Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Creator Playbook Mashable Voices Trending Now Say More Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List Switch Off In My Bag All Series How to watch Bayern Munich vs. Stuttgart online for free Live stream select fixtures from the Bundesliga without spending anything. Joseph Green is the Global Shopping Editor for Mashable. He covers VPNs, headphones, fitness gear, dating sites, streaming, and shopping events like Black Friday and Prime Day. Matt Ford is a freelance contributor to Mashable. All products featured here are independently selected by our editors and writers.

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Mashable Selects Mashable Voices Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Switch Off Trending Now In My Bag All Series Joseph Green is the Global Shopping Editor for Mashable. He covers VPNs, headphones, fitness gear, dating sites, streaming, and shopping events like Black Friday and Prime Day. All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission. Access this free live stream from anywhere in the world with ExpressVPN .

German drone maker Helsing has enlisted Japanese e-commerce and finance firm Rakuten to help finalize a deal to sell its unmanned systems to Japan's army, a spokesperson for Rakuten said on Monday. Japan's Ground Self-Defense Force is currently testing Helsing's HX-2 strike drone, as Tokyo looks to modernize its military and counter an increasingly assertive China. The field tests will run until the end of September, the spokesperson said. Helsing struck an "initial agreement" for the Japanese government to use its systems earlier this year, a spokesperson for the Munich-based company said, adding that the move was made possible by an unnamed "local brokerage partner." Japan's Ground Self-Defense Force did not immediately respond to a request for comment.

From branding irons to iris scans, the ancient business of proving who you are has never been stranger--or more lucrative. There are now more bots online than people. The challenge used to be telling who was a person from who was a bot; today, the challenge is telling good bots and bad bots apart--with humanity in the lurch. Three summers ago, Sam Altman, the C.E.O. of OpenAI, posted on X a fifteen-second video of dozens of people--mostly young, veiled in face masks, and wearing super-hip, white-soled sneakers--waiting in a long, snaky line in the traffic-light-lit but otherwise dusky dark of an unnamed city that appears to have been Nagoya, Japan. "Crazy lines around the world," Altman wrote. "One person getting verified every 8 seconds now." In the video, the camera pans to the front of the line, where a woman is kneeling on the sidewalk as if in prayer, staring into a gleaming silver orb about the size of a volleyball that has been propped up on a stand about the height of an end table. It looks like the eyeball of a giant robot Cyclops. She is bowing before an artificially intelligent machine, seeking a certification that she is human. That eyeball, which is known as the Orb, was developed by Tools for Humanity, a San Francisco-and Munich-based tech company that Altman founded in 2019 with a twenty-five-year-old German physicist named Alex Blania, who is now its C.E.O.

For the festival's 150th anniversary, the creative team have turned to AI to generate visuals reflecting Wagner's tetralogy's past, present - and future. U nveiled the same week in which tech company Anthropic admitted that its AI model Claude had gone rogue during testing, Bayreuth festival's new AI-generated staging of Wagner's four-part Ring of the Nibelungen is at least timely. But anyone who assumes that the cycle's obsession with knowledge, power and possession might make it an ideal forum for reflection on AI as a major contemporary frontier will be disappointed, as will anyone hopeful about AI's creative potential within opera. Commissioned to mark the festival's 150th anniversary, the production is "curated" - emphatically not "directed" - by a team led by German stage director Marcus Lobbes . He apparently spent weeks in dialogue with AI models about possible interpretations of the Ring.

Recent cooperative perception datasets have played a crucial role in advancing smart mobility applications by enabling information exchange between intelligent agents, helping to overcome challenges such as occlusions and improving overall scene understanding. While some existing real-world datasets incorporate both vehicle-to-vehicle and vehicle-to-infrastructure interactions, they are typically limited to a single intersection or a single vehicle. A comprehensive perception dataset featuring multiple connected vehicles and infrastructure sensors across several intersections remains unavailable, limiting the benchmarking of algorithms in diverse traffic environments. Consequently, overfitting can occur, and models may demonstrate misleadingly high performance due to similar intersection layouts and traffic participant behavior. To address this gap, we introduce UrbanIng-V2X, the first large-scale, multi-modal dataset supporting cooperative perception involving vehicles and infrastructure sensors deployed across three urban intersections in Ingolstadt, Germany. UrbanIng-V2X consists of 34 temporally aligned and spatially calibrated sensor sequences, each lasting 20 seconds. All sequences contain recordings from one of three intersections, involving two vehicles and up to three infrastructure-mounted sensor poles operating in coordinated scenarios. In total, UrbanIng-V2X provides data from 12 vehicle-mounted RGB cameras, 2 vehicle LiDARs, 17 infrastructure thermal cameras, and 12 infrastructure LiDARs. All sequences are annotated at a frequency of 10 Hz with 3D bounding boxes spanning 13 object classes, resulting in approximately 712k annotated instances across the dataset.

