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.

According to a new study, malware can be embedded directly into the artificial neurons that make up machine learning models in a way that keeps them from being detected. The neural network would even be able to continue performing its set tasks normally. Motherboard reports: "As neural networks become more widely used, this method will be universal in delivering malware in the future," the authors, from the University of the Chinese Academy of Sciences, write. Using real malware samples, their experiments found that replacing up to around 50 percent of the neurons in the AlexNet model -- a benchmark-setting classic in the AI field -- with malware still kept the model's accuracy rate above 93.1 percent. The authors concluded that a 178MB AlexNet model can have up to 36.9MB of malware embedded into its structure without being detected using a technique called steganography. Some of the models were tested against 58 common antivirus systems and the malware was not detected.

Other methods of hacking into businesses or organizations, such as attaching malware to documents or files, often cannot deliver malicious software en masse without being detected. The new research, on the other hand, envisions a future where an organization may bring in an off-the-shelf machine learning model for any given task (say, a chat bot, or image detection) that could be loaded with malware while performing its task well enough not to arouse suspicion. According to the study, this is because AlexNet (like many machine learning models) is made up of millions of parameters and many complex layers of neurons including what are known as fully-connected "hidden" layers. By keeping the huge hidden layers in AlexNet completely intact, the researchers found that changing some other neurons had little effect on performance.

According to the paper, in this approach the malware is "disassembled" when embedded into the network's neurons, and assembled into functioning malware by a malicious receiver program that can also be used to download the poisoned model via an update. The malware can still be stopped if the target device verifies the model before launching it, according to the paper. It can also be detected using "traditional methods" like static and dynamic analysis. "Today it would not be simple to detect it by antivirus software, but this is only because nobody is looking in there," cybersecurity researcher and consultant Dr. Lukasz Olejnik told Motherboard. Olejnik also warned that the malware extraction step in the process could also risk detection. Once the malware hidden in the model was compiled into, well, malware, then it could be picked up. It also might just be overkill.

An anonymous reader quotes a report from Ars Technica: Just one week after Google's DeepMind AI group finally described its biology efforts in detail, the company is releasing a paper that explains how it analyzed nearly every protein encoded in the human genome and predicted its likely three-dimensional structure -- a structure that can be critical for understanding disease and designing treatments. In the very near future, all of these structures will be released under a Creative Commons license via the European Bioinformatics Institute, which already hosts a major database of protein structures. In a press conference associated with the paper's release, DeepMind's Demis Hassabis made clear that the company isn't stopping there. In addition to the work described in the paper, the company will release structural predictions for the genomes of 20 major research organisms, from yeast to fruit flies to mice. In total, the database launch will include roughly 350,000 protein structures.
[...]
At some point in the near future (possibly by the time you read this), all this data will be available on a dedicated website hosted by the European Bioinformatics Institute, a European Union-funded organization that describes itself in part as follows: "We make the world's public biological data freely available to the scientific community via a range of services and tools." The AlphaFold data will be no exception; once the above link is live, anyone can use it to download information on the human protein of their choice. Or, as mentioned above, the mouse, yeast, or fruit fly version. The 20 organisms that will see their data released are also just a start. DeepMind's Demis Hassabis said that over the next few months, the team will target every gene sequence available in DNA databases. By the time this work is done, over 100 million proteins should have predicted structures. Hassabis wrapped up his part of the announcement by saying, "We think this is the most significant contribution AI has made to science to date." It would be difficult to argue otherwise. Further reading: Google details its protein-folding software, academics offer an alternative (Ars Technica)

Last year the artificial intelligence group DeepMind cracked a mystery that has flummoxed scientists for decades: stripping bare the structure of proteins, the building blocks of life. Now, having amassed a database of nearly all human protein structures, the company is making the resource available online free for researchers to use. From a report: The key to understanding our basic biological machinery is its architecture. The chains of amino acids that comprise proteins twist and turn to make the most confounding of 3D shapes. It is this elaborate form that explains protein function; from enzymes that are crucial to metabolism to antibodies that fight infectious attacks. Despite years of onerous and expensive lab work that began in the 1950s, scientists have only decoded the structure of a fraction of human proteins.

