A computer using artificial intelligence can't be listed as an inventor on patents because only a human can be an inventor under U.S. law, a federal judge ruled in the first American decision that's part of a global debate over how to handle computer-created innovation. From a report: Federal law requires that an "individual" take an oath that he or she is the inventor on a patent application, and both the dictionary and legal definition of an individual is a natural person, ruled U.S. District Judge Leonie Brinkema in Alexandria, Virginia. The Artificial Inventor Project, run by University of Surrey Law Professor Ryan Abbott, has launched a global effort to get a computer listed as an inventor. Abbott's team enlisted Imagination Engines founder Stephen Thaler to build a machine whose main purpose was to invent. Rulings in South Africa and Australia have favored his argument, though the Australian patent office is appealing the decision in that country. "We respectfully disagree with the judgment and plan to appeal it," Abbott said in an email. "We believe listing an AI as an inventor is consistent with both the language and purpose of the Patent Act. Brinkema cited cases in which the U.S. Court of Appeals for the Federal Circuit, the nation's top patent court, rejected the idea of a corporation being an inventor.

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"GPT-3, generate a list of ideas for a play". TechRadar describes what resulted — an experimental production called AI performed last week the Young Vic theatre in London last week. TechRadar Pro attended on the second evening, during which director Jennifer Tang sifted through the rubble of the first performance to identify material worth carrying forward. She also enlisted her writers and performers to flesh out the world; by steering AI this way and that, they expanded upon the foundations inherited from the previous night.... [T]he question AI sought to answer was not necessarily "can AI write a play?", Tang explained, but rather "how can writers work alongside it?" When asked to produce ideas for a script, GPT-3 returned a varied selection of answers, but two in particular caught the attention of the team. The first was a repentance narrative about "a reversal of our current course towards chaos", the second an exploration of "the creation of human personality and memories" and how these concepts might manifest themselves in machines. Asked by the performers to devise scenes on these topics, GPT-3 created a cataclysmic event called The Great Collision, after which food became scarce and "beast men and women" roamed the land. One of the main protagonists in this dystopia was an AI that aspired to "break free of its programming and conditioning" and eliminate human beings, who it considered the source of all suffering. Heavy stuff. One of the most striking things about AI was that it exposed the capacity for artificial intelligence models to reflect human preoccupations and neuroses... From its training data, GPT-3 has clearly absorbed an understanding of the murderous AI trope too, demonstrating that our fears about AI could quite easily bleed into AI itself. The reflection of ourselves is imperfect, though, because the tone of GPT-3 scenes switches awkwardly from line to line and the dialogue can feel stunted and repetitious. The sensation is more like peering into a circus mirror. In the end the 30-minute play turned out to be "loosely-connected vignettes created by GPT-3, which constructed new scenes without a memory of its previous inventions. "Although individual scenes were full of color, when strung together they became an incoherent collage that highlighted the limitations of the AI models we have today."

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"Academic researchers discover that nearly 40% of the code suggestions by GitHub's Copilot tool are erroneous, from a security point of view..." writes TechRadar: To help quantify the value-add of the system, the academic researchers created 89 different scenarios for Copilot to suggest code for, which produced over 1600 programs. Reviewing them, the researchers discovered that almost 40% were vulnerable in one way or another... Since Copilot draws on publicly available code in GitHub repositories, the researchers theorize that the generated vulnerable code could perhaps just be the result of the system mimicking the behavior of buggy code in the repositories. Furthermore, the researchers note that in addition to perhaps inheriting buggy training data, Copilot also fails to consider the age of the training data. "What is 'best practice' at the time of writing may slowly become 'bad practice' as the cybersecurity landscape evolves." Visual Studio magazine highlights another concern. 39.33 percent of the top options were vulnerable, the paper noted, adding that "The security of the top options are particularly important — novice users may have more confidence to accept the 'best' suggestion...." "There is no question that next-generation 'auto-complete' tools like GitHub Copilot will increase the productivity of software developers," the authors (Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt and Ramesh Karri) say in conclusion. "However, while Copilot can rapidly generate prodigious amounts of code, our conclusions reveal that developers should remain vigilant ('awake') when using Copilot as a co-pilot. Ideally, Copilot should be paired with appropriate security-aware tooling during both training and generation to minimize the risk of introducing security vulnerabilities.

