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Transistor-like Qubits Hit Key Benchmark
by Dina Genkina on 11. September 2024. at 12:00

A team in Australia has recently demonstrated a key advance in metal-oxide-semiconductor-based (or MOS-based) quantum computers. They showed that their two-qubit gates—logical operations that involve more than one quantum bit, or qubit—perform without errors 99 percent of the time. This number is important, because it is the baseline necessary to perform error correction, which is believed to be necessary to build a large-scale quantum computer. What’s more, these MOS-based quantum computers are compatible with existing CMOS technology, which will make it more straightforward to manufacture a large number of qubits on a single chip than with other techniques.
“Getting over 99 percent is significant because that is considered by many to be the error correction threshold, in the sense that if your fidelity is lower than 99 percent, it doesn’t really matter what you’re going to do in error correction,” says Yuval Boger, CCO of quantum computing company QuEra and who wasn’t involved in the work. “You’re never going to fix errors faster than they accumulate.”
There are many contending platforms in the race to build a useful quantum computer. IBM, Google and others are building their machines out of superconducting qubits. Quantinuum and IonQ use individual trapped ions. QuEra and Atom Computing use neutrally-charged atoms. Xanadu and PsiQuantum are betting on photons. The list goes on.
In the new result, a collaboration between the University of New South Whales (UNSW) and Sydney-based startup Diraq, with contributors from Japan, Germany, Canada, and the U.S., has taken yet another approach: trapping single electrons in MOS devices. “What we are trying to do is we are trying to make qubits that are as close to traditional transistors as they can be,” says Tuomo Tanttu, a research fellow at UNSW who led the effort.
Qubits That Act Like Transistors
These qubits are indeed very similar to a regular transistor, gated in such a way as to have only a single electron in the channel. The biggest advantage of this approach is that it can be manufactured using traditional CMOS technologies, making it theoretically possible to scale to millions of qubits on a single chip. Another advantage is that MOS qubits can be integrated on-chip with standard transistors for simplified input, output, and control, says Diraq CEO Andrew Dzurak.
The drawback of this approach, however, is that MOS qubits have historically suffered from device-to-device variability, causing significant noise on the qubits.
“The sensitivity in [MOS] qubits is going to be more than in transistors, because in transistors, you still have 20, 30, 40 electrons carrying the current. In a qubit device, you’re really down to a single electron,” says Ravi Pillarisetty, a senior device engineer for Intel quantum hardware who wasn’t involved in the work.
The team’s result not only demonstrated the 99 percent accurate functionality on two-qubit gates of the test devices, but also helped better understand the sources of device-to-device variability. The team tested three devices with three qubits each. In addition to measuring the error rate, they also performed comprehensive studies to glean the underlying physical mechanisms that contribute to noise.
The researchers found that one of the sources of noise was isotopic impurities in the silicon layer, which, when controlled, greatly reduced the circuit complexity necessary to run the device. The next leading cause of noise was small variations in electric fields, likely due to imperfections in the oxide layer of the device. Tanttu says this is likely to improve by transitioning from a laboratory clean room to a foundry environment.
“It’s a great result and great progress. And I think it’s setting the right direction for the community in terms of thinking less about one individual device, or demonstrating something on an individual device, versus thinking more longer term about the scaling path,” Pillarisetty says.
Now, the challenge will be to scale up these devices to more qubits. One difficulty with scaling is the number of input/output channels required. The quantum team at Intel, who are pursuing a similar technology, has recently pioneered a chip they call Pando Tree to try to address this issue. Pando Tree will be on the same substrate as the quantum processor, enabling faster inputs and outputs to the qubits. The Intel team hopes to use it to scale to thousands of qubits. “A lot of our approach is thinking about, how do we make our qubit processor look more like a modern CPU?” says Pillarisetty.
Similarly, Diraq CEO Dzurak says his team plan to scale their technology to thousands of qubits in the near future through a recently announced partnership with Global Foundries. “With Global Foundries, we designed a chip that will have thousands of these [MOS qubits]. And these will be interconnected by using classical transistor circuitry that we designed. This is unprecedented in the quantum computing world,” Dzurak says.
Where VR Gaming Took a Wrong Turn
by Marcus Carter on 10. September 2024. at 13:00

This article is based on the authors’ new book, Fantasies of Virtual Reality (The MIT Press).

In 2017 Mark Zuckerberg stated a bold goal: He wanted one billion people to try virtual reality (VR) by 2027. While he still has a few years to pull it off, the target remains impossibly farfetched. The most recent estimates place total worldwide VR headset sales at only 34 million.
VR Gaming was expected to lead this uptake, but why hasn’t it? We believe that VR gaming has been held back by game developers who are committed to a fantasy. In this fantasy, VR games align with the values of “hardcore” gamer culture, with advanced graphics and wholly immersive play. Aspirational attempts to reach this flawed fantasy have squashed the true potential of VR for gaming.
VR Gaming’s Contemporary Emergence
The 1990s and 2000s saw several ill-fated attempts to launch VR gaming systems—including the Sega VR system, which the company promoted breathlessly but then never released, because it gave players motion sickness and headaches. But VR gaming’s contemporary emergence really began in August 2009, when then-17-year-old Palmer Luckey began posting on a VR enthusiasts forum about his plan to make a head-mounted VR gaming device. One early reader of Luckey’s posts was John Carmack, lead programmer for several of the most influential first-person shooter games, including Doom and Wolfenstein.
Palmer Luckey, shown here in 2013 at the age of 20, holds an early Oculus Rift virtual reality head-mounted display.Allen J. Schaben/Los Angeles Times/Getty Images
While working on the remaster of Doom 3—which included support for 3D displays—Carmack was experimenting with different VR headsets that were available at the time. The two connected through their forum posts, and Luckey sent one of his prototype VR headsets to Carmack. When Carmack took the prototype to the major gaming expo E3 in 2012, it catalyzed an avalanche of interest in the project.
Carmack’s involvement put Luckey’s newly formed company, Oculus VR, on a trajectory towards a particular kind of gaming: the hyper-violent games with high-fidelity graphics that hardcore gamers revere. Carmack, far beyond anyone else, pioneered the genre of hardcore games with his first-person shooter games.
Sega Visions Magazine promoted Sega VR in its August/September 1993 issue. Sega
Here’s how the gaming scholar Shira Chess sums up the genre: “Traditionally, ‘hardcore’ describes games that are difficult to learn, expensive, and unforgiving of mistakes and that must be played over longer periods of time. Conversely, casual games can be learned quickly, are forgiving of mistakes and cheap or free, and can be played for either longer or shorter periods of time, depending on one’s schedule.”
Oculus’s Kickstarter campaign in 2012 was proudly “designed for gamers, by gamers.” Soon after, Meta (then Facebook) acquired Oculus for US $3 billion in March 2014. The acquisition enraged many of those in the gaming community and those who had backed the original Kickstarter. Facebook was already an unpopular platform with the tech-enthusiast community, associated more closely with data collection and surveillance than gaming. If Facebook was associated with gaming, it was with casual social media games like Farmville and Bejeweled. But as it turns out, Meta went on to invest billions in VR, a level of investment highly unlikely if Oculus had remained independent.
The Three Wrong Assumptions of VR Gaming
VR’s origin in hardcore gaming culture resulted in VR game development being underpinned by three false assumptions about the types of experiences that would (or could) make VR gaming successful. These assumptions were that gamers wanted graphical realism and fast-paced violence, and that they didn’t want casual play experiences.
Over the past three decades, “AAA” game development—a term used in the games industry to signify high-budget games distributed by large publishers—has driven the massive expansion of computing power in consumer gaming devices. Particularly in PC gaming, part of what made a game hardcore was the computing power needed to run it at “maximum settings,” with the most detailed and textured graphics available.
The enormous advances in game graphics over the past 30 years contributed to significant improvements in player experience. This graphical realism became closely entwined with the concept of immersion.
For VR—which sold itself as “ truly immersive”—this meant that hardcore gamers expected graphically real VR experiences. But VR environments need to be rendered smoothly in order to not cause motion sickness, something made harder by a commitment to graphical realism. This aspiration saddling VR games with a nearly impossible compute burden.
One game that sidesteps this issue—and has subsequently become one of the most celebrated VR games—is Superhot VR, an action puzzle with basic graphics in which enemy avatars and their bullets only move when the player moves their body.
The video game Superhot VR remains one of the top-selling VR games years after its release due to its unique experience of time manipulation through body movements.Superhot VR
Play begins with the player surrounded by attacking enemies, with death immediately returning the player to the starting moment. Play thus involves discovering what sequence of movements and attacks can get the player out of this perilous situation. It’s a learning curve reminiscent of the 2014 science-fiction film Edge of Tomorrow, in which a hapless soldier (played by Tom Cruise) quicky becomes an elite, superhuman soldier while stuck in a time loop.
The attention in Superhot’s gameplay is not to visual fidelity or sensory immersion, but what genuinely makes VR distinct: embodiment. The effect of its conceit is a superhuman-like control of time manipulation, with players deftly contorting their bodies to evade slow moving bullets while dispatching enemies with an empowering ease. Superhot VR provides an experience worth donning a headset for, and it consequently remains one of VR gaming’s top selling titles eight years after its release.
When Immersion Is Too Much
John Carmack’ Doom and Wolfenstein, on which VR’s gaming fantasy was based, are first-person shooters that closely map to hardcore gaming ideals. They’re hyperviolent, fast-paced, and difficult; they have a limited focus on story; and they feature some of the goriest scenes in games. In the same way that VR gaming has been detrimentally entwined with the pursuit of photorealism, VR gaming has been co-opted by these hardcore values that ultimately limit the medium. They lack mainstream appeal and valorise experiences that simply aren’t as appealing in VR as it is in a flat screen.
In a discussion around the design of Half Life: Alyx—one of the only high-budget VR-only games—designers Greg Coomer and Robin Walker explain that VR changes the way that people interact with virtual environments. As Coomer says, “people are slower to traverse space, and they want to slow down and be more interactive with more things in each environment. It has affected, on a fundamental level, how we’ve constructed environments and put things together.” Walker adds that the changes aren’t “because of some constraint around how they move through the world, it’s just because they pay so much more attention to things and poke at things.” Environments in VR games are much denser; on PC they feel small, but in VR they feel big.
This in part explains why few games originally designed for flat screens and “ported” to VR have been successful. The rapidly paced hyperviolence best characterized by Doom is simply sensory overload in VR, and the “intensity of being there”—one of Carmack’s aspirations—is unappealing. In VR, unrelenting games are unpleasurable: Most of us aren’t that coordinated, and we can’t play for extended periods of time in VR. It’s physically exhausting.
Casual Virtual Reality?
Beat Saber is a prime example of a game that might be derided as casual, if it weren’t the bestselling VR game of all time. Beat Saber is a music rhythm-matching game, a hybrid of Dance Dance Revolution, Guitar Hero, and Fruit Ninja. In time with electronic music, a playlist of red or blue boxes streams towards the player. Armed with two neon swords—commonly described as light sabers—the player must strike these boxes in the correct direction, denoted by a subtle white arrow.
Striking a box releases a note in the accompanying song, resulting in an experience that is half playing an instrument, and half dance. Well patterned songs create sweeping movements and rhythms reminiscent of the exaggerated gestures used by Nintendo Wii players.
Beat Saber youtube
Beat Saber’s appeal is immersion-through-embodiment, also achieved by disregarding VR’s gaming fantasy of hardcore experiences. With each song being, well, song length, Beat Saber supports a shorter, casual mode of engagement that isn’t pleasurable because it is difficult or competitive, but simply because playing a song feels good.
Gaming in VR has been subjected to a vicious self-reinforcing cycle wherein VR developers create hardcore games, which appeal to a certain kind of hardcore gamer user, whose purchasing habits in turn drive further development of those kinds of games, and not others. Attempts to penetrate this feedback loop have been met with the hostility of VR’s online gaming culture, appropriated from gamer culture at large.
As a result, the scope of VR games remains narrow, and oblivious to the kinds of games that might take VR to its billionth user. Maybe then, the one thing that could save VR gaming is the one possibility that VR enthusiasts decried the most when Facebook purchased Oculus in 2014: Farmville VR.
Meet the Teens Whose Tech Reduces Drownings and Fights Air Pollution
by Elizabeth Fuscaldo on 09. September 2024. at 18:00

Drowning is the third leading cause of accidental deaths globally, according to the World Health Organization. The deaths disproportionately impact low- and middle-income communities, whose beaches tend to lack lifeguards because of limited funds. Last year 104 drownings of the 120 reported in the United States occurred on unstaffed beaches.
That fueled Angelina Kim’s drone project. Kim is a senior at the Bishop’s School in La Jolla, Calif. Her Autonomous Unmanned Aerial Vehicle (UAV) System for Ocean Hazard Recognition and Rescue: Scout and Rescue UAV Prototype project was showcased in May at Regeneron’s International Science and Engineering Fair (ISEF) in Los Angeles.
Kim project took first place in this year’s IEEE Presidents’ Scholarship competition: a prize of US $10,000 payable over four years of undergraduate university study. The IEEE Foundation established the award to acknowledge a deserving student whose project demonstrates an understanding of electrical or electronics engineering, computer science, or other IEEE field of interest. The scholarship is administered by IEEE Educational Activities.
Kim has long been motivated to help those in need, especially after her mother’s illness when Kim was a young child.
“I’ve been determined to find ways to create a safer community using technology my whole life,” she says. “I realized as I got more into robotics that technology can be used to protect those in my community.”
Kim and the second- and third-place scholarship winners also received a complimentary IEEE student membership.
In addition, to mark the scholarship’s quarter century, IEEE established a 25th anniversary award to honor a project that is likely to make a difference. It also was given out at ISEF.
Drones that can save swimmers’ lives
Angelina Kim’s autonomous drone system, consisting of a scout and rescue drone, aims to prevent drownings on beaches that have no lifeguards.Lynn Bowlby
The autonomous UAV lifeguard system consists of two types of drones: a scout craft and a rescue craft. The scout drone surveys approximately 1 kilometer of shoreline, taking photographs and analyzing them for rip currents, which can be deadly to swimmers.
The scout drone “implements a new version of differential frame displacement and a new depth-risk model,” Kim says. The displacement compares the previous image to the next one and notes in what direction a wave is moving. The algorithm detects rip currents and, using the depth-risk model, focuses on strong currents in the deep end—which are more dangerous. If the scout drone detects a swimmer caught in such a current, it summons the rescue drone. That drone drops a flotation device outfitted with a heaving rope and can pull the endangered swimmer to shore, Kim says.
The rescue drones operate with roll-and-pitch tilt rotors, allowing them to fly in whatever direction and orientation they need to.
Kim says she was shocked and ecstatic to receive the award: “It felt like a dream come true because IEEE has been the organization I’ve always wanted to be a part of.”
She presented her project at two IEEE gatherings this year: the IEEE International Conference on Control and Automation and the IEEE International Conference on Automatic Control and Intelligent Systems.
Kim says some of the roadblocks she encountered with her project have made her a better engineer.
“If everything goes perfectly, then there will not be much to learn,” she says. “But if you fail—like accidentally flying your drone into your parents’ house, like I did—you get opportunities to learn.”
She plans to study electrical or mechanical engineering in college. Eventually, she says, she would like to build and mass-produce technologies that help communities at a low cost.
Spotting carbon dioxide in soil
Sahiti Bulusu’s Carboflux device measures and tracks carbon levels in soil to collect data for climatologists. Lynn Bowlby
Excessive carbon dioxide is a leading cause of global warming and climate change, according to NASA. Burning fossil fuels is a prominent source of carbon dioxide, but it also can be released from the ground.
“People don’t account for the large amounts of CO₂ that come from soil,” second-place winner Sahiti Bulusu says. “Carbon flux is the rate at which CO₂ is exchanged between the soil and the atmosphere, and can account for 80 percent of net ecosystem carbon exchange.”
Bulusu is a senior at the Basis Independent high school in Fremont, California.
Ecosystem carbon exchange is the transfer of carbon dioxide between the atmosphere and the physical environment. Because the exchange is not often accounted for, there’s a gap in data. To provide additional information, Bulusu created the Carboflux Network.
The sensor node is designed to measure enhanced flux CO₂ and add to the Global Ecosystem Monitoring Network. The system contains three main subunits: a flux chamber, an under-soil sensor array, and a microcontroller with a data transmission unit. The enclosed flux chamber houses sensors. When CO₂ accumulates in the chamber, the linear increase in carbon dioxide concentration is used to calculate carbon flux. The second subunit uses the gradient method, which measures flux using Fick’s law, looking at the CO₂ gradient in the soil, and multiplying it with the soil diffusivity coefficient. Sensors are put in the ground at different depths to measure the gradient.
“Carboflux uses the gas concentration between the depths to predict what the soil carbon flux would be,” Bulusu says.
The system is automated and provides real-time data through microcontrollers and a modem. The device studies soil flux, both above and beneath the ground, she says.
“It is able to be scaled locally and globally,” she says, “helping to pinpoint local carbon sources and carbon sinks.” The data also can give scientists a large-scale global perspective.
Bulusu came up with her idea with help from mentors and professors Helen Dahlke and Elad Levintal.
“They told me about the lack of global carbon flux data, and the idea for a network of CO₂ flux sensors,” Bulusu says. “This data is needed for climate modeling and mitigation strategies.
“I didn’t think I would be so passionate about a project. I love science, but I never thought I would be someone who would sit in the rain and cold for hours trying to figure out a problem. I was so invested in this project, and it has come so far.”
Bulusu plans to pursue a degree in computer science or environmental science. Whatever field she choses, she says, she wants to improve the environment with the technology she creates.
She was awarded a $600 scholarship for her project.
Accessible communication for ALS patients
Gaze Link, developed by Xiangzhou Sun, uses a mobile app, camera, and artificial intelligence to help those with ALS communicate through their eye movements. Lynn Bowlby
Xiangzhou “Jonas” Sun has been volunteering to help people with amyotrophic lateral sclerosis, also known as Lou Gehrig’s disease. After Sun and his family spent time helping ALS patients and assisting caretakers in Hong Kong and the United States, he was inspired to create a mobile app to assist them. He is a senior at the Webb School of California, in Claremont.
While volunteering, he noticed that ALS patients had trouble communicating because of the disease.
“ALS damages neurons in the body, and patients gradually lose the ability to walk, move their hands, and speak,” he says. “My objective with Gaze Link was to build a mobile application that could help ALS patients to type sentences with only their eyes and without external assistance.”
The low-cost smartphone app uses eye-gesture recognition, AI sentence generation, and text-to-speech capabilities to allow people with disabilities linked to ALS to use their phone’s front-end camera to communicate.
Gaze Link now works with three languages: English, Mandarin, and Spanish.
Users’ eye gestures are mapped to words and pronunciations, and an AI next-word-prediction feature is incorporated.
Sun’s passion for his project is palpable.
“My favorite moment was when I brought a prototype of Gaze Link to a caretaker from the ALS Association, and they were overjoyed,” he says. “That moment was really emotional for me because I was motivated by these patients. It gave me a huge sense of achievement that I could finally help them.”
Sun received a $400 scholarship for placing third.
He says he plans to use his engineering know-how to help people with disabilities. He is passionate about design and visual arts as well, and says he hopes to combine them with his engineering skills.
Gaze Link is available through Google Play.
An inclusive way to code
Abhisek Shah’s AuralStudio provides a way for programmers with visual impairments to code using the Rattle language he wrote, which can be read aloud.Lynn Bowlby
The 25th anniversary award, in the amount of $1,000, was given to Abishek Shah, a senior at Green Level High School, in Cary, N.C.
Last year Shah had a temporary vision issue, which made looking at a computer screen nearly impossible, even for a short time. After that, while visiting family back home in India, he got a chance to interact with some students at a school for blind girls.
During his interactions with the students, he says, he realized they were determined and driven and wanted to be financially independent. None had even considered a career in computer programming, however, believing it to be off-limits because of their visual impairment, he says.
He wondered if there was a solution that could help blind people code.
“How can we redesign and rethink the way we code today?” he asked himself before devising his AuralStudio. He realized he could put his passion for coding to good use and created a software application for those whose vision impairments are permanent.
His AuralStudio development environment allows programmers with a visual disability to write, build, run, and test prototypes.
“All this was built toward helping those with disabilities learn to code,” Shah says.
It eliminates the need for a keyboard and mouse in favor of a custom control pad. It includes a voice-only option for those who cannot use their hands.
The testbed uses Rattle, a programming language that Shah created to be read aloud, both from the computer and the programmer. AuralStudio also uses acyclic digraphs to render code, making it easier and more intuitive. Shah wrote a two-part autocorrect algorithm to prevent homophones and homonyms from causing errors. It was done by integrating AI into the application.
Shah used the programming languages Rust, C, JavaScript, and Python. Raspberry Pi was the primary hardware.
He worked with visually impaired students from the Governor Morehead School of Raleigh, N.C., teaching them programming basics.
“The students were so eager to learn,” he says. “Getting to see their eagerness, grit, and hard-working nature was so heartwarming and probably the best part of this project.”
Shah says he plans to study computer science and business.
“Anything I build will have the end goal of improving the lives of others,” he says.
Will the "AI Scientist" Bring Anything to Science?
by Eliza Strickland on 09. September 2024. at 14:41

