The Guardian revealed how people were being put at risk of harm by false and misleading health information in Google AI Overviews. The Guardian revealed how people were being put at risk of harm by false and misleading health information in Google AI Overviews. Exclusive: England and Wales charity to examine safeguards after Guardian exposed'very dangerous' advice on Google AI Overviews'Very dangerous': a Mind mental health expert on Google's AI summaries Mind is launching a significant inquiry into artificial intelligence and mental health after a Guardian investigation exposed how Google's AI Overviews gave people "very dangerous" medical advice. In a year-long commission, the mental health charity, which operates in England and Wales, will examine the risks and safeguards required as AI increasingly influences the lives of millions of people affected by mental health issues worldwide. The inquiry - the first of its kind globally - will bring together the world's leading doctors and mental health professionals, as well as people with lived experience, health providers, policymakers and tech companies.

Indian Prime Minister Narendra Modi takes a group photo with leaders of artificial intelligence companies at the AI Impact Summit in New Delhi on Thursday. But analysts said the country was unlikely to have a "DeepSeek moment" -- the sort of boom China had last year with a high-performance, low-cost chatbot -- any time soon. Still, building custom AI tools could bring benefits to the world's most populous nation. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories.

Documents say customs officers in the US Virgin Islands had friendly relationships with Epstein years after his 2008 conviction, showing how the infamous sex offender tried to cultivate allies. United States prosecutors and federal law enforcement spent over a year examining ties between Jeffrey Epstein and Customs and Border Protection officers stationed in the US Virgin Islands (USVI), according to documents recently released by the Department of Justice. As The Guardian and New York Times have reported, emails, text messages, and investigative records show that Epstein cultivated friendships with several officers, entertaining them on his island and offering to take them for whale-watching trips in his helicopter. He even brought one cannolis for Christmas Eve. In turn, Epstein would bring certain officers his complaints about his treatment at the hands of other CBP and federal agents.

How the US left Ukraine exposed to Russia's winter war Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? Russian forces launched 448 attacks on 34 settlements in Ukraine's front-line Zaporizhia region in a single day, injuring a six-year-old child and damaging homes, cars and other infrastructure, regional governor Ivan Fedorov wrote on the Telegram app. Russian drone, missile and artillery attacks on Ukraine's Kherson region injured five people and damaged homes, including seven high-rise buildings, the local military administration said on Telegram. Russian attacks also continued in Ukraine's Dnipropetrovsk and Sumy regions, but local officials there noted that "fortunately, no people were injured".

The Fulu Foundation, a nonprofit that pays out bounties for removing user-hostile features, is hunting for a way to keep Ring cameras from sending data to Amazon--without breaking the hardware. Usually, when you see a feel-good story about finding a lost dog, you don't immediately react with fear and revulsion. But that was indeed the case in response to a Super Bowl commercial from Amazon-owned security camera company Ring. There's now a group offering to dole out a $10,000 bounty to wrest back control of the user data Ring controls. The ad showed off a new feature from Ring called Search Party.

On the 30th floor of the Chartered Square building in downtown Bangkok, the low-key office of Skyhub Technologies serves as a nexus for a burgeoning and contentious trade. The space, rented out by a serviced office provider, is visited only rarely by the company's sole director and occasionally by Chinese nationals, according to building staff who asked not to be identified speaking about clients. No contact number is listed on its online registration documents. No one was available during a visit in late January. Despite the appearance of inactivity, this is a busy conduit for advanced drones. Trade documents show that Skyhub Technologies is Thailand's second-biggest importer of unmanned aerial vehicles from China.

This paper introduces a distillation framework for an ensemble of entropy-optimal Sparse Probabilistic Approximation (eSPA) models, trained exclusively on satellite-era observational and reanalysis data to predict ENSO phase up to 24 months in advance. While eSPA ensembles yield state-of-the-art forecast skill, they are harder to interpret than individual eSPA models. We show how to compress the ensemble into a compact set of "distilled" models by aggregating the structure of only those ensemble members that make correct predictions. This process yields a single, diagnostically tractable model for each forecast lead time that preserves forecast performance while also enabling diagnostics that are impractical to implement on the full ensemble. An analysis of the regime persistence of the distilled model "superclusters", as well as cross-lead clustering consistency, shows that the discretised system accurately captures the spatiotemporal dynamics of ENSO. By considering the effective dimension of the feature importance vectors, the complexity of the input space required for correct ENSO phase prediction is shown to peak when forecasts must cross the boreal spring predictability barrier. Spatial importance maps derived from the feature importance vectors are introduced to identify where predictive information resides in each field and are shown to include known physical precursors at certain lead times. Case studies of key events are also presented, showing how fields reconstructed from distilled model centroids trace the evolution from extratropical and inter-basin precursors to the mature ENSO state. Overall, the distillation framework enables a rigorous investigation of long-range ENSO predictability that complements real-time data-driven operational forecasts.

Learning to Defer (L2D) enables a classifier to abstain from predictions and defer to an expert, and has recently been extended to multi-expert settings. In this work, we show that multi-expert L2D is fundamentally more challenging than the single-expert case. With multiple experts, the classifier's underfitting becomes inherent, which seriously degrades prediction performance, whereas in the single-expert setting it arises only under specific conditions. We theoretically reveal that this stems from an intrinsic expert identifiability issue: learning which expert to trust from a diverse pool, a problem absent in the single-expert case and renders existing underfitting remedies failed. To tackle this issue, we propose PiCCE (Pick the Confident and Correct Expert), a surrogate-based method that adaptively identifies a reliable expert based on empirical evidence. PiCCE effectively reduces multi-expert L2D to a single-expert-like learning problem, thereby resolving multi expert underfitting. We further prove its statistical consistency and ability to recover class probabilities and expert accuracies. Extensive experiments across diverse settings, including real-world expert scenarios, validate our theoretical results and demonstrate improved performance.

The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments. Existing methods typically require labeled data from multiple training environments, limiting their applicability when labeled data are scarce. In this work, we study domain generalization in an anti-causal setting, where the outcome causes the observed covariates. Under this structure, environment perturbations that affect the covariates do not propagate to the outcome, which motivates regularizing the model's sensitivity to these perturbations. Crucially, estimating these perturbation directions does not require labels, enabling us to leverage unlabeled data from multiple environments. We propose two methods that penalize the model's sensitivity to variations in the mean and covariance of the covariates across environments, respectively, and prove that these methods have worst-case optimality guarantees under certain classes of environments. Finally, we demonstrate the empirical performance of our approach on a controlled physical system and a physiological signal dataset.

Graph neural networks (GNNs) work remarkably well in semi-supervised node regression, yet a rigorous theory explaining when and why they succeed remains lacking. To address this gap, we study an aggregate-and-readout model that encompasses several common message passing architectures: node features are first propagated over the graph then mapped to responses via a nonlinear function. For least-squares estimation over GNNs with linear graph convolutions and a deep ReLU readout, we prove a sharp non-asymptotic risk bound that separates approximation, stochastic, and optimization errors. The bound makes explicit how performance scales with the fraction of labeled nodes and graph-induced dependence. Approximation guarantees are further derived for graph-smoothing followed by smooth nonlinear readouts, yielding convergence rates that recover classical nonparametric behavior under full supervision while characterizing performance when labels are scarce. Numerical experiments validate our theory, providing a systematic framework for understanding GNN performance and limitations.