An 18-year-old man has been arrested for his suspected involvement in a cyberattack on an internet cafe operator. An 18-year-old man has been arrested for his suspected involvement in a cyberattack on the operator of the Kaikatsu Club internet cafe chain, according to investigative sources. On Wednesday, the Metropolitan Police Department's cybercrime countermeasure division arrested the company employee from Tokyo's Katsushika Ward, who was in the second year of high school at the time of the incident, on suspicion of fraudulent obstruction of business and violation of the law against unauthorized computer access. He has denied parts of the allegations, the sources said. In the cyberattack on the internet cafe chain operator Kaikatsu Frontier, a computer program that a high school boy from the city of Osaka developed using ChatGPT was used.

The Canadian province of British Columbia is preparing to sue OpenAI, alleging the US company failed to alert police after its staff internally flagged violent ChatGPT conversations linked to the person responsible for February's Tumbler Ridge mass shooting . Attorney General Niki Sharma announced Tuesday that the province has hired legal teams in British Columbia and California to "explore all legal avenues to hold OpenAI and its decision-makers accountable for its documented failure to notify law enforcement regarding explicit, flagged threats made by the perpetrator on the company's ChatGPT platform." The move stems from the February 10 attack in the remote mountain community of Tumbler Ridge, where authorities say 18-year-old Jesse Van Rootselaar killed their mother and half-brother before going to the Tumbler Ridge Secondary School and opening fire. Five children between the ages of 11 and 13 and one educator were killed at the school. Twenty-seven other people were wounded before Van Rootselaar died from what police described as a self-inflicted gunshot wound.

Starship Technologies is pulling 1,200 delivery robots from U.S. college campuses to focus on grocery delivery in cities across the United States and Europe.

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New York City's Summer of Ludd festival is teaching people how to live offline amid the suffocating presence of Big Tech. A papier-mâché woman is the backdrop to a play about the Luddite movement. On a Sunday evening in the middle of Tompkins Square Park in New York City's East Village, hundreds of people gather in front of a giant papier-mâché face of a woman wearing a crown. She's the backdrop of a play, her body made up of curtains that look like a dress but serve a dual purpose, allowing actors to scurry on and offstage. I'm here to watch a performance called " Luddite Recreations," which is a history of the Luddite movement--a group of artisans and textile workers who resisted the adoption of machines during the early years of the Industrial Revolution in England and whose resistance to being displaced from their work was met with violence by the British monarchy.

Protein engineering is a field primed for artificial intelligence research. Each protein is made up of amino acids; to optimize a protein function, researchers modify proteins by switching out one of 20 different amino acids for another. For a protein that is just 50 amino acids in length, this leads to approximately 1.13 10 potential combinations to test. This number of potential combinations, impossible to test in the lab, makes protein engineering an ideal challenge for AI. Modeling which of these combinations will give the best results is a perfect problem for the technology's massive computing power.

Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet existing methods either depend on extensively curated or teacher-generated training data, or, when the generator runs unsupervised, reward it by a difficulty heuristic that need not improve the solver. We introduce INFUSER, an iterative co-training framework with two co-evolving roles: a Generator that drafts questions and reference golden answers from a pool of unstructured, automatically collected documents, and a Solver that improves by training on them. The solver is trained with standard correctness rewards against the generator-provided answers, while the generator is rewarded by an optimizer-aware influence score that measures whether each proposed question would actually improve the solver on the target distribution. Because this continuous, noisy influence score is poorly served by standard GRPO, we propose DuGRPO, a dual-normalized variant of GRPO, for generator training. Together, these turn the document pool into an adaptive curriculum that favors questions useful to the current solver, not just hard ones. On Qwen3-8B-Base, INFUSER outperforms strong self-evolution baselines with over 20% relative improvement on Olympiad and SuperGPQA benchmarks, and an 8B INFUSER co-evolving generator outperforms a frozen 32B thinking generator on math and coding. Ablations confirm each design choice is necessary, and two extensions, applying INFUSER to an instruction-finetuned anchor and augmenting it with rule-verifiable RLVR data, further demonstrate the flexibility and generalizability of the framework. Code is available at https://github.com/FFishy-git/INFUSER.

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Plus: The US House has passed new youth online safety legislation. AI agents are not your "coworkers" Imagine coming in to work to learn that a new underling will report to you. The worker is not a person but an AI tool--one that your company nonetheless calls Alex, an "employee" with a title and defined responsibilities. How well do you think you would work with Alex? If you're anything like the managers studied by Boston University professor Emma Wiles, treating that AI as a coworker would lead you to do a worse job. They caught 18% fewer errors when the work was attributed to an agentic AI employee rather than a chatbot. This is an alarming glimpse of the future Silicon Valley is hurling us toward.