DeepMind's AI program, AlphaFold, has predicted the structure of nearly all 20,000 proteins expressed by humans. In an independent benchmark test that compared predictions to known structures, the system was able to predict the shape of a protein to a good standard 95% of time. DeepMind, which has partnered with the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI), hopes the database will help researchers to analyse how life works at an atomic scale by unpacking the apparatus that drives some diseases, make strides in the field of personalised medicine, create more nutritious crops and develop "green enzymes" that can break down plastic.

China's capabilities in artificial intelligence are "much closer than I thought" to catching up to the US, former Google chief executive Eric Schmidt told Nikkei Asia, stressing that America would not succeed without a "very strong partnership with our Asian friends." From a report: In an online interview, Schmidt, now chair of the National Security Commission on Artificial Intelligence, said China was closing in on the US in certain areas of AI and quantum computing -- faster than his previous estimate of "a couple of years." That's a really, really big deal," he said. Schmidt stepped down as executive chair of Google parent Alphabet in 2018. He was nominated as the commission chair in 2019 to make AI-related policy recommendations to the US president and Congress.

The commission's final report, released in March, warned that "if the United States does not act, it will probably lose its leadership position in AI to China in the next decade and become more vulnerable to a spectrum of AI-enabled threats from a host of state and non-state actors." To win the tech competition with China, the US had to maintain its lead in "strategic" areas such as AI, semiconductors, energy, quantum computing and synthetic biology, Schmidt said. And for that, he said, "we need much closer relationships with Japanese researchers, Japanese universities, Japanese government -- the same thing for South Koreans and same thing for Europeans."

OpenAI has disbanded its robotics team after years of research into machines that can learn to perform tasks like solving a Rubik's Cube. Company cofounder Wojciech Zaremba quietly revealed on a podcast hosted by startup Weights & Biases that OpenAI has shifted its focus to other domains, where data is more readily available. From a report: "So it turns out that we can make a gigantic progress whenever we have access to data. And I kept all of our machinery unsupervised, [using] reinforcement learning -- [it] work[s] extremely well. There [are] actually plenty of domains that are very, very rich with data. And ultimately that was holding us back in terms of robotics," Zaremba said. "The decision [to disband the robotics team] was quite hard for me. But I got the realization some time ago that actually, that's for the best from the perspective of the company."

In a statement, an OpenAI spokesperson told VentureBeat: "After advancing the state of the art in reinforcement learning through our Rubik's Cube project and other initiatives, last October we decided not to pursue further robotics research and instead refocus the team on other projects. Because of the rapid progress in AI and its capabilities, we've found that other approaches, such as reinforcement learning with human feedback, lead to faster progress in our reinforcement learning research."

Since 2006 Baldrson (Slashdot reader #78,598) has been part of the team verifying "The Hutter Prize for Lossless Compression of Human Knowledge," an ongoing challenge to compress a 100-MB excerpt of Wikipedia (approximately the amount a human can read in a lifetime).

"The intention of this prize is to encourage development of intelligent compressors/programs as a path to Artificial General Intelligence," explains the project's web site. 15 years ago, Baldrson wrote a Slashdot post explaining the logic (titled "Compress Wikipedia and Win AI Prize"): The basic theory, for which Hutter provides a proof, is that after any set of observations the optimal move by an AI is find the smallest program that predicts those observations and then assume its environment is controlled by that program. Think of it as Ockham's Razor on steroids.
The amount of the prize also increases based on how much compression is achieved. (So if you compress the 1GB file x% better than the current record, you'll receive x% of the prize...) The first prize was awarded in 2006. And now Baldrson writes with the official news that this Spring another prize was claimed after reaching a brand new milestone: Artemiy Margaritov's STARLIT algorithm's 1.13% cleared the 1% improvement hurdle to beat the last benchmark, set by Alexander Rhatushnyak. He receives a bonus in proportion to the time since the last benchmark was set, raising his award by 60% to €9000. [$10,632 USD]

Congratulations to Artemiy Margaritov for his winning submission!

