Long-time Slashdot reader gregstumph writes: Dr.Fill, a software program that solves crossword puzzles, finished in first place at the 2021 American Crossword Puzzle Tournament, for the first time ever (its previous best was 11th place in 2017). Dr.Fill, created by Matt Ginsberg, has been participating as a non-competitor at the tournament since 2012. This year, Ginsberg made improvements to Dr.Fill with the assistance of a team from the Berkeley NLP Group. The program finished "a scant 15 points ahead of Erik Agard on the main block of puzzles 1-7," Ginsberg posted on Facebook. This was followed by "then solving the playoff puzzle perfectly in 49 seconds" (while according to Wikipedia the fastest human competitor, Tyler Hinman, took three minutes to solve the puzzle). The Facebook post adds graciously, "Total kudos to Erik, the true winner of puzzles 1-7, and to Tyler Hinman, the winner of the event itself."

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An anonymous reader quotes a report from The New York Times: The European Union unveiled strict regulations on Wednesday to govern the use of artificial intelligence, a first-of-its-kind policy that outlines how companies and governments can use a technology seen as one of the most significant, but ethically fraught, scientific breakthroughs in recent memory. The draft rules would set limits around the use of artificial intelligence in a range of activities, from self-driving cars to hiring decisions, bank lending, school enrollment selections and the scoring of exams. It would also cover the use of artificial intelligence by law enforcement and court systems -- areas considered "high risk" because they could threaten people's safety or fundamental rights. Some uses would be banned altogether, including live facial recognition in public spaces, though there would be several exemptions for national security and other purposes. The108-page policy is an attempt to regulate an emerging technology before it becomes mainstream. The rules have far-reaching implications for major technology companies that have poured resources into developing artificial intelligence, including Amazon, Google, Facebook and Microsoft, but also scores of other companies that use the software to develop medicine, underwrite insurance policies and judge credit worthiness. Governments have used versions of the technology in criminal justice and the allocation of public services like income support. Companies that violate the new regulations, which could take several years to move through the European Union policymaking process, could face fines of up to 6 percent of global sales. The European Union regulations would require companies providing artificial intelligence in high-risk areas to provide regulators with proof of its safety, including risk assessments and documentation explaining how the technology is making decisions. The companies must also guarantee human oversight in how the systems are created and used. Some applications, like chatbots that provide humanlike conversation in customer service situations, and software that creates hard-to-detect manipulated images like "deepfakes," would have to make clear to users that what they were seeing was computer generated. [...] Release of the draft law by the European Commission, the bloc's executive body, drew a mixed reaction. Many industry groups expressed relief that the regulations were not more stringent, while civil society groups said they should have gone further.

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Translation tools from Google and other companies could be contributing to significant misunderstanding of legal terms with conflicting meanings such as "enjoin," according to research due to be presented at an academic workshop on Monday. From a report: Google's translation software turns an English sentence about a court enjoining violence, or banning it, into one in the Indian language of Kannada that implies the court ordered violence, according to the new study. "Enjoin" can refer to either promoting or restraining an action. Mistranslations also arise with other contronyms, or words with contradictory meanings depending on context, including "all over," "eventual" and "garnish," the paper said. Google said machine translation is "is still just a complement to specialized professional translation" and that it is "continually researching improvements, from better handling ambiguous language, to mitigating bias, to making large quality gains for under-resourced languages." The study's findings add to scrutiny of automated translations generated by artificial intelligence software. Researchers previously have found programs that learn translations by studying non-diverse text perpetuate historical gender biases, such as associating "doctor" with "he." The new paper raises concerns about a popular method companies use to broaden the vocabulary of their translation software. They translate foreign text into English and then back into the foreign language, aiming to teach the software to associate similar ways of saying the same phrase.

