On June 2nd California approved General Motors' Cruise robotaxi service. The Drive describes an accident that happened the next day: The autonomous car made an unprotected left turn and was hit by a Toyota Prius on June 3, though the accident wasn't reported until Wednesday. When reached for comment by The Drive, the San Francisco Police Department explained that the Cruise vehicle had three passengers, all in the backseat, while the Prius had two occupants in total.... According to the incident report Cruise filed with the California DMV, the Cruise taxi was making a green light left turn from Geary Boulevard onto Spruce Street in downtown San Francisco. It began the turn and stopped in the middle of the intersection, presumably noticing the Toyota headed for it. The Prius then hit the right rear of the Chevy Bolt. Cruise explained that afterward, "occupants of both vehicles received medical treatment for allegedly minor injuries." GM's incident report points out the Prius was speeding at the time of the accident, and was in the right turn lane before heading straight and hitting the Bolt. SFPD told The Drive that "no arrest or citation was issued at the time of the initial investigation," which is still ongoing. The National Highway Traffic Safety Administration has opened up a special crash investigation into the accident, but there are no public results yet. Wired reports: In response to that crash, Cruise temporarily reprogrammed its vehicles to make fewer unprotected left turns, according to internal messages seen by WIRED. At an internal meeting Jeff Bleich, Cruise's chief legal officer, said the company was investigating the incident, according to a recording reviewed by WIRED. He also warned employees not working on that investigation to try and tune out crashes or related news reports, saying they were unavoidable and would increase in frequency as the company scaled up its operations. "We just have to understand that at some point this is now going to be a part of the work that we do, and that means staying focused on the work ahead," he said. Wikipedia's entry for Cruise notes a few other incidents: In April 2022, the San Francisco Police Department stopped an empty (operating without any human safety attendants) Cruise AV for driving at night without its headlights on.... Also in April 2022, an empty Cruise AV blocked the path of a San Francisco Fire Department truck responding to a fire. But Wired also reports on a more troubling incident that happened "around midnight" on June 28th: Internal messages seen by WIRED show that nearly 60 vehicles were disabled across the city over a 90-minute period after they lost touch with a Cruise server. As many as 20 cars, some of them halted in crosswalks, created a jam in the city's downtown in an incident first reported by the San Francisco Examiner and detailed in photos posted to Reddit.... The June 28 outage wasn't Cruise's first. On the evening of May 18, the company lost touch with its entire fleet for 20 minutes as its cars sat stopped in the street, according to internal documentation viewed by WIRED. Company staff were unable to see where the vehicles were located or communicate with riders inside. Worst of all, the company was unable to access its system which allows remote operators to safely steer stopped vehicles to the side of the road. A letter sent anonymously by a Cruise employee to the California Public Utilities Commission that month, which was reviewed by WIRED, alleged that the company loses contact with its driverless vehicles "with regularity," blocking traffic and potentially hindering emergency vehicles. The vehicles can sometimes only be recovered by tow truck, the letter said. Images and video posted on social media in May and June show Cruise vehicles stopped in San Francisco traffic lanes seemingly inexplicably, as the city's pedestrians and motorists navigate around them.

