Computer science

“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.

In Shang-Hua Teng’s work, theoretical and practical questions have long been intertwined. Now he’s turning his focus to the impractical.

Researchers can now find the shortest route through a network nearly as fast as theoretically possible, even when some steps can cancel out others.

Random circuit sampling, a popular technique for showing the power of quantum computers, doesn’t scale up if errors go unchecked.

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.

A quantum approach to data analysis that relies on the study of shapes will likely remain an example of a quantum advantage — albeit for increasingly unlikely scenarios.