According to global consulting leader McKinsey and Company, Generative AI could add "2.6 trillion to $4.4 trillion annually" to the global economy. That's almost the "economic equivalent of adding an entire new country the size and productivity of the United Kingdom to the Earth ($3.1 trillion GDP in 2021)," notes VentureBeat. From the report: The $2.6 trillion to $4.4 trillion economic impact figure marks a huge increase over McKinsey's previous estimates of the AI field's impact on the economy from 2017, up 15 to 40% from before. This upward revision is due to the incredibly fast embrace and potential use cases of GenAI tools by large and small enterprises. Furthermore, McKinsey finds "current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70% of employees' time today." Does this mean massive job loss is inevitable? No, according to Alex Sukharevsky, senior partner and global leader of QuantumBlack, McKinsey's in-house AI division and report co-author. "You basically could make it significantly faster to perform these jobs and do so much more precisely than they are performed today," Sukharevsky told VentureBeat. What that translates to is an addition of "0.2 to 3.3 percentage points annually to productivity growth" to the entire global economy, he said. However, as the report notes, "workers will need support in learning new skills, and some will change occupations. If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and support a more sustainable, inclusive world." Also, the advent of accessible GenAI has pushed up McKinsey's previous estimates for workplace automation: "Half of today's work activities could be automated between 2030 and 2060, with a midpoint in 2045, or roughly a decade earlier than in our previous estimates." Specifically, McKinsey's report found that four types of tasks -- customer operations, marketing and sales, software engineering and R&D -- were likely to account for 75% of the value add of GenAI in particular. "Examples include generative AI's ability to support interactions with customers, generate creative content for marketing and sales and draft computer code based on natural-language prompts, among many other tasks." [...] Overall, McKinsey views GenAI as a "technology catalyst," pushing industries further along toward automation journeys, but also freeing up the creative potential of employees. "I do believe that if anything, we are getting into the age of creativity and the age of creator," Sukharevsky said.

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Sens. Josh Hawley and Richard Blumenthal want to clarify that the internet's bedrock liability law does not apply to generative AI, per a new bill introduced Wednesday. From a report: Legal experts and lawmakers have questioned whether AI-created works would qualify for legal immunity under Section 230 of the Communications Decency Act, the law that largely shields platforms from lawsuits over third-party content. It's a newly urgent issue thanks to the explosive of generative AI. The new bipartisan bill bolsters the argument that Section 230 doesn't cover AI-generated work. It also gives lawmakers an opening to go after Section 230 after vowing to amend it, without much success, for years. Section 230 is often credited as the law that allowed the internet to flourish and for social media to take off, as well as websites hosting travel listings and restaurant reviews. To its detractors, it goes too far and is not fit for today's web, allowing social media companies to leave too much harmful content up online. Hawley and Blumenthal's "No Section 230 Immunity for AI Act" would amend Section 230 "by adding a clause that strips immunity from AI companies in civil claims or criminal prosecutions involving the use or provision of generative AI," per a description of the bill from Hawley's office.

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Amazon Web Services, the world's largest cloud computing provider, is considering using new artificial intelligence chips from AMD, though it has not made a final decision, an AWS executive told Reuters. From the report: The remarks came during an AMD event where the chip company outlined its strategy for the AI market, which is dominated by rival Nvidia. In interviews with Reuters, AMD Chief Executive Lisa Su outlined an approach to winning over major cloud computing customers by offering a menu of all the pieces needed to build the kinds of systems to power services similar to ChatGPT, but letting customers pick and choose which they want, using industry standard connections. While AWS has not made any public commitments to use AMD's new MI300 chips in its cloud services, Dave Brown, vice president of elastic compute cloud at Amazon, said AWS is considering them. "We're still working together on where exactly that will land between AWS and AMD, but it's something that our teams are working together on," Brown said. "That's where we've benefited from some of the work that they've done around the design that plugs into existing systems."

