and illustrator John MacNeill, Polzin and Schleicher\u2019s idea has moved from a napkin to the pages of this month\u2019s issue. Says Cass, \u201cThe whole point of the article is to say, \u2018Here\u2019s our napkin. Noodle on this with us and tell us what you think.\u2019\u201d We invite you to do so in the comments beneath the web version of this article. Or do it the old-fashioned way and mail the authors your own napkin.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/oscar-winner-jernej-barbic\" target=\"_blank\" rel=\" noopener\" title=\"Oscar Winner Brings Monsters to Life With His Simulation Software\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/a-white-man-in-business-casual-attire-seated-at-his-desk-with-a-laptop-and-desktop-computer-each-playing-a-different-movie-on-t.jpg?id=67690500&amp;width=980\" title=\"Oscar Winner Brings Monsters to Life With His Simulation Software\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/oscar-winner-jernej-barbic\" target=\"_blank\" rel=\" noopener\">Oscar Winner Brings Monsters to Life With His Simulation Software<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Joanna Goodrich<\/a> on 28. Augusta 2026. at 18:00 <\/small><p>While walking to school as a child, Jernej Barbi\u010d would marvel at how beautiful his home was. He was born and raised in a picturesque village in northwestern Slovenia (formerly Yugoslavia), located in the European Alps. Surrounded by alpine and beech trees, Barbic dreamed of replicating their swaying in the wind for others to enjoy.At the time, he didn\u2019t have the tools or the knowledge to create a system that could do that, but it sparked his interest in computer graphics, he says.Jernej Barbi\u010dEmployer University of Southern California, in Los AngelesTitle Professor of computer scienceMember grade Senior memberAlma maters University of Ljubljana, in Slovenia; Carnegie MellonTwenty years later, in 2016, Barbi\u010d, a professor of computer science at the University of Southern California, in Los Angeles, made his mark. His Ziva VFX software system allows for the creation of realistic muscle, fat, and skin simulations for 3D digital humans and creatures.The technology was launched in 2016 by a startup he helped found, Ziva Dynamics, headquartered in Vancouver. It was acquired in 2021 by Unity Technologies of San Francisco.Ziva VFX has been used in more than 60 movies including Aquaman and the Lost Kingdom; Godzilla x Kong: The New Empire; and Venom: Let There Be Carnage.For the design and development of Ziva VFX, Barbi\u010d, an IEEE senior member, received a 2025 technical achievement Academy Award. It was a \u201ctremendous honor,\u201d he says, as the award recognizes technologies that have had a significant impact on motion picture production.\u201cComputer graphics and simulation can sometimes feel like a specialized technical field,\u201d he says, \u201cbut the award showed that these ideas affect not just science but also art and how stories are told on screen.\u201cThe digital characters enabled by mathematics become important parts of people\u2019s lives.\u201dSparking an interest in computer graphicsBarbi\u010d says he was inspired to pursue engineering by his father, an engineer who headed a cement factory\u2019s research department and invented a technology that uses magnetic resonance imaging to test the integrity of cement. His father\u2019s work showed him that \u201cmathematics and physics are beautiful on their own, but engineering lets you build something that other people can use,\u201d he says.It was Barbi\u010d\u2019s mother, an elementary school teacher, who introduced him to computer science. When he was 8 years old, the school his mother taught at bought a ZX Spectrum computer. With permission from the principal, she brought it home for her son to play on for two weeks. But he didn\u2019t just play games; he created his own game using the BASIC programming language.The machine came with a booklet that contained instructions on how to write a computer program, he says.\u201cAt first,\u201d he says, \u201cI copied them verbatim without understanding what they did. But then I started realizing there is structure, and I modified the instructions.\u201d Of all the creatures brought to life using his technology, Barbi\u010d is particularly enamored with King Kong from 2024\u2019s Godzilla vs. Kong.DNEG\/Warner Bros. Entertainment Inc.\/LegendaryBy the end of the two weeks, he\u2019d developed a computer game where players guided a snowman along a winding road. It shifted unpredictably to the left or right, and players accumulated points by remaining on the road for as long as possible.Barbi\u010d went on to earn a bachelor\u2019s degree in mathematics in 2000 from the University of Ljubljana, in Slovenia. The following year, he moved to the United States to begin a doctoral program in computer science at Carnegie Mellon. It was a major turning point in his life, he says.His doctoral research focused on developing simulation methods for objects that can change their shape when an outside force is applied to them, known as \u201cdeformable objects.\u201dThat project shaped much of his later research, he says: \u201cI became interested not only in making simulations accurate but also in making them practical: fast enough, robust enough, and controllable enough to be used in real applications.