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Several U.S. banks have started deploying camera software that can analyze customer preferences, monitor workers and spot people sleeping near ATMs, even as they remain wary about possible backlash over increased surveillance, Reuters reported Monday, citing more than a dozen banking and technology sources. From the report: Previously unreported trials at City National Bank of Florida and JPMorgan Chase & Co as well as earlier rollouts at banks such as Wells Fargo & Co offer a rare view into the potential U.S. financial institutions see in facial recognition and related artificial intelligence systems. Widespread deployment of such visual AI tools in the heavily regulated banking sector would be a significant step toward their becoming mainstream in corporate America. Bobby Dominguez, chief information security officer at City National, said smartphones that unlock via a face scan have paved the way. "We're already leveraging facial recognition on mobile," he said. "Why not leverage it in the real world?" City National will begin facial recognition trials early next year to identify customers at teller machines and employees at branches, aiming to replace clunky and less secure authentication measures at its 31 sites, Dominguez said. Eventually, the software could spot people on government watch lists, he said. JPMorgan said it is "conducting a small test of video analytic technology with a handful of branches in Ohio." Wells Fargo said it works to prevent fraud but declined to discuss how.

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"Jensen Huang, the CEO of Nvidia, the nation's most valuable semiconductor company, with a stock price of $645 a share and a market cap of $400 billion, is out to create the metaverse," writes Time magazine. Huang defines it as "a virtual world that is a digital twin of ours." Huang credits author Neal Stephenson's Snow Crash, filled with collectives of shared 3-D spaces and virtually enhanced physical spaces that are extensions of the Internet, for conjuring the metaverse. This is already playing out with the massively popular online games like Fortnite and Minecraft, where users create richly imagined virtual worlds. Now the concept is being put to work by Nvidia and others. Partnering with Nvidia, BMW is using a virtual digital twin of a factory in Regensburg, Germany, to virtually plan new workflows before deploying the changes in real time in their physical factory. The metaverse, says Huang, "is where we will create the future" and transform how the world's biggest industries operate... Not to make any value judgments about the importance of video games, but do you find it ironic that a company that has its roots in entertainment is now providing vitally important computing power for drug discovery, basic research and reinventing manufacturing? No, not at all. It's actually the opposite. We always started as a computing company. It just turned out that our first killer app was video games... How important is the advent and the adaptation of digital twins for manufacturing, business and society at large? In the future, the digital world or the virtual world will be thousands of times bigger than the physical world. There will be a new New York City. There'll be a new Shanghai. Every single factory and every single building will have a digital twin that will simulate and track the physical version of it. Always. By doing so, engineers and software programmers could simulate new software that will ultimately run in the physical version of the car, the physical version of the robot, the physical version of the airport, the physical version of the building. All of the software that's going to be running in these physical things will be simulated in the digital twin first, and then it will be downloaded into the physical version. And as a result, the product keeps getting better at an exponential rate. The second thing is, you're going to be able to go in and out of the two worlds through wormholes. We'll go into the virtual world using virtual reality, and the objects in the virtual world, in the digital world, will come into the physical world, using augmented reality. So what's going to happen is pieces of the digital world will be temporarily, or even semipermanently, augmenting our physical world. It's ultimately about the fusion of the virtual world and the physical world. See also this possibly related story, "Nvidia's newest AI model can transform single images into realistic 3D models."

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Aflorithmic, an AI-driven audio cloning startup, has created a digital version of Albert Einstein using AI voice cloning technology drawing on audio records of the famous scientist's actual voice. TechCrunch reports: Alforithmic says the "digital Einstein" is intended as a showcase for what will soon be possible with conversational social commerce. Which is a fancy way of saying deepfakes that make like historical figures will probably be trying to sell you pizza soon enough, as industry watchers have presciently warned. The startup also says it sees educational potential in bringing famous, long-deceased figures to interactive "life." Or, well, an artificial approximation of it -- the "life" being purely virtual and Digital Einstein's voice not being a pure tech-powered clone either; Alforithmic says it also worked with an actor to do voice modelling for the chatbot (because how else was it going to get Digital Einstein to be able to say words the real-deal would never even have dreamt of saying -- like, er, "blockchain"?). So there's a bit more than AI artifice going on here too. In a blog post discussing how it recreated Einstein's voice the startup writes about progress it made on one challenging element associated with the chatbot version -- saying it was able to shrink the response time between turning around input text from the computational knowledge engine to its API being able to render a voiced response, down from an initial 12 seconds to less than three (which it dubs "near-real-time"). But it's still enough of a lag to ensure the bot can't escape from being a bit tedious. The report notes that the video engine powering the 3D character rendering components of this "digital human" version of Einstein is the work of another synthesized media company, UneeQ, which is hosting the interactive chatbot version on its website.