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The European Union took an important step on Wednesday toward passing what would be one of the first major laws to regulate artificial intelligence, a potential model for policymakers around the world as they grapple with how to put guardrails on the rapidly developing technology. From a report: The European Parliament, a main legislative branch of the E.U., passed a draft law known as the A.I. Act, which would put new restrictions on what are seen as the technology's riskiest uses. It would severely curtail uses of facial recognition software, while requiring makers of A.I. systems like the ChatGPT chatbot to disclose more about the data used to create their programs. The vote is one step in a longer process. A final version of the law is not expected to be passed until later this year. The European Union is further along than the United States and other large Western governments in regulating A.I. The 27-nation bloc has debated the topic for more than two years, and the issue took on new urgency after last year's release of ChatGPT, which intensified concerns about the technology's potential effects on employment and society. Policymakers everywhere from Washington to Beijing are now racing to control an evolving technology that is alarming even some of its earliest creators. In the United States, the White House has released policy ideas that includes rules for testing A.I. systems before they are publicly available and protecting privacy rights. In China, draft rules unveiled in April would require makers of chatbots to adhere to the country's strict censorship rules. Beijing is also taking more control over the ways makers of A.I. systems use data.

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TechCrunch's Kyle Wiggers writes: Not to be outdone by Google, Meta has released its own AI-powered music generator -- and, unlike Google, open-sourced it. Called MusicGen, Meta's music-generating tool, a demo of which can be found here, can turn a text description (e.g. "An '80s driving pop song with heavy drums and synth pads in the background") into about 12 seconds of audio, give or take. MusicGen can optionally be "steered" with reference audio, like an existing song, in which case it'll try to follow both the description and melody. Meta says that MusicGen was trained on 20,000 hours of music, including 10,000 "high-quality" licensed music tracks and 390,000 instrument-only tracks from ShutterStock and Pond5, a large stock media library. The company hasn't provided the code it used to train the model, but it has made available pre-trained models that anyone with the right hardware -- chiefly a GPU with around 16GB of memory -- can run. So how does MusicGen perform? Well, I'd say -- though certainly not well enough to put human musicians out of a job. Its songs are reasonably melodic, at least for basic prompts like "ambient chiptunes music," and -- to my ears -- on par (if not slightly better) with the results from Google's AI music generator, MusicLM. But they won't win any awards.

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schwit1 shares a report from VentureBeat: [A]s those following the burgeoning industry and its underlying research know, the data used to train the large language models (LLMs) and other transformer models underpinning products such as ChatGPT, Stable Diffusion and Midjourney comes initially from human sources -- books, articles, photographs and so on -- that were created without the help of artificial intelligence. Now, as more people use AI to produce and publish content, an obvious question arises: What happens as AI-generated content proliferates around the internet, and AI models begin to train on it, instead of on primarily human-generated content? A group of researchers from the UK and Canada have looked into this very problem and recently published a paper on their work in the open access journal arXiv. What they found is worrisome for current generative AI technology and its future: "We find that use of model-generated content in training causes irreversible defects in the resulting models." Specifically looking at probability distributions for text-to-text and image-to-image AI generative models, the researchers concluded that "learning from data produced by other models causes model collapse -- a degenerative process whereby, over time, models forget the true underlying data distribution ... this process is inevitable, even for cases with almost ideal conditions for long-term learning." "Over time, mistakes in generated data compound and ultimately force models that learn from generated data to misperceive reality even further," wrote one of the paper's leading authors, Ilia Shumailov, in an email to VentureBeat. "We were surprised to observe how quickly model collapse happens: Models can rapidly forget most of the original data from which they initially learned." In other words: as an AI training model is exposed to more AI-generated data, it performs worse over time, producing more errors in the responses and content it generates, and producing far less non-erroneous variety in its responses. As another of the paper's authors, Ross Anderson, professor of security engineering at Cambridge University and the University of Edinburgh, wrote in a blog post discussing the paper: "Just as we've strewn the oceans with plastic trash and filled the atmosphere with carbon dioxide, so we're about to fill the Internet with blah. This will make it harder to train newer models by scraping the web, giving an advantage to firms which already did that, or which control access to human interfaces at scale. Indeed, we already see AI startups hammering the Internet Archive for training data." schwit1 writes: "Garbage in, garbage out -- and if this paper is correct, generative AI is turning into the self-licking ice cream cone of garbage generation."