Random splitting of datasets in image segmentation often leads to unrepresentative test sets, resulting in biased evaluations and poor model generalization. While stratified sampling has proven effective for addressing label distribution imbalance in classification tasks, extending these ideas to segmentation remains challenging due to the multi-label structure and class imbalance typically present in such data. Building on existing stratification concepts, we introduce Iterative Pixel Stratification (IPS), a straightforward, label-aware sampling method tailored for segmentation tasks. Additionally, we present Wasserstein-Driven Evolutionary Stratification (WDES), a novel genetic algorithm designed to minimize the Wasserstein distance, thereby optimizing the similarity of label distributions across dataset splits. We prove that WDES is globally optimal given enough generations. Using newly proposed statistical heterogeneity metrics, we evaluate both methods against random sampling and find that WDES consistently produces more representative splits. Applying WDES across diverse segmentation tasks, including street scenes, medical imaging, and satellite imagery, leads to lower performance variance and improved model evaluation. Our results also highlight the particular value of WDES in handling small, imbalanced, and low-diversity datasets, where conventional splitting strategies are most prone to bias.

Recent cooperative perception datasets have played a crucial role in advancing smart mobility applications by enabling information exchange between intelligent agents, helping to overcome challenges such as occlusions and improving overall scene understanding. While some existing real-world datasets incorporate both vehicle-to-vehicle and vehicle-to-infrastructure interactions, they are typically limited to a single intersection or a single vehicle. A comprehensive perception dataset featuring multiple connected vehicles and infrastructure sensors across several intersections remains unavailable, limiting the benchmarking of algorithms in diverse traffic environments. Consequently, overfitting can occur, and models may demonstrate misleadingly high performance due to similar intersection layouts and traffic participant behavior. To address this gap, we introduce UrbanIng-V2X, the first large-scale, multi-modal dataset supporting cooperative perception involving vehicles and infrastructure sensors deployed across three urban intersections in Ingolstadt, Germany. UrbanIng-V2X consists of 34 temporally aligned and spatially calibrated sensor sequences, each lasting 20 seconds. All sequences contain recordings from one of three intersections, involving two vehicles and up to three infrastructure-mounted sensor poles operating in coordinated scenarios. In total, UrbanIng-V2X provides data from 12 vehicle-mounted RGB cameras, 2 vehicle LiDARs, 17 infrastructure thermal cameras, and 12 infrastructure LiDARs. All sequences are annotated at a frequency of 10 Hz with 3D bounding boxes spanning 13 object classes, resulting in approximately 712k annotated instances across the dataset.

The ruling holds that a company that designs, trains, operates, and manages an AI system must assume legal liability for any damages caused by the responses it generates. A local court in Germany has issued a ruling that could reshape the operation of search engines and artificial-intelligence-based chatbots worldwide. The Munich Regional Court preliminarily ruled that Google is liable for a series of false statements generated by its AI Overviews feature, requiring the company to prevent the dissemination of erroneous or inaccurate claims through its search engine. The ruling stems from a case first reported by the Decoder, in which two publishers discovered that Google's AI-generated summaries linked them, in certain searches, to questionable business practices, scams, and subscription-related frauds, without any basis for doing so. Earlier this year, the affected companies sent the tech giant a cease-and-desist letter, according to the report.

Inverse optimization (IO) seeks to infer the parameters of a decision-maker's objective from observed context--action data. We study noiseless IO, where demonstrations are generated by a ground-truth objective. We provide a high-probability ${O}(\frac{d}{T})$ generalization bound for the induced action set, where $d$ is the number of unknown parameters and $T$ is the size of the training dataset. We strengthen these guarantees under additional conditions that ensure uniqueness of the chosen action, bringing our IO guarantees in line with best-arm identification results in the bandit literature. We further show that the ${O}(\frac{d}{T})$ rate is tight over all consistent estimators considered here, and extend the result to both instantaneous and cumulative regret. Notably, the resulting regret lower bound matches the corresponding upper bounds in the adversarial setting, indicating that the stochastic IO setting is effectively adversarial for the class of estimators studied here. Finally, we propose a parameter-free algorithm with lower per-iteration complexity than generic solvers. Experiments validate the predicted rates and illustrate the tightness of our bounds.