A new documentary film has harnessed artificial intelligence to artificially voice quotes from its subject, the late Anthony Bourdain. From a report: Details of the dubious decision are outlined in a piece in The New Yorker, and raise a heap of uncomfortable questions about whether or not it's ethical to put words in the mouths of the deceased, whether or not they penned them during their life. The lines appear in filmmaker Morgan Neville's new documentary, Roadrunner, when an email from Bourdain is initially read by the recipient, but the audio then transitions into Bourdain's own voice.

Pepper, SoftBank's robot, malfunctioned during scripture readings, took breaks in exercise class and couldn't recognize the faces of family members. From a report: Having a robot read scripture to mourners seemed like a cost-effective idea to the people at Nissei Eco, a plastics manufacturer with a sideline in the funeral business. The company hired child-sized robot Pepper, clothed it in the vestments of Buddhist clergy and programmed it to chant several sutras, or Buddhist scriptures, depending on the sect of the deceased. Alas, the robot, made by SoftBank Group, kept breaking down during practice runs. "What if it refused to operate in the middle of a ceremony?" said funeral-business manager Osamu Funaki. "It would be such a disaster." Pepper was fired. The company ended its lease of the robot and sent it back to the manufacturer. After a rash of similar mishaps across Japan, in which Pepper botched its job at a nursing home and gave baseball fans a creepy feeling, some people are saying the humanoid itself will need a funeral soon.

"Because it has the shape of a person, people expect the intelligence of a human," said Takayuki Furuta, head of the Future Robotics Technology Center at Chiba Institute of Technology, which wasn't involved in Pepper's development. "The level of the technology completely falls short of that. It's like the difference between a toy car and an actual car." The robotics unit of SoftBank, a Tokyo-based technology investor, said in late June that it halted production of Pepper last year and was planning to restructure its global robotics teams, including a French unit involved in Pepper's development. Still, the company says the machine shouldn't be sent to the product graveyard. Spokeswoman Ai Kitamura said Pepper is SoftBank's icon and still doing good work as a teacher and a temperature taker at hospitals. She declined to comment on any of its individual mishaps.

SoftBank introduced the humanoid to the world in 2014 and started selling it the next year. "Today might become a day that people 100, 200 or 300 years later would remember as a historic day," SoftBank Chief Executive Masayoshi Son said at the introduction. SoftBank sold the robots to individuals for about $2,000, plus monthly fees for subscription services, and rented them to businesses starting at $550 a month. Japan has had a love affair with humanlike robots going back to Astro Boy, a robot featured in a 1960s animated television series, but there have also been breakups. Honda Motor's Asimo once kicked a soccer ball to then-President Barack Obama. Toshiba's Aiko Chihira, an android with a woman's name and appearance, briefly worked as a department store receptionist. After a while, both disappeared. More recently, a Japanese hotel chain created a robot-operated hotel, with dinosaur-shaped robots handling front-desk duties, only to reverse course after the plan failed to save money and created more work for humans.

Pablo Samuel Castro, Staff Software Engineer at Google Research, writes on Google AI blog: It is widely accepted that the enormous growth of deep reinforcement learning research, which combines traditional reinforcement learning with deep neural networks, began with the publication of the seminal DQN algorithm. This paper demonstrated the potential of this combination, showing that it could produce agents that could play a number of Atari 2600 games very effectively. Since then, there have been several approaches that have built on and improved the original DQN. The popular Rainbow algorithm combined a number of these recent advances to achieve state-of-the-art performance on the ALE benchmark. This advance, however, came at a very high computational cost, which has the unfortunate side effect of widening the gap between those with ample access to computational resources and those without.

In "Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning Research," to be presented at ICML 2021, we revisit this algorithm on a set of small- and medium-sized tasks. We first discuss the computational cost associated with the Rainbow algorithm. We explore how the same conclusions regarding the benefits of combining the various algorithmic components can be reached with smaller-scale experiments, and further generalize that idea to how research done on a smaller computational budget can provide valuable scientific insights.

