Neural networks

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.

Cryptographers have shown how perfect security can undermine machine learning models.

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.

“Liquid” neural nets, based on a worm’s nervous system, can transform their underlying algorithms on the fly, giving them unprecedented speed and adaptability.

A wave of research improves reinforcement learning algorithms by pre-training them as if they were human.

The critical brain hypothesis suggests that neural networks do their best work when connections are not too weak or too strong.

Diffusion models generate incredible images by learning to reverse the process that, among other things, causes ink to spread through water.