\u201dAfter earning his Ph.D. in computer science in 2007, he worked as a postdoctoral researcher at MIT. Two years later, he joined USC as an assistant professor.Making movie magic possibleIt was at USC that Barbi\u010d merged his passion for computer science with film. He developed Vega FEM, an open-source software program that allowed people to animate realistic 3D deformable objects. But Vega FEM was narrow in scope and not exactly what filmmakers needed, he says, so he started exploring how to create a version suitable for the movie industry.\u201cA major theme of my career has been the translation of research ideas into practical tools,\u201d he says. \u201cAcademic research often produces beautiful algorithms, but it can be difficult to make those algorithms usable by artists, engineers, or production teams. I have always been interested in that bridge: taking rigorous computational methods and turning them into systems that people can actually use.\u201dIn 2011 he attended an Association for Computing Machinery conference presented by its Special Interest Group on Computer Graphics and Interactive Techniques (SIGGraph). There he met James Jacobs, the creature supervisor at visual effects company Weta FX of Wellington, New Zealand. The company is behind the effects in the Lord of the Rings and Hobbit trilogies and other movies. Jacobs used Barbi\u010d\u2019s software to create animals and fantastical creatures.Two years later, Weta FX offered Barbi\u010d a summerlong research position in New Zealand. He accepted and spent the time studying the process of creating visual effects and learning what roadblocks existed in the film industry, he says.At the time, the technology to create realistic soft-tissue and anatomical simulation for digital characters didn\u2019t exist.\u201cThe visual effects industry had reached a point where surface-level realism was not enough,\u201d Barbi\u010d says. \u201cA creature could have beautiful skin textures and detailed geometry, but if the bones, muscles, and fat underneath did not move correctly, the illusion would break.\u201cThe problem was especially difficult for creatures and characters that need to feel alive: animals, monsters, fantasy creatures, or digital doubles. Their bodies may have unfamiliar anatomy, but the audience still anticipates them to move in a way that matches real-world expectations. Muscles should bulge and contract, skin should stretch and slide, fat should have inertia, and tissue should respond to motion and impact.\u201d In an effort to solve the problem Jacobs in 2014 approached Barbi\u010d about founding a startup. In 2015 they launched Ziva Dynamics, where they began what is now Ziva VFX.The software uses physics-based simulations to model the internal anatomy of a character. It numerically solves the partial differential equations of nonlinear elasticity for musculoskeletal human and creature tissues, Barbi\u010d says. The equations describe how muscles, fat, skin, and connective tissue deform, interact with bones, and connect, and how muscles activate. Instead of animating only the outside surface, artists can create a model with underlying muscles, bones, soft tissue, and fat. Each component is assigned material properties, constraints, attachments, and activations. The simulator then computes how they deform and interact over time.The technology uses ideas from computational mechanics, finite element methods, numerical optimization, contact handling, and computer graphics, Barbi\u010d says. Finite element simulation, a method used to predict how a product or structure reacts to heat and other real-world forces, provides a way to model deformable materials volumetrically, not just as surfaces, he says. The tool computes internal elastic forces and solves the equations of motion so the character\u2019s tissues respond plausibly to animation, pose changes, muscle activation, and dynamic motion.But the system had to be designed for artists, Barbi\u010d says. In production, he says, the goal is not only physical realism but also controllable realism.\u201cArtists need to direct the result, iterate, and fit the simulation into a larger animation pipeline,\u201d he says. \u201cSo the technology had to combine scientific simulation with practical controls, robustness, and integration with visual effects workflows.\u201dBarbi\u010d says Ziva VFX has been used in more than 60 movies. Of all the creatures brought to life using his technology, he is particularly enamored with King Kong from 2024\u2019s Godzilla vs. Kong.\u201cWhen King Kong is walking, you can see the muscles, how they\u2019re very pronounced, and how they influence the shape of the skin. You can really feel the strength of King Kong,\u201d he says. \u201cAnd this was made through my software, so I think it\u2019s amazing.\u201dAfter Ziva Dynamics was acquired by Unity, Barbi\u010d consulted for the company for almost two years.In 2024 DNEG, a London-based visual effects and computer animation company, acquired the exclusive license to Ziva VFX.Animating the human handBarbi\u010d strives to improve visual effects as an entrepreneur and an academic. His most recent research, funded by the U.S. National Science Foundation, focused on the modeling, simulation, and animation of human hands. The goal is to create computer models of hands that can be used to design tools, medical prosthetics, and robotic hands.