Machine learning

Large language models do better at solving problems when they show their work. Researchers are beginning to understand why.

Erasing key information during training results in machine learning models that can learn new languages faster and more easily.

A new study suggests that so-called emergent abilities actually develop gradually and predictably, depending on how you measure them.

Far from being “stochastic parrots,” the biggest large language models seem to learn enough skills to understand the words they’re processing.

As computer-driven cars and planes become more common, the key to preventing accidents, researchers show, is to know what you don’t know.

Artificial intelligence learned how to generate text and art better than ever before, while computer scientists developed algorithms that solved long-standing problems.

By bringing together disparate approaches, machines can reach a new level of creative problem-solving.

Alexei Efros has spent his career learning how machines see differently from humans. Now he’s helping to bridge the gap.

To better understand how neural networks learn to simulate writing, researchers trained simpler versions on synthetic children’s stories.