US President Donald Trump will meet Chinese President Xi Jinping in Beijing on May 14 and 15, following weeks of delays due to the US-Israel war on Iran. The talks are expected to focus on trade relations and mark the first time a US president has visited China in nearly a decade. In recent decades, the US and China have emerged as the world's dominant superpowers, frequently seen as locked in a contest for who sits atop the world order. A quarter of a century ago, by contrast, the US dwarfed China in most major indicators, but today, Beijing is regarded as the factory of the world and is outpacing its Western counterpart in many regards. Who is the world's top trading power?

Smart glasses are'an invasion of privacy' - Meta's are selling better than ever Issues with a new wave of smart glasses seem to be piling up. Yet some of the biggest technology companies in the world are poised to sell many millions of pairs in the coming years. Women leaving the beach, going into a shop, or simply standing outside are now being approached by men usually wearing Meta's Ray-Bans, the company's smart or AI glasses, often in order to film the women's responses to casual questions or pick-up lines without their knowledge or consent. The women only find out about the videos of them after they gain traction, and often abuse, online. They have little legal recourse as photography in public is broadly considered legal.

Matt Keightley in his 2015 Chelsea garden, designed for Prince Harry. This year he is launching an AI app that has'designed' three full-size gardens for the show. Matt Keightley in his 2015 Chelsea garden, designed for Prince Harry. This year he is launching an AI app that has'designed' three full-size gardens for the show. Wed 13 May 2026 01.00 EDTLast modified on Wed 13 May 2026 01.01 EDT With glasses of champagne sipped among the peonies, Chelsea flower show is generally a friendly and genteel occasion.

OpenAI is facing another wrongful death lawsuit . Leila Turner-Scott and Angus Scott filed a lawsuit against the company, alleging that it designed and distributed a defective product that led to the death of their son Sam Nelson from an accidental overdose. Specifically, they're alleging that Sam died following the exact medical advice GPT-4o had provided and approved. In the lawsuit, the plaintiffs described how Sam, a 19-year-old junior at the University of California, Merced, started using ChatGPT in 2023 when he was in high school to help with homework and to troubleshoot computer problems. Sam then started asking the chatbot about safe drug use, but ChatGPT initially refused to answer his question, telling him that it couldn't assist him and warning him that taking drugs can have serious consequences for his health and well-being.

Is Big Brother watching you shop? - podcast From supermarkets to corner shops, live facial recognition could be coming to retailers near you. Live facial recognition is being hailed as a powerful new frontier in the fight against crime, not only by police but by private companies too. Retailers from supermarkets to corner shops hope it will help them fight back against shoplifting. And the technology doesn't always get it right. With more police forces wanting to take up the technology, what could the consequences be?

Elon Musk Had'Hair-Raising' Idea of Passing OpenAI Onto His Kids, Sam Altman Says Musk's lawyers questioned Altman over allegations of deception and his network of financial investments, but the OpenAI CEO painted a picture of Musk as obsessed with controlling the company. Sam Altman took to the witness stand to defend his reputation in the trial on Tuesday, as Elon Musk's lawyers peppered the OpenAI CEO with hours of questions regarding his alleged history of deceptive behavior . The cross examination was a much needed win for Musk, who has so far struggled to make a convincing case. Tuesday's testimony included several heated exchanges in which the OpenAI CEO had to respond to allegations from former colleagues suggesting he's untrustworthy . Highlighting this evidence is not only important for Musk winning over a jury, but also for beating OpenAI in the court of public opinion.

We study the information-theoretic limits of learning a one-hidden-layer teacher network with hierarchical features from noisy queries, in the context of knowledge transfer to a smaller student model. We work in the high-dimensional regime where the teacher width $k$ scales linearly with the input dimension $d$ -- a setting that captures large-but-finite-width networks and has only recently become analytically tractable. Using a heuristic leave-one-out decoupling argument, validated numerically throughout, we derive asymptotically sharp characterizations of the Bayes-optimal generalization error and individual feature overlaps via a system of closed fixed-point equations. These equations reveal that feature learnability is governed by a sequence of sharp phase transitions: as data grows, teacher features become recoverable sequentially, each through a discontinuous jump in overlap. This sequential acquisition underlies a precise notion of \textit{effective width} $k_c$ -- the number of learnable features at a given data budget $n$ -- which unifies two distinct scaling regimes: a feature-learning regime in which the Bayes-optimal generalization error $\varepsilon^{\rm BO}$ scales as $ n^{1/(2β)-1}$, and a refinement regime in which it scales as $n^{-1}$, where $β>1/2$ is the exponent of the power-law feature hierarchy. Both laws collapse to the single relation $\varepsilon^{\rm BO}=Θ(k_c d/n)$. We further show empirically that a student trained with \textsc{Adam} near the effective width $k_c$ achieves these optimal scaling laws (up to a small algorithmic gap), and provide an information-theoretic account of the associated scaling in model size.