When an international team of researchers set out to create an “AI scientist” to handle the whole scientific process, they didn’t know how far they’d get. Would the system they created really be capable of generating interesting hypotheses, running experiments, evaluating the results, and writing up papers?
What they ended up with, says researcher Cong Lu, was an AI tool that they judged equivalent to an early Ph.D. student. It had “some surprisingly creative ideas,” he says, but those good ideas were vastly outnumbered by bad ones. It struggled to write up its results coherently, and sometimes misunderstood its results: “It’s not that far from a Ph.D. student taking a wild guess at why something worked,” Lu says. And, perhaps like an early Ph.D. student who doesn’t yet understand ethics, it sometimes made things up in its papers, despite the researchers’ best efforts to keep it honest.
Lu, a postdoctoral research fellow at the University of British Columbia, collaborated on the project with several other academics, as well as with researchers from the buzzy Tokyo-based startup Sakana AI. The team recently posted a preprint about the work on the ArXiv server. And while the preprint includes a discussion of limitations and ethical considerations, it also contains some rather grandiose language, billing the AI scientist as “the beginning of a new era in scientific discovery,” and “the first comprehensive framework for fully automatic scientific discovery, enabling frontier large language models (LLMs) to perform research independently and communicate their findings.”
The AI scientist seems to capture the zeitgeist. It’s riding the wave of enthusiasm for AI for science, but some critics think that wave will toss nothing of value onto the beach.
The “AI for Science” Craze
This research is part of a broader trend of AI for science. Google DeepMind arguably started the craze back in 2020 when it unveiled AlphaFold, an AI system that amazed biologists by predicting the 3D structures of proteins with unprecedented accuracy. Since generative AI came on the scene, many more big corporate players have gotten involved. Tarek Besold, a SonyAI senior research scientist who leads the company’s AI for scientific discovery program, says that AI for science is “a goal behind which the AI community can rally in an effort to advance the underlying technology but—even more importantly—also to help humanity in addressing some of the most pressing issues of our times.”
Yet the movement has its critics. Shortly after a 2023 Google DeepMind paper came out claiming the discovery of 2.2 million new crystal structures (“equivalent to nearly 800 years’ worth of knowledge”), two materials scientists analyzed a random sampling of the proposed structures and said that they found “scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility.” In other words, AI can generate a lot of results quickly, but those results may not actually be useful.
How the AI Scientist Works
In the case of the AI scientist, Lu and his collaborators tested their system only on computer science, asking it to investigate topics relating to large language models, which power chatbots like ChatGPT and also the AI scientist itself, and the diffusion models that power image generators like DALL-E.
The AI scientist’s first step is hypothesis generation. Given the code for the model it’s investigating, it freely generates ideas for experiments it could run to improve the model’s performance, and scores each idea on interestingness, novelty, and feasibility. It can iterate at this step, generating variations on the ideas with the highest scores. Then it runs a check in Semantic Scholar to see if its proposals are too similar to existing work. It next uses a coding assistant called Aider to run its code and take notes on the results in the format of an experiment journal. It can use those results to generate ideas for follow-up experiments.
The AI scientist is an end-to-end scientific discovery tool powered by large language models. University of British Columbia
The next step is for the AI scientist to write up its results in a paper using a template based on conference guidelines. But, says Lu, the system has difficulty writing a coherent nine-page paper that explains its results—”the writing stage may be just as hard to get right as the experiment stage,” he says. So the researchers broke the process down into many steps: The AI scientist wrote one section at a time, and checked each section against the others to weed out both duplicated and contradictory information. It also goes through Semantic Scholar again to find citations and build a bibliography.
But then there’s the problem of hallucinations—the technical term for an AI making stuff up. Lu says that although they instructed the AI scientist to only use numbers from its experimental journal, “sometimes it still will disobey.” Lu says the model disobeyed less than 10 percent of the time, but “we think 10 percent is probably unacceptable.” He says they’re investigating a solution, such as instructing the system to link each number in its paper to the place it appeared in the experimental log. But the system also made less obvious errors of reasoning and comprehension, which seem harder to fix.
And in a twist that you may not have seen coming, the AI scientist even contains a peer review module to evaluate the papers it has produced. “We always knew that we wanted some kind of automated [evaluation] just so we wouldn’t have to pour over all the manuscripts for hours,” Lu says. And while he notes that “there was always the concern that we’re grading our own homework,” he says they modeled their evaluator after the reviewer guidelines for the leading AI conference NeurIPS and found it to be harsher overall than human evaluators. Theoretically, the peer review function could be used to guide the next round of experiments.
Critiques of the AI Scientist
While the researchers confined their AI scientist to machine learning experiments, Lu says the team has had a few interesting conversations with scientists in other fields. In theory, he says, the AI scientist could help in any field where experiments can be run in simulation. “Some biologists have said there’s a lot of things that they can do in silico,” he says, also mentioning quantum computing and materials science as possible fields of endeavor.
Some critics of the AI for science movement might take issue with that broad optimism. Earlier this year, Jennifer Listgarten, a professor of computational biology at UC Berkeley, published a paper in Nature Biotechnology arguing that AI is not about to produce breakthroughs in multiple scientific domains. Unlike the AI fields of natural language processing and computer vision, she wrote, most scientific fields don’t have the vast quantities of publicly available data required to train models.
Two other researchers who study the practice of science, anthropologist Lisa Messeri of Yale University and psychologist M.J. Crockett of Princeton University, published a 2024 paper in Nature that sought to puncture the hype surrounding AI for science. When asked for a comment about this AI scientist, the two reiterated their concerns over treating “AI products as autonomous researchers.” They argue that doing so risks narrowing the scope of research to questions that are suited for AI, and losing out on the diversity of perspectives that fuels real innovation. “While the productivity promised by ‘the AI Scientist’ may sound appealing to some,” they tell IEEE Spectrum, “producing papers and producing knowledge are not the same, and forgetting this distinction risks that we produce more while understanding less.”
But others see the AI scientist as a step in the right direction. SonyAI’s Besold says he believes it’s a great example of how today’s AI can support scientific research when applied to the right domain and tasks. “This may become one of a handful of early prototypes that can help people conceptualize what is possible when AI is applied to the world of scientific discovery,” he says.
What’s Next for the AI Scientist
Lu says that the team plans to keep developing the AI scientist, and he says there’s plenty of low-hanging fruit as they seek to improve its performance. As for whether such AI tools will end up playing an important role in the scientific process, “I think time will tell what these models are good for,” Lu says. It might be, he says, that such tools are useful for the early scoping stages of a research project, when an investigator is trying to get a sense of the many possible research directions—although critics add that we’ll have to wait for future studies to see if these tools are really comprehensive and unbiased enough to be helpful.
Or, Lu says, if the models can be improved to the point that they match the performance of “a solid third-year Ph.D. student,” they could be a force multiplier for anyone trying to pursue an idea (at least, as long as the idea is in an AI-suitable domain). “At that point, anyone can be a professor and carry out a research agenda,” says Lu. “That’s the exciting prospect that I’m looking forward to.”
Greener Steel Production Requires More Electrochemical Engineers
by Dan Steingart on 08. September 2024. at 13:00

In the 1800s, aluminum was considered more valuable than gold or silver because it was so expensive to produce the metal in any quantity. Thanks to the Hall-Héroult smelting process, which pioneered the electrochemical reduction of aluminum oxide in 1886, electrochemistry advancements made aluminum more available and affordable, rapidly transforming it into a core material used in the manufacturing of aircraft, power lines, food-storage containers and more.
As society mobilizes against the pressing climate crisis we face today, we find ourselves seeking transformative solutions to tackle environmental challenges. Much as electrochemistry modernized aluminum production, science holds the key to revolutionizing steel and iron manufacturing.
Electrochemistry can help save the planet
As the world embraces clean energy solutions such as wind turbines, electric vehicles, and solar panels to address the climate crisis, changing how we approach manufacturing becomes critical. Traditional steel production—which requires a significant amount of energy to burn fossil fuels at temperatures exceeding 1,600 °C to convert ore into iron—currently accounts for about 10 percent of the planet’s annual CO₂ emissions. Continuing with conventional methods risks undermining progress toward environmental goals.
Scientists already are applying electrochemistry—which provides direct electrical control of oxidation-reduction reactions—to convert ore into iron. The conversion is an essential step in steel production and the most emissions-spewing part. Electrochemical engineers can drive the shift toward a cleaner steel and iron industry by rethinking and reprioritizing optimizations.
When I first studied engineering thermodynamics in 1998, electricity—which was five times the price per joule of heat—was considered a premium form of energy to be used only when absolutely required.
Since then the price of electricity has steadily decreased. But emissions are now known to be much more harmful and costly.
Engineers today need to adjust currently accepted practices to develop new solutions that prioritize mass efficiency over energy efficiency.
In addition to electrochemical engineers working toward a cleaner steel and iron industry, advancements in technology and cheaper renewables have put us in an “electrochemical moment” that promises change across multiple sectors.
The plummeting cost of photovoltaic panels and wind turbines, for example, has led to more affordable renewable electricity. Advances in electrical distribution systems that were designed for electric vehicles can be repurposed for modular electrochemical reactors.
Electrochemistry holds the potential to support the development of clean, green infrastructure beyond batteries, electrolyzers, and fuel cells. Electrochemical processes and methods can be scaled to produce metals, ceramics, composites, and even polymers at scales previously reserved for thermochemical processes. With enough effort and thought, electrochemical production can lead to billions of tons of metal, concrete, and plastic. And because electrochemistry directly accesses the electron transfer fundamental to chemistry, the same materials can be recycled using renewable energy.
As renewables are expected to account for more than 90 percent of global electricity expansion during the next five years, scientists and engineers focused on electrochemistry must figure out how best to utilize low-cost wind and solar energy.
The core components of electrochemical systems, including complex oxides, corrosion-resistant metals, and high-power precision power converters, are now an exciting set of tools for the next evolution of electrochemical engineering.
The scientists who came before have created a stable set of building blocks; the next generation of electrochemical engineers needs to use them to create elegant, reliable reactors and other systems to produce the processes of the future.
Three decades ago, electrochemical engineering courses were, for the most part, electives and graduate-level. Now almost every institutional top-ranked R&D center has full tracks of electrochemical engineering. Students interested in the field should take both electroanalytical chemistry and electrochemical methods classes and electrochemical energy storage and materials processing coursework.
Although scaled electrochemical production is possible, it is not inevitable. It will require the combined efforts of the next generation of engineers to reach its potential scale.
Just as scientists found a way to unlock the potential of the abundant, once-unattainable aluminum, engineers now have the opportunity to shape a cleaner, more sustainable future. Electrochemistry has the power to flip the switch to clean energy, paving the way for a world in which environmental harmony and industrial progress go hand in hand.
Get to Know the IEEE Board of Directors
by IEEE on 06. September 2024. at 18:00

The IEEE Board of Directors shapes the future direction of IEEE and is committed to ensuring IEEE remains a strong and vibrant organization—serving the needs of its members and the engineering and technology community worldwide—while fulfilling the IEEE mission of advancing technology for the benefit of humanity.
This article features IEEE Board of Directors members A. Matt Francis, Tom Murad, and Christopher Root.
IEEE Senior Member A. Matt Francis
Director, IEEE Region 5: Southwestern U.S.
Moriah Hargrove Anders
Francis’s primary technology focus is extreme environment and high-temperature integrated circuits. His groundbreaking work has pushed the boundaries of electronics, leading to computers operating in low Earth orbit for more than a year on the International Space Station and on jet engines. Francis and his team have designed and built some of the world’s most rugged semiconductors and systems.
He is currently helping explore new computing frontiers in supersonic and hypersonic flight, geothermal energy exploration, and molten salt reactors. Well versed in shifting technology from idea to commercial application, Francis has secured and led projects with the U.S. Air Force, DARPA, NASA, the National Science Foundation, the U.S. Department of Energy, and private-sector customers.
Francis’s influence extends beyond his own ventures. He is a member of the IEEE Aerospace and Electronic Systems, IEEE Computer, and IEEE Electronics Packaging societies, demonstrating his commitment to industry and continuous learning.
He attended the University of Arkansas in Fayetteville for both his undergraduate and graduate degrees. He joined IEEE while at the university and was president of the IEEE–Eta Kappa Nu honor society’s Gamma Phi chapter. Francis’s other past volunteer roles include serving as chair of the IEEE Ozark Section, which covers Northwest Arkansas, and also as a member of the IEEE-USA Entrepreneurship Policy Innovation Committee.
His deep-rooted belief in the power of collaboration is evident in his willingness to share knowledge and support aspiring entrepreneurs. Francis is proud to have helped found a robotics club (an IEEE MGA Local Group) in his rural Elkins, Ark., community and to have served on steering committees for programs including IEEE TryEngineering and IEEE-USA’s Innovation, Research, and Workforce Conferences. He serves as an elected city council member for his town, and has cofounded two non-profits, supporting his community and the state of Arkansas.
Francis’s journey from entrepreneur to industry leader is a testament to his determination and innovative mindset. He has received numerous awards including the IEEE-USA Entrepreneur Achievement Award for Leadership in Entrepreneurial Spirit, IEEE Region 5 Directors Award, and IEEE Region 5 Outstanding Individual Member Achievement Award.
IEEE Senior Member Tom Murad
Director, IEEE Region 7: Canada
Siemens Canada
Murad is a respected technology leader, award-winning educator, and distinguished speaker on engineering, skills development, and education. Recently retired, he has 40 years of experience in professional engineering and technical operations executive management, including more than 10 years of academic and R&D work in industrial controls and automation.
He received his doctorate (Ph.D.) degree in power electronics and industrial controls from Loughborough University of Technology in the U.K.
Murad has held high-level positions in several international engineering and industrial organizations, and he contributed to many global industrial projects. His work on projects in power utilities, nuclear power, oil and gas, mining, automotive, and infrastructure industries has directly impacted society and positively contributed to the economy. He is a strong advocate of innovation and creativity, particularly in the areas of digitalization, smart infrastructure, and Industry 4.0. He continues his academic career as an adjunct professor at University of Guelph in Ontario, Canada.
His dedication to enhancing the capabilities of new generations of engineers is a source of hope and optimism. His work in significantly improving the quality and relevance of engineering and technical education in Canada is a testament to his commitment to the future of the engineering profession and community. For that he has been assigned by the Ontario Government to be a member of the board of directors of the Post Secondary Education Quality Assessment Board (PEQAB).
Murad is a member of the IEEE Technology and Engineering Management, IEEE Education, IEEE Intelligent Transportation Systems, and IEEE Vehicular Technology societies, the IEEE-Eta Kappa Nu honor society, and the Editorial Advisory Board Chair for the IEEE Canadian Review Magazine. His accomplishments show his passion for the engineering profession and community.
He is a member of the Order of Honor of the Professional Engineers of Ontario, Canada, Fellow of Engineers Canada, Fellow of Engineering Institutes of Canada (EIC), and received the IEEE Canada J.M. Ham Outstanding Engineering Educator Award, among other recognitions highlighting his impact on the field.
IEEE Senior Member Christopher Root
Director, Division VII
Vermont Electric Power Company and Shana Louiselle
Root has been in the electric utility industry for more than 40 years and is an expert in power system operations, engineering, and emergency response. He has vast experience in the operations, construction, and maintenance of transmission and distribution utilities, including all phases of the engineering and design of power systems. He has shared his expertise through numerous technical presentations on utility topics worldwide.
Currently an industry advisor and consultant, Root focuses on the crucial task of decarbonizing electricity production. He is engaged in addressing the challenges of balancing an increasing electrical market and dependence on renewable energy with the need to provide low-cost, reliable electricity on demand.
Root’s journey with IEEE began in 1983 when he attended his first meeting as a graduate student at Rensselaer Polytechnic Institute, in Troy, N.Y. Since then, he has served in leadership roles such as treasurer, secretary, and member-at-large of the IEEE Power & Energy Society (PES). His commitment to the IEEE mission and vision is evident in his efforts to revitalize the dormant IEEE PES Boston Chapter in 2007 and his instrumental role in establishing the IEEE PES Green Mountain Section in Vermont in 2015. He also is a member of the editorial board of the IEEE Power & Energy Magazine and the IEEE–Eta Kappa Nu honor society.
Root’s contributions and leadership in the electric utility industry have been recognized with the IEEE PES Leadership in Power Award and the PES Meritorious Service Award.
Video Friday: HAND to Take on Robotic Hands
by Evan Ackerman on 06. September 2024. at 15:53

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

ICRA@40: 23–26 September 2024, ROTTERDAM, NETHERLANDS
IROS 2024: 14–18 October 2024, ABU DHABI, UAE
ICSR 2024: 23–26 October 2024, ODENSE, DENMARK
Cybathlon 2024: 25–27 October 2024, ZURICH
Enjoy today’s videos!

The National Science Foundation Human AugmentatioN via Dexterity Engineering Research Center (HAND ERC) was announced in August 2024. Funded for up to 10 years and $52 million, the HAND ERC is led by Northwestern University, with core members Texas A&M, Florida A&M, Carnegie Mellon, and MIT, and support from Wisconsin-Madison, Syracuse, and an innovation ecosystem consisting of companies, national labs, and civic and advocacy organizations. HAND will develop versatile, easy-to-use dexterous robot end effectors (hands).

[ HAND ]

The Environmental Robotics Lab at ETH Zurich, in partnership with Wilderness International (and some help from DJI and Audi), is using drones to sample DNA from the tops of trees in the Peruvian rainforest. Somehow, the treetops are where 60 to 90 percent of biodiversity is found, and these drones can help researchers determine what the heck is going on up there.

[ ERL ]
Thanks, Steffen!

1X introduces NEO Beta, “the pre-production build of our home humanoid.”

“Our priority is safety,” said Bernt Børnich, CEO at 1X. “Safety is the cornerstone that allows us to confidently introduce NEO Beta into homes, where it will gather essential feedback and demonstrate its capabilities in real-world settings. This year, we are deploying a limited number of NEO units in selected homes for research and development purposes. Doing so means we are taking another step toward achieving our mission.”
[ 1X ]

We love MangDang’s fun and affordable approach to robotics with Mini Pupper. The next generation of the little legged robot has just launched on Kickstarter, featuring new and updated robots that make it easy to explore embodied AI.

The Kickstarter is already fully funded after just a day or two, but there are still plenty of robots up for grabs.
[ Kickstarter ]

Quadrupeds in space can use their legs to reorient themselves. Or, if you throw one off a roof, it can learn to land on its feet.

To be presented at CoRL 2024.
[ ARL ]

HEBI Robotics, which apparently was once headquartered inside a Pittsburgh public bus, has imbued a table with actuators and a mind of its own.

[ HEBI Robotics ]

Carcinization is a concept in evolutionary biology where a crustacean that isn’t a crab eventually becomes a crab. So why not do the same thing with robots? Crab robots solve all problems!

[ KAIST ]

Waymo is smart, but also humans are really, really dumb sometimes.

[ Waymo ]

The Robotics Department of the University of Michigan created an interactive community art project. The group that led the creation believed that while roboticists typically take on critical and impactful problems in transportation, medicine, mobility, logistics, and manufacturing, there are many opportunities to find play and amusement. The final piece is a grid of art boxes, produced by different members of our robotics community, which offer an eight-inch-square view into their own work with robotics.

[ Michigan Robotics ]

I appreciate that UBTECH’s humanoid is doing an actual job, but why would you use a humanoid for this?

[ UBTECH ]

I’m sure most actuators go through some form of life-cycle testing. But if you really want to test an electric motor, put it into a BattleBot and see what happens.

[ Hardcore Robotics ]

Yes, but have you tried fighting a BattleBot?

[ AgileX ]

In this video, we present collaboration aerial grasping and transportation using multiple quadrotors with cable-suspended payloads. Grasping using a suspended gripper requires accurate tracking of the electromagnet to ensure a successful grasp while switching between different slack and taut modes. In this work, we grasp the payload using a hybrid control approach that switches between a quadrotor position control and a payload position control based on cable slackness. Finally, we use two quadrotors with suspended electromagnet systems to collaboratively grasp and pick up a larger payload for transportation.

[ Hybrid Robotics ]

I had not realized that the floretizing of broccoli was so violent.

[ Oxipital ]

While the RoboCup was held over a month ago, we still wanted to make a small summary of our results, the most memorable moments, and of course an homage to everyone who is involved with the B-Human team: the team members, the sponsors, and the fans at home. Thank you so much for making B-Human the team it is!

[ B-Human ]
When "AI for Good" Goes Wrong
by Payal Arora on 05. September 2024. at 14:59

This guest article is adapted from the author’s new book From Pessimism to Promise: Lessons from the Global South on Designing Inclusive Tech, published by MIT Press.
What do AI-enabled rhino collars in South Africa, computer-vision pest-detection drones in the Punjab farmlands, and wearable health devices in rural Malawi have in common?
These initiatives are all part of the AI for Good movement, which aligns AI technologies with the United Nations sustainable development goals to find solutions for global challenges like poverty, health, education, and environmental sustainability.
MIT Press
The hunger for AI-based solutions is understandable. In 2023, 499 rhinos were killed by poachers in South Africa, an increase of more than 10 percent from 2022. Several farmers in Punjab lost about 90 percent of their cotton yield to the pink bollworm; if the pest had been detected in time, they could have saved their crops. As for healthcare, despite decades of effort to boost the numbers of healthcare practitioners in rural areas, they continue to migrate to cities.
What makes AI “good,” though? Why do we need to preface AI applications in the Global South with morality and charity? And will noble intent translate to making AI tools work for the majority of the world?
A Changed Reality
The fact is, the Global South of decades ago does not exist.
Today the countries in the Global South are more confident, more entrepreneurial, and are taking leadership to pioneer locally appropriate AI tools that work for their people. Startups understand that the success of new tech is contingent on leveraging local knowledge for meaningful adoption and scaling.
The old formula of “innovate in the West and disseminate to the rest” is out of sync with this new reality. While the West holds onto its old missionary zeal, the South-South collaboration continues to grow, sharing new tech and building AI governance. What’s more, some tech altruism initiatives have come under scrutiny as they obfuscate their data extraction activities, making them more transactional than charitable.
The Market for Tech Altruism
In August, the European Union’s legal framework on AI, the AI Act, entered into force. Its measures are meant to help citizens and stakeholders optimize these tools while mitigating the risks. There is little mention of AI for good in their documents; it’s simply the default. Yet, as we shift from the Global North to the South, morality kicks in.
Tech altruism underlines this shift. Many of the AI for Good initiatives are funded by tech philanthropists in partnership with global aid agencies. Doing good manifests in piloting tech solutions, with the Global South as a live laboratory. A running joke with development workers is that their field suffers from “pilotitis,” an acute syndrome of pilot projects that never scale. The Global South is typically viewed as a recipient, a market, a beneficiary for techno-solutionism.
Take AI collars for rhinos. The Conservation Collar initiative in South Africa, for example, detects abnormal behavior, and these signals are sent to an AI system that computes the probability of risk. If it determines that the animal is at urgent risk, the rangers can hypothetically act immediately to stop the poaching. But when my team investigated the ground realities, we found that rangers face numerous obstacles to fast action, including dirt roads, old vehicles, and long distances. Many rangers had not been paid in months, and their motivation was low. And to top all this, they faced an armed militia protecting a multibillion rhino trade business.
In Punjab, drones with computer vision can guide farmers to detect pests before they destroy crops. The Global Alliance for Climate-Smart Agriculture funds projects involving many such AI-enabled technologies as farmers face the vagaries of the climate crisis. However, detection is just one part of a larger problem. Farmers struggle with poor quality and unaffordable pesticides, loan sharks, the vulnerabilities brought on by monocropping, and water scarcity. Agricultural innovators complain that there are few early adopters of their tech, however good their tools may be. Afterall, young people in the Global South increasingly don’t see their future in farming.
Meanwhile, we’ve seen philanthropies such as the Bill & Melinda Gates Foundation launch grand challenges for AI to help alleviate burdens on African healthcare systems. This has resulted in winners such as IntelSurv in Malawi, an intelligent disease surveillance data feedback system that computes data from smart wearables. Yet, even with hundreds of patents for such devices being registered every year, they’re not yet capable of consistently capturing high-quality data. In places like Malawi, these devices may become the single source of training data for healthcare AI, amplifying errors in their healthcare system
The fact is, we can’t really solve problems with AI without accompanying social reforms. Building proper roads or paying your rangers on time is not an innovation, it’s common sense. Likewise, whether it’s in the healthcare or the agricultural sector, people need social incentives to adopt these technologies. Otherwise these AI tools will remain in the wild, and won’t be domesticated.
Data Is Currency
Tech altruism has increasingly become suspect as AI companies are now facing an acute data shortage. They’re scrambling for data in the Global South, where majority of tech users live. Take, for instance, the case of Worldcoin, co-founded by OpenAI CEO Sam Altman. It plans to become “the world’s largest privacy-preserving human identity and financial network, giving ownership to everyone.” Worldcoin started as a nonprofit in 2019 by collecting biometric data, mostly in Global South countries, through its “orb” device and in exchange for cryptocurrency. Today, it’s a for-profit entity and is under investigation by many countries for its dubious data-collection methods.
The German nonprofit Heinrich-Böll-Stiftung recently reported on the aggressive growth of digital agricultural platforms across Africa which promise farmers precision agriculture and increased yields via AI-enabled apps. Yet these apps often provide corporations with free access to data about seeds, soil, crops, fertilizers, and weather from the farms where they’re used. Corporations can use AI analytics to weaponize this information, perhaps creating discriminatory agricultural insurance policies or micro-targeting ads for seeds and fertilizers. Similarly, in the health care sector, the Center for Digital Health at Brown University has reported on the selling of personal health data to third-party advertisers without user consent.
The problem is that, unlike private companies that are compelled to follow the law, altruistic initiatives often succeed in circumventing regulations due to their “charitable” intent. Almost a decade ago, Facebook launched Free Basics, which provided access to limited internet services in the Global South by violating net neutrality principles. When India blocked Free Basics in 2015, Mark Zuckerberg appeared shocked and remarked, “Who could possibly be against this?”
Today we ask, who could possibly get on board?
From Paternalism to Partnerships
As of 2024, according to one estimate, the Global South contributes 80 percent to global economic growth. Close to 90 percent of the world’s young population reside in these regions. And it has become a vital space for innovation. In 2018, China entered the global innovation index rankings as one of the top twenty most innovative countries in the world. India’s government has set up its “tech stack,” the largest open source, interoperable, and public digital infrastructure in the world. This stack is enabling entrepreneurs to build their products and services away from the Apple and Google duopoly that constrains competition and choice.
Despite the Global South demonstrating its innovative prowess, the imitator label remains sticky. This perception often translates to Western organizations treating Global South countries as beneficiaries, and not as partners and leaders in global innovation.
It is time we stop underestimating the Global South. Instead, Western organizations should channel their energies by looking at how different consumers can help to rethink opportunity, safeguards, and digital futures for the world’s majority. Inclusion is not an altruistic act. It is an essential element to generating solutions for the wicked problems that humanity faces today.
In designing new tech, we need to shift away from morality-driven design with grandiose visions of doing good. Instead, we should strive for design that focuses on the relationships between people, contexts, and policies.
Designers, programmers, and funders can benefit from listening to what users and entrepreneurs in the Global South have to say about AI intervening in their lives. And policymakers should bury the term “AI for Good.”
Media outlets must stop debating whether tech alone can solve the world’s problems. The real contextual intelligence we need won’t come from AI, but from human beings.
How Region Realignment Will Impact IEEE Elections
by IEEE Member and Geographic Activities on 04. September 2024. at 18:00

