Machine learning

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.

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.