Robots have a hard time improvising, and encountering an unusual surface or obstacle usually means an abrupt stop or hard fall. But researchers have created a new model for robotic locomotion that adapts in real time to any terrain it encounters, changing its gait on the fly to keep trucking when it hits sand, rocks, stairs and other sudden changes. From a report: Although robotic movement can be versatile and exact, and robots can "learn" to climb steps, cross broken terrain and so on, these behaviors are more like individual trained skills that the robot switches between. Although robots like Spot famously can spring back from being pushed or kicked, the system is really just working to correct a physical anomaly while pursuing an unchanged policy of walking. There are some adaptive movement models, but some are very specific (for instance this one based on real insect movements) and others take long enough to work that the robot will certainly have fallen by the time they take effect.

The team, from Facebook AI, UC Berkeley and Carnegie Mellon University, call it Rapid Motor Adaptation. It came from the fact that humans and other animals are able to quickly, effectively and unconsciously change the way they walk to fit different circumstances. "Say you learn to walk and for the first time you go to the beach. Your foot sinks in, and to pull it out you have to apply more force. It feels weird, but in a few steps you'll be walking naturally just as you do on hard ground. What's the secret there?" asked senior researcher Jitendra Malik, who is affiliated with Facebook AI and UC Berkeley. Certainly if you've never encountered a beach before, but even later in life when you have, you aren't entering some special "sand mode" that lets you walk on soft surfaces. The way you change your movement happens automatically and without any real understanding of the external environment.

The US Department of Commerce has sanctioned 14 Chinese tech companies over links to human rights abuses against Uyghur Muslims in Xinjiang, including one backed by a top Silicon Valley investment firm. From a report: DeepGlint, also known as Beijing Geling Shentong Information Technology Co., Ltd., is a facial recognition company with deep ties to Chinese police surveillance, and funding from US-based Sequoia Capital. Today the Commerce Department added it to its Entity List, which restricts US companies from doing business with listed firms without a special license. Sequoia did not immediately respond to a request for comment. DeepGlint co-founded a facial recognition lab in 2018 with Chinese authorities in Urumqi, the capital of Xinjiang, according to the South China Morning Post. It has also gained international bragging rights through the US National Institute of Standards and Technology's (NIST) Face Recognition Vendor Test. DeepGlint claimed top accuracy in the test as of January 2021, giving it a potent marketing tool in the security and surveillance industry. While DeepGlint has been accepted for a public offering on Shanghai's STAR stock exchange, the firm hasn't seen the commercial success of other AI startups in the country, explained Jeffrey Ding in his ChinAI newsletter last month. Since the firm is so heavily invested in government work, it has to follow slow government procurement cycles and is unlikely to score huge infrastructure projects, Ding writes.

ASUS and Qualcomm have teamed up to make a smartphone that shows off some of the latter's mobile tech. Although the phone is ostensibly for the 1.6 million members of the Snapdragon Insiders program (which is a bit like Microsoft's Windows Insider early-access scheme), it'll be more broadly available by August. From a report: The snappily named Smartphone for Snapdragon Insiders harnesses Qualcomm's Snapdragon 888 5G chipset with a 2.84 GHz octa-core processor and the Adreno 660 GPU. It has what Qualcomm describes as "the most comprehensive support for all key 5G sub-6 and mmWave bands" of any device, along with WiFi 6 and WiFi 6E support with speeds of up to 3.6 Gbps. You'll get 16GB of LPDDR5 memory and 512GB of storage. The 6.78-inch AMOLED display from Samsung has a 144 Hz refresh rate, which could help make it a solid gaming phone. The screen has up to 1,200 nits of brightness and it's HDR10 and HDR10+ certified. The phone has three rear cameras: a 64MP main lens, 12MP ultrawide camera and 8MP telephoto. The array can capture video in up to 8K. The device also has a 24MP front camera and AI auto-zoom. You'll be able to buy the $1,499 device at ASUSTeK's eShop and other retailers.