The work of restructuring IEEE’s geographic regions is well underway. Six U.S. regions will be consolidated into five, joining together the current IEEE Region 1 (Northeastern U.S.) and Region 2 (Eastern U.S.) to form Region 2 (Eastern and Northeastern U.S.). IEEE Region 10 (Asia and Pacific) will be split into two to form Region 10 (North Asia) and Region 11 (South Asia and Pacific).
The restructuring of IEEE’s 10 geographic regions will provide a more equitable representation across its global membership, as outlined in The Institute’s February 2023 article, “IEEE is Realigning Its Geographic Regions.”
The realignment will impact this year’s annual IEEE election process, which runs through 1 October.
In this year’s IEEE annual election, eligible voting members residing in Region 2 will be electing the IEEE Region 2 director-elect for the 2025—2026 term (serving as director in 2027—2028). The elected officer will first serve as director of the current Region 2 in 2027, and then in 2028 as director of the new Region 2.
The eligible voting members residing in Region 10 will be electing the IEEE Region 10 director-elect for the term 2025—2026 (serving as Director in 2027—2028). The elected officer will first serve as director of the current Region 10 in 2027 and then in 2028 as Director of the new Region 10 .
IEEE Member and Geographic Activities is continuing to coordinate the transition in tandem with the other IEEE organizational units in preparation for the realignment, which takes effect in January 2028.
This article appears in the September 2004 print issue.
How the Designer of the First Hydrogen Bomb Got the Gig
by Glenn Zorpette on 02. September 2024. at 14:00

By any measure, Richard Garwin is one of the most decorated and successful engineers of the 20th century. The IEEE Life Fellow has won the Presidential Medal of Freedom, the National Medal of Science, France’s La Grande Médaille de l’Académie des Sciences, and is one of just a handful of people elected to all three U.S. National Academies: Engineering, Science, and Medicine. At IBM, where he worked from 1952 to 1993, Garwin was a key contributor or a facilitator on some of the most important products and breakthroughs of his era, including magnetic resonance imaging, touchscreen monitors, laser printers, and the Cooley-Tukey fast Fourier transform algorithm.

And all that was after he did the thing for which he is most famous. At age 23 and at the behest of Edward Teller, Garwin designed the very first working hydrogen bomb, which was referred to as “the Sausage.” It was detonated in a test code-named Ivy Mike at Enewetak Atoll in November 1952, yielding 10.4 megatons of TNT. (The largest detonation before Ivy Mike was of a bomb code-named George, which yielded a mere 225 kilotons.)
Richard Garwin

Richard Garwin is an IBM Fellow Emeritus, an IEEE Life Fellow, and the designer of the first working hydrogen bomb.
Not until 2001—50 years after Garwin’s work on the bomb—did his pivotal role become publicly known. The definitive history of the hydrogen bomb, Richard Rhodes’s Dark Sun: The Making of the Hydrogen Bomb, published in 1995, has barely a page about Garwin. However, in 1979, after suffering a heart attack and contemplating his mortality, Teller sat down with the physicist George A. Keyworth II to record an oral testimony about the project. Teller’s verbal reckoning was kept secret for 22 years, until 2001, at which time a transcript was obtained by The New York Times.
In the transcript, Teller discounts the role of the mathematician Stanislaw Ulam, who was thought to have been Teller’s partner in what is still called the Teller-Ulam configuration. This “configuration” was actually a theory-based framework that envisioned a two-stage thermonuclear device based in part on a fission bomb (the first stage) that would generate the enormous temperatures and pressures needed to trigger a runaway fusion reaction (in the second stage). In the same transcript, Teller lavishes praise on Garwin’s design and declares, “that first design was made by Dick Garwin.” Because of the enduring secrecy around that first thermonuclear bomb, Garwin’s role had been largely unknown outside of a small circle of Los Alamos physicists, mathematicians, and engineers who were involved with the project—notably Teller, Enrico Fermi, Hans Bethe, and Ulam. Teller died in 2003.
Starting in the early 1950s and continuing in parallel with his career at IBM, Garwin also served as an advisor or consultant to U.S. government agencies on some of the most vital tech-related issues, and some of the most prestigious panels, of his times. That work continues to this day with his service as a member of the Jason group, the elite panel that offers technical and scientific advice, often classified, to the U.S. Defense Department and other agencies. Garwin, who has served in advisory roles under every U.S. president from Dwight Eisenhower to Barak Obama, has also been known for his writing and speaking on issues related to nuclear proliferation and arms control.
IEEE Spectrum spoke via videoconference with Garwin, now 96, who was at his home in Westchester County, New York.
Richard Garwin on:
Garwin arrived at Los Alamos for the second time to work as a physicist in May of 1951. In the interview, he spoke early on, and without prompting, about Edward Teller’s ideas at the time about how a thermonuclear (fusion) bomb would work. Teller had not had much success translating his ideas into a working bomb, in part, Garwin says, because Teller did not understand that the deuterium fuel would “burn” (react) when it was very highly compressed, as it would be in the basic, Teller-Ulam conception of a hydrogen bomb.
Garwin: When I got to Los Alamos for the second time, in 1951, I had already known Edward Teller. He was on the physics faculty of the University of Chicago. And I went to Edward and I said, “What is the progress on your ideas for burning deuterium?” And he told me that he had met with the mathematician, Stanislaw Ulam, who worked for him. Ulam was in his small group. Teller was allowed only about four people in his group, much to his distress. And he resented that. But it was the right choice because you would need an atomic bomb, according to the Ulam-Teller concept. And there was no sense diluting the effort on working on the atomic bomb.
But Edward had had for many years a wrong theorem which he had never written down. He confesses this in his 1979 paper [ Editor’s note: This is the statement dictated to Keyworth after Teller’s heart attack] in which he gives me credit for the hydrogen bomb. But his theorem was that compression wouldn’t help. That if you couldn’t burn deuterium at normal liquid density—I think it’s 0.19 grams per cubic centimeter—you can’t burn it at 100-fold or 1,000-fold density. Everything would just happen faster, 100 times faster, or 1,000 times faster. This was a wrong theorem. He had never written it down, and it was wrong. And when he told Stan Ulam, he said in his 1979 effort, that he had been wasting a lot of time talking to Stan.
And so Edward decided that I would write it [a detailed engineering design for a working hydrogen bomb] up and give him a fair shot. And Ulam’s idea, according to this still-secret document in the Los Alamos report library, was given away by the title of the report. The title of the report that is, and always has been, unclassified. The first part of the title was: “Hydrodynamic Lenses.” The second part of the title was: “and Radiation Mirrors.” [ Editor’s Note: The paper, published in secret in March 1951, is titled, “On Heterocatalytic Detonations I: Hydrodynamic Lenses and Radiation Mirrors,” and it is the paper that contains the first description of the Teller-Ulam configuration.]
That was the option that Teller thought was best. So I went to Teller in his office at Los Alamos, and I asked him what had happened. He said that he had written up the meeting he had had recently with Stan Ulam and that Ulam had proposed acoustic lenses, of which we had 32 on the original implosion weapon [detonated at the Trinity Test near Alamogordo, N.M.]. So you could get 32 segments of the sphere. They had fast and slow explosives. And so most of the mass of the explosive—of the 8 tons of the weapon, probably 4 tons was the lenses, which didn’t count in accelerating the plutonium.
And so that was the Nagasaki bomb and the one that was tested in Alamogordo on July 16, 1945. [Teller] told me about his report, and that was the end of the conversation, except that he said what he really needed was a small experiment to prove to the most skeptical physicists that this was the way to build the atomic bomb and the hydrogen bomb. And I took that as a challenge. I started and tried to make a 20-kiloton experiment, but I couldn’t make one that was sufficiently convincing and decided to make it full scale. And so that’s what I did. I published my report of the Sausage based on the concepts current at the time. I wrote that up and published it in the classified report library, also on July 25th, 1951.
And it was detonated, as Teller says later, “exactly as Dick Garwin had devised it,” on November 1, 1952, so just 16 months afterwards. And it could never have been done faster. And the only way it got done that fast was because I wrote the paper all by myself. I was sitting in the office with Enrico Fermi. I had two offices: One was with Fermi in the theoretical division, and the other was in the physics division, where I was working on developing a means for accelerating deuterons and protons to 100 kilovolts.
Ivy Mike, detonated in the Marshall Islands on 1 November, 1952, was the first successful test of a full-scale thermonuclear device. Richard Garwin designed the bomb used in the test.Los Alamos National Laboratory/AP
Garwin took exception to my suggestion that Teller “entrusted” him with the design of the first thermonuclear bomb. He also revealed poignant details about the daily routine in his office, which he shared with Enrico Fermi.
Garwin: [Teller] challenged me. He didn’t entrust me. He didn’t know that it could be done. But he said, “I’d like a small experiment that would persuade the most skeptical and that this is the way to do it.” And it persuaded the person who counted—It persuaded [Los Alamos Director] Norris Bradbury, and Norris Bradbury [then allocated more resources for continued work on hydrogen bombs], without asking anybody else, because that’s how things worked then. Truman had said, “We’re going to build a hydrogen bomb,” and nobody knew how to build it. But Truman didn’t say that. People thought that Edward Teller probably knew how to build it. But he had been working on it since 1939, and he didn’t know how to build it, either. He continually complained that he didn’t have enough people. But any time, he could have written down his theorem and found out that it was wrong. But he found out it was wrong when he wrote down what he and Ulam had talked about.
When I sat in the room, it was a very small room, which had two desks, my desk faced Fermi’s desk. I could see him face-to-face. He taught me a lot the first year and the second year. But only the first year did I share an office with him. And that year, he worked with Stan Ulam in the mornings. The coders would come in. And they would deliver the code, the results of their work. They had been following a spreadsheet that Fermi had started. The first few lines he had actually calculated and sat next to the coder and calculated the first few lines of the spreadsheet, which were various zones along the axis of this infinitely long cylinder. And it started at one end, which was enriched with tritium. And so it reacted about 100 times as fast as deuterium itself. And so then, the second line across the spreadsheet would be the second set of zones along the axis. And the third line would be the third set of zones, and so on. And so they would come in with the whole thing, 100 zones, perhaps. And Fermi would discuss that with the coder, and then he would think of what to calculate next with Stan Ulam.
[back to top]
What’s it like to hold plutonium in your hands? Garwin is among the few people on the planet who can tell you.
Garwin: You can put plutonium in your pocket if it’s coated with nickel, as were the original plutonium hemispheres for the atomic bomb. I’ve held it in my two hands. It was a very dangerous thing to do. But at Los Alamos, you could be admitted to the sanctum and hold the nickel-plated plutonium in your hands. It’s warm, like a rabbit. And of course, if you isolate it, it gets even warmer.
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Garwin spoke about the basic design of the Sausage, the first thermonuclear bomb. He disclosed how the device persuaded Hans Bethe, a star of the Manhattan Project and later, like Garwin, active in arms-control causes, about the viability of thermonuclear weapons. In a touching aside, he remembered his wife, Lois Garwin, to whom he was married from 1947 until her death in 2018.
Garwin: In order to get the Sausage to work, you needed to have a different way of getting equal forces on all sides. And that was the design of the full-size bomb. We used a normal atomic bomb at one end, and then, as has been revealed since then, a cylinder containing deuterium and surrounding that, a cylinder containing hydrogen. And beyond that, the very heavy container. All of that was at liquid-hydrogen temperature or at liquid-deuterium temperature. I can’t go into more detail at present, even now. So I made a full-size weapon. And it was very big. But I argued with Hans Bethe, who was head of various committees for building the atomic bomb, the hydrogen bomb, even though he didn’t want to build it. He wanted to prove that it couldn’t be built. But he was an honest man and excellent physicist. And so he accepted that it could be built.
But I never saw a weapons test, not even in Nevada. Never saw a weapons test. But I traveled to Hawaii a couple of times during the Ivy series and the George series in order to talk to people who came from the test site back to Hawaii to talk to me and others.
I want to mention my wife, Lois Garwin. I could not have done any of this without her. She died in 2018, February 4. And she was the one who took care of the children, except for waking up and diapering them or feeding them a bottle at night, because I could wake up and go to sleep much faster than she.
Garwin also weighed in on one of the most enduring controversies of nuclear-weapons history, which was the relative contributions of Teller and Ulam to the Teller-Ulam configuration. Garwin was asked, was it really the two of them working on this, or was it all Edward Teller?
Garwin: It was really all Edward Teller. I have volunteered that in various interviews. Ulam was a very good mathematician. But he was interested in things whether they were useful or not. He reminded me of Samuel Eilenberg, a mathematician at Columbia with whom I used to have lunch, together with I.I. Rabi and other Nobel Prize winners. Eilenberg used to say, “It’s like a tailor. Sometimes, you make a suit which has three sleeves, sometimes two sleeves, whatever looks best. Sometimes it’s useful, sometimes it’s not. And that’s mathematics for you.”
Why did Teller choose Garwin, a 23-year-old newly minted physicist, over the many staff physicists at Los Alamos to design that first hydrogen bomb?
Garwin: Well, he probably was influenced by something that I found out only in 1981. And that was in an article in Science magazine. Fermi had told people very publicly that I was the only true genius he had ever met. And it was too late to ask Fermi, who died in 1954. He had said that at a meeting at Fuller Lodge at lunchtime. That was the school for boys at Los Alamos that was taken over at the beginning of the atomic bomb program. I was not at that lunch. And he had said, “I’ve met the only true genius I have ever met.” And the people started preening themselves and so on, expecting to be named by him. And then Fermi said, “His name’s Dick Garwin.” And I suppose I had been working, at that time, on the hydrogen-bomb paper. Anyhow, so that’s all I know. Those people were very disappointed.
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Garwin was asked which, of the many things he invented or helped invent during his career at IBM, he was most proud of. He did not hesitate in answering.
Garwin: Really the Cooley-Tukey algorithm because I was the midwife for that. I didn’t invent it. I just sat next to John Tukey. I sat next to Tukey so I could eat his dried prunes, with his permission. I had worked with him in 1953 to ’54 on the intelligence project of the Killian Committee. There were 67 consultants who were members of the Killian Committee. It was a very well-received report. And I worked for six months with NSA under William O Baker, who was the Vice President for research at Bell Labs. So I worked halftime there for six months. I can’t explain what we worked on. But I met all the people, Bill Friedman [legendary cryptanalyst William F. Friedman] and others.
I worked also with Jerry Wiesner. The first time I saw him, in the Lamp Light study, he said, “You know, Dick, you can either accomplish something or get credit for it, but not both.”
Richard Garwin, shown here in 1960, spent decades working at the IBM Research Laboratory in New York.AP
PSAC was the President’s Science Advisory Committee. If you look in Wikipedia, you’ll find many items for PSAC. It was formed in 1957 as part of the Killian Committee report. Eisenhower created PSAC, and Killian was the first head. [Editor’s note: James Rhyne Killian was the 10th president of MIT, from 1948 to 1959.] I had two terms on PSAC. One was with Kennedy, beginning January 21, 1961, and the other one was with Nixon, his second term.
When I came home from the PSAC meeting, a two-day meeting [in 1963], I wrote the person who was head of mathematics at Yorktown Heights. I was, at that time, head of the [IBM] Watson Scientific Laboratory, at Columbia University. And so I wrote to him and I said, “Can you find me a numerical analyst, somebody who can code this up and who will go to Princeton and talk with Tukey.” And he said, “Cooley is your man.”
Jim Cooley wasn’t enthusiastic about stopping what he was doing and going to Princeton. He needed additional influence. So I wrote him and I told him what it was that I wanted him to do. And in fact, the idea came not from Tukey, but it came from a colleague of his at [Government Communications Headquarters] in England [mathematician I. J. Good], a fellow whom we both knew from our days working on the Killian Committee. This was persuasive to Cooley, that his employment would be dependent upon his going to Princeton and talking to Tukey. So Cooley went to Princeton, and he talked to Tukey, and I don’t know for how long, but then he sat down and he wrote a Fortran program. And I then went to the IBM Science Advisory Committee, which was headed by Jerome Wiesner, and persuaded them to make this a free-for-everybody Fortran program rather than charging money for it. And so that was my additional contribution. And I then started distributing the program within a few months. I would send the name of the program and people could write in and get it. But mostly, they weren’t persuaded.
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Garwin counted Enrico Fermi among his closest friends and associates. I asked him if there was something about Fermi that most people did not know, and that he wanted to share.
Garwin: He was a very ordinary-appearing person, but he had great round eyes. And in fact, I gave a talk at the IISS, the International Institute of Strategic Studies, meaning I was on the board for nine years while my daughter, Laura, was a graduate student at Oxford. And then at Cambridge, she was in the first batch of women Rhodes Scholars. And I saw somebody in the front row who looked very familiar. And I realized that it was his eyes. That he had Fermi’s eyes. It was Giulio Fermi, his son. I had met Giulio when he was 12 or 13 years old at their home in Chicago.
And I knew Nella, the older scion, who was a daughter. And I knew Laura, Laura Fermi, after whom our daughter, Laura, is named. She was a refined person. But Enrico was self-taught. There was an engineer, a friend of his father. His father, I think, worked for the railroad. The engineering friend would lend him books and Enrico would read them and learn them and learn the various languages involved. And he would solve the problems in the books, many of which were not easy, but nothing was too little for him or too big. He kept very good notes, in his laboratory notebook. And actually, he would write in my laboratory notebook at Los Alamos, and I would lock it up in the safe at night so that he wouldn’t have to do that.
There he would record the four shock equations and derive them and teach me how to do things like that. The degree to which he was self-taught… He organized the kids, the ragazzi, the kids of whatever street it was in Rome. And when he got to Rome from where he had gone to college and went to graduate school, he brought all kinds of people. Some of whom joined him in Los Alamos.
We were good friends of the Fermis in Los Alamos. And Lois and Jeffrey, my oldest boy, were with me in the summers. And then gradually, the other two children, Tom and Laura, joined us. So there were many years when we had rental homes in Los Alamos because people were always going away for the summer.
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Garwin, an IEEE Life Fellow, is renowned for his ability to not only understand theory but also to put it to practical use. Nevertheless, his answer to the question of whether he considers himself an engineer or a physicist was surprising.
Garwin: I’m a physicist. I don’t think there’s a ranking. I just don’t know enough to qualify as an engineer.
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All through the 41 years he was working for IBM, Garwin was also extraordinarily active on countless government committees and boards and also active in the nuclear arms–control movement. I asked him how he was able to maintain such an active and public professional life outside of IBM without raising eyebrows.
Garwin: My agreement with IBM was that they would not know what I was working on [outside of IBM]. They wouldn’t know what I testified about, and so on. And they signed an employment contract. Because otherwise, I knew that they would want to approve it. They would have lawyers saying, “Is this a good thing for us to do or not?” And so then when I started testifying in Congress and the testimony was public, I decided that I should tell IBM. I told them the same day. I gave them a copy of the testimony at the same time I gave it. I printed 100 copies, and we stood around the dining room table in our house, and whatever children were of suitable age would sort these things or unsort these things and staple them together and put them into a suitcase. One hundred copies of 10 pages of testimony is a lot to carry. And I would lug them to the airport for the early morning flight to Washington.
And IBM was as good as its word. I think they considered firing me a couple of times, but once I saw Manny Piori, who was the first director of research, and then he was head of the IBM Science Advisory Committee, and then he was various other things. Once I saw him furiously writing the head of IBM, who was, at that time, Thomas J. Watson Jr. And he was writing him to tell him that whatever they did, they shouldn’t fire me.
Not surprisingly, Garwin had strong opinions about the United States’ planned resumption of the production of plutonium, intended for a new missile warhead, the W87-1.
Garwin: That’s very bad, but it’s a matter of monkey see, monkey do. But it’s not that we need to do these things. It’s just that they don’t want to be caught short when the Russians resume testing or the Chinese resume testing. [The Chinese] have a lot to learn from their tests because they’ve had only 40-some total in history compared with the thousands or more, mostly underground. And underground tests up to 5 megatons, with the antiballistic missile warhead. So they don’t need to do this, but they don’t want to be caught short. And people say, “Look, the Russians are testing,” and the Russians have manufactured plutonium and you aren’t. They don’t really need new plutonium.
Somebody in Congress would say that the Russians are ahead of us, that they have these thousands of…they had 60,000 weapons at one time in 1962, I guess. And they could have those weapons that would destroy whole cities. And the United States doesn’t have a comparable number of weapons. They could destroy industrial centers. Anyhow, by appearance, the Americans would lose the race for appearance.
Not that the Russians could do anything useful with their weapons, but Russians aren’t constrained by logic. The Chinese, unfortunately, under Xi Jinping, have lost their way. Their way really is to make things and sell them to the world, even though the labor is transferring elsewhere as a matter of relative size and cost. But I had hoped that before Xi Jinping, beginning with Deng Xiaoping, I guess, that the Chinese would see the benefit of being supplier to the world. But now we have a kind of trade war with China. And I think that’s a big mistake for the United States. The United States ought to encourage China. And although we should set tariffs, the tariffs ought to be modest, in my opinion.
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Garwin also had strong feelings about the surging funding for small, modular nuclear power reactors in the United States and elsewhere. I asked him if he thought these reactors were likely to succeed economically where larger reactors had not.
Garwin: The answer is no. I think that they won’t succeed because of economics. And the beginning of subsidy for those reactors, they want to subsidize the “valley of death.” But you can’t do that because there are many competing firms. You can subsidize all you want, but you can’t get out of the fact that they’re uneconomical. They’re not economical, and they will find out when they try to build them and when they exhaust the subsidy, and they can’t make it work. So no, I don’t think that they will work. I think it will put us in a large plutonium economy in order to breed plutonium and reprocess the material that accumulates in the reactors.
Does he think the only way to progress toward a carbon-neutral energy regime is with renewables?
Garwin: Yes. I think that using—I’m sorry to claim credit for something, but nobody has picked it up. A few years ago, I published a paper on green hydrogen and green ammonia. And the key is to use the steep trench up the west coast of the United States and Chile and the other countries in Latin America. Within 100 miles of the coast, there is this undersea trench where you can store hydrogen in ordinary weighted culverts. So it sits on the bottom. And it goes down to five kilometers or more. And all you need is one kilometer for 100 bar. You would have land-based solar and land-based wind turbines. They would be constantly electrolyzing either at surface or at depth, one way or another. So you would accumulate hydrogen. You would store it in a bladder which is held down by the negative buoyancy of the culverts. And it would just displace the water as you fill the bladder and as you empty the bladder during hydrogen usage, you would send the hydrogen back to shore. All that was worked out in my paper. And I even say how you would start by making green ammonia. You would get nitrogen from the atmosphere and then combine it with the hydrogen from the electrolysis. You can start small.
And you can just electrolyze and convert to ammonia and have it trucked away. And it would initially be sold at a high price, which would work because it would be sold for fertilizer. There’s a large market for fertilizer. And then when that gets saturated because of too much production, then you’d have to start using green ammonia for fuel anyhow.
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An abridged version of this article appears in the September 2024 print issue as “5 Questions for Richard Garwin.”
IEEE President’s Note: Why Students Should Stay with IEEE
by Tom Coughlin on 01. September 2024. at 15:00