\u201cThe hand is a fascinating and difficult system,\u201d Barbi\u010d says. \u201cIt contains many small bones, muscles, tendons, ligaments, skin, fat, and other soft tissues, all packed into a compact structure and interacting mechanically in complex ways.\u201dHe and his team built a digital twin of the human hand.He aimed to move toward \u201canatomically meaningful simulation,\u201d he says. He used medical imaging, geometric modeling, finite element methods, and multibody simulation to represent the internal structures of the hand and its motions.\u201cIEEE lets me place my work not only in the world of images and animation but also in the world of engineering systems that must be accurate, stable, interactive, and useful.\u201dHe worked with Bohan Wang, who at the time was a USC doctoral candidate, and George Matcuk, an associate professor of radiology. Wang is now an assistant professor of computer science at the National University of Singapore.Barbi\u010d, Wang, and Matcuk scanned four people\u2019s hands with an MRI machine. The two men and two women would position their hands in 12 poses, which allowed the team to gather data about how the bones, muscles, and fat move with each pose.The data sets are available for anyone to use in their own studies.\u201cThis project can help medical doctors learn more about how the hand is moving,\u201d Barbi\u010d says. \u201cIt\u2019s also great for roboticists to better understand how the human hand actually works, so [the movements] can be replicated.\u201dIEEE: Integral in interdisciplinary researchBarbi\u010d joined IEEE in 2008, when he published his research paper on simulation methods for deformable objects in the inaugural issue of the IEEE Transactions on Haptics. He has since published several papers in the IEEE Transactions on Visualization and Computer Graphics, which he says connected his work to a wider community interested in visual computing and computational methods. You can find his research in the IEEE Xplore Digital Library.\u201cIEEE recognizes the engineering side of computer science,\u201d he says. \u201cMy work is often presented as computer graphics, but at its core, it is also simulation, mechanics, numerical methods, haptics, visualization, and software systems.\u201cIEEE is a community where that broader identity makes sense. It lets me place my work not only in the world of images and animation but also in the world of engineering systems that must be accurate, stable, interactive, and useful.\u201dHe believes the organization is key in supporting a healthy interdisciplinary research ecosystem at a global scale\u2014which, he says, is why he has served as an associate editor for Transactions on Visualization and Computer Graphics and Transactions on Haptics.Being a member has made it easier for Barbi\u010d to connect with engineers in different fields, he says.\u201cMy research often lives between categories: It is mathematical but also practical; visual but also mechanical; artistic but also engineering-driven,\u201d he says. \u201cIEEE is one of the professional communities where that mixture is understood.\u201d<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/andrew-ng-data-centric-ai\" target=\"_blank\" rel=\" noopener\" title=\"Andrew Ng: Unbiggen AI\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/andrew-ng-listens-during-the-power-of-data-sooner-than-you-think-global-technology-conference-in-brooklyn-new-york-on-wednes.jpg?id=29206806&amp;width=980\" title=\"Andrew Ng: Unbiggen AI\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/andrew-ng-data-centric-ai\" target=\"_blank\" rel=\" noopener\">Andrew Ng: Unbiggen AI<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Eliza Strickland<\/a> on 9. Februara 2022. at 15:31 <\/small><p>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\u2019s AI group. So when he says he has identified the next big shift in artificial intelligence, people listen. And that\u2019s what he told IEEE Spectrum in an exclusive Q&amp;A.\n\tNg\u2019s current efforts are focused on his company \n\tLanding 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 \u201csmall data\u201d solutions to big issues in AI, including model efficiency, accuracy, and bias.\n\n\tAndrew Ng on...\n\nWhat\u2019s next for really big models\nThe career advice he didn\u2019t listen to\nDefining the data-centric AI movement\nSynthetic data\nWhy Landing AI asks its customers to do the work\n\nThe 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\u2019s an unsustainable trajectory. Do you agree that it can\u2019t go on that way?\n\nAndrew Ng: This is a big question. We\u2019ve seen foundation models in NLP [natural language processing]. I\u2019m excited about NLP models getting even bigger, and also about the potential of building foundation models in computer vision. I think there\u2019s 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\u2019s a set of other problems that need small data solutions.\n\nWhen you say you want a foundation model for computer vision, what do you mean by that?\n\nNg: 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\u2019re reasonably fair and free from bias, especially if many of us will be building on top of them.