Machine learning

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.

To combat resistant bacteria and refill the trickling antibiotic pipeline, scientists are getting help from deep learning networks.

By teaching machines to understand our true desires, one scientist hopes to avoid the potentially disastrous consequences of having them do what we command.

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.