Five big questions about the UK's under-16s social media ban After the government's announcement on Monday, we know a social media ban is coming for under-16s in the UK . However, details on which apps are and are not included, besides those named by the government, and how the measures will extend to gaming sites like Roblox, remain sparse. And many are already asking whether enforcing the ban will mean cracking down on virtual private networks (VPNs), which can disguise someone's location online. Ministers have said they will provide an update on further restrictions like potential curfews, curbing of addictive features like infinite scroll and AI chatbots, in July. But here are some of the big unanswered questions about the UK social media ban.

On Friday, the U.S. ordered Anthropic to deny foreign nationals access to the company's newest artificial intelligence models. With the recent crackdown on Anthropic, the White House has given global leaders another reason to panic about their place in the technology race. On Friday, the U.S. ordered Anthropic to deny foreign nationals access to the company's newest artificial intelligence models. The export ban asserted a broad, unprecedented authority over the technology. Until then, conversations in Europe about losing access to U.S. tech -- sometimes posed as a presidential "kill switch" -- were theoretical. To many on the continent, Friday's move underscored the dire need to find alternatives to American AI, and fast.

AlphaZero has achieved remarkable success in complex decision-making problems through self-play and neural network training. However, its self-play process remains inefficient due to limited exploration of high-uncertainty positions, the overlooked runner-up decisions in Monte Carlo Tree Search (MCTS), and high variance in value labels. To address these challenges, we propose and evaluate uncertainty-guided exploration by branching from high-uncertainty positions using our proposed Label Change Rate (LCR) metric, which is further refined by a Bayesian inference framework. Our proposed approach leverages runner-up MCTS decisions to create multiple variations, and ensembles value labels across these variations to reduce variance. We investigate three key design parameters for our branching strategy: where to branch, how many variations to branch, and which move to play in the new branch. Our empirical findings indicate that branching with 10 variations per game provides the best performance-exploration balance. Overall, our end-to-end results show an improved sample efficiency over the baseline by 58.5% on 9x9 Go in the early stage of training and by 47.3% on 19x19 Go in the late stage of training.

The rapid proliferation of domain-specialized machine learning models presents a challenge: while individual models excel in specific domains, their performance varies significantly across diverse applications. This makes selecting the optimal model when faced with an unknown mixture of tasks, especially with limited or no data to estimate the mixture, a difficult problem. We address this challenge by formulating it as a multiple-source domain adaptation (MSA) problem. We introduce a novel, scalable algorithm that effectively routes each input to the best-suited model from a pool of available models. Our approach provides a strong performance guarantee: remarkably, for any mixture domain, the accuracy achieved by the best source model is maintained. This guarantee is established through a theoretical bound on the regret for new domains, expressed as a convex combination of the best regrets in the source domains, plus a concentration term that diminishes as the amount of source data increases. While our primary contributions are theoretical and algorithmic, we also present empirical results demonstrating the effectiveness of our approach.

We study online convex optimisation on ℓp-balls in Rd for p > 2. While always sub-linear, the optimal regret exhibits a shift between the high-dimensional setting (d > T), when the dimension d is greater than the time horizon T and the low-dimensional setting (d T). We show that Follow-the-Regularised-Leader (FTRL) with time-varying regularisation which is adaptive to the dimension regime is anytime optimal for all dimension regimes. Motivated by this, we ask whether it is possible to obtain anytime optimality of FTRL with fixed non-adaptive regularisation. Our main result establishes that for separable regularisers, adaptivity in the regulariser is necessary, and that any fixed regulariser will be sub-optimal in one of the two dimension regimes. Finally, we provide lower bounds which rule out sublinear regret bounds for the linear bandit problem in sufficiently high-dimension for all ℓp-balls with p 1.

The law of supply and demand asserts that in a perfectly competitive market, the price of a good adjusts to a market clearing price. In a market clearing price p the number of sellers willing to sell the good at p equals the number of sellers willing to buy the good at price p . In this work, we provide a mathematical foundation on the law of supply and demand through the lens of online learning. Specifically, we demonstrate that if each seller employs a no-swap regret algorithm to set their individual selling price--aiming to maximize its individual revenue--the collective pricing dynamics converge to the market-clearing price p . Our findings offer a novel perspective on the law of supply and demand, framing it as the emergent outcome of an adaptive learning processes among sellers.

Causal discovery for dynamical systems poses a major challenge in fields where active interventions are infeasible. Most methods used to investigate these systems and their associated benchmarks are tailored to deterministic, low-dimensional and weakly nonlinear time-series data. To address these limitations, we present CausalDynamics, a large-scale benchmark and extensible data generation framework to advance the structural discovery of dynamical causal models. Our benchmark consists of true causal graphs derived from thousands of both linearly and nonlinearly coupled ordinary and stochastic differential equations as well as two idealized climate models. We perform a comprehensive evaluation of state-of-the-art causal discovery algorithms for graph reconstruction on systems with noisy, confounded, and lagged dynamics. CausalDynamics consists of a plug-and-play, build-yourown coupling workflow that enables the construction of a hierarchy of physical systems. We anticipate that our framework will facilitate the development of robust causal discovery algorithms that are broadly applicable across domains while addressing their unique challenges. We provide a user-friendly implementation and documentation on https://kausable.github.io/CausalDynamics.

Ultrasound (US) is a widely used medical imaging modality due to its real-time capabilities, non-invasive nature, and cost-effectiveness. Robotic ultrasound can further enhance its utility by reducing operator dependence and improving access to complex anatomical regions. For this, while deep reinforcement learning (DRL) and imitation learning (IL) have shown potential for autonomous navigation, their use in complex surgical tasks such as anatomy reconstruction and surgical guidance remains limited -- largely due to the lack of realistic and efficient simulation environments tailored to these tasks. We introduce SonoGym, a scalable simulation platform for complex robotic ultrasound tasks that enables parallel simulation across tens to hundreds of environments. Our framework supports realistic and real-time simulation of US data from CT-derived 3D models of the anatomy through both a physics-based and a generative modeling approach.

Police in Poland are investigating the execution-style murder of a Russian artist and vocal critic of President Vladimir Putin. Polish prosecutors said Robert K, known as the artist Semyon Skrepetsky, was shot dead on Monday morning in the Polish city of Biała Podlaska, about 40km (25 miles) from the Belarusian border. The 44-year-old was shot five times in the head, chest and back in a car park in the city, located about 600m from the Belarusian consulate. He was known for his caricatures of politicians, including Putin, Belarusian leader Alexander Lukashenko and Chechen leader Ramzan Kadyrov. Marcin Kozak, spokesman for the District Prosecutor's Office in Lublin, said the artist was approached by an unidentified gunman who fired two shots at him.

We introduce a class of neural networks named PLEIADES (PoLynomial Expansion In Adaptive Distributed Event-based Systems), which contains temporal convolution kernels generated from orthogonal polynomial basis functions. We focus on interfacing these networks with event-based data to perform online spatiotemporal classification and detection with low latency. By virtue of using structured temporal kernels and event-based data, we have the freedom to vary the sample rate of the data along with the discretization step-size of the network without additional finetuning. We experimented with three event-based benchmarks and obtained state-of-the-art results on all three by large margins with significantly smaller memory and compute costs. We achieved: 1) 99.59% accuracy with 192K parameters on the DVS128 hand gesture recognition dataset and 100% with a small additional output filter; 2) 99.58% test accuracy with 277K parameters on the AIS 2024 eye tracking challenge; and 3) 0.556 mAP with 576k parameters on the PROPHESEE 1 Megapixel Automotive Detection Dataset.