The UK's most powerful supercomputer, which its creators hope will make the process of preventing, diagnosing and treating disease better, faster and cheaper, is operational. The Guardian reports: Christened Cambridge-1, the supercomputer represents a $100m investment by US-based computing company Nvidia. The idea capitalizes on artificial intelligence (AI) -- which combines big data with computer science to facilitate problem-solving -- in healthcare. [...] Cambridge-1's first projects will be with AstraZeneca, GSK, Guy's and St Thomas' NHS foundation trust, King's College London and Oxford Nanopore. They will seek to develop a deeper understanding of diseases such as dementia, design new drugs, and improve the accuracy of finding disease-causing variations in human genomes.

A key way the supercomputer can help, said Dr Kim Branson, global head of artificial intelligence and machine learning at GSK, is in patient care. In the field of immuno-oncology, for instance, existing medicines harness the patient's own immune system to fight cancer. But it isn't always apparent which patients will gain the most benefit from these drugs -- some of that information is hidden in the imaging of the tumors and in numerical clues found in blood. Cambridge-1 can be key to helping fuse these different datasets, and building large models to help determine the best course of treatment for patients, Branson said.

An anonymous reader quotes a report from Motherboard: With only 30 minutes of audio, companies can now create a digital clone of your voice and make it say words you never said. Using machine learning, voice AI companies like VocaliD can create synthetic voices from a person's recorded speech -- adopting unique qualities like speaking rhythm, pronunciation of consonants and vowels, and intonation. For tech companies, the ability to generate any sentence with a realistic-sounding human voice is an exciting, cost-saving frontier. But for the voice actors whose recordings form the foundation of text-to-speech (TTS) voices, this technology threatens to disrupt their livelihoods, raising questions about fair compensation and human agency in the age of AI.

At the center of this reckoning is voice actress Bev Standing, who is suing TikTok after alleging the company used her voice for its text-to-speech feature without compensation or consent. This is not the first case like this; voice actress Susan Bennett discovered that audio she recorded for another company was repurposed to be the voice of Siri after Apple launched the feature in 2011. She was paid for the initial recording session but not for being Siri. Rallying behind Standing, voice actors donated to a GoFundMe that has raised nearly $7,000 towards her legal expenses and posted TikTok videos under the #StandingWithBev hashtag warning users about the feature. Standing's supporters say the TikTok lawsuit is not just about Standing's voice -- it's about the future of an entire industry attempting to adapt to new advancements in the field of machine learning.

Standing's case materializes some performers' worst fears about the control this technology gives companies over their voices. Her lawsuit claims TikTok did not pay or notify her to use her likeness for its text-to-speech feature, and that some videos using it voiced "foul and offensive language" causing "irreparable harm" to her reputation. Brands advertising on TikTok also had the text-to-speech voice at their disposal, meaning her voice could be used for explicitly commercial purposes. [...] Laws protecting individuals from unauthorized clones of their voices are also in their infancy. Standing's lawsuit invokes her right of publicity, which grants individuals the right to control commercial uses of their likeness, including their voice. In November 2020, New York became the first state to apply this right to digital replicas after years of advocacy from SAG-AFTRA, a performers' union. "We look to make sure that state rights of publicity are as strong as they can be, that any limitations on people being able to protect their image and voice are very narrowly drawn on first amendment lines," Jeffrey Bennett, a general counsel for SAG-AFTRA, told Motherboard. "We look at this as a potentially great right of publicity case for this voice professional whose voice is being used in a commercial manner without her consent."

An anonymous reader shares a report: For years YouTube's video-recommending algorithm has stood accused of fuelling a grab bag of societal ills by feeding users an AI-amplified diet of hate speech, political extremism and/or conspiracy junk/disinformation for the profiteering motive of trying to keep billions of eyeballs stuck to its ad inventory. And while YouTube's tech giant parent Google has, sporadically, responded to negative publicity flaring up around the algorithm's antisocial recommendations -- announcing a few policy tweaks or limiting/purging the odd hateful account -- it's not clear how far the platform's penchant for promoting horribly unhealthy clickbait has actually been rebooted. The suspicion remains nowhere near far enough.

