Machine learning

By screening films in a brain scanner, neuroscientists discovered a rich library of neural scripts — from a trip through an airport to a marriage proposal — that form scaffolds for memories of our experiences.

By treating DNA as a language, Brian Hie’s “ChatGPT for genomes” could pick up patterns that humans can’t see, accelerating biological design.

Recent results show that large language models struggle with compositional tasks, suggesting a hard limit to their abilities.

Certain grammatical rules never appear in any known language. By constructing artificial languages that have these rules, linguists can use neural networks to explore how people learn.

Researchers got a better look at the thoughts of chatbots, amateurs learned exactly how complicated simple systems can be, and quantum computers passed an essential milestone.

To better understand machine learning algorithms, Lenka Zdeborová treats them like physical materials.

Across the social and biological sciences, statisticians use a technique that leverages randomness to deal with the unknown.

Machines work with words by embedding their relationships with other words in a string of numbers.

By tapping into a decades-old mathematical principle, researchers are hoping that Kolmogorov-Arnold networks will facilitate scientific discovery.