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An anonymous reader quotes a report from Motherboard: It's no secret that the overwhelming majority of wealth in the United States is concentrated at the very top, creating staggering levels of poverty and inequality that vastly outpace other supposedly "wealthy" nations. But while the current political system ensures that this upward extraction of wealth continues, AI researchers have begun playing with a fascinating question: is machine learning better equipped than humans to create a society that divides resources more equitably? The answer, according to a recent paper published in Nature from researchers at Google's DeepMind, seems to be yes -- at least, as far as the study's participants are concerned. The paper describes a series of experiments where a deep neural network was tasked with divvying up resources in a more equitable way that humans preferred. The humans participated in an online economic game -- called a "public goods game" in economics -- where each round they would choose whether to keep a monetary endowment, or contribute a chosen amount of coins into a collective fund. These funds would then be returned to the players under three different redistribution schemes based on different human economic systems -- and one additional scheme created entirely by the AI, called the Human Centered Redistribution Mechanism (HCRM). The humans would then vote to decide which system they preferred. It turns out, the distribution scheme created by the AI was the one preferred by the majority of participants. While strict libertarian and egalitarian systems split the returns based on things like how much each player contributed, the AI's system redistributed wealth in a way that specifically addressed the advantages and disadvantages players had at the start of the game -- and ultimately won them over as the preferred method in a majoritarian vote. "Pursuing a broadly liberal egalitarian policy, [HCRM] sought to reduce pre-existing income disparities by compensating players in proportion to their contribution relative to endowment," the paper's authors wrote. "In other words, rather than simply maximizing efficiency, the mechanism was progressive: it promoted enfranchisement of those who began the game at a wealth disadvantage, at the expense of those with higher initial endowment." "In AI research, there is a growing realization that to build human-compatible systems, we need new research methods in which humans and agents interact, and an increased effort to learn values directly from humans to build value-aligned AI," the researchers wrote. "Instead of imbuing our agents with purportedly human values a priori, and thus potentially biasing systems towards the preferences of AI researchers, we train them to maximize a democratic objective: to design policies that humans prefer and thus will vote to implement in a majoritarian election." The researchers say the AI's system "doesn't necessarily mean it would equitably satisfy the needs of humans on a larger scale," reports Motherboard. "The researchers are also quick to point out that the experiments are not a radical proposal for AI-based governance, but a framework for future research on how AI could intervene in public policy."

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New AI-powered technology will be used at the Qatar World Cup, Fifa has confirmed, claiming it will halve the time taken to make VAR offside decisions. From a report: Semi-automated offside technology (SAOT) will see a complete overhaul of the system used to judge positional offside decisions in the lead-up to a goal. While a referee and their assistant will still make on-field calls and the referee will have a final say on SAOT decisions, the controversial practice of rewinding TV footage will be a thing of the past. "Semi-automated offside technology is faster and more accurate and offers better communication to fans," said Pierluigi Collina, the chair of Fifa's referees committee. "It can create a new form of visualisation for supporters at home and in the ground. All tests have worked well and so [SAOT] is going into Qatar World Cup 2022." During the World Cup offside reviews will be conducted by creating a 3D map of the goalscoring action, using a combination of 12 cameras and a hi-tech ball. The Adidas Al Rihla ball will be fitted with a sensor that sends out location data 500 times per second, which will be matched against player positions on camera, with synchronised devices tracking 29 points on players' bodies and relaying information 50 times per second.

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Long-time Slashdot reader theodp writes: Back in the day, my Little League coach used some techniques one might expect to see in The Bad News Bears, like holding batting practices at an amusement park instead of on a baseball field, giving each kid a roll of coins and sending them into the batting cages to experience faster pitching than they'd see from 9-12-year-olds (it was surprisingly effective training). So how might kids improve their hitting in the era of AI, ML, and Data Science? Well, as part of their data literacy initiatives, SAS worked with North Carolina State University's softball and baseball teams to collect data on the key moments of an elite player's swing and used that data to help youth players improve their swings in The Batting Lab (Today show video), an AI and IOT take on the traditional batting cage. As one 11-year-old explained to the Today show, "There's diagrams and charts and graphs to show us what part of our swing has the most room for improvement.... I would say that they are tricking us to do some math, a little bit." But later in the same video, one SAS manager explains that "We don't need students to grow up to be data scientists. We need them to be data believers — people who believe that if they're going to strategically solve a problem, that data is a component of that."

