artificial intelligence

The learning algorithm that enables the runaway success of deep neural networks doesn’t work in biological brains, but researchers are finding alternatives that could.

Deep neural networks, often criticized as “black boxes,” are helping neuroscientists understand the organization of living brains.

To tame urban traffic, the computer scientist Carlos Gershenson finds that letting transportation systems adapt and self-organize often works better than trying to predict and control them.

Computer scientists are trying to build an AI system that can win a gold medal at the world’s premier math competition.

AI tools are shaping next-generation theorem provers, and with them the relationship between math and machine.

At the molecular level, glass looks like a liquid. But an artificial neural network has picked up on hidden structure in its molecules that may explain why glass is rigid like a solid.

Neural networks have been taught to quickly read the surfaces of proteins — molecules critical to many biological processes.

After translating some of math’s complicated equations, researchers have created an AI system that they hope will answer even bigger questions.

The problem of common-sense reasoning has plagued the field of artificial intelligence for over 50 years. Now a new approach, borrowing from two disparate lines of thinking, has made important progress.