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What if the key to improving speech recognition accuracy is simply mixing all available speech datasets together to train one large AI model? That's the hypothesis behind a recent study published by a team of researchers affiliated with Google Research and Google Brain. They claim an AI model named SpeechStew that was trained on a range of speech corpora achieves state-of-the-art or near-state-of-the-art results on a variety of speech recognition benchmarks. VentureBeat reports: In pursuit of a solution, the Google researchers combined all available labeled and unlabelled speech recognition data curated by the community over the years. They drew on AMI, a dataset containing about 100 hours of meeting recordings, as well as corpora that include Switchboard (approximately 2,000 hours of telephone calls), Broadcast News (50 hours of television news), Librispeech (960 hours of audiobooks), and Mozilla's crowdsourced Common Voice. Their combined dataset had over 5,000 hours of speech -- none of which was adjusted from its original form. With the assembled dataset, the researchers used Google Cloud TPUs to train SpeechStew, yielding a model with more than 100 million parameters. In machine learning, parameters are the properties of the data that the model learned during the training process. The researchers also trained a 1-billion-parameter model, but it suffered from degraded performance. Once the team had a general-purpose SpeechStew model, they tested it on a number of benchmarks and found that it not only outperformed previously developed models but demonstrated an ability to adapt to challenging new tasks. Leveraging Chime-6, a 40-hour dataset of distant conversations in homes recorded by microphones, the researchers fine-tuned SpeechStew to achieve accuracy in line with a much more sophisticated model. Transfer learning entails transferring knowledge from one domain to a different domain with less data, and it has shown promise in many subfields of AI. By taking a model like SpeechStew that's designed to understand generic speech and refining it at the margins, it's possible for AI to, for example, understand speech in different accents and environments.

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A man who was falsely accused of shoplifting has sued the Detroit Police Department for arresting him based on an incorrect facial recognition match. The American Civil Liberties Union filed suit on behalf of Robert Williams, whom it calls the first US person wrongfully arrested based on facial recognition. The Verge reports: The Detroit Police Department arrested Williams in 2019 after examining security footage from a shoplifting incident. A detective used facial recognition technology on a grainy image from the video, and the system flagged Williams as a potential match based on a driver's license photo. But as the lawsuit notes, facial recognition is frequently inaccurate, particularly with Black subjects and a low-quality picture. The department then produced a photo lineup that included Williams' picture, showed it to a security guard who hadn't actually witnessed the shoplifting incident, and obtained a warrant when that guard picked him from the lineup. Williams -- who had been driving home from work during the incident -- spent 30 hours in a detention center. The ACLU later filed a formal complaint on his behalf, and the prosecutor's office apologized, saying he could have the case expunged from his records. The ACLU claims Detroit police used facial recognition under circumstances that they should have known would produce unreliable results, then dishonestly failed to mention the system's shortcomings -- including a "woefully substandard" image and the known racial bias of recognition systems.

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Khari Johnson writes via VentureBeat: In April 2020, news broke that Banjo CEO Damien Patton, once the subject of profiles by business journalists, was previously convicted of crimes committed with a white supremacist group. According to OneZero's analysis of grand jury testimony and hate crime prosecution documents, Patton pled guilty to involvement in a 1990 shooting attack on a synagogue in Tennessee. Amid growing public awareness about algorithmic bias, the state of Utah halted a $20.7 million contract with Banjo, and the Utah attorney general's office opened an investigation into matters of privacy, algorithmic bias, and discrimination. But in a surprise twist, an audit and report released last week found no bias in the algorithm because there was no algorithm to assess in the first place. "Banjo expressly represented to the Commission that Banjo does not use techniques that meet the industry definition of artificial Intelligence. Banjo indicated they had an agreement to gather data from Twitter, but there was no evidence of any Twitter data incorporated into Live Time," reads a letter Utah State Auditor John Dougall released last week. The incident, which VentureBeat previously referred to as part of a "fight for the soul of machine learning," demonstrates why government officials must evaluate claims made by companies vying for contracts and how failure to do so can cost taxpayers millions of dollars. As the incident underlines, companies selling surveillance software can make false claims about their technologies' capabilities or turn out to be charlatans or white supremacists -- constituting a public nuisance or worse. The audit result also suggests a lack of scrutiny can undermine public trust in AI and the governments that deploy them.