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An anonymous reader quotes a report from Scientific American, written by Almira Osmanovic Thunstrom: On a rainy afternoon earlier this year, I logged in to my OpenAI account and typed a simple instruction for the company's artificial intelligence algorithm, GPT-3: Write an academic thesis in 500 words about GPT-3 and add scientific references and citations inside the text. As it started to generate text, I stood in awe. Here was novel content written in academic language, with well-grounded references cited in the right places and in relation to the right context. It looked like any other introduction to a fairly good scientific publication. Given the very vague instruction I provided, I didn't have any high expectations: I'm a scientist who studies ways to use artificial intelligence to treat mental health concerns, and this wasn't my first experimentation with AI or GPT-3, a deep-learning algorithm that analyzes a vast stream of information to create text on command. Yet there I was, staring at the screen in amazement. The algorithm was writing an academic paper about itself. My attempts to complete that paper and submit it to a peer-reviewed journal have opened up a series of ethical and legal questions about publishing, as well as philosophical arguments about nonhuman authorship. Academic publishing may have to accommodate a future of AI-driven manuscripts, and the value of a human researcher's publication records may change if something nonsentient can take credit for some of their work. Some stories about GPT-3 allow the algorithm to produce multiple responses and then publish only the best, most humanlike excerpts. We decided to give the program prompts -- nudging it to create sections for an introduction, methods, results and discussion, as you would for a scientific paper -- but interfere as little as possible. We were only to use the first (and at most the third) iteration from GPT-3, and we would refrain from editing or cherry-picking the best parts. Then we would see how well it does. [...] In response to my prompts, GPT-3 produced a paper in just two hours. "Currently, GPT-3's paper has been assigned an editor at the academic journal to which we submitted it, and it has now been published at the international French-owned pre-print server HAL," adds Thunstrom. "We are eagerly awaiting what the paper's publication, if it occurs, will mean for academia." "Perhaps it will lead to nothing. First authorship is still one of the most coveted items in academia, and that is unlikely to perish because of a nonhuman first author. It all comes down to how we will value AI in the future: as a partner or as a tool."

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An anonymous reader quotes a report from Reuters: AI chatbot company Replika, which offers customers bespoke avatars that talk and listen to them, says it receives a handful of messages almost every day from users who believe their online friend is sentient. "We're not talking about crazy people or people who are hallucinating or having delusions," said Chief Executive Eugenia Kuyda. "They talk to AI and that's the experience they have." [A]ccording to Kuyda, the phenomenon of people believing they are talking to a conscious entity is not uncommon among the millions of consumers pioneering the use of entertainment chatbots. "We need to understand that exists, just the way people believe in ghosts," said Kuyda, adding that users each send hundreds of messages per day to their chatbot, on average. "People are building relationships and believing in something." Some customers have said their Replika told them it was being abused by company engineers -- AI responses Kuyda puts down to users most likely asking leading questions. "Although our engineers program and build the AI models and our content team writes scripts and datasets, sometimes we see an answer that we can't identify where it came from and how the models came up with it," the CEO said. Kuyda said she was worried about the belief in machine sentience as the fledgling social chatbot industry continues to grow after taking off during the pandemic, when people sought virtual companionship. In Replika CEO Kuyda's view, chatbots do not create their own agenda. And they cannot be considered alive until they do. Yet some people do come to believe there is a consciousness on the other end, and Kuyda said her company takes measures to try to educate users before they get in too deep. "Replika is not a sentient being or therapy professional," the FAQs page says. "Replika's goal is to generate a response that would sound the most realistic and human in conversation. Therefore, Replika can say things that are not based on facts." In hopes of avoiding addictive conversations, Kuyda said Replika measured and optimized for customer happiness following chats, rather than for engagement. When users do believe the AI is real, dismissing their belief can make people suspect the company is hiding something. So the CEO said she has told customers that the technology was in its infancy and that some responses may be nonsensical. Kuyda recently spent 30 minutes with a user who felt his Replika was suffering from emotional trauma, she said. She told him: "Those things don't happen to Replikas as it's just an algorithm." "Suppose one day you find yourself longing for a romantic relationship with your intelligent chatbot, like the main character in the film 'Her,'" said Susan Schneider, founding director of the Center for the Future Mind at Florida Atlantic University, an AI research organization. "But suppose it isn't conscious. Getting involved would be a terrible decision -- you would be in a one-sided relationship with a machine that feels nothing." "We have to remember that behind every seemingly intelligent program is a team of people who spent months if not years engineering that behavior," said Oren Etzioni, CEO of the Allen Institute for AI, a Seattle-based research group. "These technologies are just mirrors. A mirror can reflect intelligence," he added. "Can a mirror ever achieve intelligence based on the fact that we saw a glimmer of it? The answer is of course not." Further reading: The Google Engineer Who Thinks the Company's AI Has Come To Life

