Tapping into the “hidden states” in generative large language models (LLMs) can allow users to access more of the model’s information content, research published by the Bank of England finds.

New research shows that several top AI models are actively ignoring explicit instructions to shut themselves down.

Large language models (LLMs) may not reliably acknowledge a user's incorrect beliefs, according to a new paper published in ...

Writes Stanford University researchers, "Most models lack a robust understanding of the factive nature of knowledge — that ...

Anthropic’s Claude models showed early signs of self-awareness, detecting “injected thoughts" and both thrilling and ...

Although heart cells and skin cells contain identical instructions for creating proteins encoded in their DNA, they're able ...

The technique can also be used to produce more training data for AI models. Model developers are currently grappling with a ...

The solution proposed by DeepSeek in its latest paper is to convert text tokens into images, or pixels, using a vision ...

A ninth grader from New York City won the most prestigious middle school STEM competition in the nation this week, and he did ...

A new benchmark shows that AI agents are embarrassingly terrible at doing remote work tasks -- which is bad news for the AI ...

Chinese AI company DeepSeek may have found a way to help large language models see more, remember more, and cost less.

Paper argues that large language models can improve through experience on the job without needing to change their parameters.