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Franklin “Frank” Kuo
Codeveloper of ALOHAnet
Fellow, 91; died 14 April
Kuo helped develop ALOHAnet, a pioneering computer system at the University of Hawaii at Mānoa, 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’s development of Ethernet a couple of years later. In 2020 ALOHAnet was designated as an IEEE Milestone.
Kuo earned bachelor’s, master’s, 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—which 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’s 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’s office. He oversaw computer communications applications used in command, control, and intelligence programs.
During the 1980s and ’90s, he helped develop China’s 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’s 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’s 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 Karim
Bell Labs researcher
Life senior member, 86; died 18 May
Karim 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’s degree in electrical engineering from the Bangladesh University of Engineering and Technology, in Dhaka. He then earned a master’s 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 Bostic
Former IEEE Region 4 director
Life senior member, 86; died 18 March
Bostic was an active IEEE volunteer who served as the 1998–1999 director of IEEE Region 4. In 2007 he received a lifetime achievement award from the IEEE Central Indiana Section for “outstanding commitment and dedicated service as regional advisor to the volunteers and members of Region 4 and the Institute.”
He was an engineer for 30 years at U.S. Navy’s 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.
Professor
Life Fellow, 91; died 10 March
Jones 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’s 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’s 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’s 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’s systems engineering program.
An active IEEE volunteer, he served as 1975–1976 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 Spitzer
Clinical neurology researcher
Life senior member, 70; died 27 February
Spitzer 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’ 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 patients 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 1977–1979 director of the IEEE Central Indiana Section.
Donald Leo Dietmeyer
Professor
Life Fellow, 93; died 13 February
Dietmeyer 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’d joined the university’s electrical engineering faculty as a professor in 1958 while pursuing his doctorate.
Dietmeyer’s 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’s electrical and computer engineering department from 1983 to 1995. In 1998 he retired and was named professor emeritus.
This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free!
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 income
For many of us, our work became our identity, so we don’t just think, “I don’t have a job anymore.” We start thinking, “I’m not an engineer anymore.”
An apple tree in winter is still an apple tree. A car parked in a driveway is still a car. You’re still an engineer, the same way you were still one every evening you clocked out, and the same way you’ll be one at the next job. Your skills took years to build and won’t 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’ve been putting off: working out, being present with your kids, writing, whatever hobby got shelved for a deadline three years ago.
Once you’ve caught your breath, here are some tips for when the actual work starts:
1. Don’t sprint on day one
I 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’t 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 spending
Go through every subscription and recurring charge and cut what isn’t essential. Every dollar you stop bleeding buys you patience instead of forcing a bad offer out of fear.
3. Build a list wider than LinkedIn
Start 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’t mean your search should stop there. Try Facebook, Instagram, and other social media channels too, where plenty of people who aren’t 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’t the only game in town.
Don’t 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’ve contacted.
4. Eight hours is a long time
With no work to fill your day, you may default to treating the search like an eight-hour shift. You can’t apply productively for 8 hours straight, and that’s 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’ve been putting off.
5. Catalog your wins before you study interview questions
Cataloging 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’ll come up once you’re 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’s work is generating interest in you, or leading you to someone who might hire you. If not, consider skipping it.
One last thing
A layoff rarely reflects your skills. It’s usually a company protecting revenue—nothing more personal than that. Knowing that doesn’t make it easier, but hopefully this gets you back on your feet faster.
—Brian
Looking 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.
While 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.
When 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’s 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.
I’m 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’s Volkswagen SUV, named Stanley, became the world’s 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’s bid to make self-driving cars a reality, for robotaxis and—eventually—for 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’s vastly more advanced descendant. Rivian’s 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’s 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’s $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’s 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’s 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 “check out” behind the wheel for limited periods, to scroll through emails or watch a movie—but 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’s seat—or in which you could spend the ride lounging alone in the back seat, enjoying a private slice while reading a newspaper.
At Rivian’s software lab in Palo Alto, Calif., a technician evaluated code for the company’s self-driving system.Jason Henry/Bloomberg/Getty Images
Robotaxis 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—including Tesla, Toyota, Mercedes, Volkswagen, and China’s BYD—are racing to bring that level of self-guided mobility to the masses. Rivian’s 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’s 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’ systems. The race is on to funnel those data through fast-improving AI models with “end to end” capability: an AI architecture, powered by deep learning, that processes raw sensor data directly into physical vehicle commands. So equipped, engineers anticipate they’ll be able to solve the tricky edge cases—tangled urban streets, unique geographies, swarms of pedestrians, inclement weather—that skeptics once deemed intractable.