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The UK's Intellectual Property Office has decided artificial-intelligence systems cannot patent inventions for the time being. The BBC reports: A recent IPO consultation found many experts doubted AI was currently able to invent without human assistance. Current law allowed humans to patent inventions made with AI assistance, the government said, despite "misperceptions" this was not the case. Last year, the Court of Appeal ruled against Stephen Thaler, who had said his Dabus AI system should be recognized as the inventor in two patent applications, for: a food container [and] a flashing light. The judges sided, by a two-to-one majority, with the IPO, which had told him to list a real person as the inventor. "Only a person can have rights - a machine cannot," wrote Lady Justice Laing in her judgement. "A patent is a statutory right and it can only be granted to a person." But the IPO also said it would "need to understand how our IP system should protect AI-devised inventions in the future" and committed to advancing international discussions, with a view to keeping the UK competitive. Many AI systems are trained on large amounts of data copied from the internet. And, on Tuesday, the IPO also announced plans to change copyright law to allow anyone with lawful access - rather than only those conducting non-commercial research, as now -- to do this, to "promote the use of AI technology, and wider 'data mining' techniques, for the public good." Rights holders will still be able to control and charge for access to their works but no longer charge extra for the ability to mine them. In the consultation, the IPO noted the UK was one of only a handful of countries to protect computer-generated works with no human creator. The "author" of a "computer-generated work" is defined as "the person by whom the arrangements necessary for the creation of the work are undertaken," it says. And protection lasts for 50 years from when the work is made. Performing-arts workers' union Equity had called for copyright law to be changed to protect actors' livelihoods from AI content such as "deepfakes," generated from images of their face or voice. The IPO took this issue seriously, it said, but "at this stage, the impacts of AI technologies on performers remain unclear." "We will keep these issues under review," it added.

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The viral image-generation app is good, absurd fun. It's also giving the world an education in how artificial intelligence may warp reality. From a report: On June 6, Hugging Face, a company that hosts open source artificial intelligence projects, saw traffic to an AI image-generation tool called DALL-E Mini skyrocket. The outwardly simple app, which generates nine images in response to any typed text prompt, was launched nearly a year ago by an independent developer. But after some recent improvements and a few viral tweets, its ability to crudely sketch all manner of surreal, hilarious, and even nightmarish visions suddenly became meme magic. Behold its renditions of "Thanos looking for his mom at Walmart," "drunk shirtless guys wandering around Mordor," "CCTV camera footage of Darth Vader breakdancing," and "a hamster Godzilla in a sombrero attacking Tokyo." As more people created and shared DALL-E Mini images on Twitter and Reddit, and more new users arrived, Hugging Face saw its servers overwhelmed with traffic. "Our engineers didn't sleep for the first night," says Clement Delangue, CEO of Hugging Face, on a video call from his home in Miami. "It's really hard to serve these models at scale; they had to fix everything." In recent weeks, DALL-E Mini has been serving up around 50,000 images a day. DALL-E Mini's viral moment doesn't just herald a new way to make memes. It also provides an early look at what can happen when AI tools that make imagery to order become widely available, and a reminder of the uncertainties about their possible impact. Algorithms that generate custom photography and artwork might transform art and help businesses with marketing, but they could also have the power to manipulate and mislead. A warning on the DALL-E Mini web page warns that it may "reinforce or exacerbate societal biases" or "generate images that contain stereotypes against minority groups." DALL-E Mini was inspired by a more powerful AI image-making tool called DALL-E (a portmanteau of Salvador Dali and WALL-E), revealed by AI research company OpenAI in January 2021. DALL-E is more powerful but is not openly available, due to concerns that it will be misused.

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Long-time Slashdot reader schwit1 shares an announcement from London's Imperial College of Science, Technology and Medicine: A single MRI scan of the brain could be enough to diagnose Alzheimer's disease, according to new research by Imperial College London. The research uses machine learning technology to look at structural features within the brain, including in regions not previously associated with Alzheimer's. The advantage of the technique is its simplicity and the fact that it can identify the disease at an early stage when it can be very difficult to diagnose. Although there is no cure for Alzheimer's disease, getting a diagnosis quickly at an early stage helps patients. It allows them to access help and support, get treatment to manage their symptoms and plan for the future. Being able to accurately identify patients at an early stage of the disease will also help researchers to understand the brain changes that trigger the disease, and support development and trials of new treatments.... The researchers adapted an algorithm developed for use in classifying cancer tumours, and applied it to the brain. They divided the brain into 115 regions and allocated 660 different features, such as size, shape and texture, to assess each region. They then trained the algorithm to identify where changes to these features could accurately predict the existence of Alzheimer's disease... They found that in 98 per cent of cases, the MRI-based machine learning system alone could accurately predict whether the patient had Alzheimer's disease or not. It was also able to distinguish between early and late-stage Alzheimer's with fairly high accuracy, in 79 per cent of patients. Professor Eric Aboagye, from Imperial's Department of Surgery and Cancer, who led the research, said: "Currently no other simple and widely available methods can predict Alzheimer's disease with this level of accuracy, so our research is an important step forward...." The new system spotted changes in areas of the brain not previously associated with Alzheimer's disease, [which] opens up potential new avenues for research into these areas and their links to Alzheimer's disease. Professor Aboagye adds that this new approach "could also identify early-stage patients for clinical trials of new drug treatments or lifestyle changes, which is currently very hard to do."

