Machine learning

Sarah Demers, chair of the physics department at Yale University, sees the potential for large language models to both help and harm her field.

Computer scientist Melanie Mitchell discusses why artificial intelligence doesn’t “think” or “reason” like humans, and how we can create better methods for measuring machine cognition.

The idea that artificial intelligence can “reason” is more intuitive than ever. But intuitions can be wrong, and the science is far from settled.

AI is being used to prove new results at a rapid pace. Mathematicians think this is just the beginning.

Our tales of AI developing the will to survive, commandeer resources, and manipulate people say more about us than they do about language models.

A vast meshwork of soil-bound fungi governs life aboveground. In Alaska, and at field sites around the world, researchers are racing to understand exactly how, with essential stores of carbon at stake.

Around the world, volcanologists are following the path of magma as it travels between connected volcanoes, in an effort that could lead to improved eruption forecasts.

The last decade has seen vast improvements in humanoid robots, but graduating to widespread use might require going back to the fundamentals.

Machine learning is helping neuroscientists organize vast quantities of cells’ genetic data in the latest neurobiological cartography effort.