Machine learning

As Scarlett Howard taught honeybees to do arithmetic, they showed her how fundamental numbers might be to all brains.

The laws of physics stay the same no matter one’s perspective. Now this idea is allowing computers to detect features in curved and higher-dimensional space.

Mathematicians and computer scientists made big progress in number theory, graph theory, machine learning and quantum computing, even as they reexamined our fundamental understanding of mathematics and neural networks.

Machine learning and deep neural networks can capture and analyze the “language” of animal behavior in ways that go beyond what’s humanly possible.

After millions of games, machine learning algorithms found creative solutions and unexpected new strategies that could transfer to the real world.

By ignoring their goals, evolutionary algorithms have solved longstanding challenges in artificial intelligence.

A tool known as BERT can now beat humans on advanced reading-comprehension tests. But it's also revealed how far AI has to go.

In some ways, machine vision is superior to human vision. In other ways, it may never catch up.

Iyad Rahwan’s radical idea: The best way to understand algorithms is to observe their behavior in the wild.