#machine-learning-risks

#machine-learning-risks

Companies hoping to sign contracts with the state of California will have to show they have policies to keep AI from distributing child sexual abuse material and violent pornography.

Four terabytes of data have reportedly been stolen, including database records and source code. Allegedly stolen data has been published on a leak site, containing Slack information, internal ticketing data, and videos of conversations between Mercor's AI systems and contractors.

Anthropic's political activities have ramped up as the company continues to be enmeshed in a nasty legal battle with the Defense Department. The dispute erupted earlier this year over the government's use of Anthropic's AI models and what guidelines (if any) should exist for that usage.

The savings disappear the moment you hit real-world complexity. Disparate data sources and messy inputs, ambiguous situations without clear rule sets, or actually any domain where the rules aren't already obvious. And someone still has to write all those rules.

One of the strangest things about large language models is not what they get wrong, but what they assume to be correct. LLMs behave as if every question already has an answer. It's as if reality itself is always a kind of crossword puzzle. The clues may be hard, the grid may be vast and complex, but the solution is presumed to exist. Somewhere, just waiting to be filled in.