deep learning

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.

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.

Neural networks originally designed for language processing turn out to be great models of how our brains understand places.

Anima Anandkumar wants computer scientists to move beyond the matrix, among other challenges.

Self-supervised learning allows a neural network to figure out for itself what matters. The process might be what makes our own brains so successful.

Christopher Kanan is building algorithms that can continuously learn over time — the way we do.