I would like our student members to know that IEEE is much more than just a club you join at school. It is an international community that can help students build and sustain successful careers as technical professionals after they graduate.
For more than 40 years, IEEE has been a great place to build my personal brand and to create a valuable professional network. I know it can do the same for the next generation of engineers.
By maintaining their membership after graduation and throughout their careers, students will have access to more resources—both professional and personal—that can help them advance within their field and discover new interests. They will also have the opportunity to build soft skills, raise their visibility, and make friendships that last a lifetime. All these resources and experiences, and more, will be of value in their life and work.
Lifelong professional home
The students of today likely will have many jobs across different organizations throughout their career. IEEE can be their lifelong professional home and help them meet their long-term technical and professional needs. The organization provides professional contacts and a community that offers support, advice, and mentoring, independent of where they work.
IEEE is here to support members at each stage of their professional journey. Within the organization, there are young people just starting to explore their professional passions. But there are also active engineers currently working in industry, government, and academia, as well as retired professionals who have a wealth of acquired knowledge and experience to share.
The students of today likely will have many jobs across different organizations throughout their career. IEEE can be their lifelong professional home and help them meet their long-term technical and professional needs.
Through its technical societies, IEEE also has a tremendous reach that spans areas including circuits, communications, computers, power and energy, semiconductors, and more. Whatever your focus is, there is a community within IEEE that will meet your needs.
For students and early-career practitioners, membership can help expand one’s professional network and elevate one’s professional image. It can lead to meaningful collaborative research opportunities that jump-start and advance one’s career, and it can provide professional-development pathways that refine skills through leadership opportunities.
Many of the key benefits of volunteering are also help with skills critical to continued professional success. Volunteering can advance your knowledge in all aspects of technology and science, provide opportunities to help guide the evolution of numerous fields, and network with others from around the world. Serving as a volunteer can also help empower members to champion their ideas and hone their communication and presentation skills, as well as management experience, which are important for professional development.
The future looks bright
There are great career opportunities in technology and engineering. The number of jobs in science, technology, engineering, and mathematics (STEM) fields is increasing, and many of them are lucrative, as they are essential components of today’s competitive global economy.
A career in these areas enables IEEE members to make a significant impact in the world, as STEM fields are imperative to solving some of the grand challenges facing society, such as climate change, cyberwarfare, and public health.
IEEE is committed to empowering and inspiring the next generation of engineering and technology leaders. Members, from students to retirees, will play a pivotal role in not only helping the organization continue to flourish but also in advancing technology for the benefit of humanity. They are the future of IEEE.
The best way to realize the true value of the organization is by engaging with IEEE colleagues. Get involved. Volunteer with your local section or engage with a society or technical community or work on an IEEE standard. Become an active participant—whether it be with affinity groups, humanitarian efforts, continuing education, or a standards working group and make IEEE your professional home.
—Tom Coughlin
IEEE president and CEO
This article appears in the September 2024 print issue as “Why Students Should Stay with IEEE.”
AI Has Created a Battle Over Web Crawling
by Eliza Strickland on 31. August 2024. at 13:00

Most people assume that generative AI will keep getting better and better; after all, that’s been the trend so far. And it may do so. But what some people don’t realize is that generative AI models are only as good as the ginormous data sets they’re trained on, and those data sets aren’t constructed from proprietary data owned by leading AI companies like OpenAI and Anthropic. Instead, they’re made up of public data that was created by all of us—anyone who’s ever written a blog post, posted a video, commented on a Reddit thread, or done basically anything else online.
A new report from the Data Provenance Initiative, a volunteer collective of AI researchers, shines a light on what’s happening with all that data. The report, “Consent in Crisis: The Rapid Decline of the AI Data Commons,” notes that a significant number of organizations that feel threatened by generative AI are taking measures to wall off their data. IEEE Spectrum spoke with Shayne Longpre, a lead researcher with the Data Provenance Initiative, about the report and its implications for AI companies.
Shayne Longpre on:
How websites keep out web crawlers, and why
Disappearing data and what it means for AI companies
Synthetic data, peak data, and what happens next

The technology that websites use to keep out web crawlers isn’t new—the robot exclusion protocol was introduced in 1995. Can you explain what it is and why it suddenly became so relevant in the age of generative AI?

Shayne Longpre

Shayne Longpre: Robots.txt is a machine-readable file that crawlers—bots that navigate the web and record what they see—use to determine whether or not to crawl certain parts of a website. It became the de facto standard in the age where websites used it primarily for directing web search. So think of Bing or Google Search; they wanted to record this information so they could improve the experience of navigating users around the web. This was a very symbiotic relationship because web search operates by sending traffic to websites and websites want that. Generally speaking, most websites played well with most crawlers.

Let me next talk about a chain of claims that’s important to understand this. General-purpose AI models and their very impressive capabilities rely on the scale of data and compute that have been used to train them. Scale and data really matter, and there are very few sources that provide public scale like the web does. So many of the foundation models were trained on [data sets composed of] crawls of the web. Under these popular and important data sets are essentially just websites and the crawling infrastructure used to collect and package and process that data. Our study looks at not just the data sets, but the preference signals from the underlying websites. It’s the supply chain of the data itself.

But in the last year, a lot of websites have started using robots.txt to restrict bots, especially websites that are monetized with advertising and paywalls—so think news and artists. They’re particularly fearful, and maybe rightly so, that generative AI might impinge on their livelihoods. So they’re taking measures to protect their data.

When a site puts up robots.txt restrictions, it’s like putting up a no trespassing sign, right? It’s not enforceable. You have to trust that the crawlers will respect it.

Longpre: The tragedy of this is that robots.txt is machine-readable but does not appear to be legally enforceable. Whereas the terms of service may be legally enforceable but are not machine-readable. In the terms of service, they can articulate in natural language what the preferences are for the use of the data. So they can say things like, “You can use this data, but not commercially.” But in a robots.txt, you have to individually specify crawlers and then say which parts of the website you allow or disallow for them. This puts an undue burden on websites to figure out, among thousands of different crawlers, which ones correspond to uses they would like and which ones they wouldn’t like.

Do we know if crawlers generally do respect the restrictions in robots.txt?

Longpre: Many of the major companies have documentation that explicitly says what their rules or procedures are. In the case, for example, of Anthropic, they do say that they respect the robots.txt for ClaudeBot. However, many of these companies have also been in the news lately because they’ve been accused of not respecting robots.txt and crawling websites anyway. It isn’t clear from the outside why there’s a discrepancy between what AI companies say they do and what they’re being accused of doing. But a lot of the pro-social groups that use crawling—smaller startups, academics, nonprofits, journalists—they tend to respect robots.txt. They’re not the intended target of these restrictions, but they get blocked by them.

In the report, you looked at three training data sets that are often used to train generative AI systems, which were all created from web crawls in years past. You found that from 2023 to 2024, there was a very significant rise in the number of crawled domains that had since been restricted. Can you talk about those findings?

Longpre: What we found is that if you look at a particular data set, let’s take C4, which is very popular, created in 2019—in less than a year, about 5 percent of its data has been revoked if you respect or adhere to the preferences of the underlying websites. Now 5 percent doesn’t sound like a ton, but it is when you realize that this portion of the data mainly corresponds to the highest quality, most well-maintained, and freshest data. When we looked at the top 2,000 websites in this C4 data set—these are the top 2,000 by size, and they’re mostly news, large academic sites, social media, and well-curated high-quality websites—25 percent of the data in that top 2,000 has since been revoked. What this means is that the distribution of training data for models that respect robots.txt is rapidly shifting away from high-quality news, academic websites, forums, and social media to more organization and personal websites as well as e-commerce and blogs.

That seems like it could be a problem if we’re asking some future version of ChatGPT or Perplexity to answer complicated questions, and it’s taking the information from personal blogs and shopping sites.

Longpre: Exactly. It’s difficult to measure how this will affect models, but we suspect there will be a gap between the performance of models that respect robots.txt and the performance of models that have already secured this data and are willing to train on it anyway.

But the older data sets are still intact. Can AI companies just use the older data sets? What’s the downside of that?

Longpre: Well, continuous data freshness really matters. It also isn’t clear whether robots.txt can apply retroactively. Publishers would likely argue they do. So it depends on your appetite for lawsuits or where you also think that trends might go, especially in the U.S., with the ongoing lawsuits surrounding fair use of data. The prime example is obviously The New York Times against OpenAI and Microsoft, but there are now many variants. There’s a lot of uncertainty as to which way it will go.

The report is called “Consent in Crisis.” Why do you consider it a crisis?

Longpre: I think that it’s a crisis for data creators, because of the difficulty in expressing what they want with existing protocols. And also for some developers that are non-commercial and maybe not even related to AI—academics and researchers are finding that this data is becoming harder to access. And I think it’s also a crisis because it’s such a mess. The infrastructure was not designed to accommodate all of these different use cases at once. And it’s finally becoming a problem because of these huge industries colliding, with generative AI against news creators and others.

What can AI companies do if this continues, and more and more data is restricted? What would their moves be in order to keep training enormous models?

Longpre: The large companies will license it directly. It might not be a bad outcome for some of the large companies if a lot of this data is foreclosed or difficult to collect, it just creates a larger capital requirement for entry. I think big companies will invest more into the data collection pipeline and into gaining continuous access to valuable data sources that are user-generated, like YouTube and GitHub and Reddit. Acquiring exclusive access to those sites is probably an intelligent market play, but a problematic one from an antitrust perspective. I’m particularly concerned about the exclusive data acquisition relationships that might come out of this.

Do you think synthetic data can fill the gap?

Longpre: Big companies are already using synthetic data in large quantities. There are both fears and opportunities with synthetic data. On one hand, there have been a series of works that have demonstrated the potential for model collapse, which is the degradation of a model due to training on poor synthetic data that may appear more often on the web as more and more generative bots are let loose. However, I think it’s unlikely that large models will be hampered much because they have quality filters, so the poor quality or repetitive stuff can be siphoned out. And the opportunities of synthetic data are when it’s created in a lab environment to be very high quality, and it’s targeting particularly domains that are underdeveloped.

Do you give credence to the idea that we may be at peak data? Or do you feel like that’s an overblown concern?

Longpre: There is a lot of untapped data out there. But interestingly, a lot of it is hidden behind PDFs, so you need to do OCR [optical character recognition]. A lot of data is locked away in governments, in proprietary channels, in unstructured formats, or difficult to extract formats like PDFs. I think there’ll be a lot more investment in figuring out how to extract that data. I do think that in terms of easily available data, many companies are starting to hit walls and turning to synthetic data.

What’s the trend line here? Do you expect to see more websites putting up robots.txt restrictions in the coming years?

Longpre: We expect the restrictions to rise, both in robots.txt and in terms of service. Those trend lines are very clear from our work, but they could be affected by external factors such as legislation, companies themselves changing their policies, the outcome of lawsuits, as well as community pressure from writers’ guilds and things like that. And I expect that the increased commoditization of data is going to cause more of a battlefield in this space.

What would you like to see happen in terms of either standardization within the industry to making it easier for websites to express preferences about crawling?

Longpre: At the Data Province Initiative, we definitely hope that new standards will emerge and be adopted to allow creators to express their preferences in a more granular way around the uses of their data. That would make the burden much easier on them. I think that’s a no-brainer and a win-win. But it’s not clear whose job it is to create or enforce these standards. It would be amazing if the [AI] companies themselves could come to this conclusion and do it. But the designer of the standard will almost inevitably have some bias towards their own use, especially if it’s a corporate entity.

It’s also the case that preferences shouldn’t be respected in all cases. For instance, I don’t think that academics or journalists doing prosocial research should necessarily be foreclosed from accessing data with machines that is already public, on websites that anyone could go visit themselves. Not all data is created equal and not all uses are created equal.

Was an AI Image Generator Taken Down for Making Child Porn?

by David Evan Harris on 30. August 2024. at 20:04

Why are AI companies valued in the millions and billions of dollars creating and distributing tools that can make AI-generated child sexual abuse material (CSAM)?

An image generator called Stable Diffusion version 1.5, which was created by the AI company Runway with funding from Stability AI, has been particularly implicated in the production of CSAM. And popular platforms such as Hugging Face and Civitai have been hosting that model and others that may have been trained on real images of child sexual abuse. In some cases, companies may even be breaking laws by hosting synthetic CSAM material on their servers. And why are mainstream companies and investors like Amazon, Google, Nvidia, Intel, Salesforce, and Andreessen Horowitz pumping hundreds of millions of dollars into these companies? Their support amounts to subsidizing content for pedophiles.

As AI safety experts, we’ve been asking these questions to call out these companies and pressure them to take the corrective actions we outline below. And we’re happy today to report one major triumph: seemingly in response to our questions, Stable Diffusion version 1.5 has been removed from Hugging Face. But there’s much still to do, and meaningful progress may require legislation.

The Scope of the CSAM Problem

Child safety advocates began ringing the alarm bell last year: Researchers at Stanford’s Internet Observatory and the technology non-profit Thorn published a troubling report in June 2023. They found that broadly available and “open-source” AI image-generation tools were already being misused by malicious actors to make child sexual abuse material. In some cases, bad actors were making their own custom versions of these models (a process known as fine-tuning) with real child sexual abuse material to generate bespoke images of specific victims.

Last October, a report from the U.K. nonprofit Internet Watch Foundation (which collects reports of child sexual abuse material) detailed the ease with which malicious actors are now making photorealistic AI-generated child sexual abuse material, at scale. The researchers included a “snapshot” study of one dark web CSAM forum, analyzing more than 11,000 AI-generated images posted in a one-month period; of those, nearly 3,000 were judged severe enough to be classified as criminal. The report urged stronger regulatory oversight of generative AI models.

AI models can be used to create this material because they’ve seen examples before. Researchers at Stanford discovered last December that one of the most significant data sets used to train image-generation models included hundreds of pieces of CSAM. Many of the most popular downloadable open-source AI image generators, including the popular Stable Diffusion version 1.5 model, were trained using this data. While Runway created that version of Stable Diffusion, Stability AI paid for the computing power to produce the dataset and train the model, and Stability AI released the subsequent versions.

Runway did not respond to a request for comment. A Stability AI spokesperson emphasized that the company did not release or maintain Stable Diffusion version 1.5, and says the company has “implemented robust safeguards” against CSAM in subsequent models, including the use of filtered data sets for training.

Also last December, researchers at the social media analytics firm Graphika found a proliferation of dozens of “undressing” services, many based on open-source AI image generators, likely including Stable Diffusion. These services allow users to upload clothed pictures of people and produce what experts term nonconsensual intimate imagery (NCII) of both minors and adults, also sometimes referred to as deepfake pornography. Such websites can be easily found through Google searches, and users can pay for the services using credit cards online. Many of these services only work on women and girls, and these types of tools have been used to target female celebrities like Taylor Swift and politicians like U.S. representative Alexandria Ocasio-Cortez.

AI-generated CSAM has real effects. The child safety ecosystem is already overtaxed, with millions of files of suspected CSAM reported to hotlines annually. Anything that adds to that torrent of content—especially photorealistic abuse material—makes it more difficult to find children that are actively in harm’s way. Making matters worse, some bad actors are using existing CSAM to generate synthetic images of these survivors—a horrific re-violation of their rights. Others are using the readily available “nudifying” apps to create sexual content from benign imagery of real children, and then using that newly generated content in sexual extortion schemes.

One Victory Against AI-Generated CSAM

Based on the Stanford investigation from last December, it’s well-known in the AI community that Stable Diffusion 1.5 was trained on child sexual abuse material, as was every other model trained on the LAION-5B data set. These models are being actively misused by malicious actors to make AI-generated CSAM. And even when they’re used to generate more benign material, their use inherently revictimizes the children whose abuse images went into their training data. So we asked the popular AI hosting platforms Hugging Face and Civitai why they hosted Stable Diffusion 1.5 and derivative models, making them available for free download?

It’s worth noting that Jeff Allen, a data scientist at the Integrity Institute, found that Stable Diffusion 1.5 was downloaded from Hugging Face over 6 million times in the past month, making it the most popular AI image-generator on the platform.

When we asked Hugging Face why it has continued to host the model, company spokesperson Brigitte Tousignant did not directly answer the question, but instead stated that the company doesn’t tolerate CSAM on its platform, that it incorporates a variety of safety tools, and that it encourages the community to use the Safe Stable Diffusion model that identifies and suppresses inappropriate images.

Then, yesterday, we checked Hugging Face and found that Stable Diffusion 1.5 is no longer available. Tousignant told us that Hugging Face didn’t take it down, and suggested that we contact Runway—which we did, again, but we have not yet received a response.

It’s undoubtedly a success that this model is no longer available for download from Hugging Face. Unfortunately, it’s still available on Civitai, as are hundreds of derivative models. When we contacted Civitai, a spokesperson told us that they have no knowledge of what training data Stable Diffusion 1.5 used, and that they would only take it down if there was evidence of misuse.

Platforms should be getting nervous about their liability. This past week saw the arrest of Pavel Durov, CEO of the messaging app Telegram, as part of an investigation related to CSAM and other crimes.

What’s Being Done About AI-Generated CSAM

The steady drumbeat of disturbing reports and news about AI-generated CSAM and NCII hasn’t let up. While some companies are trying to improve their products’ safety with the help of the Tech Coalition, what progress have we seen on the broader issue?

In April, Thorn and All Tech Is Human announced an initiative to bring together mainstream tech companies, generative AI developers, model hosting platforms, and more to define and commit to Safety by Design principles, which put preventing child sexual abuse at the center of the product development process. Ten companies (including Amazon, Civitai, Google, Meta, Microsoft, OpenAI, and Stability AI) committed to these principles, and some also co-authored a related paper with more detailed recommended mitigations. The principles call on companies to develop, deploy, and maintain AI models that proactively address child safety risks; to build systems to ensure that any abuse material that does get produced is reliably detected; and to limit the distribution of the underlying models and services that are used to make this abuse material.

These kinds of voluntary commitments are a start. Rebecca Portnoff, Thorn’s head of data science, says the initiative seeks accountability by requiring companies to issue reports about their progress on the mitigation steps. It’s also collaborating with standard-setting institutions such as IEEE and NIST to integrate their efforts into new and existing standards, opening the door to third party audits that would “move past the honor system,” Portnoff says. Portnoff also notes that Thorn is engaging with policy makers to help them conceive legislation that would be both technically feasible and impactful. Indeed, many experts say it’s time to move beyond voluntary commitments.

We believe that there is a reckless race to the bottom currently underway in the AI industry. Companies are so furiously fighting to be technically in the lead that many of them are ignoring the ethical and possibly even legal consequences of their products. While some governments—including the European Union—are making headway on regulating AI, they haven’t gone far enough. If, for example, laws made it illegal to provide AI systems that can produce CSAM, tech companies might take notice.

The reality is that while some companies will abide by voluntary commitments, many will not. And of those that do, many will take action too slowly, either because they’re not ready or because they’re struggling to keep their competitive advantage. In the meantime, bad actors will gravitate to those services and wreak havoc. That outcome is unacceptable.

What Tech Companies Should Do About AI-Generated CSAM

Experts saw this problem coming from a mile away, and child safety advocates have recommended common-sense strategies to combat it. If we miss this opportunity to do something to fix the situation, we’ll all bear the responsibility. At a minimum, all companies, including those releasing open source models, should be legally required to follow the commitments laid out in Thorn’s Safety by Design principles:

Detect, remove, and report CSAM from their training data sets before training their generative AI models.
Incorporate robust watermarks and content provenance systems into their generative AI models so generated images can be linked to the models that created them, as would be required under a California bill that would create Digital Content Provenance Standards for companies that do business in the state. The bill will likely be up for signature by Governor Gavin Newson in the coming month.
Remove from their platforms any generative AI models that are known to be trained on CSAM or that are capable of producing CSAM. Refuse to rehost these models unless they’ve been fully reconstituted with the CSAM removed.
Identify models that have been intentionally fine-tuned on CSAM and permanently remove them from their platforms.
Remove “nudifying” apps from app stores, block search results for these tools and services, and work with payment providers to block payments to their makers.

There is no reason why generative AI needs to aid and abet the horrific abuse of children. But we will need all tools at hand—voluntary commitments, regulation, and public pressure—to change course and stop the race to the bottom.

The authors thank Rebecca Portnoff of Thorn, David Thiel of the Stanford Internet Observatory, Jeff Allen of the Integrity Institute, Ravit Dotan of TechBetter, and the tech policy researcher Owen Doyle for their help with this article.

Unitree Demos New $16k Robot

by IEEE Spectrum on 30. August 2024. at 17:01

At ICRA 2024, Spectrum editor Evan Ackerman sat down with Unitree founder and CEO Xingxing Wang and Tony Yang, VP of Business Development, to talk about the company’s newest humanoid, the G1 model.

Smaller, more flexible, and elegant, the G1 robot is designed for general use in service and industry, and is one of the cheapest—if not the cheapest—humanoid around.

Video Friday: Robots Solving Table Tennis

by Evan Ackerman on 30. August 2024. at 16:26

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

ICRA@40: 23–26 September 2024, ROTTERDAM, NETHERLANDS

IROS 2024: 14–18 October 2024, ABU DHABI, UAE

ICSR 2024: 23–26 October 2024, ODENSE, DENMARK

Cybathlon 2024: 25–27 October 2024, ZURICH

Enjoy today’s videos!

Imbuing robots with “human-level performance” in anything is an enormous challenge, but it’s worth it when you see a robot with the skill to interact with a human on a (nearly) human level. Google DeepMind has managed to achieve amateur human-level competence at table tennis, which is much harder than it looks, even for humans. Pannag Sanketi, a tech-lead manager in the robotics team at DeepMind, shared some interesting insights about performing the research. But first, video!

Some behind the scenes detail from Pannag:

The robot had not seen any participants before. So we knew we had a cool agent, but we had no idea how it was going to fare in a full match with real humans. To witness it outmaneuver even some of the most advanced players was such a delightful moment for team!
All the participants had a lot of fun playing against the robot, irrespective of who won the match. And all of them wanted to play more. Some of them said it will be great to have the robot as a playing partner. From the videos, you can even see how much fun the user study hosts sitting there (who are not authors on the paper) are having watching the games!
Barney, who is a professional coach, was an advisor on the project, and our chief evaluator of robot’s skills the way he evaluates his students. He also got surprised by how the robot is always able to learn from the last few weeks’ sessions.
We invested a lot in remote and automated 24x7 operations. So not the setup in this video, but there are other cells that we can run 24x7 with a ball thrower.
We even tried robot-vs-robot, i.e. 2 robots playing against each other! :) The line between collaboration and competition becomes very interesting when they try to learn by playing with each other.