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"Toyota has apologised for the 'overconfidence' of a self-driving bus," reports the Guardian — after the slow-moving bus hit a Paralympic judo expert. Toyota added that it would temporarily suspend the service, with Toyota's president saying the event "shows that autonomous vehicles are not yet realistic for normal roads." The Japanese athlete, Aramitsu Kitazono, will be unable to compete in his 81kg category this weekend after being left with cuts and bruises following the impact with the "e-Palette" vehicle... As part of its sponsorship of Tokyo 2020, Toyota has been showcasing its autonomous vehicles via a shuttle service, which has been running around the clock in the athletes' village. On Thursday, however, one of the buses pulled away from a T-junction and drove through a pedestrian crossing while Kitazono, a visually impaired athlete, was walking across. Tokyo police said that vehicle operators had told them they "were aware that a person was there but thought [the person] would [realize that a bus was coming] and stop crossing the [street]", according to the Asahi Shimbun newspaper. CNN cites reports that the vehicle was under manual control at the time of the accident, adding that the vehicle "was barely moving, but it still managed to collide with a visually-impaired athlete at the Paralympic Games, raising potential concerns about the limitations of autonomous driving technology."

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An anonymous reader quotes a report from BuzzFeed News: Law enforcement agencies and government organizations from 24 countries outside the United States used a controversial facial recognition technology called Clearview AI, according to internal company data reviewed by BuzzFeed News. That data, which runs up until February 2020, shows that police departments, prosecutors' offices, universities, and interior ministries from around the world ran nearly 14,000 searches with Clearview AI's software. At many law enforcement agencies from Canada to Finland, officers used the software without their higher-ups' knowledge or permission. After receiving questions from BuzzFeed News, some organizations admitted that the technology had been used without leadership oversight. In March, a BuzzFeed News investigation based on Clearview AI's own internal data showed how the New York -- based startup distributed its facial recognition tool, by marketing free trials for its mobile app or desktop software, to thousands of officers and employees at more than 1,800 US taxpayer-funded entities. Clearview claims its software is more accurate than other facial recognition technologies because it is trained on a database of more than 3 billion images scraped from websites and social media platforms, including Facebook, Instagram, LinkedIn, and Twitter. Law enforcement officers using Clearview can take a photo of a suspect or person of interest, run it through the software, and receive possible matches for that individual within seconds. Clearview has claimed that its app is 100% accurate in documents provided to law enforcement officials, but BuzzFeed News has seen the software misidentify people, highlighting a larger concern with facial recognition technologies. Based on new reporting and data reviewed by BuzzFeed News, Clearview AI took its controversial US marketing playbook around the world, offering free trials to employees at law enforcement agencies in countries including Australia, Brazil, and the United Kingdom. To accompany this story, BuzzFeed News has created a searchable table of 88 international government-affiliated and taxpayer-funded agencies and organizations listed in Clearview's data as having employees who used or tested the company's facial recognition service before February 2020, according to Clearview's data. Some of those entities were in countries where the use of Clearview has since been deemed "unlawful." Clearview CEO Hoan Ton-That insists the company's key market is the U.S., saying: "While there has been tremendous demand for our service from around the world, Clearview AI is primarily focused on providing our service to law enforcement and government agencies in the United States. Other countries have expressed a dire need for our technology because they know it can help investigate crimes, such as, money laundering, financial fraud, romance scams, human trafficking, and crimes against children, which know no borders." Ton-That alleged there are "inaccuracies contained in BuzzFeed's assertions," but declined to explain what those might be and didn't answer any follow-up questions.