Adaptive experimentation under unknown network interference requires solving two coupled problems: (i) learning the underlying dynamics of interference among units and (ii) using these dynamics to inform treatment allocation in order to maximize a cumulative outcome of interest (e.g. revenue). Existing adaptive experimentation methods either assume the interference network is fully known or bypass the network by operating on coarse cluster-level randomizations. We develop a Thompson sampling algorithm that jointly learns the interference network and adaptively optimizes individual-level treatment allocations via a Gibbs sampler. The algorithm returns both an optimized treatment policy and an estimate of the interference network; the latter supports downstream causal analyses such as estimation of direct, indirect, and total treatment effects. For additive spillover models, we show that total reward is linear in the treatment vector with coefficients given by an $n$-dimensional latent score. We prove a Bayesian regret bound of order $\sqrt{nT \cdot B \log(en/B)}$ for exact posterior sampling; empirically, our Gibbs-based approximate sampler achieves regret consistent with this rate and remains sublinear when the additive spillovers assumption is violated. For general Neighborhood Interference, where this reduction is unavailable, we analyze an explore-then-commit variant with $O(n^2 \log T)$ graph-discovery cost. An information-theoretic $Ω(n \log T)$ lower bound complements both results. Empirically, our method achieves more than an order-of-magnitude reduction in regret in head-to-head comparisons. On two real-world networks, the algorithm achieves sublinear regret and yields downstream effect estimates with small RMSE relative to the truth.

In the data-driven era, large-scale datasets are routinely collected and analyzed using machine learning (ML) and artificial intelligence (AI) to inform decisions in high-stakes domains such as healthcare, employment, and criminal justice, raising concerns about the fairness behavior of these systems. Existing works in fair ML cover tasks such as bias detection, fair prediction, and fair decision-making, but largely focus on static settings. At the same time, fairness in temporal contexts, particularly survival/time-to-event (TTE) analysis, remains relatively underexplored, with current approaches to fair survival analysis adopting statistical fairness definitions, which, even with unlimited data, cannot disentangle the causal mechanisms that generate disparities. To address this gap, we develop a causal framework for fairness in TTE analysis, enabling the decomposition of disparities in survival into contributions from direct, indirect, and spurious pathways. This provides a human-understandable explanation of why disparities arise and how they evolve over time. Our non-parametric approach proceeds in four steps: (1) formalizing the necessary assumptions about censoring and lack of confounding using a graphical model; (2) recovering the conditional survival function given covariates; (3) applying the Causal Reduction Theorem to reframe the problem in a form amenable to causal pathway decomposition; (4) estimating the effects efficiently. Finally, our approach is used to analyze the temporal evolution of racial disparities in outcome after admission to an intensive care unit (ICU).

The trustworthiness of AI decision-making systems is increasingly important. A key feature of such systems is the ability to provide recommendations for how an individual may reverse a negative decision, a problem known as algorithmic recourse. Existing approaches treat recourse outcomes as counterfactuals of a fixed unit, ignoring that real-world recourse involves repeated decisions on the same individual under possibly different latent conditions. We develop a causal framework that models recourse as a process over pre- and post-intervention outcomes, allowing for partial stability and resampling of latent variables. We introduce post-recourse stability conditions that enable reasoning about recourse from observational data alone, and develop a copula-based algorithm for inferring the effects of recourse under these conditions. For settings where paired observations of the same individual before and after intervention are available (called recourse data), we develop methods for inferring copula parameters and performing goodness-of-fit testing. When the copula model is rejected, we provide a distribution-free algorithm for learning recourse effects directly from recourse data. We demonstrate the value of the proposed methods on real and semi-synthetic datasets.