More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. It's not just nostalgia that made summer break feel so long. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . Do you remember the last day of school before summer break? You have summer to do literally anything you want. Cut to summers in adulthood, where you blink and suddenly there are Halloween decorations up. Why do summers seem to last forever when you're growing up but only a couple of days as an adult?

We can no longer ignore the growing threat of fully autonomous weapons. Should drones be allowed to kill autonomously? For years, we have had unconfirmed reports and rumours that AI-controlled weapons have killed soldiers on the battlefield without a human in the loop. Now, we know it has happened. As we report here, the use of autonomous killers in a test exercise marks a watershed in warfare .

The AAMAS 2026 best paper awards were presented at the 25th International Conference on Autonomous Agents and Multiagent Systems, which took place from 25-29 May 2025 in Paphos, Cyprus. Lucy Smith is Senior Managing Editor for Robohub and AIhub. Lucy Smith is Senior Managing Editor for Robohub and AIhub. In this special live recording at the Great Exhibition Road Festival in London, Claire chatted to George Mylonas (Imperial College London), Antonia Tzemanaki (University of Bristol) and Tom Vercauteren (King's College London) about robotics and AI in medicine and healthcare. Researchers are developing AI models that could one day enable vision prosthetics able to restore meaningful, object-level sight for the blind.

Claire chatted to Edward Mehr from Machina Labs about their RoboCraftsman that shapes complex metal parts for the aerospace, defence, and automotive industries. Edward Mehr is an entrepreneur and engineer specializing in advanced manufacturing, robotics, and artificial intelligence. As the Co-Founder and CEO of Machina Labs, he leads efforts to integrate AI-driven robotics into flexible, on-demand production systems. Under his leadership, Machina Labs is reshaping how industries such as aerospace, defence, and automotive approach metal forming and modern manufacturing. Before founding Machina Labs, Ed worked at leading technology companies, including Relativity Space, Averon, SpaceX, Google, and Microsoft.

France-Germany jet plans crash: Can Europe end reliance on US for security? France and Germany have announced this week that they are ditching a landmark project to jointly develop a sixth-generation fighter jet. French President Emmanuel Macron confirmed on Monday that the project is being terminated, in what is being seen as a major blow to efforts to boost defence cooperation between European Union states, a key issue amid uncertainty cast by United States President Donald Trump over the readiness of the US to help defend its NATO allies. Since 2019, the US president has been flirting with the idea of obtaining Greenland . His remarks about his desire for the island, a self-governing territory which is part of the Kingdom of Denmark, built to a crescendo at the start of this year, with European leaders signalling their displeasure with the idea and Trump even threatening additional trade tariffs on those countries standing in his way.

This work introduces a novel approach, Pairwise Epistemic Estimators (PairEpEsts), for epistemic uncertainty estimation in ensemble models for regression tasks using pairwise-distance estimators (PaiDEs). By utilizing the pairwise distances between model components, PaiDEs establish bounds on entropy. We leverage this capability to enhance the performance of Bayesian Active Learning by Disagreement (BALD). Notably, unlike sample-based Monte Carlo estimators, PairEpEsts can estimate epistemic uncertainty up to 100 times faster and demonstrate superior performance in higher dimensions. To validate our approach, we conducted a varied series of regression experiments on commonly used benchmarks: 1D sinusoidal data,,,, and, demonstrating PairEpEsts' advantage over baselines in high-dimensional regression active learning.

Pokemon Go became a worldwide hit after its launch - but players may not know that their game data trained AI that will potentially help military drones during war. Pokemon Go became a worldwide hit after its launch - but players may not know that their game data trained AI that will potentially help military drones during war. Fri 12 Jun 2026 03.06 EDTLast modified on Fri 12 Jun 2026 03.38 EDT An AI model trained on data collected from users of Pokémon Go will potentially help military drones find their location in war zones. Pokémon Go, a 2016 augmented reality mobile game, allowed players to find and catch Pokémon in the real world using the cameras on their mobile phones, and exploded in popularity. In 2018, the company reported having more than 800m downloads worldwide.

A penalty is used to encourage smoothness of transitions over time, while robustness is achieved throughthe use of aTukey's biweight loss function. Anadditional parameter controls the variability of feature weights across states, allowing the model to assign state-specific relevance to each feature. We illustrate in simulation how the method accurately recovers the true cluster sequence and reliably identifies relevant features, outperforming competing approaches, particularly in the presence of outliers. We conclude with two empirical applications, one on the number of conflict-related homicides in Kosovo in the period 1998-2000, and another on macroeconomic performance of twelve European countries in the period 1949-2024.Keywords: Dissimilarity-based clustering, regime-switching models, time series analysis, unsupervised learning, variable importance.

Two-sample testing is a fundamental tool for detecting distributional differences across scientific domains, but classical tests (including kernel-based tests) can be ineffective on high-dimensional structured data such as images. Recent deep two-sample tests improve sensitivity in these settings by learning informative representations, yet they provide limited insight into which data features drive rejection of the null hypothesis $H_0$. To address this issue, we propose a counterfactual explanation framework for deep two-sample testing that generates sample-level edits moving observations from a source group toward a target group while explicitly reducing the discrepancy measured by the test. Our method combines a diffusion autoencoder with a pretrained deep two-sample test model and optimizes a maximum mean discrepancy (MMD) objective in the test model's representation space to produce plausible counterfactuals. We quantify distribution-level effects through changes in the test statistic and the resulting two-sample p-values. We evaluate the method on synthetic 2D shape datasets and two MRI cohorts. Across both settings, the counterfactual transformations consistently increase p-values relative to the original samples, indicating that the edited source set becomes statistically closer to the target distribution under the test. We measure minimality using LPIPS to ensure the counterfactuals remain close to the original samples. The resulting edits provide interpretable evidence of the features associated with the detected group differences. On MRI, the localized changes are consistent with known anatomical differences between cohorts.

Vine copulas provide a flexible framework for modeling complex multivariate dependence structures using only bivariate building blocks. Their practical success relies heavily on the simplifying assumption, which restricts conditional pair copulas to be independent of the specific conditioning values. While this assumption greatly facilitates estimation, it may lead to model misspecification in applications with pronounced varying conditional dependence. We propose a novel calibration strategy for simplified vine copula models based on observation-specific correction factors. These factors are derived using noise contrastive estimation (NCE), a supervised learning technique for density estimation that reframes the problem as a binary classification task with an easily sampled noise distribution. Treating the fitted simplified vine copula as the noise model, the NCE approach yields corrected log-likelihood estimates for individual observations, thereby locally adjusting the simplified vine toward the underlying data-generating dependence structure. Simulation studies demonstrate that the proposed calibration provides sensible and effective adjustments, improving model accuracy when the simplifying assumption is violated while remaining neutral when the simplified model is adequate. Two real-data applications further illustrate the practical benefits of the method. The results highlight NCE-based calibration as a promising tool to enhance simplified vine copula models without abandoning their computational tractability.