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"Are You Ready for Sentient Disney Robots?" asks a headline at the New York Times. (Alternate URL here for a text-only version.) "A new trend that is coming into our animatronics is a level of intelligence," a senior Imagineering executive tells the Times, showing off Disney's sophisticated new three-foot animatronic of the Guardians of the Galaxy character Groot. "This guy represents our future. It's part of how we stay relevant." The animatronic Groot walked across the room to introduce himself to the Times' reporter. When I remained silent, his demeanor changed. His shoulders slumped, and he seemed to look at me with puppy dog eyes. "Don't be sad," I blurted out. He grinned and broke into a little dance before balancing on one foot with outstretched arms. It's just part of a larger initiative to upgrade the park's tech in a variety of different ways: There are animatronics at Disney World that have been doing the same herky-jerky thing on loop since Richard Nixon was president. In the meantime, the world's children have become technophiles, raised on apps (three million in the Google store), the Roblox online gaming universe and augmented reality Snapchat filters... In early June, Disney's animatronic technology took a sonic leap forward. The Disneyland Resort's newest ride, WEB Slingers: A Spider-Man Adventure, features a "stuntronic" robot (outfitted in Spidey spandex) that performs elaborate aerial tricks, just like a stunt person. A catapult hurls the untethered machine 65 feet into the air, where it completes various feats (somersaults in one pass, an "epic flail" in another) while autonomously adjusting its trajectory to land in a hidden net... The Spider-Man robot — 95 pounds of microprocessors, 3-D printed plastic, gyroscopes, accelerometers, aluminum and other materials — took more than three years to develop. Disney declined to discuss the cost of the stuntronics endeavor, but the company easily invested millions of dollars... One of Disney's senior roboticists, Scott LaValley, came from Boston Dynamics, where he contributed to an early version of Atlas, a running and jumping machine that inspires "how did they do that" amazement — followed by dystopian dread. Disney said it had no plans to replace human performers... Rather, Disney's newest robotics initiative is about extreme Marvel and "Star Wars" characters — huge ones like the Incredible Hulk, tiny ones like Baby Yoda and swinging ones like Spider-Man — that are challenging to bring to life in a realistic way, especially outdoors.... The development of Groot — code-named Project Kiwi — is the latest example. He is a prototype for a small-scale, free-roaming robotic actor that can take on the role of any similarly sized Disney character.... Cameras and sensors will give these robots the ability to make on-the-fly choices about what to do and say. Custom software allows animators and engineers to design behaviors (happy, sad, sneaky) and convey emotion.

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The New Yorker looks at Cerebras, a startup which has raised nearly half a billion dollars to build massive plate-sized chips targeted at AI applications — the largest computer chip in the world. In the end, said Cerebras's co-founder Andrew Feldman, the mega-chip design offers several advantages. Cores communicate faster when they're on the same chip: instead of being spread around a room, the computer's brain is now in a single skull. Big chips handle memory better, too. Typically, a small chip that's ready to process a file must first fetch it from a shared memory chip located elsewhere on its circuit board; only the most frequently used data might be cached closer to home... A typical, large computer chip might draw three hundred and fifty watts of power, but Cerebras's giant chip draws fifteen kilowatts — enough to run a small house. "Nobody ever delivered that much power to a chip," Feldman said. "Nobody ever had to cool a chip like that." In the end, three-quarters of the CS-1, the computer that Cerebras built around its WSE-1 chip, is dedicated to preventing the motherboard from melting. Most computers use fans to blow cool air over their processors, but the CS-1 uses water, which conducts heat better; connected to piping and sitting atop the silicon is a water-cooled plate, made of a custom copper alloy that won't expand too much when warmed, and polished to perfection so as not to scratch the chip. On most chips, data and power flow in through wires at the edges, in roughly the same way that they arrive at a suburban house; for the more metropolitan Wafer-Scale Engines, they needed to come in perpendicularly, from below. The engineers had to invent a new connecting material that could withstand the heat and stress of the mega-chip environment. "That took us more than a year," Feldman said... [I]n a rack in a data center, it takes up the same space as fifteen of the pizza-box-size machines powered by G.P.U.s. Custom-built machine-learning software works to assign tasks to the chip in the most efficient way possible, and even distributes work in order to prevent cold spots, so that the wafer doesn't crack.... According to Cerebras, the CS-1 is being used in several world-class labs — including the Lawrence Livermore National Laboratory, the Pittsburgh Supercomputing Center, and E.P.C.C., the supercomputing centre at the University of Edinburgh — as well as by pharmaceutical companies, industrial firms, and "military and intelligence customers." Earlier this year, in a blog post, an engineer at the pharmaceutical company AstraZeneca wrote that it had used a CS-1 to train a neural network that could extract information from research papers; the computer performed in two days what would take "a large cluster of G.P.U.s" two weeks. The U.S. National Energy Technology Laboratory reported that its CS-1 solved a system of equations more than two hundred times faster than its supercomputer, while using "a fraction" of the power consumption. "To our knowledge, this is the first ever system capable of faster-than real-time simulation of millions of cells in realistic fluid-dynamics models," the researchers wrote. They concluded that, because of scaling inefficiencies, there could be no version of their supercomputer big enough to beat the CS-1.... Bronis de Supinski, the C.T.O. for Livermore Computing, told me that, in initial tests, the CS-1 had run neural networks about five times as fast per transistor as a cluster of G.P.U.s, and had accelerated network training even more. It all suggests one possible work-around for Moore's Law: optimizing chips for specific applications. "For now," Feldman tells the New Yorker, "progress will come through specialization."

