Computer science

Neural networks and other forms of machine learning ultimately learn by trial and error, one improvement at a time.

After decades of uncertainty, a motley team of programmers has proved precisely how complicated simple computer programs can get.

Three years ago, Google’s AlphaFold pulled off the biggest artificial intelligence breakthrough in science to date, accelerating molecular research and kindling deep questions about why we do science.

Computational complexity theorists have discovered a surprising new way to understand what makes certain problems hard.

Computer scientist Lance Fortnow writes that by embracing the computations that surround us, we can begin to understand and tame our seemingly random world.

Researchers have proved that secure quantum encryption is possible in a world without hard problems.

Optical neural networks, which use photons instead of electrons, have advantages over traditional systems. They also face major obstacles.

By making use of randomness, a team has created a simple algorithm for estimating large numbers of distinct objects in a stream of data.

Researchers are drawing on ideas from game theory to improve large language models and make them more consistent.