The last decade has seen tremendous progress in our ability to generate realistic-looking data, be it images, text, audio, or video. Here, we discuss the closely related problem of quantifying realism, that is, designing functions that can reliably tell realistic data from unrealistic data. This problem turns out to be significantly harder to solve and remains poorly understood, despite its prevalence in machine learning and recent breakthroughs in generative AI. Drawing on insights from algorithmic information theory, we discuss why this problem is challenging, why a good generative model alone is insufficient to solve it, and what a good solution would look like. In particular, we introduce the notion of a universal critic, which unlike adversarial critics does not require adversarial training. While universal critics are not immediately practical, they can serve both as a North Star for guiding practical implementations and as a tool for analyzing existing attempts to capture realism.

Tech giants procure huge amounts of computing power through data centers and have access to GPUs -- specialized computer chips that are necessary for running the gargantuan calculations needed for AI. These resources are expensive: A recent report from Stanford University researchers estimated Google DeepMind's large language model, Chinchilla, cost 2.1 million to develop. More than 100 top artificial intelligence researchers on Tuesday urged generative AI companies to offer a legal and technical safe harbor to researchers so they can scrutinize their products without the fear that internet platforms will suspend their accounts or threaten legal action.

Britain's hard-pressed carers need all the help they can get. But that should not include using unregulated AI bots, according to researchers who say the AI revolution in social care needs a hard ethical edge. 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.

In 1914, on the eve of the First World War, HG Wells published a novel about the possibilities of an even greater conflagration. The World Set Free imagines, 30 years before the Manhattan Project, the creation of atomic weapons that allow "a man [to] carry about in a handbag an amount of latent energy sufficient to wreck half a city". It takes the "establishment of a world government" to bring about peace. What concerned Wells was not simply the perils of a new technology, it was also the dangers of democracy. Wells' world government was not created through democratic will but imposed as a benign dictatorship.

Understanding the potential of generative AI (GenAI)-based attacks on the power grid is a fundamental challenge that must be addressed in order to protect the power grid by realizing and validating risk in new attack vectors. In this paper, a novel zero trust framework for a power grid supply chain (PGSC) is proposed. This framework facilitates early detection of potential GenAI-driven attack vectors (e.g., replay and protocol-type attacks), assessment of tail risk-based stability measures, and mitigation of such threats. First, a new zero trust system model of PGSC is designed and formulated as a zero-trust problem that seeks to guarantee for a stable PGSC by realizing and defending against GenAI-driven cyber attacks. Second, in which a domain-specific generative adversarial networks (GAN)-based attack generation mechanism is developed to create a new vulnerability cyberspace for further understanding that threat. Third, tail-based risk realization metrics are developed and implemented for quantifying the extreme risk of a potential attack while leveraging a trust measurement approach for continuous validation. Fourth, an ensemble learning-based bootstrap aggregation scheme is devised to detect the attacks that are generating synthetic identities with convincing user and distributed energy resources device profiles. Experimental results show the efficacy of the proposed zero trust framework that achieves an accuracy of 95.7% on attack vector generation, a risk measure of 9.61% for a 95% stable PGSC, and a 99% confidence in defense against GenAI-driven attack.

The day after OpenAI launched in December 2015, its co-founder Sam Altman sat down with Vanity Fair to discuss what the magazine described as "a non-profit company to save the world from a dystopian future". Altman talked up his vision for keeping artificial intelligence safe and distributing it widely, as well as his good working relationship with his co-chair – Tesla CEO Elon Musk. "I really trust him, which is obviously important to everyone involved," Altman said. Almost a decade later, Musk and Altman are locked in a public spat and looming legal battle that revolves around the end of their previous partnership and OpenAI's creation of a for-profit subsidiary now valued at 80bn. Musk filed a suit against OpenAI in a California court last week, alleging that Altman and other executives had "breached the founding agreement" of the company by pursuing private commercial success instead of working to benefit humanity.

America's founding fathers depicted as Black women and Ancient Greek warriors as Asian women and men – this was the world reimagined by Google's generative AI tool, Gemini, in late February. The launch of the new image generation feature sent social media platforms into a flurry of intrigue and confusion. When users entered any prompts to create AI-generated images of people, Gemini was largely showing them results featuring people of colour – whether appropriate or not. X users shared laughs while repeatedly trying to generate images of white people on Gemini and failing to do so. While some instances were deemed humorous online, others, such as images of brown people wearing World War II Nazi uniforms with swastikas on them, prompted outrage, prompting Google to temporarily disable the tool.

OpenAI's chief executive Sam Altman will return to the company's board of directors after a probe into his brief sacking and subsequent rehiring. An investigation by law firm WilmerHale found that Altman's conduct "did not mandate his removal" last year, OpenAI said in a blog post on Friday. Altman's firing was instead due to a "breakdown in the relationship and loss of trust" between the 38-year-old entrepreneur and the previous board, the company said. OpenAI said it had "full confidence" in Altman's ongoing leadership at the artificial intelligence startup after reviewing the law firm's findings. "WilmerHale found that the prior Board acted within its broad discretion to terminate Mr. Altman, but also found that his conduct did not mandate removal," the company said in a summary of the report.

OpenAI Chief Executive Officer Sam Altman was cleared of any wrongdoing that would have mandated being fired from the company late last year, according to a report based on a monthslong investigation into the events leading up to his brief ouster. WilmerHale, the law firm that conducted the inquiry, found that the board's decision to fire Altman was a "consequence of a breakdown in the relationship and loss of trust between the prior Board and Mr. Altman," the artificial intelligence startup said Friday. It did not arise from concerns about product safety, the pace of development, or OpenAI's finances, it said. Altman is rejoining the company's board following the findings, the artificial intelligence startup said. OpenAI's board is also adding Sue Desmond-Hellmann, who previously was head of the Bill and Melinda Gates Foundation; Nicole Seligman, an ex-Sony Entertainment executive; and Instacart CEO Fidji Simo.

Sam Altman is back on the board of OpenAI, nearly four months after the CEO was ousted, and quickly reinstated, from the company he founded. Although Altman had returned as the AI company's top executive in November, a temporary board oversaw his return and the subsequent investigation into his conduct. That investigation is now complete, according to the company, which added three new members to its board of directors. The additions include: Instacart CEO and former Meta executive Fidji Simo, former Sony executive Nicole Seligman and Dr. Sue Desmond-Hellmann, former CEO of the Bill and Melinda Gates Foundation. Salesforce co-CEO Bret Taylor, economist Larry Summers and OpenAI co-founder Greg Brockman, who served on the temporary three-seat board, will remain in their positions with Taylor continuing as chair.