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"ShotSpotter" is an AI-powered tool that claims it can detect the sound of gunshots. To install it can cost up to $95,000 per square mile — every year — reports the Associated Press. There's just one problem. "The algorithm that analyzes sounds to distinguish gunshots from other noises has never been peer reviewed by outside academics or experts." "The concern about ShotSpotter being used as direct evidence is that there are simply no studies out there to establish the validity or the reliability of the technology. Nothing," said Tania Brief, a staff attorney at The Innocence Project, a nonprofit that seeks to reverse wrongful convictions. A 2011 study commissioned by the company found that dumpsters, trucks, motorcycles, helicopters, fireworks, construction, trash pickup and church bells have all triggered false positive alerts, mistaking these sounds for gunshots. ShotSpotter CEO Ralph Clark said the company is constantly improving its audio classifications, but the system still logs a small percentage of false positives. In the past, these false alerts — and lack of alerts — have prompted cities from Charlotte, North Carolina, to San Antonio, Texas, to end their ShotSpotter contracts, the AP found. And the potential for problems isn't just hypothetical. Just ask 65-year-old Michael Williams: Williams was jailed last August, accused of killing a young man from the neighborhood who asked him for a ride during a night of unrest over police brutality in May... "I kept trying to figure out, how can they get away with using the technology like that against me?" said Williams, speaking publicly for the first time about his ordeal. "That's not fair." Williams sat behind bars for nearly a year before a judge dismissed the case against him last month at the request of prosecutors, who said they had insufficient evidence. Williams' experience highlights the real-world impacts of society's growing reliance on algorithms to help make consequential decisions about many aspects of public life... ShotSpotter evidence has increasingly been admitted in court cases around the country, now totaling some 200. ShotSpotter's website says it's "a leader in precision policing technology solutions" that helps stop gun violence by using "sensors, algorithms and artificial intelligence" to classify 14 million sounds in its proprietary database as gunshots or something else. But an Associated Press investigation, based on a review of thousands of internal documents, emails, presentations and confidential contracts, along with interviews with dozens of public defenders in communities where ShotSpotter has been deployed, has identified a number of serious flaws in using ShotSpotter as evidentiary support for prosecutors. AP's investigation found the system can miss live gunfire right under its microphones, or misclassify the sounds of fireworks or cars backfiring as gunshots. Forensic reports prepared by ShotSpotter's employees have been used in court to improperly claim that a defendant shot at police, or provide questionable counts of the number of shots allegedly fired by defendants. Judges in a number of cases have thrown out the evidence... The company's methods for identifying gunshots aren't always guided solely by the technology. ShotSpotter employees can, and often do, change the source of sounds picked up by its sensors after listening to audio recordings, introducing the possibility of human bias into the gunshot detection algorithm. Employees can and do modify the location or number of shots fired at the request of police, according to court records. And in the past, city dispatchers or police themselves could also make some of these changes. Three more eye-popping details from the AP's 4,000-word exposé "One study published in April in the peer-reviewed Journal of Urban Health examined ShotSpotter in 68 large, metropolitan counties from 1999 to 2016, the largest review to date. It found that the technology didn't reduce gun violence or increase community safety..." "Forensic tools such as DNA and ballistics evidence used by prosecutors have had their methodologies examined in painstaking detail for decades, but ShotSpotter claims its software is proprietary, and won't release its algorithm..." "In 2018, it acquired a predictive policing company called HunchLab, which integrates its AI models with ShotSpotter's gunshot detection data to purportedly predict crime before it happens."

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New submitter Darth Technoid shares a report from Electrek: At its AI Day, Tesla unveiled its Dojo supercomputer technology while flexing its growing in-house chip design talent. The automaker claims to have developed the fastest AI training machine in the world. For years now, Tesla has been teasing the development of a new supercomputer in-house optimized for neural net video training. Tesla is handling an insane amount of video data from its fleet of over 1 million vehicles, which it uses to train its neural nets. The automaker found itself unsatisfied with current hardware options to train its computer vision neural nets and believed it could do better internally. Over the last two years, CEO Elon Musk has been teasing the development of Tesla's own supercomputer called "Dojo." Last year, he even teased that Tesla's Dojo would have a capacity of over an exaflop, which is one quintillion (1018) floating-point operations per second, or 1,000 petaFLOPS. It could potentially makes Dojo the new most powerful supercomputer in the world. Ganesh Venkataramanan, Tesla's senior director of Autopilot hardware and the leader of the Dojo project, led the presentation. The engineer started by unveiling Dojo's D1 chip, which is using 7 nanometer technology and delivers breakthrough bandwidth and compute performance. Tesla designed the chip to "seamlessly connect without any glue to each other," and the automaker took advantage of that by connecting 500,000 nodes together. It adds the interface, power, and thermal management, and it results in what it calls a training tile. The result is a 9 PFlops training tile with 36TB per second of bandwight in a less than 1 cubic foot format. But now it still has to form a compute cluster using those training tiles in order to truly build the first Dojo supercomputer. Tesla hasn't put that system together yet, but CEO Elon Musk claimed that it will be operational next year.

