Philippe Dufresne, the Privacy Commissioner of Canada, has found OpenAI was not compliant with Canadian federal and provincial privacy laws in the training of its AI models. Following an investigation, Dufresne and his counterparts in Alberta, Quebec and British Columbia say OpenAI's approach to things like data collection and consent stepped on multiple laws, including Canada's Personal Information Protection and Electronic Documents Act (PIPEDA), which governs how companies collect and use personal information during the normal course of business. The commissioners participating in the investigation identified multiple privacy issues with OpenAI's approach, including that the company gathered vast amounts of personal information without adequate safeguards to prevent use of that information to train its models, and that it failed to acquire consent to collect and use that personal information in the first place. Warnings in ChatGPT note that interactions with the AI could be used in training, but third-party data OpenAI has purchased or scraped also includes personal details people likely aren't even aware of. The fact that ChatGPT users have no way to access, correct or delete that data was another issue that the commissioners identified, according to a summary of the investigation's findings, along with OpenAI's lackluster attempts to acknowledge the inaccuracy of some of ChatGPT's responses.

A former OpenAI board member has explained how her unconventional personal relationship with Elon Musk evolved into having four of his children. Shivon Zilis testified in a federal courtroom in Oakland, California for hours on Wednesday as part of Musk's lawsuit trying to reverse OpenAI's change to a for-profit company. The focus of Zilis's appearance was her direct involvement in early talks with Musk around the company becoming a for-profit, but also how she worked for and became involved with Musk as she advised OpenAI. I still really wanted to be a mum and Elon made the offer around that time and I accepted, she said, explaining Musk in 2020 had offered to donate sperm. He was encouraging everyone around him at that time to have kids and he'd noticed I did not.

Anthropic has partnered with SpaceX to double Claude Code usage limits across Pro, Max, Team, and Enterprise plans, according to PCWorld. The deal provides access to SpaceX's Colossus 1 data center featuring over 220,000 Nvidia GPUs, significantly boosting Anthropic's computing capacity. This partnership marks a surprising shift, as Elon Musk previously criticized Anthropic but recently expressed being impressed after meetings with company staff. Instead of downgrading its most affordable Claude subscription plan by dropping access to Claude Code, Anthropic has instead doubled Claude Code usage rates for subscribers, starting today. All it took was an eyebrow-raising alliance with an unlikely partner.

In an unexpected turn, the two companies signed a deal for Anthropic to use computing resources from Elon Musk's xAI. Anthropic and Elon Musk's SpaceX said on Wednesday that the two entities have signed an agreement for Anthropic to use computing resources from xAI's data center in Memphis, Tennessee. It's the latest tie up in an industry that is scrambling to find enough computers to run complex AI software. SpaceX and xAI were previously separate companies, but the two merged earlier this year. The combined entity, also owned by Musk, is called SpaceXAI.

The company behind the long-running space sim has entered into a partnership with Google in which the search giant will take a minority stake. In exchange, Google's DeepMind will train its AI technology on the game, according to a report by . CCP Games, the dev who made and maintains, has also been rebranded as Fenris Creations . This happened just after the company purchased the rights to the game back from Korean developer Pearl Abyss. Google's investment is in the millions of dollars, according to Fenris Creations Chief Executive Officer Hilmar Veigar Pétursson.

Evolutionary methods have long been useful for analysis and explanation in genetics, biology, ecology, and related fields. In this work, we extend these methods to neural networks, specifically large language models (LLMs), to better analyze and explain relationships among models. We show how relating weights to genotypes and output text to phenotypes can improve our understanding of model lineage, important datasets, the roles of different model layers, and visualization of model relationships. We demonstrate this in a controlled experiment, where our estimated evolutionary trees reliably recover the topology of the ground-truth training tree. We further identify the most important weight layers according to weight differences and show through phenotypic experiments that one training dataset appears to contribute more useful information than the others. Finally, we generate an unsupervised evolutionary tree of black-box foundation models. Throughout, we provide visualizations that support a clearer understanding of evolutionary relationships among LLMs.

Transformers are effective at inferring the latent task from context via two inference modes: recognizing a task seen during training, and adapting to a novel one. Recent interpretability studies have identified from middle-layer representations task-specific directions, or task vectors, that steer model behavior. However, a lack of rigorous foundations hinders connecting internal representations to external model behavior: existing work fails to explain how task-vector geometry is shaped by the training distribution, and what geometry enables out-of-distribution (OOD) generalization. In this paper, we study these questions in a controlled synthetic setting by training small transformers from scratch on latent-task sequence distributions, which allows a principled mathematical characterization. We show that two inference modes can coexist within a single model. In-distribution behavior is governed by Bayesian task retrieval, implemented internally through convex combinations of learned task vectors. OOD behavior, by contrast, arises through extrapolative task learning, whose representations occupy a subspace nearly orthogonal to the task-vector subspace. Taken together, our results suggest that task-vector geometry, training distributions, and generalization behaviors are closely related.

Xbox is ditching Microsoft's Copilot AI Xbox is ditching Microsoft's Copilot AI Microsoft announced plans to start stripping Copilot out of select Windows apps in March after criticism of the company's mishandling of its operating system reached a fever pitch. As it turns out though, Windows isn't the only place where you'll see less Copilot: Xbox CEO Asha Sharma has announced that the AI assistant will also be removed from the gaming brand's mobile app and Xbox consoles. Under previous Xbox leadership, Copilot was introduced as a sort of in-game assistant that would be aware of what you're playing and able to offer contextual advice based on what's on your screen. Microsoft launched a beta version of the experience by adding Copilot to the Xbox mobile app in May 2025, but based on a GDC presentation the company gave in March, the plan was to also bring Copilot to Xbox consoles later this year. Those plans apparently don't align with where Xbox is headed, Sharma said in a post announcing new hires to the Xbox division.

PCWorld reports the update delivers 52.5% fewer hallucinations and 37.3% fewer inaccurate claims while providing more concise answers. Enhanced features include improved context integration from previous chats, files, and Gmail, plus transparency showing which memory sources influenced responses. One reason I took a break from ChatGPT a few months ago (I'm back now) was how sick to death I got of its constant emojis, especially when it came to lists. The brain emoji was my least favorite, along with the green checkmarks, the pointy fingers, and the yellow "hazard" signs. Well, I'll believe it when I see it, but with its latest "instant" model, OpenAI promises that we'll be getting way less of those "gratuitous" emojis in ChatGPT's responses.

New artificial intelligence (AI) tools and capabilities from Google, Microsoft and xAI will now be tested by the US Department of Commerce before they are released to the public. The tech firms have agreed to voluntarily submit their models for testing through Commerce's Center for AI Standards and Innovation (CAISI). The new pacts are an expansion on agreements by AI companies like OpenAI and Anthropic that were reached during the Biden Administration, and will see AI models from all of the companies evaluated for their capabilities and security. These expanded industry collaborations help us scale our work in the public interest at a critical moment, CAISI's director Chris Fall said. Overall, the evaluations of the AI tools will cover testing, collaborative research and best practice development related to commercial AI systems.