Machine learning

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.

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

Computer scientists this year learned how to transmit perfect secrets, why transformers seem so good at everything, and how to improve on decades-old algorithms (with a little help from AI).

Making sure our machines understand the intent behind our instructions is an important problem that requires understanding intelligence itself.

Inspired by the results of a game-playing neural network, mathematicians have been making unexpected advances on an age-old math problem.