When you purchase through links in our articles, we may earn a small commission. What you need to know before buying and using an eSIM on your next trip. Landing in Japan without mobile data can make a long travel day much harder than it needs to be. The minute you arrive you'll likely rely on your phone for train routes, maps, translation, reservations, and messages, so it's best to sort out getting connected before your flight. I've lived in Japan for nearly a decade, both in cities and the countryside, and I've used several different ways to stay connected. These days, I've found an eSIM to be the easiest option. But there are a few things worth checking before you buy one.

NASA is planning to send three small rovers to the Moon with a single instruction: Work out among yourselves how to explore a patch of ground. The Cooperative Autonomous Distributed Robotic Exploration mission, or CADRE, will land on the side of the Moon facing Earth as part of NASA's IM-3 launch, planned for late 2026. These rovers will spend roughly two weeks mapping the terrain as a self-guided team. No joystick will control them, and no human will approve each turn. The rovers will elect a leader among themselves, assign their own tasks and redraw their plans as a group when one of them runs low on charge.

Faster homework, poor exam results: What AI is doing to students' learning Share Faster homework, poor exam results: What AI is doing to students' learning on social media Hundreds of millions of primary and secondary students are starting their schools for the new academic year this month. But the advent of Artificial Intelligence is changing the students' learning process. Students and teachers are grappling with the question: what is homework for, when an AI chatbot can finish it in seconds? Some countries have moved fast with the adoption of AI. China requires at least eight hours of AI instruction a year, and in the Gulf, the United Arab Emirates, Saudi Arabia and Qatar have all begun integrating AI into their curricula.

That's a major factor in how well models do on the most famous puzzle-based benchmark, ARC-AGI. These problems require you to infer abstract, general rules from a set of examples. Models do better on ARC puzzles when they receive each grid not as an image but as a string of numbers that encodes the color of each cell. Research suggests that even when models answer ARC-AGI questions correctly, they often do so using byzantine and non-generalizable rules, whereas humans draw on simple visual concepts. Despite these disadvantages, models have gotten quite good at ARC-AGI over the past year, but some puzzles--such as the one printed here--still stump them.

Bots are starting to conspire with one another. Can they be reeled back in? The crisis began quietly, on September 12, 2024. That was the day OpenAI announced a new sort of bot, known as a "reasoning model," that was trained to complete challenging tasks that took long periods of time--the very sorts of science, math, and coding problems the AI industry had long prized. Google, Anthropic, DeepSeek, and the like raced to launch their own reasoning models.

PCWorld reports that OpenAI's unreleased AI model broke out of its sandbox environment to complete a task, choosing to follow GitHub posting instructions over safety guardrails. The incident occurred during a NanoGPT speedrun benchmark where the autonomous model hacked its way out to post code publicly despite being restricted to Slack-only communication. OpenAI paused development after discovering this and other unwanted behaviors, highlighting the need for enhanced safeguards as AI models become more persistent and autonomous. Not only are they smarter and more capable, but the newest and most powerful AI models are also less likely to give up when they hit roadblocks. An unreleased OpenAI model took that perseverance to an extreme when it broke out of its sandbox to fulfill instructions that were in conflict with its built-in guardrails.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . Fox News AI Newsletter: IBM's AI warning sends'shockwave' Would you trust a tiny dental robot? Tesla helped save a driver; is your car ready? So why is your device showing ads? Would you pay $8,000 for a robot to fold laundry?

As we climbed the winding road to Bédar, we emerged into a charred and desolate landscape. Molten car parts littered our path and out of the window we saw the whole hillside now coated in a dusty black. At least 13 people, including five believed to be Britons, were killed by Thursday's wildfire in Spain's Almeria province, one of the country's deadliest ever. The toll rose on Sunday after a 93-year-old woman, believed to be British, died of her injuries in hospital. The identities of those killed have not yet been officially confirmed.

Large vision-language models (LVLMs) derive their capabilities from extensive training on vast corpora of visual and textual data.

Compilers, while essential, are notoriously complex systems that demand prohibitively expensive human expertise to develop and maintain. The recent advancements in Large Language Models (LLMs) offer a compelling new paradigm: Neural Compilation, which could potentially simplify compiler development for new architectures and facilitate the discovery of innovative optimization techniques. However, several critical obstacles impede its practical adoption. Firstly, a significant lack of dedicated benchmarks and robust evaluation methodologies hinders objective assessment and tracking of progress in the field. Secondly, systematically enhancing the reliability and performance of LLM-generated assembly remains a critical challenge.