Computer science

We insist that large language models repeatedly translate their mathematical processes into words. There may be a better way.

When pigeons outnumber pigeonholes, some birds must double up. This obvious statement — and its inverse — have deep connections to many areas of math and computer science.

Despite the hype, it’s been surprisingly challenging to find quantum algorithms that outperform classical ones. In this episode, Ewin Tang discusses her pioneering work in “dequantizing” quantum algorithms — and what it means for the future of quantum computing.

Randomness is essential to some research, but it’s always been prohibitively complicated. Now, we can use “pseudorandomness” instead.

It’s been difficult to find important questions that quantum computers can answer faster than classical machines, but a new algorithm appears to do it for some critical optimization tasks.

Steven Strogatz and Janna Levin return for a new season on major scientific and mathematical questions of our time, with 12 all-new episodes and a new format.

Larger models can pull off greater feats, but the accessibility and efficiency of smaller models make them attractive tools.

In math and computer science, researchers have long understood that some questions are fundamentally unanswerable. Now physicists are exploring how even ordinary physical systems put hard limits on what we can predict, even in principle.

By training machine learning models with enough examples of basic science, Miles Cranmer hopes to push the pace of scientific discovery forward.