Despite the company’s 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’s 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’s Palo Alto, Calif., lab in December, 2025. Jason Henry/Bloomberg/Getty Images
Rivian’s underdog strategy is to leverage software and tech to make itself a serious player. Volkswagen, among the world’s 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’s 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’s 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
“Rivian developed an architecture so important that VW is spending billions to buy it, as opposed to trying to re-create it themselves,” says Bryan Reimer, a research scientist in MIT’s Center for Transportation and Logistics.
But the joint venture doesn’t give VW access to Rivian’s autonomous tech. In March, that R2 architecture underpinned Rivian’s $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’s 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’s 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 Images
Until recently, most observers would have gone all-in on Tesla as the winner of the autonomous race. Elon Musk’s 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: “I’m just guessing here, but probably in the fourth quarter” 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’s make-or-break R2 SUV at a Utah state park, says that showroom Rivians will start adopting some of its robotaxis’ Level 4 capabilities no later than 2030, perhaps beginning with self-parking functions.
Like most autonomous cars, Rivian’s 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—what Rivian refers to as its “Large Driving Model,” or LDM—that 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— commands for electric motors and other systems—is backed by redundant hardware for by-wire systems such as steering and brakes.
During my demo of Rivian’s point-to-point Autonomy+ system, a company test driver sits behind the wheel. Nick Nguyen, the engineer who directs Rivian’s 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’s little room for error. “We want cliché. We want boring. Just safe, repeatable driving,” he says.
The Rivian R2 SUV plans to offer a self-driving system by roughly year’s end 2026. The R2 competes with the more urban-oriented Tesla Y.Rivian
From my brief drive, I’d say suburban boredom is achieved in this Rivian R1S. Unlike some modes of Tesla’s Full Self-Driving (Supervised), Rivian’s 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’t 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, “The Growing Proof That Autonomous Cars Save Lives”].
Rivian’s 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’s part of what Scargine calls the “data flywheel,” 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’s existing roofline, an improvement over the bulky, drag-producing units seen on Waymo Jaguars, and older partially autonomous models. Vidya Rajagopalan, Rivian’s 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’s senior vice president of electrical engineering hardware, holds a RAP1 AI processor chip.Jason Henry/Bloomberg/Getty Images
A 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—“a tire in the road, or maybe a large dinosaur,” 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 “see” through rain or snow, but with relatively low spatial resolution.
To 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’s specs by Taiwan Semiconductor Manufacturing Co. , which also makes custom chips for Tesla.
Rivian’s autonomy module contains two Rivian Autonomy Processors, each capable of 800 trillion operations per second.Rivian
Nvidia’s 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’s Thor.
Rajagopalan says developing the chip and AI software simultaneously shaved a critical full year from development. Experts say it’s 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’s general-purpose chip, designed to satisfy multiple customers with various needs, must devote computing power to onboard infotainment, displays, or other systems. Rivian’s chip is designed to run autonomy and nothing but.
During my visit to Rivian’s Silicon Valley campus, Rivian engineers Prasun Raha and Mukund Chavan tutored me on the rapid pace of the company’s 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 “black boxes” 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 “early fusion”: 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’s 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’s lens gets covered with mud.
Together, these elements make up Rivian’s 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’s Level 2+. RivLink, the automaker’s interconnect technology, can bridge multiple RAP modules to scale processing power. “It 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,” Raha says.
Rivian’s next planned milestone toward self-driving will be Level 3 autonomy—a hands-off and eyes-off system, but for highways only. (Remember, Tesla’s 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’s 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.
Ready or not, these much more autonomous systems are coming, a natural evolution of today’s 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.
“One in four cars sold globally may be equipped with smart-driving technology in five years, versus one in eight cars now,” wrote Tim Hsiao, a Morgan Stanley analyst, in a note posted on the company’s 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’s, which uses cameras alone. Rivian
MIT’s 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—while owners keep working or playing—the 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’t 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—even ones that don’t result in injuries, such as Waymos driving onto a flooded street or impeding emergency workers—give the industry a black eye. Companies like Tesla and even Waymo, Reimer says, have too often relied on an arrogant “Trust me” 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.
“Do it right, and share all your data,” he says. “Earn the right to scale…. It’s about establishing trust, and developing a framework in which we truly believe these systems can operate as a trusted part of our transportation network.”
This article was updated on 08 September 2026.
Plenty of people remain spooked by autonomous vehicles, or AVs. Some experts and policymakers have cautioned that AVs won’t necessarily make roads safer. When it comes to partial or full autonomy, the picture isn’t entirely clear, in part because there aren’t 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—some of which are already mandated on every new car—are 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’ 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’s 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.