\n\nWhat needs to happen for someone to build a foundation model for video?\n\nNg: 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\u2019s why foundation models have arisen first in NLP. Many researchers are working on this, and I think we\u2019re seeing early signs of such models being developed in computer vision. But I\u2019m 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.\n\n\tHaving said that, a lot of what\u2019s 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\u2019t work for other industries.\n\nBack to top\n\nIt\u2019s funny to hear you say that, because your early work was at a consumer-facing company with millions of users.\n\nNg: Over a decade ago, when I proposed starting the Google Brain project to use Google\u2019s 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\u2019t just be in scaling up, and that I should instead focus on architecture innovation.\n\n\t\u201cIn many industries where giant data sets simply don\u2019t 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.\u201d\n\t\u2014Andrew Ng, CEO &amp; Founder, Landing AI\n\n\tI remember when my students and I published the first \n\tNeurIPS workshop paper advocating using CUDA, a platform for processing on GPUs, for deep learning\u2014a different senior person in AI sat me down and said, \u201cCUDA is really complicated to program. As a programming paradigm, this seems like too much work.\u201d I did manage to convince him; the other person I did not convince.\n\nI expect they\u2019re both convinced now.\n\nNg: I think so, yes.\n\n\tOver the past year as I\u2019ve been speaking to people about the data-centric AI movement, I\u2019ve 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\u2019ve been getting the same mix of \u201cthere\u2019s nothing new here\u201d and \u201cthis seems like the wrong direction.\u201d\n\nBack to top\n\nHow do you define data-centric AI, and why do you consider it a movement?\n\nNg: 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\u2014the neural network architecture\u2014is basically a solved problem. So for many practical applications, it\u2019s now more productive to hold the neural network architecture fixed, and instead find ways to improve the data.\n\n\tWhen I started speaking about this, there were many practitioners who, completely appropriately, raised their hands and said, \u201cYes, we\u2019ve been doing this for 20 years.\u201d This is the time to take the things that some individuals have been doing intuitively and make it a systematic engineering discipline.\n\n\tThe data-centric AI movement is much bigger than one company or group of researchers. My collaborators and I organized a \n\tdata-centric AI workshop at NeurIPS, and I was really delighted at the number of authors and presenters that showed up.\n\nYou often talk about companies or institutions that have only a small amount of data to work with. How can data-centric AI help them?\n\nNg: You hear a lot about vision systems built with millions of images\u2014I once built a face recognition system using 350 million images. Architectures built for hundreds of millions of images don\u2019t 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\u2019t 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.\n\nWhen you talk about training a model with just 50 images, does that really mean you\u2019re 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\u2019s designed to learn only from that small data set?\n\nNg: 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\u2019s 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\u2019s a very practical problem we\u2019ve seen spanning vision, NLP, and speech, where even human annotators don\u2019t agree on the appropriate label. For big data applications, the common response has been: If the data is noisy, let\u2019s just get a lot of data and the algorithm will average over it. But if you can develop tools that flag where the data\u2019s 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.\n\n\t\u201cCollecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity.\u201d\n\t\u2014Andrew Ng\n\n\tFor 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\u2019s inconsistent. So you can very quickly relabel those images to be more consistent, and this leads to improvement in performance.\n\nCould this focus on high-quality data help with bias in data sets? If you\u2019re able to curate the data more before training?\n\nNg: 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\u2019s 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.\n\n\tOne 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\u2019s quite difficult. But if you can engineer a subset of the data you can address the problem in a much more targeted way.\n\nWhen you talk about engineering the data, what do you mean exactly?\n\nNg: 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\u2019m 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.\n\n\tFor 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.\n\nBack to top\n\nWhat about using synthetic data, is that often a good solution?\n\nNg: 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\u2019d love to see more tools to let developers use synthetic data generation as part of the closed loop of iterative machine learning development.