Computer Science

Neural networks power today’s AI boom. To understand them, all we need is a map, a cat and a few thousand dimensions.

Researchers in “natural language processing” tried to tame human language. Then came the transformer.

A simple primer to the 19 most important concepts in artificial intelligence.

Rose Yu has a plan for how to make AI better, faster and smarter — and it’s already yielding results.

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.