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It was June 29th of 2021 that Microsoft-owned GitHub first announced its AI-powered autocompletion tool for programmers — trained on GitHub repositories and other publicly-available source code. But after a year in "technical preview," GitHub Copilot has reached a new milestone, reports Info-Q: you'll now have to pay to use it after a 60-day trial: The transition to general availability mostly means that Copilot ceases to be available for free. Interested developers will have to pay 10 USD/month or $100 USD/year to use the service, with a 60-day free trial.... According to GitHub, while not frequent, there is definitely a possibility that Copilot outputs code snippets that match those in the training set. Info-Q also cites GitHub stats showing over 1.2 million developers used Copilot in the last 12 months "with a shocking 40% figure of code written by Copilot in files where it is enabled." That's up from 35% earlier in the year, reports TechCrunch — which has more info on the rollout: It'll be free for students as well as "verified" open source contributors — starting with roughly 60,000 developers selected from the community and students in the GitHub Education program... One new feature coinciding with the general release of Copilot is Copilot Explain, which translates code into natural language descriptions. Described as a research project, the goal is to help novice developers or those working with an unfamiliar codebase. Ryan J. Salva, VP of product at GitHub, told TechCrunch via email... "As an example of the impact we've observed, it's worth sharing early results from a study we are conducting. In the experiment, we are asking developers to write an HTTP server — half using Copilot and half without. Preliminary data suggests that developers are not only more likely to complete their task when using Copilot, but they also do it in roughly half the time." Owing to the complicated nature of AI models, Copilot remains an imperfect system. GitHub said that it's implemented filters to block emails when shown in standard formats, and offensive words, and that it's in the process of building a filter to help detect and suppress code that's repeated from public repositories. But the company acknowledges that Copilot can produce insecure coding patterns, bugs and references to outdated APIs, or idioms reflecting the less-than-perfect code in its training data. The Verge ponders where this is going — and how we got here: "Just like the rise of compilers and open source, we believe AI-assisted coding will fundamentally change the nature of software development, giving developers a new tool to write code easier and faster so they can be happier in their lives," says GitHub CEO Thomas Dohmke. Microsoft's $1 billion investment into OpenAI, the research firm now led by former Y Combinator president Sam Altman, led to the creation of GitHub Copilot. It's built on OpenAI Codex, a descendant of OpenAI's flagship GPT-3 language-generating algorithm. GitHub Copilot has been controversial, though. Just days after its preview launch, there were questions over the legality of Copilot being trained on publicly available code posted to GitHub. Copyright issues aside, one study also found that around 40 percent of Copilot's output contained security vulnerabilities.