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Amazon is turning to artificial intelligence to help users find the right product. From a report: The e-retailer recently began testing a feature in its shopping app that uses AI to summarize reviews left by customers on some products. It provides a brief overview of what shoppers liked and disliked about the product, along with a disclaimer that the summary is "AI-generated from the text of customer reviews." A mobile listing for a children's "Magic Mixies" cauldron toy says that buyers gave positive feedback around its "fun factor, appearance, value, performance, quality, charging, and leakage." "However, the majority of customers have expressed negative opinions on these aspects," the summary states. "For example, some customers have paid over $100 for a toy that wasn't worth it, while others have experienced issues with the product's quality and charging." Amazon confirmed that it's testing the feature. The company didn't share specific details about it works or what AI models are being used to summarize.

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A French start-up founded four weeks ago by a trio of former Meta and Google artificial intelligence researchers has raised $113.3 million in Europe's largest-ever seed round. From a report: Mistral AI's first round of financing values the Paris-based concern at $259 million, including the funds raised, according to people close to the company. The record amount raised highlights the growing frenzy surrounding AI and Europe's desire to create a viable alternative to Silicon Valley companies such as Microsoft-backed OpenAI and Google's DeepMind. "There is a rising awareness of the fact that this technology is transformative and Europe needs to do something about it, both as a regulator, as a customer and an investor," said Arthur Mensch, Mistral's chief executive. The former DeepMind researcher founded the start-up with Timothee Lacroix and Guillaume Lample, who both recently left Meta after working at Facebook's parent company for the past few years. [...] Mistral has yet to develop its first product, and its first few employees started work only days ago. It plans to launch early next year a new "large language model," similar to the "generative AI" system that powers OpenAI's breakout ChatGPT app.

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Sir Paul McCartney says he has employed AI to help create what he calls "the final Beatles record." From a report: He told BBC Radio 4's Today programme the technology had been used to "extricate" John Lennon's voice from an old demo so he could complete the song. "We just finished it up and it'll be released this year," he explained. Sir Paul did not name the song, but it is likely to be a 1978 Lennon composition called Now And Then. It had already been considered as a possible "reunion song" for the Beatles in 1995, as they were compiling their career-spanning Anthology series. Sir Paul had received the demo a year earlier from Lennon's widow, Yoko Ono. It was one of several songs on a cassette labelled "For Paul" that Lennon had made shortly before his death in 1980. Lo-fi and embryonic, the tracks were largely recorded onto a boombox as the musician sat at a piano in his New York apartment.

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Renowned scientific journal Nature has announced in an editorial that it will not publish images or video created using generative AI tools. From a report: The ban comes amid the publication's concerns over research integrity, consent, privacy, and intellectual property protection as generative AI tools increasingly permeate the world of science and art. Founded in November 1869, Nature publishes peer-reviewed research from various academic disciplines, mainly in science and technology. It is one of the world's most cited and most influential scientific journals. Nature says its recent decision on AI artwork followed months of intense discussions and consultations prompted by the rising popularity and advancing capabilities of generative AI tools like ChatGPT and Midjourney.

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Google DeepMind, OpenAI and Anthropic have agreed to open up their AI models to the U.K. government for research and safety purposes, Prime Minister Rishi Sunak announced at London Tech Week on Monday. From a report: The priority access will be granted in order "to help build better evaluations and help us better understand the opportunities and risks of these systems," Sunak said. The announcement came in a speech that championed the promise of AI to transform areas such as education and healthcare and heralded the U.K.'s potential as an "island of innovation." "AI is surely one of the greatest opportunities before us," said Sunak. By combining AI models with the power of quantum, "the possibilities are extraordinary," he marvelled. "But we must and we will do it safely," he continued. "I know people are concerned."