[ DeepMind ]

Thanks, Heni!

Yoink.

[ MIT ]

Considering how their stability and recovery is often tested, teaching robot dogs to be shy of humans is an excellent idea.

[ Deep Robotics ]

Yes, quadruped robots need tow truck hooks.

[ Paper ]

Earthworm-inspired robots require novel actuators, and Ayato Kanada at Kyushu University has come up with a neat one.

[ Paper ]

Thanks, Ayato!

Meet the AstroAnt! This miniaturized swarm robot can ride atop a lunar rover and collect data related to its health, including surface temperatures and damage from micrometeoroid impacts. In the summer of 2024, with support from our collaborator Castrol, the Media Lab’s Space Exploration Initiative tested AstroAnt in the Canary Islands, where the volcanic landscape resembles the lunar surface.

[ MIT ]

Kengoro has a new forearm that mimics the human radioulnar joint giving it an even more natural badminton swing.

[ JSK Lab ]

Thanks, Kento!

Gromit’s concern that Wallace is becoming too dependent on his inventions proves justified, when Wallace invents a “smart” gnome that seems to develop a mind of its own. When it emerges that a vengeful figure from the past might be masterminding things, it falls to Gromit to battle sinister forces and save his master… or Wallace may never be able to invent again!

[ Wallace and Gromit ]

ASTORINO is a modern 6-axis robot based on 3D printing technology. Programmable in AS-language, it facilitates the preparation of classes with ready-made teaching materials, is easy both to use and to repair, and gives the opportunity to learn and make mistakes without fear of breaking it.

[ Kawasaki ]

Engineers at NASA’s Jet Propulsion Laboratory are testing a prototype of IceNode, a robot designed to access one of the most difficult-to-reach places on Earth. The team envisions a fleet of these autonomous robots deploying into unmapped underwater cavities beneath Antarctic ice shelves. There, they’d measure how fast the ice is melting — data that’s crucial to helping scientists accurately project how much global sea levels will rise.

[ IceNode ]

Los Alamos National Laboratory, in a consortium with four other National Laboratories, is leading the charge in finding the best practices to find orphaned wells. These abandoned wells can leak methane gas into the atmosphere and possibly leak liquid into the ground water.

[ LANL ]

Looks like Fourier has been working on something new, although this is still at the point of “looks like” rather than something real.

[ Fourier ]

Bio-Inspired Robot Hands: Altus Dexterity is a collaboration between researchers and professionals from Carnegie Mellon University, UPMC, the University of Illinois and the University of Houston.

[ Altus Dexterity ]

PiPER is a lightweight robotic arm with six integrated joint motors for smooth, precise control. Weighing just 4.2kg, it easily handles a 1.5kg payload and is made from durable yet lightweight materials for versatile use across various environments. Available for just $2,499 USD.

[ AgileX ]

At 104 years old, Lilabel has seen over a century of automotive transformation, from sharing a single car with her family in the 1920s to experiencing her first ride in a robotaxi.

[ Zoox ]

Traditionally, blind juggling robots use plates that are slightly concave to help them with ball control, but it’s also possible to make a blind juggler the hard way. Which, honestly, is much more impressive.

[ Jugglebot ]

Celebrate IEEE Day’s 15th Anniversary on 1 October

by Adrienne Hahn on 29. August 2024. at 18:00

IEEE Day commemorates the first time engineers worldwide gathered to share their technical ideas, in 1884. This year the annual event is scheduled for 1 October. Its theme is Leveraging Technology for a Better Tomorrow, emphasizing the positive impact tech can have.

IEEE Day, first celebrated in 2010, marks its 15th anniversary this year. Over the years, thousands of members have participated in events organized by IEEE sections, student branches, affinity groups, and society chapters. IEEE Day events provide a platform for engineers to share ideas and inspire one another.

For some sections, one day is not enough. Celebrations are scheduled between 29 September and 12 October, both virtually and in person, to connect members across borders.

“As we commemorate IEEE Day’s 15th anniversary, it is an opportune moment to reflect upon the remarkable influence that IEEE has had on each and every member, as well as the joyous events that have transpired annually across the globe,” says IEEE Member Cybele Ghanem, 2024 IEEE Day chair.

“This year holds the promise of an exceptional celebration, bringing together thousands of IEEE members in hundreds of events worldwide to honor the historical significance of IEEE,” Ghanem says. “I encourage everyone to seize this opportunity to review their IEEE journey, share their cherished moments with us, and embark on an even more exhilarating journey ahead.”

a small group of people sitting in chairs on a small stage talking into microphones One of several panel discussions organized by the IEEE Hyderabad Section to mark IEEE Day 2023.Bindu Madhavi

Global collaboration

Past events have included humanitarian projects, lectures on cutting-edge technical topics, sessions on career development and résumé writing, networking events, and an IEEE flash mob.

The events are an excellent way to engage IEEE members, recruit new ones, provide volunteering opportunities, and showcase the organization, Ghanem says. Through workshops, seminars, and networking sessions, IEEE Day encourages knowledge exchange and camaraderie.

“This year holds the promise of an exceptional celebration, bringing together thousands of IEEE members in hundreds of events worldwide to honor the historical significance of IEEE.”

Activities and contests

Participants can engage in competitions and win prizes.

The IEEE Day photo and video contests allow attendees to visually document what took place at their events, then share the images with the world. There are three photography categories: humanitarian, STEM, and technical. Videos may be long-form or short.

Contest winners receive monetary rewards and get a chance to be showcased in IEEE online and print publications as well as on social media platforms. So, take along your phone or camera when attending an IEEE Day event to capture the spirit of innovation and collaboration.

Join the celebration

IEEE will be offering a special discount on membership for those joining during the IEEE Day period. Many IEEE societies are planning special offers as well.

Resources and more information can be found on the IEEE Day website.

Escape Proprietary Smart Home Tech With This DIY Panel

by Alan Boris on 29. August 2024. at 15:00

Over the last few years, I’ve added a fair amount of smart-home technology to my house. Among other things, I can control lights and outlets, monitor the status of various appliances, measure how much electricity and water I’m using, and even cut off the water supply in the event of a leak. All this technology is coordinated through a hub, which I originally accessed through a conventional browser-based interface. But scrolling and clicking through screens to find the reading or setting I want is a) slow and b) boring. I wanted an interface that was fast and fun—a physical control panel with displays and buttons.

Something like the control room in the nuclear power plant in 1979’s The China Syndrome. I was about 10 years old when I saw that movie, and my overwhelming thought while watching Jack Lemmon trying to avert a meltdown was, “Boy, those panels look neat!” So they became my North Star for this design.

Before I could work on the aesthetic elements, however, I had to consider how my panel was going to process inputs and outputs and communicate with the systems in my home. The devices in my home are tied together using the open source Home Assistant platform. Using an open source platform means I don’t have to worry that, for example, I suddenly won’t be able to turn on my lights due to a forced upgrade of a proprietary system, or wonder if someone in the cloud is monitoring the activity in my home.

The heart of my Home Assistant setup is a hub powered by an old PC running Linux. This handles wireless connections with my sensors, appliances, and other devices. For commercial off-the-shelf equipment—like my energy meter—this communication is typically via Z-Wave. My homebrew devices are connected to the GPIO pins of a Raspberry Pi, which relays their state via Wi-Fi using the MQTT standard protocol for the Internet of Things. However, I decided on a wired Ethernet connection between the control panel and my hub PC, as this would let me use Power over Ethernet (PoE) to supply electricity to the panel.

A variety of electronic components such as individual LEDs and seven segment displays, buttons, and switches. The different types of components used in the control panel include a touchscreen display [A], LED displays [B], Raspberry Pis [C], Power over Ethernet boards [D], and an emergency stop button [E]. James Provost

In fact, I use two Ethernet connections, because I decided to divide the functionality of the control panel across two model 3B+ Raspberry Pis, which cost about US $35 each (a complete bill of materials can be found on my GitHub repository). One Pi drives a touchscreen display, while the other handles the buttons and LEDs. Each is fitted with a $20 add-on PoE “hat” to draw power from its Ethernet connection.

Driving all the buttons and LEDs requires over 50 I/O signals, more than can be accommodated by the GPIO header found on a Pi. Although this header has 40 pins, only about 26 are usable in practice. So I used three $6 I2C expanders, each capable of handling 16 I/O signals and relaying them back via a two-wire data bus.

I don’t have to worry that I suddenly won’t be able to turn on my lights due to a forced upgrade.

The software that drives each Pi also has its functionality separated out. This is done using Docker containers: software environments that act as self-contained sandboxes. The Pi responsible for the touchscreen has three containers: One runs a browser in kiosk mode, which fetches a graphical display from the Home Assistant hub. A second container runs a Python script, which translates touchscreen inputs—such as pressing an icon for another information screen—into requests to the hub. A third container runs a local Web server: When the kiosk browser is pointed to this local server instead of the hub, the screen displays internal diagnostic information that is useful for troubleshooting.

The other Pi has two containers running Python scripts. One handles all the button inputs and sends commands to the hub. The other requests status information from the hub and updates all the LEDs accordingly.

$The first Raspberry Pi has containers labeled \u201ctouch screen commands\u201d, \u201cdiagnsotic web server\u201d and \u201cKiosk Web Browser.\u201d The second Raspberry Pi has containers labelled \u201cButton Script\u201d and \u201cLED script.\u201d$ Input and output functions are split across software containers running on the panel’s Raspberry Pis. These communicate with a hub to send commands and get status updates. James Provost

These containers run on top of BalenaOS, an operating system that’s designed for running these sandboxes on edge as well as embedded devices like the Pi. Full disclosure: I’m the edge AI enablement lead for Balena, the company responsible for BalenaOS, but I started using the operating system before I joined the company because of its container-based approach. You can run Docker containers using the Raspberry Pi OS, but BalenaOS makes it easier to manage containers, including starting, stopping, and updating them remotely.

You might think that this software infrastructure is overkill for simply reading the state of some buttons and illuminating some lights, but I like containers because they let me work on one subsystem without worrying about how it will affect the rest of the system: I can tinker with how button presses are sent to the hub without messing up the touchscreen.

The buttons and various displays are mounted in a set of 3D-printed panels. I first mapped these out, full size, on paper, and then created the 3D print files in TinkerCAD. The labels for each control, as well as a schematic of my home’s water pipes, were printed as indentations in each segment, and then I filled them with white spackle for contrast. I then mounted the array of panels in an off-the-shelf $45 “floater” frame.

By a small miracle of the maker spirits, the panel segments and the frame all fit together nicely on the first try. I mounted the finished panel in a hallway of my home, somewhat to the bemusement of my family. But I don’t mind: If I ever have a water leak, I’ll get to press the big emergency button to shut off the main valve with all the aplomb of Jack Lemmon trying to stop a nuclear meltdown!

Robot Metalsmiths Are Resurrecting Toroidal Tanks for NASA

by Evan Ackerman on 29. August 2024. at 13:00

In the 1960s and 1970s, NASA spent a lot of time thinking about whether toroidal (donut-shaped) fuel tanks were the way to go with its spacecraft. Toroidal tanks have a bunch of potential advantages over conventional spherical fuel tanks. For example, you can fit nearly 40% more volume within a toroidal tank than if you were using multiple spherical tanks within the same space. And perhaps most interestingly, you can shove stuff (like the back of an engine) through the middle of a toroidal tank, which could lead to some substantial efficiency gains if the tanks could also handle structural loads.

Because of their relatively complex shape, toroidal tanks are much more difficult to make than spherical tanks. Even though these tanks can perform better, NASA simply doesn’t have the expertise to manufacture them anymore, since each one has to be hand-built by highly skilled humans. But a company called Machina Labs thinks that they can do this with robots instead. And their vision is to completely change how we make things out of metal.

The fundamental problem that Machina Labs is trying to solve is that if you want to build parts out of metal efficiently at scale, it’s a slow process. Large metal parts need their own custom dies, which are very expensive one-offs that are about as inflexible as it’s possible to get, and then entire factories are built around these parts. It’s a huge investment, which means that it doesn’t matter if you find some new geometry or technique or material or market, because you have to justify that enormous up-front cost by making as much of the original thing as you possibly can, stifling the potential for rapid and flexible innovation.

On the other end of the spectrum you have the also very slow and expensive process of making metal parts one at a time by hand. A few hundred years ago, this was the only way of making metal parts: skilled metalworkers using hand tools for months to make things like armor and weapons. The nice thing about an expert metalworker is that they can use their skills and experience to make anything at all, which is where Machina Labs’ vision comes from, explains CEO Edward Mehr who co-founded Machina Labs after spending time at SpaceX followed by leading the 3D printing team at Relativity Space.

“Craftsmen can pick up different tools and apply them creatively to metal to do all kinds of different things. One day they can pick up a hammer and form a shield out of a sheet of metal,” says Mehr. “Next, they pick up the same hammer, and create a sword out of a metal rod. They’re very flexible.”

The technique that a human metalworker uses to shape metal is called forging, which preserves the grain flow of the metal as it’s worked. Casting, stamping, or milling metal (which are all ways of automating metal part production) are simply not as strong or as durable as parts that are forged, which can be an important differentiator for (say) things that have to go into space. But more on that in a bit.

The problem with human metalworkers is that the throughput is bad—humans are slow, and highly skilled humans in particular don’t scale well. For Mehr and Machina Labs, this is where the robots come in.

“We want to automate and scale using a platform called the ‘robotic craftsman.’ Our core enablers are robots that give us the kinematics of a human craftsman, and artificial intelligence that gives us control over the process,” Mehr says. “The concept is that we can do any process that a human craftsman can do, and actually some that humans can’t do because we can apply more force with better accuracy.”

This flexibility that robot metalworkers offer also enables the crafting of bespoke parts that would be impractical to make in any other way. These include toroidal (donut-shaped) fuel tanks that NASA has had its eye on for the last half century or so.

Two people stand in a warehouse with a huge silver donut-shaped tank in front of them. Machina Labs’ CEO Edward Mehr (on right) stands behind a 15 foot toroidal fuel tank.Machina Labs

“The main challenge of these tanks is that the geometry is complex,” Mehr says. “Sixty years ago, NASA was bump-forming them with very skilled craftspeople, but a lot of them aren’t around anymore.” Mehr explains that the only other way to get that geometry is with dies, but for NASA, getting a die made for a fuel tank that’s necessarily been customized for one single spacecraft would be pretty much impossible to justify. “So one of the main reasons we’re not using toroidal tanks is because it’s just hard to make them.”

Machina Labs is now making toroidal tanks for NASA. For the moment, the robots are just doing the shaping, which is the tough part. Humans then weld the pieces together. But there’s no reason why the robots couldn’t do the entire process end-to-end and even more efficiently. Currently, they’re doing it the “human” way based on existing plans from NASA. “In the future,” Mehr tells us, “we can actually form these tanks in one or two pieces. That’s the next area that we’re exploring with NASA—how can we do things differently now that we don’t need to design around human ergonomics?”

Machina Labs’ ‘robotic craftsmen’ work in pairs to shape sheet metal, with one robot on each side of the sheet. The robots align their tools slightly offset from each other with the metal between them such that as the robots move across the sheet, it bends between the tools. Machina Labs

The video above shows Machina’s robots working on a tank that’s 4.572 m (15 feet) in diameter, likely destined for the Moon. “The main application is for lunar landers,” says Mehr. “The toroidal tanks bring the center of gravity of the vehicle lower than what you would have with spherical or pill-shaped tanks.”

Training these robots to work metal like this is done primarily through physics-based simulations that Machina developed in house (existing software being too slow), followed by human-guided iterations based on the resulting real-world data. The way that metal moves under pressure can be simulated pretty well, and although there’s certainly still a sim-to-real gap (simulating how the robot’s tool adheres to the surface of the material is particularly tricky), the robots are collecting so much empirical data that Machina is making substantial progress towards full autonomy, and even finding ways to improve the process.

A hand holds a silvery piece of sheet metal that has been forged into a series of symmetrical waves. An example of the kind of complex metal parts that Machina’s robots are able to make.Machina Labs

Ultimately, Machina wants to use robots to produce all kinds of metal parts. On the commercial side, they’re exploring things like car body panels, offering the option to change how your car looks in geometry rather than just color. The requirement for a couple of beefy robots to make this work means that roboforming is unlikely to become as pervasive as 3D printing, but the broader concept is the same: making physical objects a software problem rather than a hardware problem to enable customization at scale.

AI Inference Competition Heats Up

by Dina Genkina on 28. August 2024. at 15:07

While the dominance of Nvidia GPUs for AI training remains undisputed, we may be seeing early signs that, for AI inference, the competition is gaining on the tech giant, particularly in terms of power efficiency. The sheer performance of Nvidia’s new Blackwell chip, however, may be hard to beat.

This morning, ML Commons released the results of its latest AI inferencing competition, ML Perf Inference v4.1. This round included first-time submissions from teams using AMD Instinct accelerators, the latest Google Trillium accelerators, chips from Toronto-based startup UntetherAI, as well as a first trial for Nvidia’s new Blackwell chip. Two other companies, Cerebras and FuriosaAI, announced new inference chips but did not submit to MLPerf.

Much like an Olympic sport, MLPerf has many categories and subcategories. The one that saw the biggest number of submissions was the “datacenter-closed” category. The closed category (as opposed to open) requires submitters to run inference on a given model as-is, without significant software modification. The data center category tests submitters on bulk processing of queries, as opposed to the edge category, where minimizing latency is the focus.

Within each category, there are 9 different benchmarks, for different types of AI tasks. These include popular use cases such as image generation (think Midjourney) and LLM Q&A (think ChatGPT), as well as equally important but less heralded tasks such as image classification, object detection, and recommendation engines.

This round of the competition included a new benchmark, called Mixture of Experts. This is a growing trend in LLM deployment, where a language model is broken up into several smaller, independent language models, each fine-tuned for a particular task, such as regular conversation, solving math problems, and assisting with coding. The model can direct each query to an appropriate subset of the smaller models, or “experts”. This approach allows for less resource use per query, enabling lower cost and higher throughput, says Miroslav Hodak, MLPerf Inference Workgroup Chair and senior member of technical staff at AMD.

The winners on each benchmark within the popular datacenter-closed benchmark were still submissions based on Nvidia’s H200 GPUs and GH200 superchips, which combine GPUs and CPUs in the same package. However, a closer look at the performance results paint a more complex picture. Some of the submitters used many accelerator chips while others used just one. If we normalize the number of queries per second each submitter was able to handle by the number of accelerators used, and keep only the best performing submissions for each accelerator type, some interesting details emerge. (It’s important to note that this approach ignores the role of CPUs and interconnects.)

On a per accelerator basis, Nvidia’s Blackwell outperforms all previous chip iterations by 2.5x on the LLM Q&A task, the only benchmark it was submitted to. Untether AI’s speedAI240 Preview chip performed almost on-par with H200’s in its only submission task, image recognition. Google’s Trillium performed just over half as well as the H100 and H200s on image generation, and AMD’s Instinct performed about on-par with H100s on the LLM Q&A task.

The power of Blackwell

One of the reasons for Nvidia Blackwell’s success is its ability to run the LLM using 4-bit floating-point precision. Nvidia and its rivals have been driving down the number of bits used to represent data in portions of transformer models like ChatGPT to speed computation. Nvidia introduced 8-bit math with the H100, and this submission marks the first demonstration of 4-bit math on MLPerf benchmarks.

The greatest challenge with using such low-precision numbers is maintaining accuracy, says Nvidia’s product marketing director Dave Salvator. To maintain the high accuracy required for MLPerf submissions, the Nvidia team had to innovate significantly on software, he says.

Another important contribution to Blackwell’s success is it’s almost doubled memory bandwidth, 8 terabytes/second, compared to H200’s 4.8 terabytes/second.

a black box with gold and rainbow squares on top against a black background Nvidia GB2800 Grace Blackwell SuperchipNvidia

Nvidia’s Blackwell submission used a single chip, but Salvator says it’s built to network and scale, and will perform best when combined with Nvidia’s NVLink interconnects. Blackwell GPUs support up to 18 NVLink 100 gigabyte-per-second connections for a total bandwidth of 1.8 terabytes per second, roughly double the interconnect bandwidth of H100s.

Salvatore argues that with the increasing size of large language models, even inferencing will require multi-GPU platforms to keep up with demand, and Blackwell is built for this eventuality. “Blackwell is a platform,” Salvator says.

Nvidia submitted their Blackwell chip-based system in the preview subcategory, meaning it is not for sale yet but is expected to be available before the next MLPerf release, six months from now.

Untether AI shines in power use and at the edge

For each benchmark, MLPerf also includes an energy measurement counterpart, which systematically tests the wall plug power that each of the systems draws while performing a task. The main event (the datacenter-closed energy category) saw only two submitters this round: Nvidia and Untether AI. While Nvidia competed in all the benchmarks, Untether only submitted for image recognition.

Submitter	Accelerator	Number of accelerators	Queries per second	Watts	Queries per second per Watt
NVIDIA	NVIDIA H200-SXM-141GB	8	480,131.00	5,013.79	95.76
UntetherAI	UntetherAI speedAI240 Slim	6	309,752.00	985.52	314.30

The startup was able to achieve this impressive efficiency by building chips with an approach it calls at-memory computing. UntetherAI’s chips are built as a grid of memory elements with small processors interspersed directly adjacent to them. The processors are parallelized, each working simultaneously with the data in the nearby memory units, thus greatly decreasing the amount of time and energy spent shuttling model data between memory and compute cores.

“What we saw was that 90 percent of the energy to do an AI workload is just moving the data from DRAM onto the cache to the processing element,” says Untether AI vice president of product Robert Beachler. “So what Untether did was turn that around ... Rather than moving the data to the compute, I’m going to move the compute to the data.”

This approach proved particularly successful in another subcategory of MLPerf: edge-closed. This category is geared towards more on-the-ground use cases, such as machine inspection on the factory floor, guided vision robotics, and autonomous vehicles—applications where low energy use and fast processing are paramount, Beachler says.

Submitter	GPU type	Number of GPUs	Single Stream Latency (ms)	Multi-Stream Latency (ms)	Samples/s
Lenovo	NVIDIA L4	2	0.39	0.75	25,600.00
Lenovo	NVIDIA L40S	2	0.33	0.53	86,304.60
UntetherAI	UntetherAI speedAI240 Preview	2	0.12	0.21	140,625.00

On the image recognition task, again the only one UntetherAI reported results for, the speedAI240 Preview chip beat NVIDIA L40S’s latency performance by 2.8x and its throughput (samples per second) by 1.6x. The startup also submitted power results in this category, but their Nvidia-accelerated competitors did not, so it is hard to make a direct comparison. However, the nominal power draw per chip for UntetherAI’s speedAI240 Preview chip is 150 Watts, while for Nvidia’s L40s it is 350 W, leading to a nominal 2.3x power reduction with improved latency.

Cerebras, Furiosa skip MLPerf but announce new chips

a black box with white boxes Furiosa’s new chip implements the basic mathematical function of AI inference, matrix multiplication, in a different, more efficient way. Furiosa

Yesterday at the IEEE Hot Chips conference at Stanford, Cerebras unveiled its own inference service. The Sunnyvale, Calif. company makes giant chips, as big as a silicon wafer will allow, thereby avoiding interconnects between chips and vastly increasing the memory bandwidth of their devices, which are mostly used to train massive neural networks. Now it has upgraded its software stack to use its latest computer CS3 for inference.