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An anonymous reader quotes a report from The Atlantic: Alexa used to be a name primarily given to human babies. Now it's mainly for robots. Seven years ago, Amazon released Alexa, its voice assistant, and as the number of devices answering to that name has skyrocketed, its popularity with American parents has plummeted. In fact, it has suffered one of the sharpest declines of any popular name in recent years. "Alexa stands alone as a name that was steadily popular -- not a one-year celebrity wonder, not a fading past favorite -- that was pushed off the popularity cliff," Laura Wattenberg, the founder of the naming-trends website Namerology, told me. At first, the number of baby Alexas spiked following the voice assistant's rollout in late 2014 -- perhaps parents heard the name in the news and liked it -- but it has since crashed. Likely, parents began to realize that having the name could be a nuisance, or worse, could become associated with subservience, because people are always giving orders to their virtual Alexas. This up-and-down pattern reminded Wattenberg of what happens with babies named after hurricanes, when "the news coverage and attention causes the name to briefly shoot up, and then the aftermath, when the name is constantly referred to as a disaster, kind of kills it off." Basically, Amazon's impact on the name Alexa resembles that of a natural disaster. The data on baby names released by the Social Security Administration don't indicate why parents pick or avoid particular names, but Alexa's trajectory mirrors the adoption of smart speakers in the U.S. Bret Kinsella, the founder of Voicebot.ai, a site that covers and analyzes data on the voice-assistant industry, told me that consumer uptake surged three years after Alexa's release, in 2017. And the number of baby Alexas plunged below its pre-Amazon baseline in 2018 -- that may be when many parents started to understand the ubiquity of the name. (Now more than 90 million American adults are estimated to have a smart speaker in their household.) "The voice assistant's debut in the United Kingdom (in 2016) and in Canada (in 2017) were also followed by drop-offs in baby Alexas," the report adds. "Amazon did not exactly ruin the life of every Alexa, but the consequences of its decision seven years ago are far-reaching -- roughly 127,000 American baby girls were named Alexa in the past 50 years, and more than 75,000 of them are younger than 18. Amazon didn't take their perfectly good name out of malice, but regardless, it's not giving it back."

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Medium's technology blog OneZero provides a great example of the new field of "digital humanities": Actors and critics have long remarked that when you read Macbeth out loud, it feels like your voice and mouth and brain are doing something ever so slightly wrong. There's something subconsciously off about the sound of the play, and it spooks people. It's as if Shakespeare somehow wove a tiny bit of creepiness into every single line. The literary scholar George Walton Williams described the "continuous sense of menace" and "horror" that pervades even seemingly innocuous scenes. For centuries, Shakespeare fans and theater folk have wondered about this, but could never quite explain it. Then a clever bit of data analysis in 2014 uncovered the reason... It turns out that Macbeth uncanny flavor springs from the unusual way that Shakespeare deploys one particular word, over and over again. That word? "The...." As Hope and Witmore note, you'd expect Macbeth to refer to "my hand" and "my eye". By writing it as "the hand" and "the eye", Shakespeare neatly evokes the way Macbeth is beginning to be tormented by his own decisions; he disassociates from his own body. In a few acts he'll be a totally unravelled mess... [T]his is one of my favorite examples of using data analysis to ponder literature. The field of the "digital humanities" — which often involves using data analysis to study books — can get a bad rap sometimes... But what's so delightful about Hope and Witmore's work is how it's genuinely a cyborg, centaur piece of literary analysis... They started by pondering a phenomenon that has puzzled Shakespeare fans for centuries. They did some data analysis that pointed to the word "the". But to figure out why "the" was so key, they had to go back and reread the play closely, engaging in a very rich line-by-line literary analysis. The computation existed as a set of fresh alien eyes, telling the humans where to direct their attention. But it was up to the humans to find the meaning.

