Deep learning

To build a general artificial intelligence, we may need to know more about our own minds, argues the computer scientist Melanie Mitchell.

While the study of the SARS-CoV-2 virus was the most urgent priority, biologists also learned more about how brains process information, how to define individuality and why sleep deprivation kills.

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

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.

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.

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

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