New research published today by Mozilla backs that notion up, suggesting YouTube's AI continues to puff up piles of "bottom-feeding"/low-grade/divisive/disinforming content -- stuff that tries to grab eyeballs by triggering people's sense of outrage, sewing division/polarization or spreading baseless/harmful disinformation -- which in turn implies that YouTube's problem with recommending terrible stuff is indeed systemic; a side effect of the platform's rapacious appetite to harvest views to serve ads. That YouTube's AI is still -- per Mozilla's study -- behaving so badly also suggests Google has been pretty successful at fuzzing criticism with superficial claims of reform. The mainstay of its deflective success here is likely the primary protection mechanism of keeping the recommender engine's algorithmic workings (and associated data) hidden from public view and external oversight -- via the convenient shield of "commercial secrecy." But regulation that could help crack open proprietary AI blackboxes is now on the cards -- at least in Europe.

Reactions are starting to come in for GitHub's new Copilot coding tool, which one site calls "a product of the partnership between Microsoft and AI research and deployment company OpenAI — which Microsoft invested $1 billion into two years ago." According to the tech preview page: GitHub Copilot is currently only available as a Visual Studio Code extension. It works wherever Visual Studio Code works — on your machine or in the cloud on GitHub Codespaces. And it's fast enough to use as you type. "Copilot looks like a potentially fantastic learning tool — for developers of all abilities," said James Governor, an analyst at RedMonk. "It can remove barriers to entry. It can help with learning new languages, and for folks working on polyglot codebases. It arguably continues GitHub's rich heritage as a world-class learning tool. It's early days but AI-assisted programming is going to be a thing, and where better to start experiencing it than GitHub...?"

The issue of scale is a concern for GitHub, according to the tech preview FAQ: "If the technical preview is successful, our plan is to build a commercial version of GitHub Copilot in the future. We want to use the preview to learn how people use GitHub Copilot and what it takes to operate it at scale." GitHub spent the last year working closely with OpenAI to build Copilot. GitHub developers, along with some users inside Microsoft, have been using it every day internally for months.

[Guillermo Rauch, CEO of developer software provider Vercel, who also is founder of Vercel and creator of Next.js], cited in a tweet a statement from the Copilot tech preview FAQ page, "GitHub Copilot is a code synthesizer, not a search engine: the vast majority of the code that it suggests is uniquely generated and has never been seen before."

To that, Rauch simply typed: "The future."

Rauch's post is relevant in that one of the knocks against Copilot is that some folks seem to be concerned that it will generate code that is identical to code that has been generated under open source licenses that don't allow derivative works, but which will then be used by a developer unknowingly...
GitHub CEO Nat Friedman has responded to those concerns, according to another article, arguing that training an AI system constitutes fair use: Friedman is not alone — a couple of actual lawyers and experts in intellectual property law took up the issue and, at least in their preliminary analysis, tended to agree with Friedman... [U.K. solicitor] Neil Brown examines the idea from an English law perspective and, while he's not so sure about the idea of "fair use" if the idea is taken outside of the U.S., he points simply to GitHub's terms of service as evidence enough that the company can likely do what it's doing. Brown points to passage D4, which grants GitHub "the right to store, archive, parse, and display Your Content, and make incidental copies, as necessary to provide the Service, including improving the Service over time." "The license is broadly worded, and I'm confident that there is scope for argument, but if it turns out that Github does not require a license for its activities then, in respect of the code hosted on Github, I suspect it could make a reasonable case that the mandatory license grant in its terms covers this as against the uploader," writes Brown. Overall, though, Brown says that he has "more questions than answers."
Armin Ronacher, the creator of the Flask web framework for Python, shared an interesting example on Twitter (which apparently came from the game Quake III Arena) in which Copilot apparently reproduces a chunk of code including not only its original comment ("what the fuck?") but also its original copyright notice.

An anonymous reader quotes a report from BuzzFeed News: A government inquiry into federal agencies' deployment of facial recognition may have overlooked some organizations' use of popular biometric identification software Clearview AI, calling into question whether authorities can understand the extent to which the emerging technology has been used by taxpayer-funded entities. In a 92-page report published by the Government Accountability Office on Tuesday, five agencies -- the US Capitol Police, the US Probation Office, the Pentagon Force Protection Agency, Transportation Security Administration, and the Criminal Investigation Division at the Internal Revenue Service -- said they didn't use Clearview AI between April 2018 and March 2020. This, however, contradicts internal Clearview data previously reviewed by BuzzFeed News.