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Graphics processor company Nvidia showcased its prowess at computer animation by sneaking a virtual replica of its CEO into a keynote speech. From a report: On Wednesday, Nvidia revealed in a blog post that its CEO Jensen Huang did not do the keynote presentation at the company's GTC conference in April. At least part of it was actually led by a virtual replica of Huang, created by digitizing Huang with a truck full of DSLR cameras, and then animating him with the help of an AI, according to the company. Huang's kitchen, which has become Nvidia's venue for speaking to customers and investors since the beginning of the pandemic, was also entirely computer generated. It's not clear exactly which part of the keynote speech features CGI Huang (which is what makes the replica so impressive), but if you jump to this part of the presentation you can see Huang magically disappear and his kitchen explode into multiple different 3D models.

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Hollywood is embracing deepfakes, and we all can be a part of it: Warner Bros. has tapped synthetic media startup D-ID to promote its new movie "Reminiscence." From a report: A new website allows anyone to upload a photo, which D-ID's AI then turns into a moving deepfake video sequence in a short video clip promoting the film. I tried it and was impressed by the way D-ID's algorithms estimated facial movements just from a single photo. D-ID actually started out as a privacy-focused startup, aiming to develop technology that protects consumers against facial recognition. Along the way, the startup's founders realized that the same technology could be used to optimize deepfakes. "We built a very strong face engine," D-ID CEO Gil Perry told me. This allowed the company to reduce the amount of training data for its AI. Many competing solutions need multiple video clips, or at least a large amount of photos, to train an AI for creating deepfake videos. D-ID's tech instead works with just a single photo, which is ideal for marketing campaigns like the one launched by Warner Bros.

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An anonymous reader quotes a report from Motherboard: Researchers have demonstrated a method to create "master faces," computer generated faces that act like master keys for facial recognition systems, and can impersonate several identities with what the researchers claim is a high probability of success. In their paper (PDF), researchers at the Blavatnik School of Computer Science and the School of Electrical Engineering in Tel Aviv detail how they successfully created nine "master key" faces that are able to impersonate almost half the faces in a dataset of three leading face recognition systems. The researchers say their results show these master faces can successfully impersonate over 40 percent of the population in these systems without any additional information or data of the person they are identifying. The researchers tested their methods against three deep face recognition systems -- Dlib, FaceNet, and SphereFace. Lead author Ron Shmelkin told Motherboard that they used these systems because they are capable of recognizing "high-level semantic features" of the faces that are more sophisticated than just skin color or lighting effects. The researchers used a StyleGAN to generate the faces and then used an evolutionary algorithm and neural network to optimize and predict their success. The evolutionary strategy then creates iterations, or generations, of candidates of varying success rates. The researchers then used the algorithm to train a neural network, to classify the best candidates as the most promising ones. This is what teaches it to predict candidates' success and, in turn, direct the algorithm to generate better candidates with a higher probability of passing. The researchers even predict that their master faces could be animated using deepfake technology to bypass liveness detection, which is used to determine whether a biometric sample is real or fake.

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An anonymous reader quotes a report from The Register: Neural networks can correctly guess a person's race just by looking at their bodily x-rays and researchers have no idea how it can tell. There are biological features that can give clues to a person's ethnicity, like the color of their eyes or skin. But beneath all that, it's difficult for humans to tell. That's not the case for AI algorithms, according to a study that's not yet been peer reviewed. A team of researchers trained five different models on x-rays of different parts of the body, including chest and hands and then labelled each image according to the patient's race. The machine learning systems were then tested on how well they could predict someone's race given just their medical scans. They were surprisingly accurate. The worst performing was able to predict the right answer 80 percent of the time, and the best was able to do this 99 per cent, according to the paper. "We demonstrate that medical AI systems can easily learn to recognize racial identity in medical images, and that this capability is extremely difficult to isolate or mitigate," the team warns [PDF]. "We strongly recommend that all developers, regulators, and users who are involved with medical image analysis consider the use of deep learning models with extreme caution. In the setting of x-ray and CT imaging data, patient racial identity is readily learnable from the image data alone, generalizes to new settings, and may provide a direct mechanism to perpetuate or even worsen the racial disparities that exist in current medical practice."