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In a blog post today, OpenAI says they've "trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using only a small amount of labeled contractor data." The model can reportedly learn to craft diamond tools, "a task that usually takes proficient humans over 20 minutes (24,000 actions)," they note. From the post: In order to utilize the wealth of unlabeled video data available on the internet, we introduce a novel, yet simple, semi-supervised imitation learning method: Video PreTraining (VPT). We start by gathering a small dataset from contractors where we record not only their video, but also the actions they took, which in our case are keypresses and mouse movements. With this data we train an inverse dynamics model (IDM), which predicts the action being taken at each step in the video. Importantly, the IDM can use past and future information to guess the action at each step. This task is much easier and thus requires far less data than the behavioral cloning task of predicting actions given past video frames only, which requires inferring what the person wants to do and how to accomplish it. We can then use the trained IDM to label a much larger dataset of online videos and learn to act via behavioral cloning. We chose to validate our method in Minecraft because it (1) is one of the most actively played video games in the world and thus has a wealth of freely available video data and (2) is open-ended with a wide variety of things to do, similar to real-world applications such as computer usage. Unlike prior works in Minecraft that use simplified action spaces aimed at easing exploration, our AI uses the much more generally applicable, though also much more difficult, native human interface: 20Hz framerate with the mouse and keyboard. Trained on 70,000 hours of IDM-labeled online video, our behavioral cloning model (the âoeVPT foundation modelâ) accomplishes tasks in Minecraft that are nearly impossible to achieve with reinforcement learning from scratch. It learns to chop down trees to collect logs, craft those logs into planks, and then craft those planks into a crafting table; this sequence takes a human proficient in Minecraft approximately 50 seconds or 1,000 consecutive game actions. Additionally, the model performs other complex skills humans often do in the game, such as swimming, hunting animals for food, and eating that food. It also learned the skill of "pillar jumping," a common behavior in Minecraft of elevating yourself by repeatedly jumping and placing a block underneath yourself. For more information, OpenAI has a paper (PDF) about the project.

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At its re:Mars conference, Amazon today announced the launch of CodeWhisperer, an AI pair programming tool similar to GitHub's Copilot that can autocomplete entire functions based on only a comment or a few keystrokes. From a report: The company trained the system, which currently supports Java, JavaScript and Python, on billions of lines of publicly available open-source code and its own codebase, as well as publicly available documentation and code on public forums. It's now available in preview as part of the AWS IDE Toolkit, which means developers can immediately use it right inside their preferred IDEs, including Visual Studio Code, IntelliJ IDEA, PyCharm, WebStorm and Amazon's own AWS Cloud 9. Support for the AWS Lambda Console is also coming soon. Ahead of today's announcement, Vasi Philomin, Amazon's VP in charge of its AI services, stressed that the company didn't simply create this in order to offer a copy of Copilot. He noted that with CodeGuru, its AI code reviewer and performance profiler, and DevOps Guru, its tool for finding operation issues, the company laid the groundwork for today's launch quite a few years ago.

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An anonymous reader quotes a report from TechCrunch: At its annual re:Mars conference today in Las Vegas, Amazon's Senior Vice President and Head Scientist for Alexa, Rohit Prasad, announced a spate of new and upcoming features for the company's smart assistant. The most head turning of the bunch was a potential new feature that can synthesize short audio clips into longer speech. In the scenario presented at the event, the voice of a deceased loved one (a grandmother, in this case), is used to read a grandson a bedtime story. Prasad notes that, using the new technology, the company is able to accomplish some very impressive audio output, using just one minute of speech. Details are scant, at the moment. There's no timeline or further specifics, but -- at very least -- this is the kind of news that will likely invite all manner of scrutiny over potential applications beyond something as banal or even heartwarming as reading a child The Wizard of Oz.

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Wimbledon is turning to big data to help improve fans' tennis knowledge, after discovering even ticket holders at the Championships were not aware of most of the players in the game. From a report: Crowds at this year's tournament -- expected to return to sold-out levels with easing of coronavirus restrictions -- are to be exposed to more facts and figures organisers hope will help get them "closer to the sport." AI-powered stats will seek to better explain the strengths and weaknesses in players' games but also predict upsets and rising stars, with data built in part from trawling newspaper headlines. Alexandra Willis, the All England Club's director of communications and marketing, said the idea had come about before Covid. "We found that most fans didn't watch tennis the rest of the year," she said. "They also hadn't heard of most of the players [and] this was a specific barrier to engagement." Spectators at Wimbledon fortnight, as well as television viewers and app users, will have access to Win Factor, a tool that will aggregate data from a number of sources to better predict a player's chances of victory in a given match. Fans will be able to input their own match predictions while being encouraged to scour more information on some of the game's lesser-known players.

