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"The music industry is urging streaming platforms not to let artificial intelligence use copyrighted songs for training, in the latest of a run of arguments over intellectual property that threaten to derail the generative AI sector's explosive growth. In a letter to streamers including Spotify and Apple Music, the record label Universal Music Group expressed fears that AI labs would scrape millions of tracks to use as training data for their models and copycat versions of pop stars."

"All of this probably means I should be worried about recent trends in artificial intelligence, which is encroaching on voice-over work in a manner similar to how it threatens the labour of visual artists and writers-both financially and ethically. The creep is only just beginning, with dubbing companies training software to replace human actors and tech companies introducing digital audiobook narration. But AI poses a threat to work opportunities across the board by giving producers the tools to recreate their favourite voices on demand, without the performer's knowledge or consent and without additional compensation. It's clear that AI will transform the arts sector, and the voice-over industry offers an early, unsettling model for what this future may look like."

"Wooldridge says copyright is a "coming storm" for AI companies. LLMs are likely to have accessed copyrighted material, such as news articles. Indeed the GPT-4-assisted chatbot attached to Microsoft's Bing search engine cites news sites in its answers. "I didn't give explicit permission for my works to be used as training data, but they almost certainly were, and now they contribute to what these models know," he says. "Many artists are gravely concerned that their livelihoods are at risk from generative AI. Expect to see legal battles," he adds."

"A pilot study by academics at the University of Oxford found some care providers had been using generative AI chatbots such as ChatGPT and Bard to create care plans for people receiving care. That presents a potential risk to patient confidentiality, according to Dr Caroline Green, an early career research fellow at the Institute for Ethics in AI at Oxford, who surveyed care organisations for the study. "If you put any type of personal data into [a generative AI chatbot], that data is used to train the language model," Green said. "That personal data could be generated and revealed to somebody else." She said carers might act on faulty or biased information and inadvertently cause harm, and an AI-generated care plan might be substandard."

"The new AI Index report notes that in 2023, AI still struggled with complex cognitive tasks like advanced math problem-solving and visual commonsense reasoning. However, 'struggled' here might be misleading; it certainly doesn't mean AI did badly. Performance on MATH, a dataset of 12,500 challenging competition-level math problems, improved dramatically in the two years since its introduction. In 2021, AI systems could solve only 6.9% of problems. By contrast, in 2023, a GPT-4-based model solved 84.3%. The human baseline is 90%. "

"Training a massive AI model, the size of GPT-4, would currently take about 8,000 H100 chips, and 15 megawatts of power, Nvidia said - enough to power about 30,000 typical British homes."

"The organisation is a pioneer of the "ransomware as a service" model, whereby it outsources the target selection and attacks to a network of semi-independent "affiliates", providing them with the tools and infrastructure and taking a commission on the ransoms in return. As well as ransomware, which typically works by encrypting data on infected machines and demanding a payment for providing the decryption key, LockBit copied stolen data and threatened to publish it if the fee was not paid, promising to delete the copies on receipt of a ransom."

"One of the key features introduced is a "Correction" capability in Azure AI Content Safety. This tool aims to address the problem of AI hallucinations - instances where AI models generate false or misleading information. "When we detect there's a mismatch between the grounding context and the response… we give that information back to the AI system," Bird explained. "With that additional information, it's usually able to do better the second try.""

"Ultimately, he suggests, each user would have their own "large language model" (LLM), the underlying technology of services such as ChatGPT, which would be trained on their own speech and behaviour patterns, to let them generate extremely personalised responses to queries and requests. Such systems could be a natural progression from AI tools that already exist today. Services such as Gmail can summarise and suggest replies to emails based on previous messages, while Microsoft Teams will transcribe and summarise video conferences, automatically generating a to-do list from the contents."

