Computer science

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.

Google DeepMind’s AlphaFold3 and other deep learning algorithms can now predict the shapes of interacting complexes of protein, DNA, RNA and other molecules, better capturing cells’ biological landscapes.

After years of false starts, a team of computer scientists has found a way to efficiently deduce the Hamiltonian of a physical system at any constant temperature.