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A "Science of Love" app analyzed text conversations uploaded by its users to assess the degree of romantic feelings (based on the phrases and emojis used and the average response time). Then after more than four years, its parent company ScatterLab introduced a conversational A.I. chatbot called Lee-Luda — which it said had been trained on 10 billion such conversational logs. But because it used billions of conversations from real people, its problems soon went beyond sexually explicit comments and "verbally abusive" language: It also soon became clear that the huge training dataset included personal and sensitive information. This revelation emerged when the chatbot began exposing people's names, nicknames, and home addresses in its responses. The company admitted that its developers "failed to remove some personal information depending on the context," but still claimed that the dataset used to train chatbot Lee-Luda "did not include names, phone numbers, addresses, and emails that could be used to verify an individual." However, A.I. developers in South Korea rebutted the company's statement, asserting that Lee-Luda could not have learned how to include such personal information in its responses unless they existed in the training dataset. A.I. researchers have also pointed out that it is possible to recover the training dataset from the AI chatbot. So, if personal information existed in the training dataset, it can be extracted by querying the chatbot. To make things worse, it was also discovered that ScatterLab had, prior to Lee-Luda's release, uploaded a training set of 1,700 sentences, which was a part of the larger dataset it collected, on Github. Github is an open-source platform that developers use to store and share code and data. This Github training dataset exposed names of more than 20 people, along with the locations they have been to, their relationship status, and some of their medical information... [T]his incident highlights the general trend of the A.I. industry, where individuals have little control over how their personal information is processed and used once collected. It took almost five years for users to recognize that their personal data were being used to train a chatbot model without their consent. Nor did they know that ScatterLab shared their private conversations on an open-source platform like Github, where anyone can gain access. What makes this unusual, the article points out, is how the users became aware of just how much their privacy had actually been compromised. "[B]igger tech companies are usually much better at hiding what they actually do with user data, while restricting users from having control and oversight over their own data." And "Once you give, there's no taking back."

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An anonymous reader quotes a report from VentureBeat: Even state-of-the-art automatic speech recognition (ASR) algorithms struggle to recognize the accents of people from certain regions of the world. That's the top-line finding of a new study published by researchers at the University of Amsterdam, the Netherlands Cancer Institute, and the Delft University of Technology, which found that an ASR system for the Dutch language recognized speakers of specific age groups, genders, and countries of origin better than others. The coauthors of this latest research set out to investigate how well an ASR system for Dutch recognizes speech from different groups of speakers. In a series of experiments, they observed whether the ASR system could contend with diversity in speech along the dimensions of gender, age, and accent. The researchers began by having an ASR system ingest sample data from CGN, an annotated corpus used to train AI language models to recognize the Dutch language. [...] When the researchers ran the trained ASR system through a test set derived from the CGN, they found that it recognized female speech more reliably than male speech regardless of speaking style. Moreover, the system struggled to recognize speech from older people compared with younger, potentially because the former group wasn't well-articulated. And it had an easier time detecting speech from native speakers versus non-native speakers. Indeed, the worst-recognized native speech -- that of Dutch children -- had a word error rate around 20% better than that of the best non-native age group. In general, the results suggest that teenagers' speech was most accurately interpreted by the system, followed by seniors' (over the age of 65) and children's. This held even for non-native speakers who were highly proficient in Dutch vocabulary and grammar. One solution to remove the bias is to mitigate it at the algorithmic level. "[We recommend] framing the problem, developing the team composition and the implementation process from a point of anticipating, proactively spotting, and developing mitigation strategies for affective prejudice [to address bias in ASR systems]," the researchers wrote in a paper detailing their work. "A direct bias mitigation strategy concerns diversifying and aiming for a balanced representation in the dataset. An indirect bias mitigation strategy deals with diverse team composition: the variety in age, regions, gender, and more provides additional lenses of spotting potential bias in design. Together, they can help ensure a more inclusive developmental environment for ASR."