A key question is whether Waymo’s 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.
The IIHS’s 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 “police-reportable crashes.” It then compared crash rates of human-driven cars against Waymo taxis in San Francisco, Phoenix, Los Angeles, and Austin. Waymo’s 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’s 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’s 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’s director of statistical services, Eric Teoh: “Those are encouraging signs for the future of driverless vehicles.” Teoh, who was also the lead author of the institute’s study, added that “Now 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.”
On July 30, in a move seen as fast-tracking the tech’s 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’s toaster-shaped robotaxis, which have no steering wheel or pedals aboard. The agency determined that Zoox’s purpose-built robotaxi “would provide an equivalent level of safety” as a compliant vehicle, thereby satisfying the standard for an exemption.
On that final day of the SAE’s Automated Transportation Symposium, NHTSA also announced a partnership with SAE Industry Technologies to develop the nation’s first performance and competency standards for AVs, via a three-year, $5 million “A2SCEND” 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—let alone the 90 percent reductions suggested by some studies—would save 580,000 lives a year. That social and economic gain would dwarf that of seat-belt adoption or anti–drunk driving campaigns.
This article was updated on 08 September 2026.
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’t 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.
“We 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,” says Kristopher Zakrzewski, the IEEE area manager for Europe, the Middle East, Africa, and parts of Central Asia. “Our goal is to give them the tools they need to increase visibility and allow them to participate in global conversations in the technology space.”
More 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’s 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.
“The great thing about these sessions,” Zakrzewski says, “is 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.”
One 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’s session, where he shared practical guidance on conducting literature reviews and identifying high-impact research.
“Researchers in Uganda typically present their paper at an IEEE conference, and that’s it,” he says. “What we’re trying to do is encourage them to take the next step and get their paper published in an IEEE journal.”
He encourages his students to submit a summary of their thesis to an IEEE conference, he says.
“Otherwise,” he says, “their thesis sits in the university’s library or collects dust on a bookshelf.
“When 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’s program globally.”
Attendees 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’s 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.
“It 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,” Zakrzewski says.
The 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.” —IEEE Senior Member Mayur Kumar Chhipa
IEEE 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.
“IEEE supports author choice,” Zakrzewski says. “We 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—which is why we’re focusing on growing our open access program to complement our traditional publishing program and offer more options to authors.”
Participants at the Uganda session told Chhipa that they appreciated the IEEE Xplore Digital Library demonstrations and found the guidance on academic publishing valuable.
“Many 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,” he says.
“I have observed increased interest among students and faculty in using IEEE Xplore as their primary research resource,” he adds. “Researchers are also more aware of ethical publishing practices and are developing stronger research proposals and manuscripts.
“The 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.”
Chhipa 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—which Chhipa says he was excited about.
He encouraged that student to apply for a travel grant.
“Maybe he’ll get it. Maybe he won’t. But at least he learned how to write a paper, apply for a visa to attend the conference, and book an airplane ticket,” Chhipa says. “It will help him grow personally and professionally.”
Presenting a paper at an IEEE conference can be life-changing, he says.
“It opens additional opportunities,” he says, “including 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.”
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.
Conventional 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’s 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.
The 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 ±450 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.
Large 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’s 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.
From automotive welding cells to high-speed machining centers, Robotrax systems adapt to severe working conditions through tailored protective accessories:
This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free!
Asking someone to be your mentor is weird.
Walking up to someone and asking, “Will you be my mentor?” has always seemed to me like the adult version of a kid walking up to another kid at a party and asking, “Will you be my friend?”
What you’re really asking is: “Will you commit some amount of unpaid time to guiding my career for an indefinite period?”
Framed that way, of course some people hesitate to say yes.
But formal mentorship isn’t the only way to benefit from the wisdom of those who came before. I’ve never formally asked anyone to mentor me. And yet I’ve had dozens of unofficial mentors.
One 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’ve 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’re 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’t?
Then I steal whatever seems useful and apply it to my own career.
Great artists steal. Engineers should too.
Still, just observing has its limits. Asking questions can get you even farther.
I’ve asked managers how they approached difficult conversations, and I’ve asked engineers what their process was for solving problems I thought were impossible.
If someone seems unusually knowledgeable: “What are you reading right now?” If I respect someone’s work: “What’s something you think I could do better?”
These aren’t profound questions. They don’t need to be. You get one useful piece of information, apply it, and move on.
And if you don’t work around exceptional engineers, you can still do this. The only real requirement is curiosity. When you encounter something you don’t understand, make it a rule to investigate instead of moving past it.
