Yesterday, Amir and I examined the shock waves that have rippled through the AI industry and far beyond since former Anthropic researcher Jacob Coxon very publicly quit his job over concerns that AI “could kill us all”—and another Anthropic AI safety specialist put the probability of such a catastrophe at greater than 10%. Coxon told me that is hardly the most pessimistic view inside Anthropic. “People have varying probabilities that—barring some substantial coordinated slowdown—the whole thing ends in doom,” he said in an interview later Wednesday. Those estimates range higher than 50% among some Anthropic employees, he said. Coxon was able to gauge the fears of his fellow employees because “Anthropic has a substantially more transparent internal culture than OpenAI,” where Coxon worked for years before joining Anthropic in May. Coxon, who focused on the earliest stages of training AI models at Anthropic, said he decided to leave the company because he was gradually “starting to viscerally feel the fear of where the tech's going to be like the next two years or even the next one year.”

ReadSource

arXiv AI/ML / 6:00 PM

arXiv paper: Neural Posterior Estimation for Tomographic Weak Lensing Mass Mapping

A new arXiv AI paper by Tim White, Shreyas Chandrashekaran, and Camille Avestruz, and 2 more studies Neural Posterior Estimation for Tomographic Weak Lensing Mass Mapping.

ReadSource

arXiv AI/ML / 5:53 PM

arXiv paper: Finding and using interpretable latents in a neutrino foundation model with sparse autoencoders

A new arXiv AI paper by Raphaël Bonnet-Guerrini, Johann Ioannou-Nikolaides, and Inar Timiryasov, and 1 more studies Finding and using interpretable latents in a neutrino foundation model with sparse autoencoders.

ReadSource

AWS Machine Learning Blog / 4:24 PM

Preparing data for supervised fine-tuning Part 2: Advanced data strategies

The advanced side of supervised fine-tuning data prep. This second post in a two-part series covers evaluating data readiness with learning curves, selecting high-value data subsets, augmenting data with synthetic and distilled examples, and mixing data sources to prevent catastrophic forgetting.

ReadSource

The Decoder / 8:12 AM

OpenAI dissolved the team built to catch catastrophic AI risks, reassigning its work to other groups

OpenAI shut down its "Preparedness" team, which evaluated whether the company's own AI models could pose catastrophic risks. The work has been parceled out to existing groups, and several safety staffers have left. Internally, unease is building, with one source describing a "burbling sense of responsibility and dread" that OpenAI isn't doing enough on safety. The article OpenAI dissolved the team built to catch catastrophic AI risks, reassigning its work to other groups appeared first on The Decoder .

ReadSource

Hacker News AI / 5:58 AM

Why Does Claude.md Keep Growing? Catastrophic Remembering in Agentic Coding

HN 1 pts · 1 comments

ReadSource

Latest story in this edition: 2:10 AM

Back to front page