Neural networks

The brain’s astounding cellular diversity and networked complexity could show how to make AI better.

Modern thinking machines owe their existence to insights from the physics of complex materials.

We insist that large language models repeatedly translate their mathematical processes into words. There may be a better way.

Larger models can pull off greater feats, but the accessibility and efficiency of smaller models make them attractive tools.

By training machine learning models with enough examples of basic science, Miles Cranmer hopes to push the pace of scientific discovery forward.

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.

How do we know if a large language model is lying? Letting AI systems argue with each other may help expose the truth.