artificial intelligence

Researchers uncover striking parallels in the ways that humans and machine learning models acquire language skills.

In steganography, an ordinary message masks the presence of a secret communication. Humans can never do it perfectly, but a new study shows it’s possible for machines.

Today’s language models are more sophisticated than ever, but they still struggle with the concept of negation. That’s unlikely to change anytime soon.

Cynthia Rudin wants machine learning models, responsible for increasingly important decisions, to show their work.

By imbuing enormous vectors with semantic meaning, we can get machines to reason more abstractly — and efficiently — than before.

Large language models like ChatGPT are now big enough that they’ve started to display startling, unpredictable behaviors.

Arvind Narayanan uses quantitative methods to expose and correct the misuse of quantitative methods.

Lek-Heng Lim uses tools from algebra, geometry and topology to answer questions in machine learning.

Large language models still struggle with basic reasoning tasks. Two new papers that apply machine learning to math provide a blueprint for how that could change.