Artificial intelligence

The computer scientist Ellie Pavlick is translating philosophical concepts such as “meaning” into concrete, testable ideas.

By apparently overtraining them, researchers have seen neural networks discover novel solutions to problems.

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

Mathematicians are working to fully explain unusual behaviors uncovered using artificial intelligence.

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.

Terence Tao, who has been called the “Mozart of Mathematics,” wrote an essay in 2007 about the common ingredients in “good” mathematical research. In this episode, the Fields Medalist joins Steven Strogatz to revisit the topic.

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

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