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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. The San Francisco-based AI startup Anthropic had already informed the general public that it planned to make an announcement today, Thursday, May 22nd with a livestream scheduled for 9:30 am PT.Many in the wider AI power user community, especially on X, began to theorize that the announcement would mark the launch of a new Claude model codenamed “Neptune,” based on earlier inspection of Anthropic documents and code online, which some took to be a new version of its powerful Claude large language model (LLM) family, potentially the long-awaited “Claude 4 Opus,” a larger-parameter (denoting more internal settings and complexity) model successor to the current Claude 3.7 Sonnet.Now, it appears that Time Magazine has accidentally confirmed that rumor, publishing and quickly removing an article on its website, according to the observations of eagle-eyed AI programmers and news bloggers on X.Someone also appears to have published a full scrape of the Time article online on the news aggregator app Newsbreak, though that too has now been taken offline. The focus of Time’s piece is on Claude 4 Opus’s safety risks and mitigations, but it does reveal that the new Claude is smart enough to potentially help novices create new bioweapons.There are scant specifics about the model size, cost, licensing terms, and performance on commonly used third-party AI benchmarks. For those, we will just have to wait until more information is revealed by Anthropic — or leaked by members of the press and AI community. Daily insights on business use cases with VB Daily

Decentralized intelligence networks could hold the key to making self-driving cars smarter and safer. Many see these vehicles as the future of travel. However, there are still several problems that manufacturers and regulators need to work out.At their core, self-driving vehicles rely on sensors and other data to stay on course, avoid obstacles, and safely deliver passengers to their destinations. These sensors can include LiDAR, radar, thermal imaging, and other advanced systems. All of these systems create a data footprint that can be used to improve the car's performance in the future.Federated LearningOne way that manufacturers have found to improve their vehicles' self-driving performance is to enable their cars to share this data using a system called federated learning. Federated Learning (FL) is a distributed machine learning approach where individual vehicles train models locally and share only model updates with a central server