\n\nDo you mean that synthetic data would allow you to try the model on more data sets?\n\nNg: Not really. Here\u2019s an example. Let\u2019s say you\u2019re 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\u2019s doing well overall but it\u2019s 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.\n\n\t\u201cIn 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.\u201d\n\t\u2014Andrew Ng\n\n\tSynthetic 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.\n\nBack to top\n\nTo 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?\n\nNg: 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.\n\n\tOne 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.\n\nHow do you deal with changing needs? If products change or lighting conditions change in the factory, can the model keep up?\n\nNg: 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\u2019t expect changes in the next five years. Those stable environments make things easier. For other manufacturers, we provide tools to flag when there\u2019s 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\u2019s 3 a.m. in the United States, I want them to be able to adapt their learning algorithm right away to maintain operations.\n\n\tIn 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?\n\nSo you\u2019re saying that to make it scale, you have to empower customers to do a lot of the training and other work.\n\nNg: 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\u2019s 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\u2019s what Landing AI is executing in computer vision, and the field of AI needs other teams to execute this in other domains.\n\nIs there anything else you think it\u2019s important for people to understand about the work you\u2019re doing or the data-centric AI movement?\n\nNg: In the last decade, the biggest shift in AI was a shift to deep learning. I think it\u2019s quite possible that in this decade the biggest shift will be to data-centric AI. With the maturity of today\u2019s 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.\n\nBack to top\nThis article appears in the April 2022 print issue as \u201cAndrew Ng, AI Minimalist.\u201d<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/ai-chip-design-matlab\" target=\"_blank\" rel=\" noopener\" title=\"How AI Will Change Chip Design\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/layered-rendering-of-colorful-semiconductor-wafers-with-a-bright-white-light-sitting-on-one.jpg?id=29285079&amp;width=980\" title=\"How AI Will Change Chip Design\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/ai-chip-design-matlab\" target=\"_blank\" rel=\" noopener\">How AI Will Change Chip Design<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Rina Diane Caballar<\/a> on 8. Februara 2022. at 14:00 <\/small><p>The end of Moore\u2019s 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\u2019re 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\u2019s 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\u2019 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\u2019s involved in most parts of the cycle, including the design and manufacturing process. There\u2019s 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\u2019re designing the light and the sensors and all the different components. There\u2019s a lot of anomaly detection and fault mitigation that you really want to consider.\n\nHeather GorrMathWorksThen, thinking about the logistical modeling that you see in any industry, there is always planned downtime that you want to mitigate; but you also end up having unplanned downtime. So, looking back at that historical data of when you\u2019ve 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\u2019ve 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\u2019re 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\u2019s like having a digital twin in a sense?Gorr: Exactly. That\u2019s 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\u2019s going to be more efficient and, as you said, cheaper?Gorr: Yeah, definitely. Especially in the experimentation and design phases, where you\u2019re trying different things. That\u2019s obviously going to yield dramatic cost savings if you\u2019re 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\u2019ve 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\u2019s why you do many simulations and parameter sweeps. But that\u2019s also the benefit of having that digital twin, where you can keep that in mind\u2014it\u2019s not going to be as accurate as that precise model that we\u2019ve 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\u2019s 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\u2019s useful to explore the frequency domain and things like data synchronization or resampling. Those can be really challenging if you\u2019re not sure where to start.One of the things I would say is, use the tools that are available. There\u2019s 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\u2019ve 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\u2019s 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\u2019re 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\u2019 jobs?Gorr: It\u2019s 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\u2019s a great example of people and technology working hand in hand. It\u2019s also an industry where all people involved\u2014even on the manufacturing floor\u2014need to have some level of understanding of what\u2019s 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\u2019s very much dependent on that human element\u2014involving 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\u2019re going to see less of those superprecise predictions and more transparency of information, sharing, and that digital twin\u2014not only using AI but also using our human knowledge and all of the work that many people have done over the years.