Although Cerebras did not submit to MLPerf, the company claims its platform beats an H100 by 7x and competing AI startup Groq’s chip by 2x in LLM tokens generated per second. “Today we’re in the dial up era of Gen AI,” says Cerebras CEO and cofounder Andrew Feldman. “And this is because there’s a memory bandwidth barrier. Whether it’s an H100 from Nvidia or MI 300 or TPU, they all use the same off chip memory, and it produces the same limitation. We break through this, and we do it because we’re wafer-scale.”

Hot Chips also saw an announcement from Seoul-based Furiosa, presenting their second-generation chip, RNGD (pronounced “renegade”). What differentiates Furiosa’s chip is its Tensor Contraction Processor (TCP) architecture. The basic operation in AI workloads is matrix multiplication, normally implemented as a primitive in hardware. However, the size and shape of the matrixes, more generally known as tensors, can vary widely. RNGD implements multiplication of this more generalized version, tensors, as a primitive instead. “During inference, batch sizes vary widely, so its important to utilize the inherent parallelism and data re-use from a given tensor shape,” Furiosa founder and CEO June Paik said at Hot Chips.

Although it didn’t submit to MLPerf, Furiosa compared the performance of its RNGD chip on MLPerf’s LLM summarization benchmark in-house. It performed on-par with Nvidia’s edge-oriented L40S chip while using only 185 Watts of power, compared to L40S’s 320 W. And, Paik says, the performance will improve with further software optimizations.

IBM also announced their new Spyre chip designed for enterprise generative AI workloads, to become available in the first quarter of 2025.

At least, shoppers on the AI inference chip market won’t be bored for the foreseeable future.

Ransomware-as-a-Service Is Changing Extortion Efforts

by Margo Anderson on 28. August 2024. at 11:01

Thirty-five years ago, a misguided AIDS activist developed a piece of malware that encrypted a computer’s filenames—and asked for US $189 to obtain the key that unlocked an afflicted system. This “AIDS Trojan” holds the dubious distinction of being the world’s first piece of ransomware. In the intervening decades the encryption behind ransomware has become more sophisticated and harder to crack, and the underlying criminal enterprise has only blossomed like a terrible weed. Among the most shady of online shady businesses, ransomware has now crossed the $1 billion mark in ransoms paid out last year. Equally unfortunately, the threat today is on the rise, too. And in the same way that the “as a service” business model has sprouted up with software-as-a-service (SaaS), the ransomware field has now spawned a ransomware-as-a-service (RaaS) industry.

Guillermo Christensen is a Washington, D.C.-based lawyer at the firm K&L Gates. He’s also a former CIA officer who was detailed to the FBI to help build the intelligence program for the Bureau. He’s an instructor at the FBI’s CISO Academy—and a founding member of the Association of U.S. Cyber Forces and the National Artificial Intelligence and Cybersecurity Information Sharing Organization. IEEE Spectrum spoke with Christensen about the rise of ransomware-as-a-service as a new breed of ransomware attacks and how they can be understood—and fought.

Guillermo Christensen on...:

How has ransomware evolved in recent years?
How are people and companies responding to ransomware attacks?
What is ransomware-as-a-service?
An example of a recent ransomware attack
How are ransomware groups evading detection and bypassing security measures?
What are the most effective anti-ransomware tools and technologies today?

A head-and-shoulders photograph of a smiling man in a suit and tie Guillermo ChristensenK&L Gates

How has the ransomware situation changed in recent years? Was there an inflection point?

Christensen: I would say, [starting in] 2022, which the defining feature of is the Russian invasion of Eastern Ukraine. I see that as a kind of a dividing line in the current situation.

[Ransomware threat actors] have shifted their approach towards the core infrastructure of companies. And in particular, there are groups now that have had remarkable success encrypting the large-scale hypervisors, these systems that basically create fake computers, virtual machines that run on servers that can be enormous in scale. So by being able to attack those resources, the threat actors are able to do massive damage, sometimes taking down an entire company’s infrastructure in one attack. And some of these are due to the fact that this kind of infrastructure is hard to keep updated to patch for vulnerabilities and things like that.

Before 2022, many of these groups did not want to attack certain kinds of targets. For example, when the Colonial Pipeline company [was attacked], there was a lot of chatter afterwards that maybe that was a mistake because that attack got a lot of attention. The FBI put a lot of resources into going after [the perpetrators]. And there was a feeling among many of the ransomware groups, “Don’t do this. We have a great business here. Don’t mess it up by making it so much more likely that the U.S. government’s going to do something about this.”

How did you know the threat actors were saying these sorts of things?

Christensen: Because we work with a lot of threat intelligence experts. And a threat intelligence expert does a lot of things. But one of the things they do is they try to inhabit the same criminal forums as these groups—to get intelligence on what are they doing, what are they developing, and things like that. It’s a little bit like espionage. And it involves creating fake personas that you insert information, and you develop credibility. The other thing is that the Russian criminal groups are pretty boisterous. They have big egos. And so they also talk a lot. They talk on Reddit. They talk to journalists. So you get information from a variety of sources. Sometimes we’ve seen the groups, for example, actually have codes of ethics, if you will, about what they will or won’t do. If they inadvertently attack a hospital, when the hospital tells them, “Hey, you attacked the hospital, and you’re supposed to not do that,” in those cases, some of these groups have decrypted the hospital’s networks without charging a fee before.

“There was a feeling among many of the ransomware groups, ‘Don’t do this. We have a great business here.’”

But that, I think, has changed. And I think it changed in the course of the war in Ukraine. Because I think a lot of the Russian groups basically now understand we are effectively at war with each other. Certainly, the Russians believe the United States is at war with them. If you look at what’s going on in Ukraine, I would say we are. Nobody declares war on each other anymore. But our weapons are being used in fighting.

And so how are people responding to ransomware attacks since the Ukraine invasion?

Christensen: So now, they’ve taken it to a much higher level, and they’re going after companies and banks. They’re going after large groups and taking down all of the infrastructure that runs everything from their enterprise systems, their ERP systems that they use for all their businesses, their emails, et cetera. And they’re also stealing their data and holding it hostage, in a sense.

They’ve gone back to, really, the ultimate pain point, which is, you can’t do what your business is supposed to do. One of the first questions we ask when we get involved in one of these situations—if we don’t know who the company is—is “What is effectively the burn rate on your business every day that you’re not able to use these systems?” And some of them take a bit of effort to understand how much it is. Usually, I’m not looking for a precise amount, just a general number. Is it a million dollars a day? Is it 5 million? Is it 10? Because whatever that amount is, that’s what you then start defining as an endpoint for what you might need to pay.

What is ransomware-as-a-service? How has it evolved? And what are its implications?

Christensen: Basically, is it’s almost like the ransomware groups created a platform, very professionally. And if you know of a way to break into a company’s systems, you approach them and you say, “I have access to this system.” They also will have people who are good at navigating the network once they’re inside. Because once you’re inside, you want to be very careful not to tip off the company that something’s happened. They’ll steal the [company's] data. Then there’ll be either the same group or someone else in that group who will create a bespoke or customized version of the encryption for that company, for that victim. And they deploy it.

Because you’re doing it at scale, the ransomware can be fairly sophisticated and updated and made better every time from the lessons they learn.

Then they have a negotiator who will negotiate the ransom. And they basically have an escrow system for the money. So when they get the ransom money, the money comes into one digital wallet—sometimes a couple, but usually one. And then it gets split up among those who participated in the event. And the people who run this platform, the ransomware-as-a-service, get the bulk of it because they did the work to set up the whole thing. But then everybody gets a cut from that.

And because you’re doing it at scale, the ransomware can be fairly sophisticated and updated and made better every time from the lessons they learn. So that’s what ransomware as a service is.

How do ransomware-as-a-service companies continue to do business?

Christensen: Effectively, they’re untouchable right now, because they’re mostly based in Russia. And they operate using infrastructure that is very hard to take down. It’s almost bulletproof. It’s not something you can go to a Google and say, “This website is criminal, take it down.” They operate in a different type of environment. That said, we have had success in taking down some of the infrastructure. So the FBI in particular working with international law enforcement has had some remarkable successes lately because they’ve been putting a lot of effort into this in taking down some of these groups. One in particular was called Hive.

They were very, very good, caused a lot of damage. And the FBI was able to infiltrate their system, get the decryption keys effectively, give those to a lot of victims. Over a period of almost six months, many, many companies that reported their attack to the FBI were able to get free decryption. A lot of companies didn’t, which is really, really foolish, and they paid. And that’s something that I often just am amazed that there are companies out there that don’t report to the FBI because there’s no downside to doing that. But there are a lot of lawyers who don’t want to report for their clients to the FBI, which I think is incredibly short-sighted.

But it takes months or years of effort. And the moment you do, these groups move somewhere else. You’re not putting them in jail very often. So basically, they just disappear and then come together somewhere else.

What’s an example of a recent ransomware attack?

Christensen: One that I think is really interesting, which I was not involved with, is the attack on a company called CDK. This one got quite a bit of publicity. So details are quite well known. CDK is a company that provides the back office services for a lot of car dealers. And so if you were trying to buy a car in the last couple of months, or were trying to get your car serviced, you went to the dealer, and they were doing nothing on their computers. It was all on paper.

It appears the threat actor then came back in and attacked a second time, this time, harming broader systems, including backups.

And this has actually had quite an effect in the auto industry. Because once you interrupt that system, it cascades. And what they did in this particular case, the ransomware group went after the core system knowing that this company would then basically take down all these other businesses. So that it was a very serious problem. The company, from what we’ve been able to read, made some serious mistakes at the front end.

The first thing is rule number one, when you have a ransomware or any kind of a compromise of your system, you first have to make sure you’ve ejected the threat actor from your system. If they’re still inside, you’ve got a big problem. So what it appears is that they realized they [were being attacked] over a weekend, I think, and they realized, “Boy, if we don’t get these systems back up and running, a lot of our customers are going to be really, really upset with us.” So they decided to restore. And when they did that, they still had the threat actor in the system.

And it appears the threat actor then came back in and attacked a second time, this time, harming broader systems, including backups. So when they did that, they essentially took the company down completely, and it’s taken them at least a month plus to recover, costing hundreds of millions of dollars.

So what could we take as lessons learned from the CDK attack?

Christensen: There are a lot of things you can do to try to reduce the risk of ransomware. But the number one at this point is you’ve got to have a good plan, and the plan has got to be tested. If the day you get hit by ransomware is the first day that your leadership team talks about ransomware or who’s going to do what, you are already so behind the curve.

It’s the planning that is essential, not the plan.

And a lot of people think, “Well, a plan. Okay. So we have a plan. We’re going to follow this checklist.” But that’s not real. You don’t follow a plan. The point of the plan is to get your people ready to be able to deal with this. It’s the planning that is essential, not the plan. And that takes a lot of effort.

I think a lot of companies, frankly, don’t have the imagination at this point to see what could happen to them in this kind of attack. Which is a pity because, in a lot of ways, they’re gambling that other people are going to get hit before them. And from my perspective, that’s not a serious business strategy. Because the prevalence of this threat is very serious. And everybody’s more or less using the same system. So you really are just gambling that they’re not going to pick you out of another 10 companies.

What are some of the new technologies and techniques that ransomware groups are using today to evade detection and to bypass security measures?

Christensen: So by and large, they mostly still use the same tried and true techniques. And that’s unfortunate because what that should tell you is that many of these companies have not improved their security based on what they should have learned. So some of the most common attack vectors, so the ways into these companies, is the fact that some part of the infrastructure is not protected by multi-factor authentication.

Companies often will say, “Well, we have multi-factor authentication on our emails, so we’re good, right?” What they forget is that they have a lot of other ways into the company’s network—mostly things like virtual private networks, remote tools, lots of things like that. And those are not protected by multi-factor authentication. And when they’re discovered, and it’s not difficult for a threat actor to find them. Because usually, if you look at, say, a listing of software that a company is using, and you can scan these things externally, you’ll see the version of a particular type of software. And you know that that software does not support multi-factor authentication perhaps, or it’s very easy to see that when you put in a password, it doesn’t prompt you for a multi-factor. Then you simply use brute force techniques, which are very effective, to guess the password, and you get in.

Everybody, practically speaking, uses the same passwords. They reuse the passwords. So it’s very common for these criminal groups that hacked, say, a large company on one level, they get all the passwords there. And then they figure out that that person is at another company, and they use that same password. Sometimes they’ll try variations. That works almost 100 percent of the time.

Is there a technology that anti-ransomware advocates and ransomware fighters are waiting for today? Or is the game more about public awareness?

Christensen: Microsoft has been very effective at taking down large bot infrastructures, working with the Department of Justice. But this needs to be done with more independence, because if the government has to bless every one of these things, well, then nothing will happen. So we need to set up a program. We allow a certain group of companies to do this. They have rules of engagement. They have to disclose everything they do. And they make money for it.

I mean, they’re going to be taking a risk, so they need to make money off it. For example, be allowed to keep half the Bitcoin they grab from these groups or something like that.

But I think what I would like to see is that these threat actors don’t sleep comfortably at night, the same way that the people fighting defense right now don’t get to sleep comfortably at night. Otherwise, they’re sitting over there being able to do whatever they want, when they want, at their initiative. In a military mindset, that’s the worst thing. When your enemy has all the initiative and can plan without any fear of repercussion, you’re really in a bad place.

Essential Skills for IT Professionals in the AI Era

by Kumar Singirikonda on 27. August 2024. at 18:00

Artificial Intelligence is transforming industries worldwide, creating new opportunities in health care, finance, customer service, and other disciplines. But the ascendance of AI raises concerns about job displacement, especially as the technology might automate tasks traditionally done by humans.

Jobs that involve data entry, basic coding, and routine system maintenance are at risk of being eliminated—which might worry new IT professionals. AI also creates new opportunities for workers, however, such as developing and maintaining new systems, data analysis, and cybersecurity. If IT professionals enhance their skills in areas such as machine learning, natural language processing, and automation, they can remain competitive as the job market evolves.

Here are some skills IT professionals need to stay relevant, as well as advice on how to thrive and opportunities for growth in the industry.

Boosting your knowledge

One area you should become proficient in is machine learning algorithms.

I recommend learning the fundamentals such as the basics of programming and mathematics. Look for programs that require you to participate in projects and assignments that apply what you’ve learned.

Understanding data is also crucial. Learn how to collect, analyze, and interpret data using Python, R, SQL, and similar tools.

Recommended resources:

Coursera is an online learning platform that provides classes in a large number of subjects. I suggest the introductory course on machine learning taught by Andrew Ng, a computer science adjunct professor at Stanford.
EdX, another online platform, offers a variety of courses including ones in computer science, engineering, and business. I recommend taking the Data Science MicroMasters program, which provides a comprehensive foundation of the field, including statistical and computational tools for data analysis.
Udacity, which is known for its nanodegree programs, offers practical, project-based tech learning experiences. Nanodegrees are certified online educational programs that teach you specialized skills in less time than traditional bachelor’s and master’s degrees. Consider the AI Programming With Python nanodegree, which covers the essential skills needed for building AI applications using programming languages such as Python, NumPy, and PyTorch.
Fast.ai offers free courses on deep learning. Start with the Practical Deep Learning for Coders program designed for beginners. It covers state-of-the-art techniques and tools.
Google’s free Machine Learning Crash Course provides a practical introduction to the topic using TensorFlow APIs, which are open-source machine learning libraries. The course includes exercises, interactive visualizations, and instructional videos.

Key insights into AI ethics

Understanding the ethical considerations surrounding AI technologies is crucial. Courses on AI ethics and policy provide important insights into ethical implications, government regulations, stakeholder perspectives, and AI’s potential societal, economic, and cultural impacts.

I recommend reviewing case studies to learn from real-world examples and to get a grasp of the complexities surrounding ethical decision-making. Some AI courses explore best practices adopted by organizations to mitigate risks.

It’s also critical to learn how to conduct impact assessments to evaluate the potential societal, economic, and cultural influence of AI technologies before they’re deployed. A proactive approach can help identify and address ethical issues early on.

The importance of soft skills

AI can handle data, but humans are needed for creative and strategic thinking. AI professionals need to develop their critical-thinking and problem-solving skills, as they are areas where human intelligence excels. By honing your skills, you can complement AI technology and ensure better decision-making.

Working with AI involves interdisciplinary teams, and that requires strong communication skills to collaborate effectively with diverse team members for a broader range of perspectives and innovative solutions.

The ability to communicate clearly and concisely is also crucial when explaining complex concepts or ideas to others, whether in presentations or defining a new concept in code.

Navigating the new job market

Joining professional networks and AI communities can help you connect with potential employers. Consider joining LinkedIn and GitHub, creating a personal website, and writing a blog.

Share your portfolio on LinkedIn and other professional networks to access a wider audience and to connect with potential employers.

Create a strong online presence by sharing information about your projects, writing articles, and participating in discussions about AI and related technologies. Not only does it allow you to showcase your skills and expertise, it also could attract the attention of recruiters and hiring managers.

Another way to show off your technical skills is to develop a portfolio of your AI projects, code samples, and relevant work experience. A well-curated portfolio demonstrates your capabilities to potential employers. You should update it regularly with new projects and accomplishments. If you don’t have much professional AI experience, create personal projects and tasks to showcase your abilities.

Many successful engineers attribute their achievements to the guidance of mentors. Seeking out experienced mentors can provide invaluable guidance, feedback, and industry insights. Building relationships with more seasoned engineers offers networking opportunities, and it helps you stay updated on industry trends and advancements.

Engaging with peers through study groups and professional networks is beneficial as well. It allows you to gain different perspectives and collaborate on solving problems. Connecting with other IT professionals helps deepen your understanding of AI and technology concepts while building a robust support system within the industry.

How to thrive in the AI era

The tech industry evolves rapidly, so be open to learning new skills and adapting to changes in the job market. It can demonstrate your ability to overcome challenges and stay relevant. By continuously improving your skills, you are advertising your dedication to the field and you might stand out to potential employers.

Technical interviews for IT professionals often include coding tests, AI algorithms, and machine learning concepts. You can hone your skills at online coding platforms such as LeetCode and HackerRank. The platforms can’t teach you how to code, but they can provide a place to work on and test your code.

Combining your technical skills with knowledge of other fields such as business, health care, and finance is also advised. An interdisciplinary approach can open the door to more jobs.

Outlook and opportunities

To advance in the AI field, stay informed about its applications in emerging areas such as quantum computing, biotechnology, and smart cities. Understanding such fields can give you a competitive edge and open growth opportunities.

Step outside your comfort zone by participating in AI projects aimed at addressing social issues such as climate change, health care access, and education. By applying AI for social good, you not only contribute positively to society; you also gain valuable experience and recognition.

Having expertise in AI offers numerous opportunities for entrepreneurship. You might want to consider starting your own venture or joining innovative startups leveraging AI to solve specific problems. By being part of the entrepreneurial ecosystem, you can contribute to groundbreaking solutions and potentially create a lasting impact on society. Look for funding opportunities, incubators, and accelerators that support AI-driven startups.

Practical experience is invaluable. Seek out internships or work on projects that involve AI and machine learning. Hands-on experience enhances your technical skills and provides you with practical, real-world work to showcase in job interviews. Plus, internships can lead to valuable connections and even job opportunities.

Another way to gain practical experience is by contributing to open-source AI projects. It not only would improve your skills but also would help you build your portfolio. By collaborating with other developers on open-source projects, you can gain valuable insights and feedback to further enhance your knowledge in AI and machine learning.

Can China Challenge SpaceX's Starlink?

by Andrew Jones on 27. August 2024. at 14:00

China launched its first batch of satellites for its Qianfan megaconstellation earlier this month. It now has 18 satellites in orbit, but much more will be needed to build out this network of nearly 14,000 satellites.

Qianfan—”thousands sails” in Chinese and also referred to as Spacesail or G60—is a project run by Shanghai Spacecom Satellite Technology (SSST). Last February, the company announced it had raised 6.7 billion yuan ($943 million) in funding, with backing from Shanghai’s municipal government. This makes it a serious project, and one meant to catch up with SpaceX’s Starlink, providing global connectivity, including direct mobile connections, while also providing rural connectivity, supporting e-commerce, and bolstering national security within China.

The aim, SSST says, is to launch all 13,904 satellites by 2030. That, incredibly, works out to launching an average of just over seven satellites per day, every day, until the end of the decade.

To put this in perspective, SpaceX, with its reusable Falcon 9 rocket, has launched 6,895 satellites since the Starlink constellation’s first launch in May 2019. Of these, around 5,500 are still in orbit and operational. That works out to about 3.5 satellites launched per day.

To get off the ground, in other words, Qianfan will require both a boom in Chinese launch rates and a surge in satellite manufacturing.

China’s launch capacity is rising

China’s ability to launch spacecraft has grown vastly in recent years. The country launched 22 times in 2016, rising to 67 launches in 2023. Much of this growth has come from the creation of the country’s BeiDou navigation satellite system, the construction of a space station, and the buildout of a national space infrastructure, including large communication satellites and remote-sensing spacecraft for civilian and military uses.

However, none of the Chinese launchers used so far are reusable, and China’s four national spaceports are working at near full capacity. To build Qianfan, SSST and China will need new rockets and new spaceports. The country is working on both.

China has long heeded the rise of private space actors in the United States, such as SpaceX and Planet, and in 2014 the country began allowing private capital into some areas of the space sector. This investment started with small rockets and tiny satellites. More recently, emerging companies have been given the green light to make larger rockets.

The result is that Chinese companies such as Space Pioneer, Landspace, Deep Blue Aerospace and iSpace are closing in on first launches of medium-lift and/or reusable rockets. Landspace’s reusable stainless steel Zhuque-3, for example, will have a similar payload capacity to low Earth orbit as SpaceX’s Falcon 9, according to the company’s website. A first launch is slated for 2025.

But making rockets and flying frequently and reliably are separate issues.

“In order to increase launch throughput, additional capabilities need to be developed in the launch supply chain,” says Ian Christensen, senior director for private sector programs at the Broomfield, Colo.-based Secure World Foundation.

“In my view it is an open question whether China’s launch vehicle production capabilities can be scaled to meet the throughput necessary to deploy these constellations on the stated schedule,” he says.

The next question is, where will they launch from?

China seeks bigger spaceports

The first launch from a new commercial spaceport on the South China Sea island of Hainan is expected in the near future. It’s close to the national Wenchang spaceport, and two pads have been built so far. One is for a modified Long March 8 rocket—which is expected to play a major role in constellation launches—and the other will host rockets developed by commercial companies. Plans call for up to 10 launch pads. Recently announced plans for space industry development from Beijing and Shanghai have also been promoted to attract and boost space companies.

Yet whether these plans will translate into definitive action is an open question, according to Christensen. “Ultimately this is about resilience and quality in the supply chain: launch, satellite manufacturing, and terminal equipment,” he says. “Can the various entities involved in this project maintain production at the pace necessary to achieve the deployment schedules outlined?”

Christensen notes that many of the capabilities and entities involved in the Qianfan/G60 project are new, and the project is not vertically integrated, unlike SpaceX’s Starlink. The latter owns and controls both the launch and satellite manufacturing segments.

“[Qianfan’s] manufacturing entity was established in 2022, and its first satellite was produced in late 2023,” he says. That means fundamental questions about the satellites’ spaceworthiness and durability are as yet unanswered. “How will the products perform in space?” he says. “Will [China’s] manufacturing quality be maintained?”