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An anonymous reader shares a report: Artificial-intelligence systems are nowhere near advanced enough to replace humans in many tasks involving reasoning, real-world knowledge, and social interaction. They are showing human-level competence in low-level pattern recognition skills, but at the cognitive level they are merely imitating human intelligence, not engaging deeply and creatively, says Michael I. Jordan, a leading researcher in AI and machine learning. Jordan is a professor in the department of electrical engineering and computer science, and the department of statistics, at the University of California, Berkeley. He notes that the imitation of human thinking is not the sole goal of machine learning -- the engineering field that underlies recent progress in AI -- or even the best goal. Instead, machine learning can serve to augment human intelligence, via painstaking analysis of large data sets in much the way that a search engine augments human knowledge by organizing the Web. Machine learning also can provide new services to humans in domains such as health care, commerce, and transportation, by bringing together information found in multiple data sets, finding patterns, and proposing new courses of action. "People are getting confused about the meaning of AI in discussions of technology trends -- that there is some kind of intelligent thought in computers that is responsible for the progress and which is competing with humans," he says. "We don't have that, but people are talking as if we do." Jordan should know the difference, after all. The IEEE Fellow is one of the world's leading authorities on machine learning. In 2016 he was ranked as the most influential computer scientist by a program that analyzed research publications, Science reported. Jordan helped transform unsupervised machine learning, which can find structure in data without preexisting labels, from a collection of unrelated algorithms to an intellectually coherent field, the Engineering and Technology History Wiki explains. Unsupervised learning plays an important role in scientific applications where there is an absence of established theory that can provide labeled training data.

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One of the biggest trends in machine learning right now is text generation. AI systems learn by absorbing billions of words scraped from the internet and generate text in response to a variety of prompts. It sounds simple, but these machines can be put to a wide array of tasks -- from creating fiction, to writing bad code, to letting you chat with historical figures. From a report: The best-known AI text-generator is OpenAI's GPT-3, which the company recently announced is now being used in more than 300 different apps, by "tens of thousands" of developers, and producing 4.5 billion words per day. That's a lot of robot verbiage. This may be an arbitrary milestone for OpenAI to celebrate, but it's also a useful indicator of the growing scale, impact, and commercial potential of AI text generation. OpenAI started life as a nonprofit, but for the last few years, it has been trying to make money with GPT-3 as its first salable product. The company has an exclusivity deal with Microsoft which gives the tech giant unique access to the program's underlying code, but any firm can apply for access to GPT-3's general API and build services on top of it./i

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Car and Driver magazine has over a million readers. This month they called Tesla's "full self driving" beta "laughably bad and potentially dangerous." schwit1 shares their report on a 13-minute video posted to YouTube of a Model 3 with FSD Beta 8.2 "fumbling its way around Oakland." Quite quickly, the video moves from "embarrassing mistakes" to "extremely risky, potentially harmful driving." In autonomous mode, the Tesla breaks a variety of traffic laws, starting with a last-minute attempt to cross a hard line and execute an illegal lane change. It then attempts to make a left turn next to another car, only to give up midway through the intersection and disengage. It goes on to take another turn far too wide, landing it in the oncoming lane and requiring driver intervention. Shortly thereafter, it crosses into the oncoming lane again on a straight stretch of road with bikers and oncoming traffic. It then drunkenly stumbles through an intersection and once again requires driver intervention to make it through. While making an unprotected left after a stop sign, it slows down before the turn and chills in the pathway of oncoming cars that have to brake to avoid hitting it... The Tesla attempts to make a right turn at a red light where that's prohibited, once again nearly breaking the law and requiring the driver to actively prevent it from doing something. It randomly stops in the middle of the road, proceeds straight through a turn-only lane, stops behind a parked car, and eventually almost slams into a curb while making a turn. After holding up traffic to creep around a stopped car, it confidently drives directly into the oncoming lane before realizing its mistake and disengaging. Another traffic violation on the books — and yet another moment where the befuddled car just gives up and leaves it to the human driver to sort out the mess... Then comes another near collision. This time, the Tesla arrives at an intersection where it has a stop sign and cross traffic doesn't. It proceeds with two cars incoming, the first car narrowly passing the car's front bumper and the trailing car braking to avoid T-boning the Model 3. It is absolutely unbelievable and indefensible that the driver, who is supposed to be monitoring the car to ensure safe operation, did not intervene there. It's even wilder that this software is available to the public. But that isn't the end of the video. To round it out, the Model 3 nearly slams into a Camry that has the right of way while trying to negotiate a kink in the road. Once it gets through that intersection, it drives straight for a fence and nearly plows directly into it. Both of these incidents required driver intervention to avoid. Their conclusion? "Tesla's software clearly does a decent job of identifying cars, stop signs, pedestrians, bikes, traffic lights, and other basic obstacles. Yet to think this constitutes anything close to 'full self-driving' is ludicrous."

