deep learning

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.

In some ways, machine vision is superior to human vision. In other ways, it may never catch up.

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.