In April, BuzzFeed News revealed that those five agencies were among more than 1,800 US taxpayer-funded entities that had employees who tried or used Clearview AI, based on internal company data. As part of that story, BuzzFeed News published a searchable table disclosing all the federal, state, and city government organizations whose employees are listed in the data as having used the facial recognition software as of February 2020. While the GAO was tasked with "review[ing] federal law enforcement use of facial recognition technology," the discrepancies between the report, which was based on survey responses and BuzzFeed News' past reporting, suggest that even the US government may not be equipped to track how its own agencies access to surveillance tools like Clearview. The GAO report surveyed 42 federal agencies in total, 20 of which reported that they either owned their own facial recognition system or used one developed by a third party between April 2018 and March 2020. Ten federal agencies -- including Immigration and Customs Enforcement and Customs and Border Protection -- said they specifically used Clearview AI.

Microsoft on Tuesday announced an artificial intelligence system that can recommend code for software developers to use as they write code. From a report: Microsoft is looking to simplify the process of programming, the area where the company got its start in 1975. That could keep programmers who already use the company's tools satisfied and also attract new ones. The system, called GitHub Copilot, draws on source code uploaded to code-sharing service GitHub, which Microsoft acquired in 2018, as well as other websites. Microsoft and GitHub developed it with help from OpenAI, an AI research start-up that Microsoft backed in 2019.

Researchers at Microsoft and other institutions have been trying to teach computers to write code for decades. The concept has yet to go mainstream, at times because programs to write programs have not been versatile enough. The GitHub Copilot effort is a notable attempt in the field, relying as it does on a large volume of code in many programming languages and vast Azure cloud computing power. Nat Friedman, CEO of GitHub, describes GitHub Copilot as a virtual version of what software creators call a pair programmer -- that's when two developers work side by side collaboratively on the same project. The tool looks at existing code and comments in the current file and the location of the cursor, and it offers up one or more lines to add. As programmers accept or reject suggestions, the model learns and becomes more sophisticated over time. The new software makes coding faster, Friedman said in an interview last week. Hundreds of developers at GitHub have been using the Copilot feature all day while coding, and the majority of them are accepting suggestions and not turning the feature off, Friedman said.

"Organizations are beginning to develop, design, and manage their own AI-powered voice assistant systems independent of platforms such as Siri and Alexa," reports VentureBeat: The transition is being driven by the desire to manage the entirety of the user experience and integrate voice assistance into multiple business processes and brand environments, from call centers to stores. In a recent survey of 500 IT and business decision-makers in the U.S., France, Germany, and the U.K., 28% of respondents said they were using voice technologies and 84% expect to be using them in the next year.

To support the evolution, the Linux Foundation launched the Open Voice Network (OVN), an alliance advocating for the adoption of open standards across voice assistant apps in automobiles, smartphones, smart home devices, and more. With founding members Target, Schwarz Gruppe, Wegmans Food Markets, Microsoft, Veritone, Deutsche Telekom, and others, the OVN's goal — much like Amazon's Voice Interoperability Initiative — is to standardize the development and use of voice assistant systems and conversational agents that use technologies including automatic speech recognition, natural language processing, advanced dialog management, and machine learning... It was first announced as the Open Voice Initiative in 2019, but expanded significantly as the COVID-19 pandemic spurred enterprises to embrace digital transformation.

"Voice is expected to be a primary interface to the digital world, connecting users to billions of sites, smart environments and AI bots ... Key to enabling enterprise adoption of these capabilities and consumer comfort and familiarity is the implementation of open standards," Mike Dolan, SVP and general manager of projects at the Linux Foundation, said in a statement. "The potential impact of voice on industries including commerce, transportation, healthcare, and entertainment is staggering and we're excited to bring it under the open governance model of the Linux foundation to grow the community and pave a way forward."
Besides a focus on standards and technology-sharing, the group plans to collaborate with existing industry associations on regulatory/legislative issues — including data privacy."