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/spectrum.ieee.org\/2d-hbn-qubit\" target=\"_blank\" rel=\" noopener\" title=\"Atomically Thin Materials Significantly Shrink Qubits\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/spectrum.ieee.org\/media-library\/a-golden-square-package-holds-a-small-processor-sitting-on-top-is-a-metal-square-with-mit-etched-into-it.jpg?id=29281587&amp;width=980\" title=\"Atomically Thin Materials Significantly Shrink Qubits\" style=\"height:150px;width:150px\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/spectrum.ieee.org\/2d-hbn-qubit\" target=\"_blank\" rel=\" noopener\">Atomically Thin Materials Significantly Shrink Qubits<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/spectrum.ieee.org\" target=\"_blank\" title=\"spectrum.ieee.org\">Dexter Johnson<\/a> on 7. Februara 2022. at 16:12 <\/small><p>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\u2019s 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.\u201cWe are addressing both qubit miniaturization and quality,\u201d said William Oliver, the director for the Center for Quantum Engineering at MIT. \u201cUnlike 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.\u201dThe 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\u2014less than 0.02 degrees above absolute zero (-273.15 \u00b0C).\n\nSuperconducting qubits are measured at temperatures as low as 20 millikelvin in a dilution refrigerator.Nathan Fiske\/MITIn that environment, insulating materials that are available for the job, such as PE-CVD silicon oxide or silicon nitride, have quite a few defects that are too lossy for quantum computing applications. To get around these material shortcomings, most superconducting circuits use what are called coplanar capacitors. In these capacitors, the plates are positioned laterally to one another, rather than on top of one another.As a result, the intrinsic silicon substrate below the plates and to a smaller degree the vacuum above the plates serve as the capacitor dielectric. Intrinsic silicon is chemically pure and therefore has few defects, and the large size dilutes the electric field at the plate interfaces, all of which leads to a low-loss capacitor. The lateral size of each plate in this open-face design ends up being quite large (typically 100 by 100 micrometers) in order to achieve the required capacitance.In an effort to move away from the large lateral configuration, the MIT researchers embarked on a search for an insulator that has very few defects and is compatible with superconducting capacitor plates.\u201cWe chose to study hBN because it is the most widely used insulator in 2D material research due to its cleanliness and chemical inertness,\u201d 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\u2019t regard this as a limiting factor.\u201cWhat determines the quality factor of the capacitor are the two interfaces between the two materials,\u201d said Wang. \u201cOnce the sandwich is made, the two interfaces are \u201csealed\u201d and we don\u2019t see any noticeable degradation over time when exposed to the atmosphere.\u201dThis 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.\u201cThe 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,\u201d 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.<\/p><\/div><\/li><\/ul> <\/div><style type=\"text\/css\" media=\"all\">.feedzy-rss .rss_item .rss_image{float:left;position:relative;border:none;text-decoration:none;max-width:100%}.feedzy-rss .rss_item .rss_image span{display:inline-block;position:absolute;width:100%;height:100%;background-position:50%;background-size:cover}.feedzy-rss .rss_item .rss_image{margin:.3em 1em 0 0;content-visibility:auto}.feedzy-rss ul{list-style:none}.feedzy-rss ul li{display:inline-block}<\/style>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":2667,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-2664","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.tk.etf.unsa.ba\/bs\/wp-json\/wp\/v2\/pages\/2664","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tk.etf.unsa.ba\/bs\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.tk.etf.unsa.ba\/bs\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.tk.etf.unsa.ba\/bs\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tk.etf.unsa.ba\/bs\/wp-json\/wp\/v2\/comments?post=2664"}],"version-history":[{"count":7,"href":"https:\/\/www.tk.etf.unsa.ba\/bs\/wp-json\/wp\/v2\/pages\/2664\/revisions"}],"predecessor-version":[{"id":2676,"href":"https:\/\/www.tk.etf.unsa.ba\/bs\/wp-json\/wp\/v2\/pages\/2664\/revisions\/2676"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.tk.etf.unsa.ba\/bs\/wp-json\/wp\/v2\/media\/2667"}],"wp:attachment":[{"href":"https:\/\/www.tk.etf.unsa.ba\/bs\/wp-json\/wp\/v2\/media?parent=2664"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}