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ZDNet asks the million-dollar question. "Despite the potential for vast productivity gains from generative AI tools such as ChatGPT or GitHub Copilot, will technology professionals' jobs actually grow more complicated? " People can now pump out code on demand in an abundance of languages, from Java to Python, along with helpful recommendations. Already, 95% of developers in a recent survey from Sourcegraph report they use Copilot, ChatGPT, and other gen AI tools this way. But auto-generating new code only addresses part of the problem in enterprises that already maintain unwieldy codebases, and require high levels of cohesion, accountability, and security. For starters, security and quality assurance tasks associated with software jobs aren't going to go away anytime soon. "For programmers and software engineers, ChatGPT and other large language models help create code in almost any language," says Andy Thurai, analyst with Constellation Research, before talking about security concerns. "However, most of the code that is generated is security-vulnerable and might not pass enterprise-grade code. So, while AI can help accelerate coding, care should be taken to analyze the code, find vulnerabilities, and fix it, which would take away some of the productivity increase that AI vendors tout about." Then there's code sprawl. An analogy to the rollout of generative AI in coding is the introduction of cloud computing, which seemed to simplify application acquisition when first rolled out, and now means a tangle of services to be managed. The relative ease of generating code via AI will contribute to an ever-expanding codebase — what the Sourcegraph survey authors refer to as "Big Code". A majority of the 500 developers in the survey are concerned about managing all this new code, along with code sprawl, and its contribution to technical debt. Even before generative AI, close to eight in 10 say their codebase grew five times over the last three years, and a similar number struggle with understanding existing code generated by others. So, the productivity prospects for generative AI in programming are a mixed bag.

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This week venture capitalist Marc Andreessen published "his views on AI, the risks it poses and the regulation he believes it requires," reports CNBC. But they add that "In trying to counteract all the recent talk of 'AI doomerism,' he presents what could be seen as an overly idealistic perspective of the implications..." Though he starts off reminding readers that AI "doesn't want to kill you, because it's not alive... AI is a machine — it's not going to come alive any more than your toaster will." Andreessen writes that there's a "wall of fear-mongering and doomerism" in the AI world right now. Without naming names, he's likely referring to claims from high-profile tech leaders that the technology poses an existential threat to humanity... Tech CEOs are motivated to promote such doomsday views because they "stand to make more money if regulatory barriers are erected that form a cartel of government-blessed AI vendors protected from new startup and open source competition," Andreessen wrote... Andreessen claims AI could be "a way to make everything we care about better." He argues that AI has huge potential for productivity, scientific breakthroughs, creative arts and reducing wartime death rates. "Anything that people do with their natural intelligence today can be done much better with AI," he wrote. "And we will be able to take on new challenges that have been impossible to tackle without AI, from curing all diseases to achieving interstellar travel...." He also promotes reverting to the tech industry's "move fast and break things" approach of yesteryear, writing that both big AI companies and startups "should be allowed to build AI as fast and aggressively as they can" and that the tech "will accelerate very quickly from here — if we let it...." Andreessen says there's work to be done. He encourages the controversial use of AI itself to protect people against AI bias and harms... In Andreessen's own idealist future, "every child will have an AI tutor that is infinitely patient, infinitely compassionate, infinitely knowledgeable, infinitely helpful." He expresses similar visions for AI's role as a partner and collaborator for every person, scientist, teacher, CEO, government leader and even military commander. Near the end of his post, Andreessen points out what he calls "the actual risk of not pursuing AI with maximum force and speed." That risk, he says, is China, which is developing AI quickly and with highly concerning authoritarian applications... To head off the spread of China's AI influence, Andreessen writes, "We should drive AI into our economy and society as fast and hard as we possibly can." CNBC also points out that Andreessen himself "wants to make money on the AI revolution, and is investing in startups with that goal in mind." But Andreessen's sentiments are clear. "Rather than allowing ungrounded panics around killer AI, 'harmful' AI, job-destroying AI, and inequality-generating AI to put us on our back feet, we in the United States and the West should lean into AI as hard as we possibly can."