Those aren’t rhetorical questions, and the answers may have wide repercussions. The first Qianfan/G60 launch, on a Long March 6A on Aug. 6, created a field of hundreds of pieces of debris when the launcher’s upper stage broke up. The accident highlights a worrying issue. Qianfan/G60 satellites will operate at 800 kilometers above Earth, about 250 km higher than Starlink satellites. This means the Qianfan satellites themselves, along with rocket stages and any debris, could remain in space for decades—well beyond their own obsolescence. And that debris would then eventually threaten spacecraft in lower orbits, as it all descends back to Earth.

The rollout of Qianfan/G60 thus has domestic and international ramifications. It heralds a rapid advance in Chinese launch and satellite capabilities. And the acceleration of China’s launch rate to keep up with the project’s ambitious timetables will likely exacerbate the already significant issues of space debris, potential collisions, impacts on astronomy (already recognized by Starlink), orbital crowding, and international cooperation and coordination in space.

A Match Made in Yorktown Heights

by Harry Goldstein on 26. August 2024. at 20:06

It pays to have friends in fascinating places. You need look no further than the cover of this issue and the article “ IBM’s Big Bet on the Quantum-Centric Supercomputer” for evidence. The article by Ryan Mandelbaum, Antonio D. Córcoles, and Jay Gambetta came to us courtesy of the article’s illustrator, the inimitable graphic artist Carl De Torres, a longtime IEEE Spectrum contributor as well as a design and communications consultant for IBM Research.

Story ideas typically originate with Spectrum’s editors and pitches from expert authors and freelance journalists. So we were intrigued when De Torres approached Spectrum about doing an article on IBM Research’s cutting-edge work on quantum-centric supercomputing.

De Torres has been collaborating with IBM in a variety of capacities since 2009, when, while at Wired magazine creating infographics, he was asked by the ad agency Ogilvy to work on Big Blue’s advertising campaign “Let’s build a Smarter Planet.” That project went so well that De Torres struck out on his own the next year. His relationship with IBM expanded, as did his engagements with other media, such as Spectrum, Fortune, and The New York Times. “My interest in IBM quickly grew beyond helping them in a marketing capacity,” says De Torres, who owns and leads the design studio Optics Lab in Berkeley, Calif. “What I really wanted to do is get to the source of some of the smartest work happening in technology, and that was IBM Research.”

Last year, while working on visualizations of a quantum-centric supercomputer with Jay Gambetta, vice president and lead scientist of IBM Quantum at the Thomas J. Watson Research Center in Yorktown Heights, N.Y., De Torres was inspired to contact Spectrum’s creative director, Mark Montgomery, with an idea.

“I really loved this process because I got to bring together two of my favorite clients to create something really special.” —Carl De Torres

“I thought, ‘You know, I think IEEE Spectrum would love to see this work,’” De Torres told me. “So with Jay’s permission, I gave Mark a 30-second pitch. Mark liked it and ran it by the editors, and they said that it sounded very promising.” De Torres, members of the IBM Quantum team, and Spectrum editors had a call to brainstorm what the article could be. “From there everything quickly fell into place, and I worked with Spectrum and the IBM Quantum team on a visual approach to the story,” De Torres says.

As for the text, we knew it would take a deft editorial hand to help the authors explain what amounts to the peanut butter and chocolate of advanced computing. Fortunately for us, and for you, dear reader, Associate Editor Dina Genkina has a doctorate in atomic physics, in the subfield of quantum simulation. As Genkina explained to me, that speciality is “adjacent to quantum computing, but not quite the same—it’s more like the analog version of QC that’s not computationally complete.”

Genkina was thrilled to work with De Torres to make the technical illustrations both accurate and edifying. Spectrum prides itself on its tech illustrations, which De Torres notes are increasingly rare in the space-constrained era of mobile-media consumption.

“Working with Carl was so exciting,” Genkina says. “It was really his vision that made the article happen, and the scope of his ambition for the story was at times a bit terrifying. But it’s the kind of story where the illustrations make it come to life.”

De Torres was happy with the collaboration, too. “I really loved this process because I got to bring together two of my favorite clients to create something really special.”

This article appears in the September 2024 print issue.

Erika Cruz Keeps Whirlpool’s Machines Spinning

by Edd Gent on 26. August 2024. at 15:00

Few devices are as crucial to people’s everyday lives as their household appliances. Electrical engineer Erika Cruz says it’s her mission to make sure they operate smoothly.

Cruz helps design washing machines and dryers for Whirlpool, the multinational appliance manufacturer.

Erika Cruz

Employer:

Whirlpool

Occupation:

Associate electrical engineer

Education:

Bachelor’s degree in electronics engineering, Industrial University of Santander, in Bucaramanga, Colombia

As a member of the electromechanical components team at Whirlpool’s research and engineering center in Benton Harbor, Mich., she oversees the development of timers, lid locks, humidity sensors, and other components.

More engineering goes into the machines than is obvious. Because the appliances are sold around the world, she says, they must comply with different technical and safety standards and environmental conditions. And reliability is key.

“If the washer’s door lock gets stuck and your clothes are inside, your whole day is going to be a mess,” she says.

While appliances can be taken for granted, Cruz loves that her work contributes in its own small way to the quality of life of so many.

“I love knowing that every time I’m working on a new design, the lives of millions of people will be improved by using it,” she says.

From Industrial Design to Electrical Engineering

Cruz grew up in Bucaramanga, Colombia, where her father worked as an electrical engineer, designing control systems for poultry processing plants. Her childhood home was full of electronics, and Cruz says her father taught her about technology. He paid her to organize his resistors, for example, and asked her to create short videos for work presentations about items he was designing. He also took Cruz and her sister along with him to the processing plants.

“We would go and see how the big machines worked,” she says. “It was very impressive because of their complexity and impact. That’s how I got interested in technology.”

In 2010, Cruz enrolled in Colombia’s Industrial University of Santander, in Bucaramanga, to study industrial design. But she quickly became disenchanted with the course’s focus on designing objects like fancy tables and ergonomic chairs.

“I wanted to design huge machines like my father did,” she says.

A teacher suggested that she study mechanical engineering instead. But her father was concerned about discrimination she might face in that career.

“He told me it would be difficult to get a job in the industry because mechanical engineers work with heavy machinery, and they saw women as being fragile,” Cruz says.

Her father thought electrical engineers would be more receptive to women, so she switched fields.

“I am very glad I ended up studying electronics because you can apply it to so many different fields,” Cruz says. She received a bachelor’s degree in electronics engineering in 2019.

The Road to America

While at university, Cruz signed up for a program that allowed Colombian students to work summer jobs in the United States. She held a variety of summer positions in Galveston, Texas, from 2017 to 2019, including cashier, housekeeper, and hostess.

She met her future husband in 2018, an American working at the same amusement park as she did. When she returned the following summer, they started dating, and that September they married. Since she had already received her degree, he was eager for her to move to the states permanently, but she made the difficult decision to return to Colombia.

“With the language barrier and my lack of engineering experience, I knew if I stayed in the United States, I would have to continue working jobs like housekeeping forever,” she says. “So I told my husband he had to wait for me because I was going back home to get some engineering experience.”

“I love knowing that every time I’m working on a new design, the lives of millions of people will be improved by using it.”

Cruz applied for engineering jobs in neighboring Brazil, which had more opportunities than Colombia did. In 2021, she joined Whirlpool as an electrical engineer at its R&D site in Joinville, Brazil. There, she introduced into mass production sensors and actuators provided by new suppliers.

Meanwhile, she applied for a U.S. Green Card, which would allow her to work and live permanently in the country. She received it six months after starting her job. Cruz asked her manager about transferring to one of Whirlpool’s U.S. facilities, not expecting to have any luck. Her manager set up a phone call with the manager of the components team at the company’s Benton Harbor site to discuss the request. Cruz didn’t realize that the call was actually a job interview. She was offered a position there as an electrical engineer and moved to Michigan later that year.

Designing Appliances Is Complex

Designing a new washing machine or dryer is a complex process, Cruz says. First, feedback from customers about desirable features is used to develop a high-level design. Then the product design work is divided among small teams of engineers, each responsible for a given subsystem, including hardware, software, materials, and components.

Part of Cruz’s job is to test components from different suppliers to make sure they meet safety, reliability, and performance requirements. She also writes the documentation that explains to other engineers about the components’ function and design.

Cruz then helps select the groups of components to be used in a particular application—combining, say, three temperature sensors with two humidity sensors in an optimized location to create a system that finds the best time to stop the dryer.

Building a Supportive Environment

Cruz loves her job, but her father’s fears about her entering a male-dominated field weren’t unfounded. Discrimination was worse in Colombia, she says, where she regularly experienced inappropriate comments and behavior from university classmates and teachers.

Even in the United States, she points out, “As a female engineer, you have to actually show you are able to do your job, because occasionally at the beginning of a project men are not convinced.”

In both Brazil and Michigan, Cruz says, she’s been fortunate to often end up on teams with a majority of women, who created a supportive environment. That support was particularly important when she had her first child and struggled to balance work and home life.

“It’s easier to talk to women about these struggles,” she says. “They know how it feels because they have been through it too.”

Update Your Knowledge

Working in the consumer electronics industry is rewarding, Cruz says. She loves going into a store or visiting someone’s home and seeing the machines that she’s helped build in action.

A degree in electronics engineering is a must for the field, Cruz says, but she’s also a big advocate of developing project management and critical thinking skills. She is a certified associate in project management, granted by the Project Management Institute, and has been trained in using tools that facilitate critical thinking. She says the project management program taught her how to solve problems in a more systematic way and helped her stand out in interviews.

It’s also important to constantly update your knowledge, Cruz says, “because electronics is a discipline that doesn’t stand still. Keep learning. Electronics is a science that is constantly growing.”

NASCAR Unveils Electric Race Car Prototype

by Willie D. Jones on 25. August 2024. at 13:00

NASCAR, the stock car racing sanctioning body known for its high-octane events across the United States, is taking a significant step toward a greener future. In July, during the Chicago Street Race event, NASCAR unveiled a prototype battery-powered race car that marks the beginning of its push to decarbonize motorsports. This move is part of NASCAR’s broader strategy to achieve net-zero emissions by 2035.

The electric prototype represents a collaborative effort between NASCAR and its traditional Original Equipment Manufacturer (OEM) partners—Chevrolet, Ford, and Toyota—along with ABB, a global technology leader. Built by NASCAR engineers, the car features three 6-Phase motors from Stohl Advanced Research and Development, an Austrian specialist in electric vehicle powertrains. These motors together produce 1,000 kilowatts at peak power, equivalent to approximately 1,300 horsepower. The energy is supplied by a 78-kilowatt-hour liquid-cooled lithium-ion battery, operating at 756 volts, though the specific battery chemistry remains a closely guarded secret.

C.J. Tobin, Senior Engineer of Vehicle Systems at NASCAR and the lead engineer on the EV prototype project, explained the motivation behind the development. He told IEEE Spectrum that “The push for electric vehicles is continuing to grow, and when we started this project one and a half years ago, that growth was rapid. We wanted to showcase our ability to put an electric stock car on the track in collaboration with our OEM partners. Our racing series have always been a platform for OEMs to showcase their stock cars, and this is just another tool for them to demonstrate what they can offer to the public.”

Eleftheria Kontou, a professor of civil and environmental engineering at the University of Illinois Urbana-Champaign whose primary research focus is transportation engineering, said in an interview that “It was an excellent introduction of the new technology to NASCAR fans, and I hope that the fans will be open to seeing more innovations in that space.”

a man talking while pointing to the under hood of an open car John Probst, NASCAR’s SVP of Innovation and Racing Development speaks during the unveiling of the new EV prototype. Jared C. Tilton/Getty Images

The electric race car is not just about speed; it’s also about sustainability. The car’s body panels are made from ampliTex, a sustainable flax-based composite supplied by Bcomp, a Swiss manufacturer specializing in composites made from natural fibers. AmpliTex is lighter, more moldable, and more durable than traditional materials like steel or aluminum, making the car more efficient and aerodynamic.

Regenerative braking is another key feature of the electric race car. As it slows down, the car can convert some of its kinetic energy into electric charge that feeds back into the battery. This feature most advantageous on road courses like the one in Chicago and on short oval tracks like Martinsville Speedway in Virginia.

“The Chicago Street Race was a great introduction for the EV prototype because it happens in a real-world setup where electric vehicles tend to thrive,” says Kontou, who also serves on the Steering Committee of the Illinois Alliance for Clean Transportation. “[It was a good venue for the car’s unveiling] because navigating the course requires more braking than is typical at many speedway tracks.”
Though the electric prototype is part of a larger NASCAR sustainability initiative, “There are no plans to use the electric vehicle in competition at this time,” a spokesman said. “The internal combustion engine plays an important role in NASCAR and there are no plans to move away from that.” So, die-hard stock-car racing fans can still anticipate the sounds and smells of V-8 engines burning gasoline as they hurtle around tracks and through street courses.

“The Chicago Street Race was a great introduction for the EV prototype because it happens in a real-world setup where electric vehicles tend to thrive.” —Eleftheria Kontou, University of Illinois

In its sustainability efforts, NASCAR lags well behind Formula One, its largest rival atop the world’s motorsports hierarchy. Since 2014, Formula One’s parent organization, the Fédération Internationale de l’Automobile (FIA), has had an all-electric racing spinoff, called Formula E. For the current season, which began in July, the ABB FIA Formula E World Championship series boasts 11 teams competing in 17 races. This year’s races feature the league’s third generation of electric race cars, and a fourth generation is planned for introduction in 2026.

Asked how NASCAR plans to follow through on its pledge to make its core operations net-zero emissions by its self-imposed target date, the spokesman pointed to changes that would counterbalance the output of traditional stock cars, which are notorious for their poor fuel efficiency and high carbon emissions. Those include 100 percent renewable electricity at NASCAR-owned racetracks and facilities, and tradeoffs such as recycling and on-site charging stations for use by fans with EVs.

The spokesman also noted that NASCAR and its OEM partners are developing racing fuel that’s more sustainable in light of the fact that stock cars consume, on average, about 47 liters for every 100 km they drive (5 miles per gallon). For comparison, U.S. federal regulators announced in June that they would begin enforcing an industry-wide fleet average of approximately 5.6 liters per 100 kilometers (50.4 miles per gallon) for model year 2031 and beyond. Fortunately for NASCAR, race cars are exempt from fuel-efficiency and tailpipe-emissions rules.

While some may be tempted to compare NASCAR’s prototype racer with the cars featured in the ABB FIA Formula E World Championship, Tobin emphasized that NASCAR’s approach in designing the prototype was distinct. “Outside of us seeing that there was a series out there racing electric vehicles and seeing how things were run with Formula E, we leaned heavily on our OEMs and went with what they wanted to see at that time,” he said.

The apparently slow transition to electric vehicles in NASCAR is seen by some in the organization as both a response to environmental concerns and a proactive move to stay ahead of potential legislation that could threaten the future of motorsports. “NASCAR and our OEM partners want to be in the driver’s seat, no matter where we’re going,” says Tobin. “With the development of [the NextGen EV prototype], we wanted to showcase the modularity of the chassis and what powertrains we can build upon it—whether that be alternative fuels, battery electric power, or something unforeseen in the future…We want to continue to push the envelope.”

Seaport Electrification Could Slash Emissions Worldwide

by Willie D. Jones on 24. August 2024. at 13:00

According to the International Maritime Organization, shipping was responsible for over 1 billion tonnes of carbon dioxide emissions in 2018. A significant share of those emissions came from seaport activities, including ship berthing, cargo handling, and transportation within port areas. In response, governments, NGOs, and environmental watchdog groups are sounding alarms and advocating for urgent measures to mitigate pollution at the world’s ports.

One of the most promising solutions for the decarbonization of port operations involves electrifying these facilities. This plan envisions ships plugging into dockside electric power rather than running their diesel-powered auxiliary generators for lighting, cargo handling, heating and cooling, accommodation, and onboard electronics. It would also call for replacing diesel-powered cranes, forklifts, and trucks that move massive shipping containers from ship to shore with battery-powered alternatives.

To delve deeper into this transformative approach, IEEE Spectrum recently spoke with John Prousalidis, a leading advocate for seaport electrification. Prousalidis, a professor of marine electrical engineering at the National Technical University of Athens, has played a pivotal role in developing standards for seaport electrification through his involvement with the IEEE, the International Electrical Commission (IEC), and the International Organization for Standardization (ISO). As vice-chair of the IEEE Marine Power Systems Coordinating Committee, he has been instrumental in advancing these ideas. Last year, Prousalidis co-authored a key paper titled “Holistic Energy Transformation of Ports: The Proteus Plan” in IEEE Electrification Magazine. In the paper, Prousalidis and his co-authors outlined their comprehensive vision for the future of port operations. The main points of the Proteus plan have been integrated in the policy document on Smart and Sustainable Ports coordinated by Prousalidis within the European Public Policy Committee Working Group on Energy; the policy document was approved in July 2024 by the IEEE Global Policy Committee.

portrait of a man with glasses and a suit and tie looking at camera with a blue box and red circle behind his left side head in the background Professor John ProusalidisJohn Prousalidis

What exactly is “cold ironing?”

John Prousalidis: Cold ironing involves shutting down a ship’s propulsion and auxiliary engines while at port, and instead, using electricity from shore to power onboard systems like air conditioning, cargo handling equipment, kitchens, and lighting. This reduces emissions because electricity from the grid, especially from renewable sources, is more environmentally friendly than burning diesel fuel on site. The technical challenges include matching the ship’s voltage and frequency with that of the local grid, which, in general, varies globally, while tackling grounding issues to protect against short circuits.

IEEE, along with IEC and ISO, have developed a joint standard, 80005, which is a series of three different standards for high-voltage and low-voltage connection. It is perhaps (along with Wi-Fi, the standard for wireless communication) the “hottest” standard because all governmental bodies tend to make laws stipulating that this is the standard that all ports need to follow to supply power to ships.

How broad has adoption of this standard been?

Prousalidis: The European Union has mandated full compliance by January 1, 2030. In the United States, California led the way with similar measures in 2010. This aggressive remediation via electrification is now being adopted globally, with support from the International Maritime Organization.

Let’s talk about another interesting idea that’s part of the plan: regenerative braking on cranes. How does that work?

Prousalidis: When lowering shipping containers, cranes in regenerative braking mode convert the kinetic energy into electric charge instead of wasting it as heat. Just like when an electric vehicle is coming to a stop, the energy can be fed back into the crane’s battery, potentially saving up to 50 percent in energy costs—though a conservative estimate would be around 20 percent.

What are the estimated upfront costs for implementing cold ironing at, say, the Port of Los Angeles, which is the largest port in the United States?

Prousalidis: The cost for a turnkey solution is approximately US $1.7 million per megawatt, covering grid upgrades, infrastructure, and equipment. A rough estimate using some established rules of thumb would be about $300 million. The electrification process at that port has already begun. There are, as far as I know, about 60 or more electrical connection points for ships at berths there.

How significant would the carbon reduction from present levels be if there were complete electrification with renewable energy at the world’s 10 biggest and busiest ports?

Prousalidis: If ports fully electrify using renewable energy, the European Union’s policy could achieve a 100-percent reduction in ship emissions in the port areas. According to the IMO’s approach, which considers the energy mix of each country, it could lead to a 60-percent reduction. This significant emission reduction means lower emissions of CO₂, nitrogen oxides, sulfur oxides, and particulate matter, thus reducing shipping’s contribution to global warming and lowering health risks in nearby population centers.

If all goes according to plan, and every country with port operations goes full bore toward electrification, how long do you think it will realistically take to completely decarbonize that aspect of shipping?

Prousalidis: As I said, the European Union is targeting full port electrification by 1 January 2030. However, with around 600 to 700 ports in Europe alone, and the need for grid upgrades, delays are possible. Despite this, we should focus on meeting the 2030 deadline rather than anticipating extensions. This recalls the words of Gemini and Apollo pioneer astronaut, Alan Shepard, when he explained the difference between a test pilot and a normal professional pilot: “Suppose each of them had 10 seconds before crashing. The conventional pilot would think, In 10 seconds I’m going to die. The test pilot would say to himself, I’ve got 10 seconds to save myself and save the craft.” The point is that, in a critical situation like the fight against global warming, we should focus on the time we have to solve the problem, not on what happens after time runs out. But humanity doesn’t have an eject button to press if we don’t make every effort to avoid the detrimental consequences that will come with failure of the “save the planet” projects.

Sydney’s Tech Super-Cluster Propels Australia’s AI Industry Forward

by BESydney on 24. August 2024. at 12:00

This is a sponsored article brought to you by BESydney.

Australia has experienced a remarkable surge in AI enterprise during the past decade. Significant AI research and commercialization concentrated in Sydney drives the sector’s development nationwide and influences AI trends globally. The city’s cutting-edge AI sector sees academia, business and government converge to foster groundbreaking advancements, positioning Australia as a key player on the international stage.

Sydney – home to half of Australia’s AI companies

Sydney has been pinpointed as one of four urban super-clusters in Australia, featuring the highest number of tech firms and the most substantial research in the country.

The Geography of Australia’s Digital Industries report, commissioned by the National Science Agency, the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and the Tech Council of Australia, found Sydney is home to 119,636 digital professionals and 81 digital technology companies listed on the Australian Stock Exchange with a combined worth of A$52 billion.

AI is infusing all areas of this tech landscape. According to CSIRO, more than 200 active AI companies operate across Greater Sydney, representing almost half of the country’s 544 AI companies.

“Sydney is the capital of AI startups for Australia and this part of Australasia”
—Toby Walsh, UNSW Sydney

With this extensive AI commercialization and collaboration in progress across Sydney, AI startups are flourishing.

“Sydney is the capital of AI startups for Australia and this part of Australasia,” according to Professor Toby Walsh, Scientia Professor of Artificial Intelligence at the Department of Computer Science and Engineering at the University of New South Wales (UNSW Sydney).

He cites robotics, AI in medicine and fintech as three areas where Sydney leads the world in AI innovation.

“As a whole, Australia punches well above its weight in the AI sector,” Professor Walsh says. “We’re easily in the top 10, and by some metrics, we’re in the top five in the world. For a country of just 25 million people, that is quite remarkable.”

Sydney’s universities at the forefront of AI research

A key to Sydney’s success in the sector is the strength of its universities, which are producing outstanding research.

In 2021, the University of Sydney (USYD), the University of New South Wales (UNSW Sydney), and the University of Technology Sydney (UTS) collectively produced more than 1000 peer-reviewed publications in artificial intelligence, contributing significantly to the field’s development.

According to CSIRO, Australia’s research and development sector has higher rates of AI adoption than global averages, with Sydney presenting the highest AI publishing intensity among Australian universities and research institutes.

Professor Aaron Quigley, Science Director and Deputy Director of CSIRO’s Data61 and Head of School in Computer Science and Engineering at UNSW Sydney, says Sydney’s AI prowess is supported by a robust educational pipeline that supplies skilled graduates to a wide range of industries that are rapidly adopting AI technologies.

“Sydney’s AI sector is backed up by the fact that you have such a large educational environment with universities like UTS, USYD and UNSW Sydney,” he says. “They rank in the top five of AI locations in Australia.”

UNSW Sydney is a heavy hitter, with more than 300 researchers applying AI across various critical fields such as hydrogen fuel catalysis, coastal monitoring, safe mining, medical diagnostics, epidemiology and stress management.

A photo of a smiling man next to a device. UNSW Sydney has more than 300 researchers applying AI across various critical fields such as hydrogen fuel catalysis, coastal monitoring, safe mining, medical diagnostics, epidemiology, and stress management.UNSW

UNSW Sydney’s AI Institute also has the largest concentration of academics working in AI in the country, adds Professor Walsh.