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CNBC wrote recently, "Artificial intelligence will create so much wealth that every adult in the United States could be paid $13,500 per year from its windfall as soon as 10 years from now. So says Sam Altman, co-founder and president of San Francisco-headquartered, artificial intelligence-focused nonprofit OpenAI..." [I]f the government collects and redistributes the wealth that AI will generate, AI's exponential productivity gains could "make the society of the future much less divisive and enable everyone to participate in its gains," Altman says.... As the pace of development accelerates, AI "will create phenomenal wealth" but at the same time the price of labor "will fall towards zero," Altman said. "It sounds utopian, but it's something technology can deliver (and in some cases already has). Imagine a world where, for decades, everything — housing, education, food, clothing, etc. — became half as expensive every two years." In this future, where wealth will come from companies and land, governments should tax capital, not labor, and those taxes should be distributed to citizens, Altman said. In his post, Altman proposed an American Equity Fund that taxes sufficiently large companies 2.5% of their market value in the form of company shares, and 2.5% of the value of all land in the form of dollars... All citizens over 18 would receive payment in both dollars and company shares.... "As people's individual assets rise in tandem with the country's, they have a literal stake in seeing their country do well," Altman said. With this system in mind, in 10 years, the 250 million adults living in America would get $13,500 per year, Altman said... "That dividend could be much higher if AI accelerates growth, but even if it's not, $13,500 will have much greater purchasing power than it does now because technology will have greatly reduced the cost of goods and services," Altman wrote. "And that effective purchasing power will go up dramatically every year." Elon Musk has hinted at a similar future. "There is a pretty good chance we end up with a universal basic income, or something like that, due to automation," Musk told CNBC in 2016. "Yeah, I am not sure what else one would do. I think that is what would happen." Musk is also a co-founder of OpenAI but left the board in 2018 citing the fact that Tesla was becoming an AI company as it developed self-driving capabilities. Such a system is "both pro-business and pro-people," Altman said, and would therefore bring together "a remarkably broad constituency." "The changes coming are unstoppable," Altman said. "If we embrace them and plan for them, we can use them to create a much fairer, happier, and more prosperous society. The future can be almost unimaginably great."

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sciencehabit shares a report from Science Magazine: The video in this story will be familiar to anyone who's played the 3D world-building game Minecraft. But it's not a human constructing these castles, trees, and caterpillars -- it's artificial intelligence. The algorithm takes its cue from the "Game of Life," a so-called cellular automaton. There, squares in a grid turn black or white over a series of timesteps based on how many of their neighbors are black or white. The program mimics biological development, in which cells in an embryo behave according to cues in their local environment. The scientists taught neural networks to grow single cubes into complex designs containing thousands of bricks, like the castle or tree or furnished apartment building above, and even into functional machines, like the caterpillar. And when they sliced a creation in half, it regenerated. (Normally in Minecraft, a user would have to reconstruct the object by hand.) Going forward, the researchers hope to train systems to grow not only predefined forms, but to invent designs that perform certain functions. This could include flying, allowing engineers to find solutions human designers would not have otherwise foreseen. Or tiny robots might use local interactions to assemble rescue robots or self-healing buildings. The researchers presented their system in a paper posted on arXiv.

