OpenAI was founded in 2015 with the aim of building an artificial general intelligence (AGI) - generally taken to mean AI that can perform any task a human being is capable of. In 2019, the firm announced a new "capped profit" structure allowing it to raise money. Microsoft made an initial 1bn investment into OpenAI shortly thereafter - increasing this to a multi-year, multi-billion dollar partnership in 2023. The lawsuit also accuses boss Sam Altman - a named defendant in the lawsuit - of "rampant self-dealing". Mr Musk's initial legal action filed in March argued the agreement had transformed it into "a closed-source de facto subsidiary" of the PC giant.

Microsoft has created an artificial language that allows AI models to talk to each other faster and more efficiently than in English, with the hope that groups of models will be able to team up without having to resort to clumsy and sprawling human words. Many researchers believe that using several artificial intelligence models, each with different specialisms and abilities, to solve problems collectively holds promise for tackling thorny problems that individual ones can't solve.

ChatGPT and other similar AI chatbots are becoming a bigger part of everyday life for many people. Though a Windows app for ChatGPT has been available for some time, it's only been for those who have ponied up for a premium plan -- until now. The Windows app for ChatGPT is now available to all, just like the web and mobile apps. Poking around a bit in the Windows app, it seems more or less the same as the interface I've tested on my Android phone, though of course in Windows it's easier to input larger amounts of text or submit images for scanning. It has the popular conversation mode along with a handful of digital voice options, but it's just as restrictive as it is elsewhere -- I hit the freebie interaction limit in just a few minutes.

This year's Web Summit, in Lisbon, was all about artificial intelligence – and a robot sorting laundry. Digit, a humanoid built by the US firm Agility Robotics, demonstrated how far AI has come in a few years by responding to voice commands – filtered through Google's Gemini AI model – to sift through a pile of coloured T-shirts and place them in a basket. It wasn't a seamless demonstration but the enthusiastic response, nearly two years on from the launch of ChatGPT, reflected the excitement about all things AI that pervaded Europe's biggest annual tech conference. Talk of a bust in the AI boom could not be heard over the shouts of encouragement for Digit as it pondered different shades of garment. Nonetheless, the voices of caution were there, discussing familiar themes such as safety, jobs and the climate, as AI comes to influence a huge range of industries.

Elon Musk has amended his lawsuit against OpenAI, adding more anti-trust claims against the company and including Microsoft as a defendant. He also added his company, xAI, as well as Shivon Zilis, a former OpenAI board member and mother to three of his children, as plaintiffs. Musk originally sued OpenAI in March, accusing founders Sam Altman and Greg Brockman of violating the organization's non-profit mission by teaming up with Microsoft. He withdrew the state court lawsuit in June before suing OpenAI and Altman again in federal court. Musk was one OpenAI's earliest backers, and one of his arguments was that he was "betrayed by Mr. Altman and his accomplices."

Back in 2022, when ChatGPT arrived, I was part of the first wave of users. Delighted but also a little uncertain what to do with it, I asked the system to generate all kinds of random things. The quality of what it produced was variable, but it made clear something that is even more apparent now than it was then. Instead its arrival is an inflection point in human history. Over coming years and decades, AI will transform every aspect of our lives.

Technological progress can excite us, politics can infuriate us, and wars can mobilize us. But faced with the risk of human extinction that the rise of artificial intelligence is causing, we have remained surprisingly passive. In part, perhaps this was because there did not seem to be a solution. This is an idea I would like to challenge. Since the release of ChatGPT two years ago, hundreds of billions of dollars have poured into AI.

The most popular writers on Substack earn up to seven figures each year primarily by persuading readers to pay for their work. The newsletter platform's subscription-driven business model offers creators different incentives than platforms like Facebook or YouTube, where traffic and engagement are king. In theory, that should help shield Substack from the wave of click-courting AI content that's flooding the internet. But a new analysis shared exclusively with WIRED indicates that Substack hosts plenty of AI-generated writing, some of which is published in newsletters with hundreds of thousands of subscribers. The AI-detection startup GPTZero scanned 25 to 30 recent posts published by the 100 most popular newsletters on Substack to see whether they contained AI-generated content.

Despite their impressive capabilities, large language models are far from perfect. Due to this hallucination problem, an LLM's responses are often verified by human fact-checkers, especially if a model is deployed in a high-stakes setting like health care or finance. However, validation processes typically require people to read through long documents cited by the model, a task so onerous and error-prone it may prevent some users from deploying generative AI models in the first place. To help human validators, MIT researchers created a user-friendly system that enables people to verify an LLM's responses much more quickly. With this tool, called SymGen, an LLM generates responses with citations that point directly to the place in a source document, such as a given cell in a database.

Artificial Intelligence is rapidly embedding itself within militaries, economies, and societies, reshaping their very foundations. Given the depth and breadth of its consequences, it has never been more pressing to understand how to ensure that AI systems are safe, ethical, and have a positive societal impact. This book aims to provide a comprehensive approach to understanding AI risk. Our primary goals include consolidating fragmented knowledge on AI risk, increasing the precision of core ideas, and reducing barriers to entry by making content simpler and more comprehensible. The book has been designed to be accessible to readers from diverse backgrounds. You do not need to have studied AI, philosophy, or other such topics. The content is skimmable and somewhat modular, so that you can choose which chapters to read. We introduce mathematical formulas in a few places to specify claims more precisely, but readers should be able to understand the main points without these.