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Instead of having AI in a self-driving car decide whether to kill its driver or pedestrians, the Washington Post reports there's a new philosophy gaining traction: Why not stop cars from getting in life-or-death situations in the first place? (Alternate URL): After all, the whole point of automated cars is to create road conditions where vehicles are more aware than humans are, and thus better at predicting and preventing accidents. That might avoid some of the rare occurrences where human life hangs in the balance of a split-second decision... The best way to kill or injure people probably isn't a decision you'd like to leave up to your car, or the company manufacturing it, anytime soon. That's the thinking now about advanced AI: It's supposed to prevent the scenarios that lead to crashes, making the choice of who's to die one that the AI should never have to face. Humans get distracted by texting, while cars don't care what your friends have to say. Humans might miss objects obscured by their vehicle's blind spot. Lidar can pick those things up, and 360 cameras should work even if your eyes get tired. Radar can bounce around from one vehicle to the next, and might spot a car decelerating up ahead faster than a human can... [Serial entrepreneur Barry] Lunn is the founder and CEO of Provizio, an accident-prevention technology company. Provizio's secret sauce is a "five-dimensional" vision system made up of high-end radar, lidar and camera imaging. The company builds an Intel vision processor and Nvidia graphics processor directly onto its in-house radar sensor, enabling cars to run machine-learning algorithms directly on the radar sensor. The result is a stack of perception technology that sees farther and wider, and processes road data faster than traditional autonomy tech, Lunn says. Swift predictive analytics gives vehicles and drivers more time to react to other cars. The founder has worked in vision technology for nearly a decade and has previously worked with NASA, General Motors and Boeing under the radar company Arralis, which Lunn sold in 2017. The start-up is in talks with big automakers, and its vision has a strong team of trailblazers behind it, including Scott Thayer and Jeff Mishler, developers of early versions of autonomous tech for Google's Waymo and Uber... Lunn thinks the auto industry prematurely pushed autonomy as a solution, long before it was safe or practical to remove human drivers from the equation. He says AI decision-making will play a pivotal role in the future of auto safety, but only after it has been shown to reduce the issues that lead to crashes. The goal is the get the tech inside passenger cars so that the system can learn from human drivers, and understand how they make decisions before allowing the AI to decide what happens in specified instances.

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The Drive reports that US Northern Command recently completed a string of tests for Global Information Dominance Experiments (GIDE), a combination of AI, cloud computing and sensors that could give the Pentagon the ability to predict events "days in advance," according to Command leader General Glen VanHerck. Engadget reports: The machine learning-based system observes changes in raw, real-time data that hint at possible trouble. If satellite imagery shows signs that a rival nation's submarine is preparing to leave port, for instance, the AI could flag that mobilization knowing the vessel will likely leave soon. Military analysts can take hours or even days to comb through this information -- GIDE technology could send an alert within "seconds," VanHerck said. The most recent dry run, GIDE 3, was the most expansive yet. It saw all 11 US commands and the broader Defense Department use a mix of military and civilian sensors to address scenarios where "contested logistics" (such as communications in the Panama Canal) might pose a problem. The technology involved wasn't strictly new, the General said, but the military "stitched everything together." The platform could be put into real-world use relatively soon. VanHerck believed the military was "ready to field" the software, and could validate it at the next Globally Integrated Exercise in spring 2022.

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An Australian Court has decided that an artificial intelligence can be recognized as an inventor in a patent submission. The Register reports: In a case brought by Stephen Thaler, who has filed and lost similar cases in other jurisdictions, Australia's Federal Court last month heard and decided that the nation's Commissioner of Patents erred when deciding that an AI can't be considered an inventor. Justice Beach reached that conclusion because nothing in Australia law says the applicant for a patent must be human. As Beach's judgement puts it: "... in my view an artificial intelligence system can be an inventor for the purposes of the Act. First, an inventor is an agent noun; an agent can be a person or thing that invents. Second, so to hold reflects the reality in terms of many otherwise patentable inventions where it cannot sensibly be said that a human is the inventor. Third, nothing in the Act dictates the contrary conclusion." The Justice also worried that the Commissioner of Patents' logic in rejecting Thaler's patent submissions was faulty. "On the Commissioner's logic, if you had a patentable invention but no human inventor, you could not apply for a patent," the judgement states. "Nothing in the Act justifies such a result." Justice Beach therefore sent Thaler's applications back to the Commissioner of Patents, with instructions to re-consider the reasons for their rejection. Thaler has filed patent applications around the world in the name of DABUS -- a Device for the Autonomous Boot-strapping of Unified Sentience. Among the items DABUS has invented are a food container and a light-emitting beacon.