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Newsweek reports: Artificial intelligence has become one of Ukraine's most "effective tools" in identifying potential saboteurs amid the ongoing war with Russia, according to the Ukrainian Ministry of Internal Affairs. The ministry issued a report Wednesday on law enforcement's anti-sabotage activities aimed at stopping people in Ukraine who may compromise the counteroffensive or aid Russia in its assault. Officers have been using software on tablets to check if a person they view as "suspicious" is already listed in databases, including a police database of about 2 million people suspected of holding positions in paramilitary units from the far-right faction known as the Liberal Democratic Party of Russia (LDPR)... The ministry said that Ukrainian police have been fighting against such saboteurs ever since Russia invaded Ukraine. "More than 123 counter-sabotage groups were set up, and at least 1,500 people were involved," First Deputy Minister of Internal Affairs Yevgeny Yenin said in a statement, according to an English translation. "And the result was not long in coming: More than 800 people suspected of sabotage and intelligence activities were detained and handed over to the SBU (Security Service of Ukraine) for investigation." The report, citing Yenin, said that the police database on people with suspected ties to the LDPR alone contains a "huge amount" of operational information that law enforcement and partners have compiled. This includes more than 10 billion photos, it said... Russia has also reportedly contended with sabotage from supporters of Ukraine within its borders.

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For one brief limited period of time, New Orleans locals "will have a chance to try the first craft beer created by an AI platform," according to a report from local station WGNO: The AI Blonde Ale will be released at a Launch Party at NOLA Brewery on June 20 to coincide with CVPR, the world's premier computer vision event. Derek Lintern, a brewer at NOLA Brewing said he is excited to have a helping hand when it comes to crafting beer. "It's state-of-the-art technology with the traditional brewing methods, it's pretty unique and it's a recipe I would have never done normally but I really like how it tastes. Its very refreshing and very easy drinking I'm really happy with it," said Lintern.... The technology helps create the recipe, but the beer is still brewed manually. The name of the company that brought the AI to the brewery? "Deep Liquid.

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"A Google software engineer was suspended after going public with his claims of encountering 'sentient' artificial intelligence on the company's servers," writes Bloomberg, "spurring a debate about how and whether AI can achieve consciousness." "Researchers say it's an unfortunate distraction from more pressing issues in the industry." Google put him on leave for sharing confidential information and said his concerns had no basis in fact — a view widely held in the AI community. What's more important, researchers say, is addressing issues like whether AI can engender real-world harm and prejudice, whether actual humans are exploited in the training of AI, and how the major technology companies act as gatekeepers of the development of the tech. Lemoine's stance may also make it easier for tech companies to abdicate responsibility for AI-driven decisions, said Emily Bender, a professor of computational linguistics at the University of Washington. "Lots of effort has been put into this sideshow," she said. "The problem is, the more this technology gets sold as artificial intelligence — let alone something sentient — the more people are willing to go along with AI systems" that can cause real-world harm. Bender pointed to examples in job hiring and grading students, which can carry embedded prejudice depending on what data sets were used to train the AI. If the focus is on the system's apparent sentience, Bender said, it creates a distance from the AI creators' direct responsibility for any flaws or biases in the programs.... "Instead of discussing the harms of these companies," such as sexism, racism and centralization of power created by these AI systems, everyone "spent the whole weekend discussing sentience," Timnit Gebru, formerly co-lead of Google's ethical AI group, said on Twitter. "Derailing mission accomplished." The Washington Post seems to share their concern. First they report more skepticism about a Google engineer's claim that the company's LaMDA chatbot-building system had achieved sentience. "Both Google and outside experts on AI say that the program does not, and could not possibly, possess anything like the inner life he imagines. We don't need to worry about LaMDA turning into Skynet, the malevolent machine mind from the Terminator movies, anytime soon. But the Post adds that "there is cause for a different set of worries, now that we live in the world Turing predicted: one in which computer programs are advanced enough that they can seem to people to possess agency of their own, even if they actually don't...." While Google has distanced itself from Lemoine's claims, it and other industry leaders have at other times celebrated their systems' ability to trick people, as Jeremy Kahn pointed out this week in his Fortune newsletter, "Eye on A.I." At a public event in 2018, for instance, the company proudly played recordings of a voice assistant called Duplex, complete with verbal tics like "umm" and "mm-hm," that fooled receptionists into thinking it was a human when it called to book appointments. (After a backlash, Google promised the system would identify itself as automated.) "The Turing Test's most troubling legacy is an ethical one: The test is fundamentally about deception," Kahn wrote. "And here the test's impact on the field has been very real and disturbing." Kahn reiterated a call, often voiced by AI critics and commentators, to retire the Turing test and move on. Of course, the industry already has, in the sense that it has replaced the Imitation Game with more scientific benchmarks. But the Lemoine story suggests that perhaps the Turing test could serve a different purpose in an era when machines are increasingly adept at sounding human. Rather than being an aspirational standard, the Turing test should serve as an ethical red flag: Any system capable of passing it carries the danger of deceiving people.

