The Good Robot podcast: what makes a drone "good"? Hosted by Eleanor Drage and Kerry McInerney, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. What makes a drone "good"? In this episode, we talk to Beryl Pong, UKRI Future Leaders Fellow at the University of Cambridge, where she leads the Centre for Drones and Culture. Beryl reflects on what it means to think about drones as "good" or "ethical" technologies and how it can be assessed through its socio-political context.

Eric Dane dead at 53: Grey's Anatomy star dies after courageous battle with ALS... less than a year after announcing diagnosis RICHARD KAY: Andrew's fall may now be complete. The question is... Will he bring down the House of Windsor with him? Alysa Liu finally ends America's 24-year wait for a Winter Olympics figure skating gold medal as she wins nerve-shredding final The tide of sleaze rolling over Beatrice, Eugenie and Fergie is going to capsize them all. My stalker said he'd rape and dismember me. Then he turned his depraved sights on my seven-year-old daughter, says EVA LARUE.

Don't make us security guards, says teacher stabbed by pupil A teacher who thought she was going to die when she was stabbed by a 13-year-old pupil in the schoolyard has said giving staff handheld scanners will not stop violence in schools. Liz Hopkin, who was attacked at Ysgol Dyffryn Aman in 2024, said she felt really worried after the Welsh government announced it would offer school staff more guidance on what to do if they suspected a pupil had brought a weapon into school. It comes as a 15-year-old boy was charged with attempted murder after a teacher was stabbed at a school in the neighbouring county. Hopkin said teachers aren't security, while the Welsh government said the resources were about prevention, building on existing guidance. Hopkin, her colleague Fiona Elias and a pupil were attacked at the school where she worked in Ammanford, Carmarthenshire, by a girl who had previously been found with a knife.

Starmer'appeasing' big tech firms, says online safety campaigner A leading campaigner has accused the prime minister of appeasing big tech companies and being late to the party in regulating social media and artificial intelligence. Crossbench peer Baroness Kidron told the BBC Sir Keir Starmer needed to get on with it rather than launching more consultations. She also criticised the PM for citing his own experience as a father of two teenage children on social media, arguing that this did not make him an expert on the subject and that his family were sheltered compared to others. The government rejected the claims, with a spokesperson saying it had already introduced some of the strongest online safety protections in the world. Sir Keir has launched a consultation on banning under-16s from social media and promised to crackdown on the addictive elements of the apps.

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.

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".

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.

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.

The proliferation of Large Language Models (LLMs) necessitates efficient mechanisms to distinguish machine-generated content from human text. While statistical watermarking has emerged as a promising solution, existing methods suffer from two critical limitations: the lack of a principled approach for selecting sampling distributions and the reliance on fixed-horizon hypothesis testing, which precludes valid early stopping. In this paper, we bridge this gap by developing the first e-value-based watermarking framework, Anchored E-Watermarking, that unifies optimal sampling with anytime-valid inference. Unlike traditional approaches where optional stopping invalidates Type-I error guarantees, our framework enables valid, anytime-inference by constructing a test supermartingale for the detection process. By leveraging an anchor distribution to approximate the target model, we characterize the optimal e-value with respect to the worst-case log-growth rate and derive the optimal expected stopping time. Our theoretical claims are substantiated by simulations and evaluations on established benchmarks, showing that our framework can significantly enhance sample efficiency, reducing the average token budget required for detection by 13-15% relative to state-of-the-art baselines.