"It's an interesting question. I'm almost not sure how to answer it, because there is no thinking happening on the part of an LLM. A large language model takes the prompts and the text that you give it and tries to come up with something that is responsive and useful in relation to that text. And what's interesting is that certain people - I'm thinking of Mark Andreessen most prominently - have talked about how amazing this is conceptually from an education perspective, because with LLMs you will have this infinitely patient teacher. But that's actually not what you want from a teacher. You want, in some sense, an impatient teacher who's going to push your thinking, who's going to try to understand what you're bringing to any task or educational experience, lift up the strengths that you have, and then work on building your knowledge in areas where you don't yet have it. I don't think LLMs are capable of doing any of that. As you say, there's no real thinking going on. It's just a prediction machine. There's an interaction, I guess, but it's an illusion. Is that the word you would use? Yes. It's the illusion of a conversation. "

""AI lowers the barriers to entry for creating content. You don't need coding skills or anything like that to generate these images. It is also symptomatic of far-right views going mainstream or being normalised," he said, adding that the far right appeared to have fewer moral concerns about AI imagery. Allchorn said more established political parties appeared warier of using AI in official campaigns: "Mainstream actors still have ethical concerns about the effectiveness, authenticity and reliability of these models that far-right or extremist actors are not beholden to.""

"But what is pitched as a more convenient way of looking up information online has prompted scrutiny over how and where these chatbots select the information they provide. Looking into the sort of evidence that large language models (LLMs, the engines on which chatbots are built) find most convincing, three computer science researchers from the University of California, Berkeley, found current chatbots overrely on the superficial relevance of information. They tend to prioritise text that includes pertinent technical language or is stuffed with related keywords, while ignoring other features we would usually use to assess trustworthiness, such as the inclusion of scientific references or objective language free of personal bias."

"Imagine sitting down with an AI model for a spoken two-hour interview. A friendly voice guides you through a conversation that ranges from your childhood, your formative memories, and your career to your thoughts on immigration policy. Not long after, a virtual replica of you is able to embody your values and preferences with stunning accuracy. That's now possible, according to a new paper from a team including researchers from Stanford and Google DeepMind, which has been published on arXiv and has not yet been peer-reviewed."

"AI Outperforms Humans in Theory of Mind Tests Large language models convincingly mimic the understanding of mental states"

"According to a new report from 404Media, as originally reported in Alexios Mantzarlis' Faked Up newsletter, the Crush AI modifier app receives the vast majority of its traffic from Meta platforms such as Facebook and Instagram. According to Similarweb's data, last month Crush AI received more than a quarter of a million visits to its service - and roughly 90 percent of that traffic was from Meta's platforms. Crush AI makes it quite clear what the purpose of its service is in its advertisements. The company's ads include photos of real-life individuals like Instagram influencer and model Mikayla Demaiter and OnlyFans creator Sophie Rain. The ads boast that users can "upload a photo" and "erase anyone's clothes.""

"Brands such as fintech company Ramp, jobs search site Indeed and Pernod Ricard-owned Scottish whisky maker Chivas Brothers have adopted the software. They are hoping to reach millions of users who regularly use generative AI products as a new method to search for information online - a shift that poses a long-term threat for Google's main business. "This is about much more than just getting your website indexed in their results. This is about recognising large language models as the ultimate influencer," said Jack Smyth, partner at marketing technology group Brandtech, which has created its own interface for brands. "

"When employees at meta started developing their flagship AI model, Llama 3, they faced a simple ethical question. The program would need to be trained on a huge amount of high-quality writing to be competitive with products such as ChatGPT, and acquiring all of that text legally could take time. Should they just pirate it instead?"

"Umi is a digital wellness platform and personalized family wellness coach that integrates AI and a community of experts. By consolidating wellness data into personalized, actionable pathways for every family, Umi helps people build healthy habits and connect through wellness routines that work for everyone. And by leveraging the power of Anthropic's Claude AI model, Umi benefits from the advanced reasoning capabilities to navigate the unique challenges faced by individuals and families."

"This cycle is particularly painful for non-engineers because they lack the mental models to understand what's actually going wrong. When an experienced developer encounters a bug, they can reason about potential causes and solutions based on years of pattern recognition. Without this background, you're essentially playing whack-a-mole with code you don't fully understand."

"But Duolingo now seems to have changed its tune, at least in terms of hiring. CEO Luis von Ahn wrote in a LinkedIn post earlier this week: "To be clear: I do not see AI as replacing what our employees do (we are, in fact, continuing to hire at the same speed as before). I see it as a tool to accelerate what we do, at the same or better level of quality. And the sooner we learn how to use it-and use it responsibly-the better off we will be in the long run." Though many language learners obviously appreciate the human touch on their materials, Duolingo isn't the only one leaning toward AI for language education. Last month, Google applied its flagship Google Gemini AI model to create three new tools, dubbed Little Language Lessons, accessible via the Google Labs page. However, Google did dub the new set of tools as "just an early exploration.""