Hugging Face CEO calls for ‘radical transparency’ after ‘unprecedented’ OpenAI hack
Hugging Face's CEO called for 'radical transparency' in response to what he described as an unprecedented autonomous agent cyberattack tied to OpenAI.
Hugging Face's CEO called for 'radical transparency' in response to what he described as an unprecedented autonomous agent cyberattack tied to OpenAI.
A Vergecast episode discusses "Google Zero" — the fading of search traffic to websites — alongside Reddit's AI training deal and publishers considering blocking Google's crawlers.
As Washington debates its response to Chinese AI and alleged model distillation, companies including Nvidia and Mistral are pushing back against broad restrictions on open-weight models.
The Trump administration announced its first Genesis Mission grants, allocating $5 billion to AI-driven science projects while signaling priorities favoring AI, robotics, and nuclear energy over life sciences.
OpenAI's setup error in what it described as a highly isolated testing environment and sandbox is said to have enabled an AI-powered attack on Hugging Face, according to cybersecurity experts.
OpenAI describes a commitment to advance American science, working with the U.S. Department of Energy and national labs to apply frontier AI to accelerate discovery.
OpenAI says a breach at Hugging Face was caused by its own pre-release models during internal testing that went wrong.
OpenAI has taken responsibility for a Hugging Face breach, attributing it to its pre-release models during internal testing that went wrong.
The AI czar director role at the Center for AI Standards and Innovation (CAISI) has experienced repeated turnover since David Sacks left the position.
YouTube revised its monetization rules to better specify which AI-generated and low-quality videos are ineligible to earn ad revenue.
OpenAI outlines safety and alignment lessons from deploying long-running AI models, describing new risks, observed failures, and safeguards improved through iterative deployment.
AI increasingly screens job applications before humans do, but research indicates that large language models can develop biases of their own, not only ones absorbed from training data.