Swatch have closed their stores in cities across the UK after hundreds of people queued outside branches eager to buy a new limited watch. The Swiss firm said it would not open its branches in the capital in light of safety considerations for both our customers and our staff. Stores in Birmingham, Cardiff, Glasgow, Liverpool, Manchester and Sheffield will also remain closed. The firm had been due to launch their new Royal Pop pocket watch collaboration with luxury watchmaker Audemars Piguet, with eight models priced from £335. However the watch has been put on resale online for up to £16,000.

The 10th International Workshop on Health Intelligence (W3PHIAI-26) celebrated a decade of bringing AI and health research together, building on a lineage that began with the AAAI-W3PHI workshops focused on population health (2014-2016), the AAAI-HIAI workshops focused on personalized health (2013-2016), and the subsequent joint W3PHIAI workshops held annually from 2017 through 2025. Over this decade, the series has produced hundreds of talks and high-impact publications that have collectively received thousands of citations, shaping the research agenda in both population health intelligence and personalized healthcare AI. This year's special theme, "Foundation Models and AI Agents," reflected the field's rapidly evolving frontier: the emergence of autonomous and semi-autonomous AI systems reshaping clinical workflows, patient management, health system operations, and public health surveillance. Day 1 of the workshop focused on medical imaging and the translation of AI for clinical ...

A new collaboration with Alphabet's Wing will only deliver sandwiches. It demonstrates the tricky parts of taking to the sky. Starting today, eager customers of the US pizza restaurant chain Papa Johns living in one corner of southern North Carolina will have the opportunity to receive their food from the sky, thanks to a new collaboration with Alphabet's drone company, Wing . But Papa Johns' signature pizzas won't be on offer. Instead, drone-loving North Carolinians will have to choose between three kinds of sandwiches, a newer product for the fast-food chain: Philly cheesesteak, chicken bacon ranch, or steak and mushroom varieties.

On the 31st March, our editorial team headed to the Royal Society for AI for Science . This day-long conference explored how AI is changing the nature of scientific discovery, and was hosted by the Fundamental Research team from the Alan Turing Institute. Nestled in a terrace of 19th century townhouses along the banks of the Thames, the Royal Society looks as grand as the names who have passed through its doors throughout the years. Prof Jason McEwen, Chief Scientist for the Turing Institute, opened the event with an insightful talk on the nature of scientific revolution, and how the bidirectional relationship between AI and science could spark the next one. Then, Prof Anna Scaife from the University of Manchester spoke on the use of foundation models for astronomical discovery.

As trained intelligent systems become increasingly pervasive, multi-agent learning has emerged as a popular framework for studying complex interactions between autonomous agents. Yet, a formal understanding of how and when learners in heterogeneous environments benefit from sharing their respective experiences is still in its infancy. In this paper, we seek answers to these questions in the context of linear contextual bandits. We present a novel distributed learning algorithm based on the upper confidence bound (UCB) algorithm, which we refer to as H-LINUCB, wherein agents cooperatively minimize the group regret under the coordination of a central server. In the setting where the level of heterogeneity or dissimilarity across the environments is known to the agents, we show that H-LINUCB is provably optimal in regimes where the tasks are highly similar or highly dissimilar.

Federated learning (FL), through its privacy-preserving collaborative learning approach, has significantly empowered decentralized devices. However, constraints in either data and/or computational resources among participating clients introduce several challenges in learning, including the inability to train large model architectures, heightened risks of overfitting, and more. In this work, we present a novel FL framework grounded in Bayesian learning to address these challenges. Our approach involves training personalized Bayesian models at each client tailored to the unique complexities of the clients' datasets and efficiently collaborating across these clients. By leveraging Bayesian neural networks and their uncertainty quantification capabilities, our local training procedure robustly learns from small datasets. And the novel collaboration procedure utilizing priors in the functional (output) space of the networks facilitates collaboration across models of varying sizes, enabling the framework to adapt well in heterogeneous data and computational settings. Furthermore, we present a differentially private version of the algorithm, accompanied by formal differential privacy guarantees that apply without any assumptions on the learning algorithm. Through experiments on popular FL datasets, we demonstrate that our approach outperforms strong baselines in both homogeneous and heterogeneous settings, and under strict privacy constraints.

The proofs that earned EPFL professor Maryna Viazovska the Fields Medal in 2022 have reached a new milestone: their complete formalization by computer, achieved through a collaboration between mathematicians and artificial intelligence tools. In 2016, Maryna Viazovska solved the sphere packing problem in dimension 8, proving that the E lattice constitutes the densest possible arrangement. Shortly after, together with collaborators, she established an analogous result in dimension 24 using the Leech lattice. Her method provided an elegant solution to a problem studied for centuries, with close ties to applied fields such as error-correcting codes. For this major contribution, Viazovska was awarded the Fields Medal in 2022, the highest distinction in mathematics.