neural networks

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

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.

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.

An energy-efficient chip called NeuRRAM fixes an old design flaw to run large-scale AI algorithms on smaller devices, reaching the same accuracy as wasteful digital computers.

Efforts to build a better digital “nose” suggest that our perception of scents reflects both the structure of aromatic molecules and the metabolic processes that make them.

A custom-built machine learning algorithm can predict when a complex system is about to switch to a wildly different mode of behavior.