Output from generative AI such as ChatGPT, can be repetitive and biased. But more worrying is that this output can mysteriously tip mid-response from good (correct) to bad (misleading or wrong) without the user noticing. In 2024 alone, this reportedly caused $67 billion in losses and several deaths. Establishing a mathematical mapping to a multispin thermal system, we reveal a hidden tipping instability at the scale of the AI's 'atom' (basic Attention head). We derive a simple but essentially exact formula for this tipping point which shows directly the impact of a user's prompt choice and the AI's training bias. We then show how the output tipping can get amplified by the AI's multilayer architecture. As well as helping improve AI transparency, explainability and performance, our results open a path to quantifying users' AI risk and legal liabilities.

With its Gemini chatbot and Pixel AI phone software, it's fair to say Google has an obsessive focus on artificial intelligence. But all that advanced computational power requires millions of computers, known as'servers', housed inside data centres across the world that operate 24/7. Now, in an attempt to cater to its vast AI needs, Google is going nuclear. The tech giant has signed a deal with California-based nuclear firm Kairos Power to build new nuclear reactors to supply its US data centres with energy. Although the location of these reactors is yet to be revealed, Google said the first will be operational in 2030, with more to follow by 2035.

Cluster analysis, which focuses on the grouping and categorization of similar elements, is widely used in various fields of research. A novel and fast clustering algorithm, fission clustering algorithm, is proposed in recent year. In this article, we propose a robust fission clustering (RFC) algorithm and a self-adaptive noise identification method. The RFC and the self-adaptive noise identification method are combine to propose a self-adaptive robust fission clustering (SARFC) algorithm. Several frequently-used datasets were applied to test the performance of the proposed clustering approach and to compare the results with those of other algorithms. The comprehensive comparisons indicate that the proposed method has advantages over other common methods.

Roger Bootle is not afraid to think and say unconventional things. He is that rare phenomenon: a professional economist who thinks that Brexit is a Good Idea. Indeed, he belongs to a group called Economists for Brexit, now renamed as Economists for Free Trade, which argues for a no-deal Brexit. Whatever you think of that, the economics consultancy that Bootle founded, Capital Economics, has been very successful financially, and in 2012 it was awarded the £250,000 Wolfson Economics Prize, the second most valuable economics prize in the world after the Nobel, for a proposal that EU member states who wanted to exit should default on a large part of their debts. A book on tech unemployment from such a high-profile economist is to be warmly welcomed.

A version of this story appeared in STAT Health Tech, our weekly newsletter about how tech is transforming health care and the life sciences. Sign up here to receive it in your inbox. You're looking at two versions of the same video of a moment in a single cell, captured under a powerful microscope. The red and yellow structures are mitochondria, and the inset magnified in the bottom left hand corner in each view captures a mitochondrion dividing. The view on the left shows the raw data as it came off the microscope; you might think of it like a social media influencer's first take, before any filters have been applied to get that Instagram-ready look.

Cluster analysis which focuses on the grouping and categorization of similar elements is widely used in various fields of research. Inspired by the phenomenon of atomic fission, a novel density-based clustering algorithm is proposed in this paper, called fission clustering (FC). It focuses on mining the dense families of a dataset and utilizes the information of the distance matrix to fissure clustering dataset into subsets. When we face the dataset which has a few points surround the dense families of clusters, K-nearest neighbors local density indicator is applied to distinguish and remove the points of sparse areas so as to obtain a dense subset that is constituted by the dense families of clusters. A number of frequently-used datasets were used to test the performance of this clustering approach, and to compare the results with those of algorithms. The proposed algorithm is found to outperform other algorithms in speed and accuracy.

These eye-opening designs propose a future of flight that is both supersonic and eco-friendly. The hypersonic plane would cut travel times between New York and London to just three hours, more than halving the duration of current flights. What's more, the amazing aircraft will produce zero carbon emissions, thanks to the compact fusion reactor that will power it. According to designer Oscar Vinals, the craft, which he hopes will revolutionise the aeronautic industry, will run primarily on a compact fusion reactor (CFR). This reactor, the plans say, would provide the Magavem with an incredible amount of electrical energy - all at no cost to the environment.

If you worry that the robots are coming, don't, because they are already here. Artificial intelligence agents are already involved in every aspect of our lives - they keep our inboxes free of spam, they help us make our web transactions, they fly our planes and if Google gets its way will also soon drive our cars for us. "AI's are embedded in the fabric of our everyday lives," head of AI at Singularity University, Neil Jacobstein, told the BBC. "They are used in medicine, in law, in design and throughout automotive industry." And each day the algorithms that power away, making decisions behind the scenes, are getting smarter.

How did you become interested in artificial intelligence (AI)? I am a documentary filmmaker, writer and speaker. I was making a film around 15 years ago about AI and got to speak to some of the major players in the field, including Ray Kurzweil, the Director of Engineering at Google who started the singularity industry, and Rodney Brookes, the premier roboticist of our time who founded iRobot (a company that created the Roomba vaccum cleaner and robots for military use) and then established a company called Rethink Robotics. Both Kurzweil and Brookes were optimistic about the time when we will share the planet with smarter-than-human machines – and I was too. I was, and still am, a gigantic proponent of AI, despite my book's title Our Final Invention: Artificial Intelligence and the End of the Human Era.

Belief fusion is the principle of combining separate beliefs or bodies of evidence originating from different sources. Depending on the situation to be modelled, different belief fusion methods can be applied. Cumulative and averaging belief fusion is defined for fusing opinions in subjective logic, and for fusing belief functions in general. The principle of fission is the opposite of fusion, namely to eliminate the contribution of a specific belief from an already fused belief, with the purpose of deriving the remaining belief. This paper describes fission of cumulative belief as well as fission of averaging belief in subjective logic. These operators can for example be applied to belief revision in Bayesian belief networks, where the belief contribution of a given evidence source can be determined as a function of a given fused belief and its other contributing beliefs.