Machine learning

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.

The computer vision scientist Greg Johnson is building systems that can recognize organelles on sight and show the dynamics of living cells more clearly than microscopy can.

Consciousness is a famously hard problem, so Hod Lipson is starting from the basics: with self-aware robots that can help us understand how we think.

To researchers’ surprise, deep learning vision algorithms often fail at classifying images because they mostly take cues from textures, not shapes.

The latest AI algorithms are probing the evolution of galaxies, calculating quantum wave functions, discovering new chemical compounds and more. Is there anything that scientists do that can’t be automated?