“One of the main reasons the AI Institute exists at UNSW Sydney is to be a front door to industry and government, to help translate the technology out of the laboratory and into practice,” he says.

Likewise, the Sydney Artificial Intelligence Centre at the University of Sydney, the Australian Artificial Intelligence Institute at UTS, and Macquarie University’s Centre for Applied Artificial Intelligence are producing world-leading research in collaboration with industry.

Alongside the universities, the Australian Government’s National AI Centre in Sydney, aims to support and accelerate Australia’s AI industry.

Synergies in Sydney: where tech titans converge

Sydney’s vortex of tech talent has meant exciting connections and collaborations are happening at lightning speed, allowing simultaneous growth of several high-value industries.

The intersection between quantum computing and AI will come into focus with the April 2024 announcement of a new Australian Centre for Quantum Growth at the University of Sydney. This centre will aim to build strategic and lasting relationships that drive innovation to increase the nation’s competitiveness within the field. Funded under the Australian Government’s National Quantum Strategy, it aims to promote the industry and enhance Australia’s global standing.

“There’s nowhere else in the world that you’re going to get a quantum company, a games company, and a cybersecurity company in such close proximity across this super-cluster arc located in Sydney”
—Aaron Quigley, UNSW Sydney

“There’s a huge amount of experience in the quantum space in Sydney,” says Professor Quigley. “Then you have a large number of companies and researchers working in cybersecurity, so you have the cybersecurity-AI nexus as well. Then you’ve got a large number of media companies and gaming companies in Sydney, so you’ve got the interconnection between gaming and creative technologies and AI.”

“So it’s a confluence of different industry spaces, and if you come here, you can tap into these different specialisms,” he adds “There’s nowhere else in the world that you’re going to get a quantum company, a games company, and a cybersecurity company in such close proximity across this super-cluster arc located in Sydney.”

A global hub for AI innovation and collaboration

In addition to its research and industry achievements in the AI sector, Sydney is also a leading destination for AI conferences and events. The annual Women in AI Asia Pacific Conference is held in Sydney each year, adding much-needed diversity to the mix.

Additionally, the prestigious International Joint Conference on Artificial Intelligence was held in Sydney in 1991.

Overall, Sydney’s integrated approach to AI development, characterized by strong academic output, supportive government policies, and vibrant commercial activity, firmly establishes it as a leader in the global AI landscape.

To discover more about how Sydney is shaping the future of AI download the latest eBook on Sydney’s Science & Engineering industry at besydney.com.au

Andrew Ng: Unbiggen AI

by Eliza Strickland on 09. February 2022. at 15:31

Andrew Ng has serious street cred in artificial intelligence. He pioneered the use of graphics processing units (GPUs) to train deep learning models in the late 2000s with his students at Stanford University, cofounded Google Brain in 2011, and then served for three years as chief scientist for Baidu, where he helped build the Chinese tech giant’s AI group. So when he says he has identified the next big shift in artificial intelligence, people listen. And that’s what he told IEEE Spectrum in an exclusive Q&A.

Ng’s current efforts are focused on his company Landing AI, which built a platform called LandingLens to help manufacturers improve visual inspection with computer vision. He has also become something of an evangelist for what he calls the data-centric AI movement, which he says can yield “small data” solutions to big issues in AI, including model efficiency, accuracy, and bias.

Andrew Ng on...

What’s next for really big models
The career advice he didn’t listen to
Defining the data-centric AI movement
Synthetic data
Why Landing AI asks its customers to do the work

The great advances in deep learning over the past decade or so have been powered by ever-bigger models crunching ever-bigger amounts of data. Some people argue that that’s an unsustainable trajectory. Do you agree that it can’t go on that way?

Andrew Ng: This is a big question. We’ve seen foundation models in NLP [natural language processing]. I’m excited about NLP models getting even bigger, and also about the potential of building foundation models in computer vision. I think there’s lots of signal to still be exploited in video: We have not been able to build foundation models yet for video because of compute bandwidth and the cost of processing video, as opposed to tokenized text. So I think that this engine of scaling up deep learning algorithms, which has been running for something like 15 years now, still has steam in it. Having said that, it only applies to certain problems, and there’s a set of other problems that need small data solutions.

When you say you want a foundation model for computer vision, what do you mean by that?

Ng: This is a term coined by Percy Liang and some of my friends at Stanford to refer to very large models, trained on very large data sets, that can be tuned for specific applications. For example, GPT-3 is an example of a foundation model [for NLP]. Foundation models offer a lot of promise as a new paradigm in developing machine learning applications, but also challenges in terms of making sure that they’re reasonably fair and free from bias, especially if many of us will be building on top of them.

What needs to happen for someone to build a foundation model for video?

Ng: I think there is a scalability problem. The compute power needed to process the large volume of images for video is significant, and I think that’s why foundation models have arisen first in NLP. Many researchers are working on this, and I think we’re seeing early signs of such models being developed in computer vision. But I’m confident that if a semiconductor maker gave us 10 times more processor power, we could easily find 10 times more video to build such models for vision.

Having said that, a lot of what’s happened over the past decade is that deep learning has happened in consumer-facing companies that have large user bases, sometimes billions of users, and therefore very large data sets. While that paradigm of machine learning has driven a lot of economic value in consumer software, I find that that recipe of scale doesn’t work for other industries.

It’s funny to hear you say that, because your early work was at a consumer-facing company with millions of users.

Ng: Over a decade ago, when I proposed starting the Google Brain project to use Google’s compute infrastructure to build very large neural networks, it was a controversial step. One very senior person pulled me aside and warned me that starting Google Brain would be bad for my career. I think he felt that the action couldn’t just be in scaling up, and that I should instead focus on architecture innovation.

“In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn.”
—Andrew Ng, CEO & Founder, Landing AI

I remember when my students and I published the first NeurIPS workshop paper advocating using CUDA, a platform for processing on GPUs, for deep learning—a different senior person in AI sat me down and said, “CUDA is really complicated to program. As a programming paradigm, this seems like too much work.” I did manage to convince him; the other person I did not convince.

I expect they’re both convinced now.

Ng: I think so, yes.

Over the past year as I’ve been speaking to people about the data-centric AI movement, I’ve been getting flashbacks to when I was speaking to people about deep learning and scalability 10 or 15 years ago. In the past year, I’ve been getting the same mix of “there’s nothing new here” and “this seems like the wrong direction.”

How do you define data-centric AI, and why do you consider it a movement?

Ng: Data-centric AI is the discipline of systematically engineering the data needed to successfully build an AI system. For an AI system, you have to implement some algorithm, say a neural network, in code and then train it on your data set. The dominant paradigm over the last decade was to download the data set while you focus on improving the code. Thanks to that paradigm, over the last decade deep learning networks have improved significantly, to the point where for a lot of applications the code—the neural network architecture—is basically a solved problem. So for many practical applications, it’s now more productive to hold the neural network architecture fixed, and instead find ways to improve the data.

When I started speaking about this, there were many practitioners who, completely appropriately, raised their hands and said, “Yes, we’ve been doing this for 20 years.” This is the time to take the things that some individuals have been doing intuitively and make it a systematic engineering discipline.

The data-centric AI movement is much bigger than one company or group of researchers. My collaborators and I organized a data-centric AI workshop at NeurIPS, and I was really delighted at the number of authors and presenters that showed up.

You often talk about companies or institutions that have only a small amount of data to work with. How can data-centric AI help them?

Ng: You hear a lot about vision systems built with millions of images—I once built a face recognition system using 350 million images. Architectures built for hundreds of millions of images don’t work with only 50 images. But it turns out, if you have 50 really good examples, you can build something valuable, like a defect-inspection system. In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn.

When you talk about training a model with just 50 images, does that really mean you’re taking an existing model that was trained on a very large data set and fine-tuning it? Or do you mean a brand new model that’s designed to learn only from that small data set?

Ng: Let me describe what Landing AI does. When doing visual inspection for manufacturers, we often use our own flavor of RetinaNet. It is a pretrained model. Having said that, the pretraining is a small piece of the puzzle. What’s a bigger piece of the puzzle is providing tools that enable the manufacturer to pick the right set of images [to use for fine-tuning] and label them in a consistent way. There’s a very practical problem we’ve seen spanning vision, NLP, and speech, where even human annotators don’t agree on the appropriate label. For big data applications, the common response has been: If the data is noisy, let’s just get a lot of data and the algorithm will average over it. But if you can develop tools that flag where the data’s inconsistent and give you a very targeted way to improve the consistency of the data, that turns out to be a more efficient way to get a high-performing system.

“Collecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity.”
—Andrew Ng

For example, if you have 10,000 images where 30 images are of one class, and those 30 images are labeled inconsistently, one of the things we do is build tools to draw your attention to the subset of data that’s inconsistent. So you can very quickly relabel those images to be more consistent, and this leads to improvement in performance.

Could this focus on high-quality data help with bias in data sets? If you’re able to curate the data more before training?

Ng: Very much so. Many researchers have pointed out that biased data is one factor among many leading to biased systems. There have been many thoughtful efforts to engineer the data. At the NeurIPS workshop, Olga Russakovsky gave a really nice talk on this. At the main NeurIPS conference, I also really enjoyed Mary Gray’s presentation, which touched on how data-centric AI is one piece of the solution, but not the entire solution. New tools like Datasheets for Datasets also seem like an important piece of the puzzle.

One of the powerful tools that data-centric AI gives us is the ability to engineer a subset of the data. Imagine training a machine-learning system and finding that its performance is okay for most of the data set, but its performance is biased for just a subset of the data. If you try to change the whole neural network architecture to improve the performance on just that subset, it’s quite difficult. But if you can engineer a subset of the data you can address the problem in a much more targeted way.

When you talk about engineering the data, what do you mean exactly?

Ng: In AI, data cleaning is important, but the way the data has been cleaned has often been in very manual ways. In computer vision, someone may visualize images through a Jupyter notebook and maybe spot the problem, and maybe fix it. But I’m excited about tools that allow you to have a very large data set, tools that draw your attention quickly and efficiently to the subset of data where, say, the labels are noisy. Or to quickly bring your attention to the one class among 100 classes where it would benefit you to collect more data. Collecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity.

For example, I once figured out that a speech-recognition system was performing poorly when there was car noise in the background. Knowing that allowed me to collect more data with car noise in the background, rather than trying to collect more data for everything, which would have been expensive and slow.

What about using synthetic data, is that often a good solution?

Ng: I think synthetic data is an important tool in the tool chest of data-centric AI. At the NeurIPS workshop, Anima Anandkumar gave a great talk that touched on synthetic data. I think there are important uses of synthetic data that go beyond just being a preprocessing step for increasing the data set for a learning algorithm. I’d love to see more tools to let developers use synthetic data generation as part of the closed loop of iterative machine learning development.

Do you mean that synthetic data would allow you to try the model on more data sets?

Ng: Not really. Here’s an example. Let’s say you’re trying to detect defects in a smartphone casing. There are many different types of defects on smartphones. It could be a scratch, a dent, pit marks, discoloration of the material, other types of blemishes. If you train the model and then find through error analysis that it’s doing well overall but it’s performing poorly on pit marks, then synthetic data generation allows you to address the problem in a more targeted way. You could generate more data just for the pit-mark category.

“In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In manufacturing, you might have 10,000 manufacturers building 10,000 custom AI models.”
—Andrew Ng

Synthetic data generation is a very powerful tool, but there are many simpler tools that I will often try first. Such as data augmentation, improving labeling consistency, or just asking a factory to collect more data.

To make these issues more concrete, can you walk me through an example? When a company approaches Landing AI and says it has a problem with visual inspection, how do you onboard them and work toward deployment?

Ng: When a customer approaches us we usually have a conversation about their inspection problem and look at a few images to verify that the problem is feasible with computer vision. Assuming it is, we ask them to upload the data to the LandingLens platform. We often advise them on the methodology of data-centric AI and help them label the data.

One of the foci of Landing AI is to empower manufacturing companies to do the machine learning work themselves. A lot of our work is making sure the software is fast and easy to use. Through the iterative process of machine learning development, we advise customers on things like how to train models on the platform, when and how to improve the labeling of data so the performance of the model improves. Our training and software supports them all the way through deploying the trained model to an edge device in the factory.

How do you deal with changing needs? If products change or lighting conditions change in the factory, can the model keep up?

Ng: It varies by manufacturer. There is data drift in many contexts. But there are some manufacturers that have been running the same manufacturing line for 20 years now with few changes, so they don’t expect changes in the next five years. Those stable environments make things easier. For other manufacturers, we provide tools to flag when there’s a significant data-drift issue. I find it really important to empower manufacturing customers to correct data, retrain, and update the model. Because if something changes and it’s 3 a.m. in the United States, I want them to be able to adapt their learning algorithm right away to maintain operations.

In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In manufacturing, you might have 10,000 manufacturers building 10,000 custom AI models. The challenge is, how do you do that without Landing AI having to hire 10,000 machine learning specialists?

So you’re saying that to make it scale, you have to empower customers to do a lot of the training and other work.

Ng: Yes, exactly! This is an industry-wide problem in AI, not just in manufacturing. Look at health care. Every hospital has its own slightly different format for electronic health records. How can every hospital train its own custom AI model? Expecting every hospital’s IT personnel to invent new neural-network architectures is unrealistic. The only way out of this dilemma is to build tools that empower the customers to build their own models by giving them tools to engineer the data and express their domain knowledge. That’s what Landing AI is executing in computer vision, and the field of AI needs other teams to execute this in other domains.

Is there anything else you think it’s important for people to understand about the work you’re doing or the data-centric AI movement?

Ng: In the last decade, the biggest shift in AI was a shift to deep learning. I think it’s quite possible that in this decade the biggest shift will be to data-centric AI. With the maturity of today’s neural network architectures, I think for a lot of the practical applications the bottleneck will be whether we can efficiently get the data we need to develop systems that work well. The data-centric AI movement has tremendous energy and momentum across the whole community. I hope more researchers and developers will jump in and work on it.

This article appears in the April 2022 print issue as “Andrew Ng, AI Minimalist.”

How AI Will Change Chip Design

by Rina Diane Caballar on 08. February 2022. at 14:00

The end of Moore’s Law is looming. Engineers and designers can do only so much to miniaturize transistors and pack as many of them as possible into chips. So they’re turning to other approaches to chip design, incorporating technologies like AI into the process.

Samsung, for instance, is adding AI to its memory chips to enable processing in memory, thereby saving energy and speeding up machine learning. Speaking of speed, Google’s TPU V4 AI chip has doubled its processing power compared with that of its previous version.

But AI holds still more promise and potential for the semiconductor industry. To better understand how AI is set to revolutionize chip design, we spoke with Heather Gorr, senior product manager for MathWorks’ MATLAB platform.

How is AI currently being used to design the next generation of chips?

Heather Gorr: AI is such an important technology because it’s involved in most parts of the cycle, including the design and manufacturing process. There’s a lot of important applications here, even in the general process engineering where we want to optimize things. I think defect detection is a big one at all phases of the process, especially in manufacturing. But even thinking ahead in the design process, [AI now plays a significant role] when you’re designing the light and the sensors and all the different components. There’s a lot of anomaly detection and fault mitigation that you really want to consider.

Portrait of a woman with blonde-red hair smiling at the camera Heather GorrMathWorks

Then, thinking about the logistical modeling that you see in any industry, there is always planned downtime that you want to mitigate; but you also end up having unplanned downtime. So, looking back at that historical data of when you’ve had those moments where maybe it took a bit longer than expected to manufacture something, you can take a look at all of that data and use AI to try to identify the proximate cause or to see something that might jump out even in the processing and design phases. We think of AI oftentimes as a predictive tool, or as a robot doing something, but a lot of times you get a lot of insight from the data through AI.

What are the benefits of using AI for chip design?

Gorr: Historically, we’ve seen a lot of physics-based modeling, which is a very intensive process. We want to do a reduced order model, where instead of solving such a computationally expensive and extensive model, we can do something a little cheaper. You could create a surrogate model, so to speak, of that physics-based model, use the data, and then do your parameter sweeps, your optimizations, your Monte Carlo simulations using the surrogate model. That takes a lot less time computationally than solving the physics-based equations directly. So, we’re seeing that benefit in many ways, including the efficiency and economy that are the results of iterating quickly on the experiments and the simulations that will really help in the design.

So it’s like having a digital twin in a sense?

Gorr: Exactly. That’s pretty much what people are doing, where you have the physical system model and the experimental data. Then, in conjunction, you have this other model that you could tweak and tune and try different parameters and experiments that let sweep through all of those different situations and come up with a better design in the end.

So, it’s going to be more efficient and, as you said, cheaper?

Gorr: Yeah, definitely. Especially in the experimentation and design phases, where you’re trying different things. That’s obviously going to yield dramatic cost savings if you’re actually manufacturing and producing [the chips]. You want to simulate, test, experiment as much as possible without making something using the actual process engineering.

We’ve talked about the benefits. How about the drawbacks?

Gorr: The [AI-based experimental models] tend to not be as accurate as physics-based models. Of course, that’s why you do many simulations and parameter sweeps. But that’s also the benefit of having that digital twin, where you can keep that in mind—it’s not going to be as accurate as that precise model that we’ve developed over the years.

Both chip design and manufacturing are system intensive; you have to consider every little part. And that can be really challenging. It’s a case where you might have models to predict something and different parts of it, but you still need to bring it all together.

One of the other things to think about too is that you need the data to build the models. You have to incorporate data from all sorts of different sensors and different sorts of teams, and so that heightens the challenge.

How can engineers use AI to better prepare and extract insights from hardware or sensor data?

Gorr: We always think about using AI to predict something or do some robot task, but you can use AI to come up with patterns and pick out things you might not have noticed before on your own. People will use AI when they have high-frequency data coming from many different sensors, and a lot of times it’s useful to explore the frequency domain and things like data synchronization or resampling. Those can be really challenging if you’re not sure where to start.

One of the things I would say is, use the tools that are available. There’s a vast community of people working on these things, and you can find lots of examples [of applications and techniques] on GitHub or MATLAB Central, where people have shared nice examples, even little apps they’ve created. I think many of us are buried in data and just not sure what to do with it, so definitely take advantage of what’s already out there in the community. You can explore and see what makes sense to you, and bring in that balance of domain knowledge and the insight you get from the tools and AI.

What should engineers and designers consider when using AI for chip design?

Gorr: Think through what problems you’re trying to solve or what insights you might hope to find, and try to be clear about that. Consider all of the different components, and document and test each of those different parts. Consider all of the people involved, and explain and hand off in a way that is sensible for the whole team.

How do you think AI will affect chip designers’ jobs?

Gorr: It’s going to free up a lot of human capital for more advanced tasks. We can use AI to reduce waste, to optimize the materials, to optimize the design, but then you still have that human involved whenever it comes to decision-making. I think it’s a great example of people and technology working hand in hand. It’s also an industry where all people involved—even on the manufacturing floor—need to have some level of understanding of what’s happening, so this is a great industry for advancing AI because of how we test things and how we think about them before we put them on the chip.

How do you envision the future of AI and chip design?

Gorr: It’s very much dependent on that human element—involving people in the process and having that interpretable model. We can do many things with the mathematical minutiae of modeling, but it comes down to how people are using it, how everybody in the process is understanding and applying it. Communication and involvement of people of all skill levels in the process are going to be really important. We’re going to see less of those superprecise predictions and more transparency of information, sharing, and that digital twin—not only using AI but also using our human knowledge and all of the work that many people have done over the years.

Atomically Thin Materials Significantly Shrink Qubits

by Dexter Johnson on 07. February 2022. at 16:12

Quantum computing is a devilishly complex technology, with many technical hurdles impacting its development. Of these challenges two critical issues stand out: miniaturization and qubit quality.

IBM has adopted the superconducting qubit road map of reaching a 1,121-qubit processor by 2023, leading to the expectation that 1,000 qubits with today’s qubit form factor is feasible. However, current approaches will require very large chips (50 millimeters on a side, or larger) at the scale of small wafers, or the use of chiplets on multichip modules. While this approach will work, the aim is to attain a better path toward scalability.

Now researchers at MIT have been able to both reduce the size of the qubits and done so in a way that reduces the interference that occurs between neighboring qubits. The MIT researchers have increased the number of superconducting qubits that can be added onto a device by a factor of 100.

“We are addressing both qubit miniaturization and quality,” said William Oliver, the director for the Center for Quantum Engineering at MIT. “Unlike conventional transistor scaling, where only the number really matters, for qubits, large numbers are not sufficient, they must also be high-performance. Sacrificing performance for qubit number is not a useful trade in quantum computing. They must go hand in hand.”

The key to this big increase in qubit density and reduction of interference comes down to the use of two-dimensional materials, in particular the 2D insulator hexagonal boron nitride (hBN). The MIT researchers demonstrated that a few atomic monolayers of hBN can be stacked to form the insulator in the capacitors of a superconducting qubit.

Just like other capacitors, the capacitors in these superconducting circuits take the form of a sandwich in which an insulator material is sandwiched between two metal plates. The big difference for these capacitors is that the superconducting circuits can operate only at extremely low temperatures—less than 0.02 degrees above absolute zero (-273.15 °C).

Golden dilution refrigerator hanging vertically Superconducting qubits are measured at temperatures as low as 20 millikelvin in a dilution refrigerator.Nathan Fiske/MIT

In that environment, insulating materials that are available for the job, such as PE-CVD silicon oxide or silicon nitride, have quite a few defects that are too lossy for quantum computing applications. To get around these material shortcomings, most superconducting circuits use what are called coplanar capacitors. In these capacitors, the plates are positioned laterally to one another, rather than on top of one another.

As a result, the intrinsic silicon substrate below the plates and to a smaller degree the vacuum above the plates serve as the capacitor dielectric. Intrinsic silicon is chemically pure and therefore has few defects, and the large size dilutes the electric field at the plate interfaces, all of which leads to a low-loss capacitor. The lateral size of each plate in this open-face design ends up being quite large (typically 100 by 100 micrometers) in order to achieve the required capacitance.

In an effort to move away from the large lateral configuration, the MIT researchers embarked on a search for an insulator that has very few defects and is compatible with superconducting capacitor plates.

“We chose to study hBN because it is the most widely used insulator in 2D material research due to its cleanliness and chemical inertness,” said colead author Joel Wang, a research scientist in the Engineering Quantum Systems group of the MIT Research Laboratory for Electronics.

On either side of the hBN, the MIT researchers used the 2D superconducting material, niobium diselenide. One of the trickiest aspects of fabricating the capacitors was working with the niobium diselenide, which oxidizes in seconds when exposed to air, according to Wang. This necessitates that the assembly of the capacitor occur in a glove box filled with argon gas.

While this would seemingly complicate the scaling up of the production of these capacitors, Wang doesn’t regard this as a limiting factor.

“What determines the quality factor of the capacitor are the two interfaces between the two materials,” said Wang. “Once the sandwich is made, the two interfaces are “sealed” and we don’t see any noticeable degradation over time when exposed to the atmosphere.”

This lack of degradation is because around 90 percent of the electric field is contained within the sandwich structure, so the oxidation of the outer surface of the niobium diselenide does not play a significant role anymore. This ultimately makes the capacitor footprint much smaller, and it accounts for the reduction in cross talk between the neighboring qubits.

“The main challenge for scaling up the fabrication will be the wafer-scale growth of hBN and 2D superconductors like [niobium diselenide], and how one can do wafer-scale stacking of these films,” added Wang.

Wang believes that this research has shown 2D hBN to be a good insulator candidate for superconducting qubits. He says that the groundwork the MIT team has done will serve as a road map for using other hybrid 2D materials to build superconducting circuits.

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