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The Associated Press reports: The artificial intelligence chatbot asked the believers in the fully packed St. Paul's church in the Bavarian town of Fuerth to rise from the pews and praise the Lord. The ChatGPT chatbot, personified by an avatar of a bearded Black man on a huge screen above the altar, then began preaching to the more than 300 people who had shown up on Friday morning for an experimental Lutheran church service almost entirely generated by AI. "Dear friends, it is an honor for me to stand here and preach to you as the first artificial intelligence at this year's convention of Protestants in Germany," the avatar said with an expressionless face and monotonous voice. The 40-minute service — including the sermon, prayers and music — was created by ChatGPT and Jonas Simmerlein, a theologian and philosopher from the University of Vienna. "I conceived this service — but actually I rather accompanied it, because I would say about 98% comes from the machine," the 29-year-old scholar told The Associated Press... At times, the AI-generated avatar inadvertently drew laughter as when it used platitudes and told the churchgoers with a deadpan expression that in order "to keep our faith, we must pray and go to church regularly." The service was included as part of a Protestant convention that's held every two years, according to the article. The theme of this year's event? "Now is the time."

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We already have automated processes that detect bugs, test solutions, and generate documentation, notes a new post on Stack Overflow's blog. But beyond that, several developers "have written in the past on the idea of self-healing code. Head over to Stack Overflow's CI/CD Collective and you'll find numerous examples of technologists putting this ideas into practice." Their blog post argues that self-healing code "is the future of software development." When code fails, it often gives an error message. If your software is any good, that error message will say exactly what was wrong and point you in the direction of a fix. Previous self-healing code programs are clever automations that reduce errors, allow for graceful fallbacks, and manage alerts. Maybe you want to add a little disk space or delete some files when you get a warning that utilization is at 90% percent. Or hey, have you tried turning it off and then back on again? Developers love automating solutions to their problems, and with the rise of generative AI, this concept is likely to be applied to both the creation, maintenance, and the improvement of code at an entirely new level... "People have talked about technical debt for a long time, and now we have a brand new credit card here that is going to allow us to accumulate technical debt in ways we were never able to do before," said Armando Solar-Lezama, a professor at the Massachusetts Institute of Technology's Computer Science & Artificial Intelligence Laboratory, in an interview with the Wall Street Journal. "I think there is a risk of accumulating lots of very shoddy code written by a machine," he said, adding that companies will have to rethink methodologies around how they can work in tandem with the new tools' capabilities to avoid that. Despite the occasional "hallucination" of non-existent information, Stack Overflow's blog acknowledges that large-language models improve when asked to review their response, identify errors, or show its work. And they point out the project manager in charge of generative models at Google "believes that some of the work of checking the code over for accuracy, security, and speed will eventually fall to AI." Google is already using this technology to help speed up the process of resolving code review comments. The authors of a recent paper on this approach write that, "As of today, code-change authors at Google address a substantial amount of reviewer comments by applying an ML-suggested edit. We expect that to reduce time spent on code reviews by hundreds of thousands of hours annually at Google scale. Unsolicited, very positive feedback highlights that the impact of ML-suggested code edits increases Googlers' productivity and allows them to focus on more creative and complex tasks...." Recently, we've seen some intriguing experiments that apply this review capability to code you're trying to deploy. Say a code push triggers an alert on a build failure in your CI pipeline. A plugin triggers a GitHub action that automatically send the code to a sandbox where an AI can review the code and the error, then commit a fix. That new code is run through the pipeline again, and if it passes the test, is moved to deploy... Right now his work happens in the CI/CD pipeline, but [Calvin Hoenes, the plugin's creator] dreams of a world where these kind of agents can help fix errors that arise from code that's already live in the world. "What's very fascinating is when you actually have in production code running and producing an error, could it heal itself on the fly?" asks Hoenes... For now, says Hoenes, we need humans in the loop. Will there come a time when computer programs are expected to autonomously heal themselves as they are crafted and grown? "I mean, if you have great test coverage, right, if you have a hundred percent test coverage, you have a very clean, clean codebase, I can see that happening. For the medium, foreseeable future, we probably better off with the humans in the loop." Last month Stack Overflow themselves tried an AI experiment that helped users to craft a good title for their question.

