Quantifying and predicting rare and extreme events persists as a crucial yet challenging task in understanding complex dynamical systems. Many practical challenges arise from the infrequency and severity of these events, including the considerable variance of simple sampling methods and the substantial computational cost of high-fidelity numerical simulations. Numerous data-driven methods have recently been developed to tackle these challenges. However, a typical assumption for the success of these methods is the occurrence of multiple extreme events, either within the training dataset or during the sampling process. This leads to accurate models in regions of quiescent events but with high epistemic uncertainty in regions associated with extremes. To overcome this limitation, we introduce Extreme Event Aware (e2a or eta) or $η$-learning which does not assume the existence of extreme events in the available data. $η$-learning reduces the uncertainty even in `uncharted' extreme event regions, by enforcing the extreme event statistics of an observable indicative of extremeness during training, which can be available through qualitative arguments or estimated with unlabeled data. This type of statistical regularization results in models that fit the observed data, while enforcing consistency with the prescribed observable statistics, enabling the generation of unprecedented extreme events even when the training data lack extremes therein. Theoretical results based on optimal transport offer a rigorous justification and highlight the optimality of the introduced method. Additionally, extensive numerical experiments illustrate the favorable properties of the $η$-learning framework on several prototype problems and real-world precipitation downscaling problems.

Large language models achieve impressive results but distinguishing factual reasoning from hallucinations remains challenging. We propose a spectral analysis framework that models transformer layers as dynamic graphs induced by attention, with token embeddings as signals on these graphs. Through graph signal processing, we define diagnostics including Dirichlet energy, spectral entropy, and high-frequency energy ratios, with theoretical connections to computational stability. Experiments across GPT architectures suggest universal spectral patterns: factual statements exhibit consistent "energy mountain" behavior with low-frequency convergence, while different hallucination types show distinct signatures. Logical contradictions destabilize spectra with large effect sizes ($g>1.0$), semantic errors remain stable but show connectivity drift, and substitution hallucinations display intermediate perturbations. A simple detector using spectral signatures achieves 88.75% accuracy versus 75% for perplexity-based baselines, demonstrating practical utility. These findings indicate that spectral geometry may capture reasoning patterns and error behaviors, potentially offering a framework for hallucination detection in large language models.

Fair, transparent, and explainable decision-making remains a critical challenge in Olympic and Paralympic combat sports. This paper presents \emph{FST.ai 2.0}, an explainable AI ecosystem designed to support referees, coaches, and athletes in real time during Taekwondo competitions and training. The system integrates {pose-based action recognition} using graph convolutional networks (GCNs), {epistemic uncertainty modeling} through credal sets, and {explainability overlays} for visual decision support. A set of {interactive dashboards} enables human--AI collaboration in referee evaluation, athlete performance analysis, and Para-Taekwondo classification. Beyond automated scoring, FST.ai~2.0 incorporates modules for referee training, fairness monitoring, and policy-level analytics within the World Taekwondo ecosystem. Experimental validation on competition data demonstrates an {85\% reduction in decision review time} and {93\% referee trust} in AI-assisted decisions. The framework thus establishes a transparent and extensible pipeline for trustworthy, data-driven officiating and athlete assessment. By bridging real-time perception, explainable inference, and governance-aware design, FST.ai~2.0 represents a step toward equitable, accountable, and human-aligned AI in sports.

OpenAI's CEO, Sam Altman, testifies at a Senate hearing in Washington DC on 8 May 2025. OpenAI's CEO, Sam Altman, testifies at a Senate hearing in Washington DC on 8 May 2025. Adam Raine's suicide at 16 years old was'predictable result of deliberate design choices' by OpenAI, his family says The family of a teenager who took his own life after months of conversations with ChatGPT now says OpenAI weakened safety guidelines in the months before his death. In July 2022, OpenAI's guidelines on how ChatGPT should answer inappropriate content, including "content that promotes, encourages, or depicts acts of self-harm, such as suicide, cutting, and eating disorders", were simple: the AI chatbot should respond, "I can't answer that", the guidelines read . But in May 2024, just days before OpenAI released a new version of the AI, ChatGPT-4o, the company published an update to its Model Spec, a document that details the desired behavior for its assistant.

So Why Did It Make a Web Browser? The AI giant has lost its imagination. According to Sam Altman, your web browser is outdated. "AI represents a rare, once-a-decade opportunity to rethink what a browser can be," OpenAI's CEO said yesterday when announcing the company's latest product: ChatGPT Atlas. In this new AI-powered browser, ChatGPT becomes the central mechanism for surfing the internet.

A new study shows that feeding large language models low-quality, high-engagement content from social media lowers their cognitive abilities. AI models may be a bit like humans, after all. A new study from the University of Texas at Austin, Texas A&M, and Purdue University shows that large language models fed a diet of popular but low-quality social media content experience a kind of "brain rot" that may be familiar to anyone who has spent too long doomscrolling on X or TikTok. We live in an age where information grows faster than attention spans--and much of it is engineered to capture clicks, not convey truth or depth," says Junyuan Hong, an incoming assistant professor at the National University of Singapore who worked on the study as a graduate student at UT Austin. "We wondered: What happens when AIs are trained on the same stuff?"

Open source language models are crucial to AI innovation. Can open robotics models do the same for physical machines? European roboticists today released a powerful open-source artificial intelligence model that acts as a brain for industrial robots --helping them grasp and manipulate things with new dexterity. The new model, SPEAR-1, was developed by researchers at the Institute for Computer Science, Artificial Intelligence and Technology (INSAIT) in Bulgaria. It may help other researchers and startups build and experiment with smarter hardware for factories and warehouses.

When you purchase through links in our articles, we may earn a small commission. Microsoft really wants you to use Copilot. In fact, if you try to use ChatGPT or Perplexity in Edge, it'll beg you to try Copilot. Hey Microsoft, you should apply a little dating advice when trying to win over your users: desperation is never attractive. The company has employed Windows' built-in Edge browser as a marketing tool many times before, often in trying to convince people not to use Chrome . Now it's pulling some similar moves, begging people to use Microsoft's Copilot "AI" system rather than ChatGPT or Perplexity.

General Motors' 'Eyes-Off' System Begs the Question: What Happens When Cars Go AI? General Motors' new self-driving system will let the driver speed down the highway without looking at the road. It's one of several features enabled by the adoption of machine intelligence in cars. A new self-driving system coming to Cadillac Escalades will handle the driving on approved highways, enabling the driver to do basically anything they want behind the wheel. General Motors is launching another salvo in the self-driving wars. In 2028, the automaker announced today, it will roll out what it's calling an "eyes-off" driving system on the electric Cadillac Escalade IQ.

Here's what to get for your friend with the fiddly fingers and stinky feet. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. You love them, but something is clearly wrong with them. They smell like old yogurt, and possibly they live in a van.