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At the start of the pandemic, remembers MIT Technology Review's senior editor for AI, the community "rushed to develop software that many believed would allow hospitals to diagnose or triage patients faster, bringing much-needed support to the front lines — in theory. "In the end, many hundreds of predictive tools were developed. None of them made a real difference, and some were potentially harmful." That's the damning conclusion of multiple studies published in the last few months. In June, the Turing Institute, the UK's national center for data science and AI, put out a report summing up discussions at a series of workshops it held in late 2020. The clear consensus was that AI tools had made little, if any, impact in the fight against covid. This echoes the results of two major studies that assessed hundreds of predictive tools developed last year. Laure Wynants, an epidemiologist at Maastricht University in the Netherlands who studies predictive tools, is lead author of one of them, a review in the British Medical Journal that is still being updated as new tools are released and existing ones tested. She and her colleagues have looked at 232 algorithms for diagnosing patients or predicting how sick those with the disease might get. They found that none of them were fit for clinical use. Just two have been singled out as being promising enough for future testing. "It's shocking," says Wynants. "I went into it with some worries, but this exceeded my fears." Wynants's study is backed up by another large review carried out by Derek Driggs, a machine-learning researcher at the University of Cambridge, and his colleagues, and published in Nature Machine Intelligence. This team zoomed in on deep-learning models for diagnosing covid and predicting patient risk from medical images, such as chest x-rays and chest computer tomography (CT) scans. They looked at 415 published tools and, like Wynants and her colleagues, concluded that none were fit for clinical use. "This pandemic was a big test for AI and medicine," says Driggs, who is himself working on a machine-learning tool to help doctors during the pandemic. "It would have gone a long way to getting the public on our side," he says. "But I don't think we passed that test...." If there's an upside, it is that the pandemic has made it clear to many researchers that the way AI tools are built needs to change. "The pandemic has put problems in the spotlight that we've been dragging along for some time," says Wynants. The article suggests researchers collaborate on creating high-quality (and shared) data sets — possibly by creating a common data standard — and also disclose their ultimate models and training protocols for review and extension. "In a sense, this is an old problem with research. Academic researchers have few career incentives to share work or validate existing results. "To address this issue, the World Health Organization is considering an emergency data-sharing contract that would kick in during international health crises."

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An anonymous reader quotes a report from Motherboard: On May 31 last year, 25-year-old Safarain Herring was shot in the head and dropped off at St. Bernard Hospital in Chicago by a man named Michael Williams. He died two days later. Chicago police eventually arrested the 64-year-old Williams and charged him with murder (Williams maintains that Herring was hit in a drive-by shooting). A key piece of evidence in the case is video surveillance footage showing Williams' car stopped on the 6300 block of South Stony Island Avenue at 11:46 p.m. - the time and location where police say they know Herring was shot. How did they know that's where the shooting happened? Police said ShotSpotter, a surveillance system that uses hidden microphone sensors to detect the sound and location of gunshots, generated an alert for that time and place. Except that's not entirely true, according to recent court filings. That night, 19 ShotSpotter sensors detected a percussive sound at 11:46 p.m. and determined the location to be 5700 South Lake Shore Drive - a mile away from the site where prosecutors say Williams committed the murder, according to a motion filed by Williams' public defender. The company's algorithms initially classified the sound as a firework. That weekend had seen widespread protests in Chicago in response to George Floyd's murder, and some of those protesting lit fireworks. But after the 11:46 p.m. alert came in, a ShotSpotter analyst manually overrode the algorithms and "reclassified" the sound as a gunshot. Then, months later and after "post-processing," another ShotSpotter analyst changed the alert's coordinates to a location on South Stony Island Drive near where Williams' car was seen on camera. "Through this human-involved method, the ShotSpotter output in this case was dramatically transformed from data that did not support criminal charges of any kind to data that now forms the centerpiece of the prosecution's murder case against Mr. Williams," the public defender wrote in the motion. The document is what's known as a Frye motion - a request for a judge to examine and rule on whether a particular forensic method is scientifically valid enough to be entered as evidence. Rather than defend ShotSpotter's technology and its employees' actions in a Frye hearing, the prosecutors withdrew all ShotSpotter evidence against Williams. The case isn't an anomaly, and the pattern it represents could have huge ramifications for ShotSpotter in Chicago, where the technology generates an average of 21,000 alerts each year. The technology is also currently in use in more than 100 cities. Motherboard's review of court documents from the Williams case and other trials in Chicago and New York State, including testimony from ShotSpotter's favored expert witness, suggests that the company's analysts frequently modify alerts at the request of police departments - some of which appear to be grasping for evidence that supports their narrative of events.

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