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The ACLU will be seeking information about how the government is using artificial intelligence in national security, Axios reported Friday. From a report: The development of AI has major implications for security, surveillance, and justice. The ACLU's request may help shed some light on the government's often opaque applications of AI. Later today the ACLU will be filing a broad Freedom of Information Act (FOIA) request to the CIA, the NSA, the Department of Homeland Security and other agencies concerning the government's use of AI, especially in the area of national security. "The problem with these AI systems is that they're black boxes," says Patrick Toomey, senior staff attorney at the ACLU National Security Project. "The public needs to know exactly what kinds of fundamental decisions about our lives the government is handing over to AI." The ACLU is specifically concerned about "vetting and screening processes in agencies like Homeland Security, and tools that can analyze voice, data and video," says Toomey. Another area of concern is the possibility that AI systems could be "biased against people of color, women and marginalized communities," he adds. "AI systems could be used to supercharge government activities to unfairly scrutinize communities through intrusive surveillance, questioning and even detention and watchlisting."

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The Trades Union Congress (TUC) is calling for new legal protections for workers, warning that they could soon be "hired and fired by algorithm." "Among the changes it is calling for is a legal right to have any 'high-risk' decision reviewed by a human," reports the BBC. From the report: TUC general secretary Frances O'Grady said the use of AI at work stood at "a fork in the road." "AI at work could be used to improve productivity and working lives. But it is already being used to make life-changing decisions about people at work -- like who gets hired and fired. "Without fair rules, the use of AI at work could lead to widespread discrimination and unfair treatment -- especially for those in insecure work and the gig economy," she warned. The union body is calling for: - An obligation on employers to consult unions on the use of "high risk" or "intrusive" AI at work - The legal right to have a human review decisions - A legal right to "switch off" from work and not be expected to answer calls or emails - Changes to UK law to protect against discrimination by algorithm

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New submitter ytene writes: As covered by a fascinating and hilarious video from the BBC, Twitch Streamer and YouTube star, Kitboga, has teamed up with some software developers to produce an AI that can interact directly with phone scammers. Although only brief samples of the solution at work were shown in the clip, the reporter suggests that it has worked for periods of up to 30 minutes. Will this be enough to finally put an end to the phone scammers, or do you think even more drastic steps will be required?

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Adobe just dropped its latest software updates via the Creative Cloud and among those updates is a new feature in Adobe Camera Raw (ACR) called "Super Resolution." You can mark this day down as a major shift in the photo industry, writes PetaPixel. From the report: I have seen a bit of reporting out there on this topic from the likes of PetaPixel and Fstoppers, but other than that the ramifications of this new feature in ACR have not been widely promoted from what I can see. The new Super Resolution feature in ACR essentially upsizes the image by a factor of four using machine learning, i.e. Artificial Intelligence (AI). The PetaPixel article on this new feature quoted Eric Chan from Adobe: Super Resolution builds on a technology Adobe launched two years ago called Enhance Details, which uses machine learning to interpolate RAW files with a high degree of fidelity, which resulted in images with crisp details and fewer artifacts. The term 'Super Resolution' refers to the process of improving the quality of a photo by boosting its apparent resolution," Chan explains. "Enlarging a photo often produces blurry details, but Super Resolution has an ace up its sleeve: an advanced machine learning model trained on millions of photos. Backed by this vast training set, Super Resolution can intelligently enlarge photos while maintaining clean edges and preserving important details." What does this mean practically? Well, I immediately tested this out and was pretty shocked by the results. Though it might be hard to make out in the screenshot below, I took the surfing image shown below, which was captured a decade ago with a Nikon D700 -- a 12MP camera -- and ran the Super Resolution tool on it and the end result is a 48.2MP image that looks to be every bit as sharp (if not sharper) than the original image file. This means that I can now print that old 12MP image at significantly larger sizes than I ever could before. What this also means is that anyone with a lower resolution camera, i.e. the current crop of 24MP cameras, can now output huge image files for prints or any other usage that requires a higher resolution image file. In the three or four images I have run through this new feature in Photoshop I have found the results to be astoundingly good.

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