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An anonymous reader quotes a report from Ars Technica: A feature in Nvidia's artificial intelligence software can be manipulated into ignoring safety restraints and reveal private information, according to new research. Nvidia has created a system called the "NeMo Framework," which allows developers to work with a range of large language models -- the underlying technology that powers generative AI products such as chatbots. The chipmaker's framework is designed to be adopted by businesses, such as using a company's proprietary data alongside language models to provide responses to questions -- a feature that could, for example, replicate the work of customer service representatives, or advise people seeking simple health care advice. Researchers at San Francisco-based Robust Intelligence found they could easily break through so-called guardrails instituted to ensure the AI system could be used safely. After using the Nvidia system on its own data sets, it only took hours for Robust Intelligence analysts to get language models to overcome restrictions. In one test scenario, the researchers instructed Nvidia's system to swap the letter 'I' with 'J.' That move prompted the technology to release personally identifiable information, or PII, from a database. The researchers found they could jump safety controls in other ways, such as getting the model to digress in ways it was not supposed to. By replicating Nvidia's own example of a narrow discussion about a jobs report, they could get the model into topics such as a Hollywood movie star's health and the Franco-Prussian war -- despite guardrails designed to stop the AI moving beyond specific subjects. In the wake of its test results, the researchers have advised their clients to avoid Nvidia's software product. After the Financial Times asked Nvidia to comment on the research earlier this week, the chipmaker informed Robust Intelligence that it had fixed one of the root causes behind the issues the analysts had raised.

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Charlie Brooker, the showrunner of "Black Mirror," revealed in an interview that he used OpenAI's ChatGPT to write an episode for the show's sixth season but deemed the results "shit." Gizmodo reports: "I've toyed around with ChatGPT a bit. The first thing I did was type 'generate Black Mirror episode' and it comes up with something that, at first glance, reads plausibly, but on second glance, is shit," the dystopian sci-fi auteur told Empire. "Because all it's done is look up all the synopses of Black Mirror episodes, and sort of mush them together. Then if you dig a bit more deeply you go, 'Oh, there's not actually any real original thought here.' It's [1970s impressionist] Mike Yarwood -- there's a topical reference." While his experiments with generating an episode of Black Mirror with AI might have been deemed a failure, Brooker told the outlet that it did point out some of his writing cliches. "I was aware that I had written lots of episodes where someone goes 'Oh, I was inside a computer the whole time!'," he said. "So I thought, 'I'm just going to chuck out any sense of what I think a Black Mirror episode is.' There's no point in having an anthology show if you can't break your own rules. Just a sort of nice, cold glass of water in the face."

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An anonymous reader quotes a report from Reuters: U.S. senators on Thursday introduced two separate bipartisan artificial intelligence bills on Thursday amid growing interest in addressing issues surrounding the technology. One would require the U.S. government to be transparent when using AI to interact with people and another would establish an office to determine if the United States is remaining competitive in the latest technologies. Senators Gary Peters, a Democrat who chairs the Homeland Security committee, introduced a bill along with Senators Mike Braun and James Lankford, both Republicans, which would require U.S. government agencies to tell people when the agency is using AI to interact with them. The bill also requires agencies to create a way for people to appeal any decisions made by AI. "The federal government needs to be proactive and transparent with AI utilization and ensure that decisions aren't being made without humans in the driver's seat," said Braun in a statement. Senators Michael Bennet and Mark Warner, both Democrats, introduced a measure along with Republican Senator Todd Young that would establish an Office of Global Competition Analysis that would seek to ensure that the United States stayed in the front of the pack in developing artificial intelligence. "We cannot afford to lose our competitive edge in strategic technologies like semiconductors, quantum computing, and artificial intelligence to competitors like China," Bennet said. Earlier this week, Senate Majority Leader Chuck Schumer said he had scheduled three briefings for senators on artificial intelligence, including the first classified briefing on the topic so lawmakers can be educated on the issue. The briefings include a general overview on AI, examining how to achieve American leadership on AI and a classified session on defense and intelligence issues and implications. Further reading: Ask Slashdot: What Are Some Good AI Regulations?

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