You don’t need one person willing to guide your career. You need a collection of people who know things you don’t.
Pay attention to them. Ask questions. And shamelessly copy the good parts.
If you have a career question you’re struggling with, like an upcoming decision, a problem at work, an interview, whatever—submit it here: https://docs.google.com/forms/d/e/1FAIpQLSdj_2BZIhrGF__7BCLH33zJ9NMv8C7Vsg9NNusASrYj7-9Idw/viewform. You can include your name or remain anonymous.
I’ll be reading through them and answering some in future articles. Consider it mentorship without the awkward “will you be my mentor?” conversation.
—Brian
IEEE 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.
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’s 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’ 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’s 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.
“It is appropriate that the 300th Milestone is the Canadarm,” says Michael Geselowitz, senior director of the IEEE History and Heritage group. “The 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.”
Seeking to collaborate with other countries on the reusable spacecraft, NASA invited Canada to participate in 1969. It took some time for the country’s officials to determine what technology it could contribute. They learned of a robot that loaded and replaced spent fuel bundles in Canada’s 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’s payload bay. NASA approved the project, and development began in 1975.
Canada had no space agency at the time, so the country’s 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’s Institute for Aerospace Studies contributed to the project.
NASA 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.
“Each 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,” Hughes said in the article. The IEEE life member worked at the Institute for Aerospace Studies.
“This milestone is a reminder of the privilege we all have at MDA Space—as engineers, designers, builders, operators—to build technology that shapes history.” —Holly Johnson, MDA Space vice president
The arm was 50 meters long and weighed 400 kilograms. It was made of materials that could withstand outer space’s harsh environment: titanium, stainless steel, and graphite epoxy. The arm was so lightweight that it couldn’t support itself under Earth’s gravity, so it lay on air bearings on the lab floor at Spar’s 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’s 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 “all singing, all dancing,” examined the arm’s 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.
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.NASA
The Canadarm was attached to the outside of the shuttle. Astronauts were able to monitor the arm’s 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’s 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’s mission.
The original Canadarm took its final flight in July 2011 aboard the Atlantis shuttle.
The IEEE Milestone dedication ceremony was held at MDA Space’s 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‑elect, gave the opening remarks at the ceremony. She emphasized that the Milestone was not only celebrating the technology but also “the 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.”
To 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’s Communications History Committee, unveiled a bronze plaque that honored the technology. Michelson was the Milestone’s proposer.
“This milestone is a reminder of the privilege we all have at MDA Space—as engineers, designers, builders, operators—to build technology that shapes history,” Johnson said. “That 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.”
The 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’s IEEE Tech History collection. IEEE Spectrum also covers aspects of tech history.
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—engineered 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’s always the worst day, because automation only quits when it’s confused or in trouble. But by then, you have a person in the chair who hasn’t 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’ve come to believe it’s 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?
The 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, “entry-level” has quietly come to mean “three years of experience required.”
Strip away the noise and you’re 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 “why on earth did that work” 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’s also an engineering problem, and safety-critical fields have already spent decades learning how to solve it.
My 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’s 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’t 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’s response is instructive, and it’s 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, “Manual Flight Operations Proficiency,” declaring that “manual flight is the foundation upon which other technical flying skills are built.” 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’s 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’t see it.
Put 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 “manual gate.”
A 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—deliberately, often a junior one—must 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’s. When the two disagree, that’s 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’d protect any other piece of critical infrastructure. It may not be efficient this quarter, but dismantling it quietly mortgages your capability a decade out.
None 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’s a hard sell in a market that judges most leaders on quarterly results. A hired executive who carries “unnecessary” 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—so that when the automation fails, as it always eventually does, there is still someone in the chair who knows how to fly.
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—to advance technology for the benefit of humanity—is 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—supported by a global network of volunteers and technical professionals—working together to apply technology to solve the world’s 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.
IEEE 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’s 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‑cultural fluency—competencies increasingly essential in a world where technology and society are deeply intertwined. They also learn to transition from R&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‑impact work. By engaging in purpose‑driven projects, they can discover their own capacity to grow, strengthen their technical skills, and become the leaders and problem‑solvers who will guide our profession forward.
Our members feel this deeply. Engagement research shows that members increasingly cited “giving back to my profession and the world community” as a reason for joining the organization and renewing their membership. Those with higher membership grades identify “participation in humanitarian technology efforts” 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 “humanitarian impact” and “large-scale collaboration.” 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’s leading convening force for developing interdisciplinary technology solutions to solve humanity’s 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.
—MARY ELLEN RANDALL
IEEE president and CEO
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