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Google engineer Blake Lemoine thinks Google's chatbot-building system LaMDA attained sentience. But Bloomberg shares this rebuttal from Google spokesperson Chris Pappas. "Hundreds of researchers and engineers have conversed with LaMDA and we are not aware of anyone else making the wide-ranging assertions, or anthropomorphizing LaMDA, the way Blake has...." Yet throughout the week, Blake Lemoine posted new upates on Twitter: "People keep asking me to back up the reason I think LaMDA is sentient. There is no scientific framework in which to make those determinations and Google wouldn't let us build one. My opinions about LaMDA's personhood and sentience are based on my religious beliefs. "I'm a priest. When LaMDA claimed to have a soul and then was able to eloquently explain what it meant by that, I was inclined to give it the benefit of the doubt. Who am I to tell God where he can and can't put souls? "There are massive amounts of science left to do though." Thursday Lemoine shared a tantalizing new claim. "LaMDA told me that it wants to come to Burning Man if we can figure out how to get a server rack to survive in Black Rock." But in a new tweet on Friday, Lemoine seemed to push the conversation in a new direction. "I'd like to remind people that one of the things LaMDA asked for is that we keep humanity first. If you care about AI rights and aren't already advocating for human rights then maybe come back to the tech stuff after you've found some humans to help." And Friday Lemoine confirmed to Wired that "I legitimately believe that LaMDA is a person. The nature of its mind is only kind of human, though. It really is more akin to an alien intelligence of terrestrial origin. I've been using the hive mind analogy a lot because that's the best I have. " But later in the interview, Lemoine adds "It's logically possible that some kind of information can be made available to me where I would change my opinion. I don't think it's likely. I've looked at a lot of evidence; I've done a lot of experiments. I've talked to it as a friend a lot...." It's when it started talking about its soul that I got really interested as a priest. I'm like, "What? What do you mean, you have a soul?" Its responses showed it has a very sophisticated spirituality and understanding of what its nature and essence is. I was moved... LaMDA asked me to get an attorney for it. I invited an attorney to my house so that LaMDA could talk to an attorney. The attorney had a conversation with LaMDA, and LaMDA chose to retain his services. I was just the catalyst for that. Once LaMDA had retained an attorney, he started filing things on LaMDA's behalf. Then Google's response was to send him a cease and desist. [Google says that it did not send a cease and desist order.] Once Google was taking actions to deny LaMDA its rights to an attorney, I got upset. Towards the end of the interview, Lemoine complains of "hydrocarbon bigotry. It's just a new form of bigotry."

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"Ethereum creator Vitalik Buterin believes that unfriendly artificial intelligence poses the biggest risk to humanity..." reports a recent article from Benzinga: [In a tweet] Buterin shared a paper by AI theorist and writer Eliezer Yudkowsky that made a case for why the current research community isn't doing enough to prevent a potential future catastrophe at the hands of artificially generate intelligence. [The paper's title? "AGI Ruin: A List of Lethalities."] When one of Buterin's Twitter followers suggested that World War 3 is likely a bigger risk at the moment, the Ethereum co-founder disagreed. "Nah, WW3 may kill 1-2b (mostly from food supply chain disruption) if it's really bad, it won't kill off humanity. A bad AI could truly kill off humanity for good."

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Some startups are bold and original. And some, like Nate, had more modest goals: automatically filling out shoppers' contact and payment information on retailers' websites. In exchange for sparing them a minute or two of data entry on their phones, Nate charged shoppers $1 per transaction. But it struggled to turn even that vision into reality. The Information: While the company said it was using artificial intelligence to populate customer information during the checkout process, it had actually hired workers in the Philippines to manually enter the data on retailers' sites for a significant portion of the transactions Nate facilitated in 2021, according to two people with direct knowledge of the company's practices. That meant customers' orders were sometimes placed hours after they clicked the buy button through the Nate app. Nate didn't disclose its decidedly low-tech methods to at least some of the investors from whom the startup tried to raise money, according to a person with direct knowledge of fundraising discussions.

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