The wars in Ukraine and Iran reveal a structural shift in modern warfare where the final ten percent of military degradation costs more than the first ninety percent.

The future of robot armies is here - and it's not what you think Robots are becoming more a part of our lives every year, and worries about a robot army rising up have long plagued the technology. The robot army that saves the world won't be anything like what you imagine. Nope, they aren't little humanoids who can do synchronised martial arts like the ones who dazzled audiences during New Year's festivities in China . And they won't help you find a can of Coke with embarrassing slowness like the man-shaped beast known as Optimus from Elon Musk's Tesla Inc. Instead, they will be microscopic, and mostly made of algae, bacteria and other single-celled organisms.

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Standard Chartered said it would cut 15% of its corporate function roles by 2030. Standard Chartered said it would cut 15% of its corporate function roles by 2030. Standard Chartered plans to cut more than 7,000 jobs over the next four years as it increasingly uses artificial intelligence. The London-headquartered lender is one of the first major global banks to lay out plans to cut thousands of jobs, citing AI as a driver to make its operations slimmer as it seeks to increase its profitability and tackle competition. StanChart said on Tuesday it would cut 15% of its back-office roles by 2030, which would result in about 7,800 redundancies out of its more than 52,000 staff in such roles.

Nintendo shares climbed as much as 6.8% in Tokyo Tuesday to mark a third straight day of gains -- their longest winning streak since mid-March. Embattled Switch 2 maker Nintendo enjoyed its biggest stock gain in two months on Tuesday as concerns about overvaluation in the AI sector sent investors on the hunt for bargains elsewhere. Nintendo shares climbed as much as 6.8% in Tokyo to mark a third straight day of gains -- their longest winning streak since mid-March. The advance is part of a broad rally in Japanese video game stocks that saw Bandai Namco Holdings and Konami Group rise more than 9% on Tuesday. The revival in Japanese gaming shares comes after months of headwinds brought on by a memory chips supply crunch and worries it will hit hardware sales.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' A Russian attack has damaged port infrastructure in Ukraine's Danube River port city of Izmail, a vital grain-export hub, while Russian authorities said they had downed four Ukrainian drones headed towards Moscow, as peace efforts remain stalled and both sides continue reciprocal attacks. Izmail, in the Odesa region, is a frequently targeted logistical centre and was hit in the early hours of Tuesday. It is Ukraine's largest port on the Danube. The attack lasted from about 1am to 3am (22:00 to 00:00 GMT), with firefighters battling a blaze in a building with blown-out windows. This followed another Russian attack on port infrastructure in Izmail on the night of May 2. In Kharkiv, two people were rescued, and one may remain trapped under the rubble after a Russian drone attack, Mayor Ihor Terekhov said on Telegram.

Banking giant Standard Chartered has become the latest major company to announce job cuts as it increases its adoption of artificial intelligence (AI). The firm, which has its headquarters in the UK, said it will cut more than 15%, or around 7,800, back-office roles by 2030. The BBC understands that Standard Chartered aims to move some of the effected workers to other roles in the business. Companies around the world have announced major job cuts in recent months as they increasingly use AI tools for roles currently carried out by humans. The company did not give details of where the roles would be cut.

Elon Musk loses case against Sam Altman over OpenAI's overhaul Elon Musk arrives at the Ronald V. Dellums Federal Building for court in Oakland, California on April 30. A jury rejected Elon Musk's claims that OpenAI under Sam Altman's leadership betrayed its mission to benefit the public by morphing into a for-profit business, finding that he waited too long to sue the company. The verdict reached Monday in federal court in Oakland, California, follows a trial over the bitter feud between the entrepreneurs who worked together to launch the startup in 2015. OpenAI has since evolved into one of the world's most valuable and powerful artificial intelligence companies. "I think there is a substantial amount of evidence to support the jury's findings," U.S. District Judge Yvonne Gonzalez Rogers said when she accepted the nine-member jury's unanimous conclusion after about two hours of deliberations.

Self-supervised learning (SSL) excels at finding general-purpose latent representations from complex data, yet lacks a unifying theoretical framework that explains the diverse existing methods and guides the design of new ones. We cast SSL as latent distribution matching (LDM): learning representations that maximize their log-probability under an assumed latent model (alignment), while maximizing latent entropy to prevent collapse (uniformity). This view unifies independent component analysis with contrastive, non-contrastive, and predictive SSL methods, including stop gradient approaches. Leveraging LDM, we derive a nonlinear, sampling-free Bayesian filtering model with a Kalman-based predictor for high-dimensional timeseries. We further prove that predictive LDM yields identifiable latent representations under mild assumptions, even with nonlinear predictors. Overall, LDM clarifies the assumptions behind established SSL methods and provides principled guidance for developing new approaches.

The population dynamics of molecules, cells, and organisms are governed by a number of unknown forces. In the last decade, population dynamics have predominantly been modeled with Wasserstein gradient flows. However, since gradient flows minimize free energy, they fail to capture important dynamical properties, such as periodicity. In this work, we propose a change in perspective by considering dynamics that minimize a population-level action under a damped Wasserstein Lagrangian. By deriving the corresponding Hamiltonian equations of motion, we formalize Wasserstein Lagrangian Mechanics, a structured class of second-order dynamics that encompasses classical mechanics, quantum mechanics, and gradient flows. We then propose WLM as the first algorithm that learns these second-order dynamics from observed marginals, without specifying the Lagrangian. By directly learning the population mechanics, WLM can both forecast and interpolate unseen marginals, and outperforms existing gradient flow and flow matching methods across a wide range of dynamics, including vortex dynamics, embryonic development, and flocking.