Computer vision

Alexei Efros has spent his career learning how machines see differently from humans. Now he’s helping to bridge the gap.

Real data can be hard to get, so researchers are turning to synthetic data to train their artificial intelligence systems.

Cynthia Rudin wants machine learning models, responsible for increasingly important decisions, to show their work.

Self-supervised learning allows a neural network to figure out for itself what matters. The process might be what makes our own brains so successful.

A simple algorithm that revolutionizes how neural networks approach language is now taking on image classification as well. It may not stop there.

Tiny amounts of artificial noise can fool neural networks, but not humans. Some researchers are looking to neuroscience for a fix.

For all their triumphs, AI systems can’t seem to generalize the concepts of “same” and “different.” Without that, researchers worry, the quest to create truly intelligent machines may be hopeless.

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