Computer Science

Is it possible to fill space “cubically” with shapes that act like spheres? A proof at the intersection of geometry and theoretical computer science says yes.

As The Joy of Why podcast returns for a second season, producer Polly Stryker and host Steven Strogatz invite listeners to join them and their brilliant new guests on another voyage of discovery.

“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).