machine learning

Melanie Mitchell has worked on digital minds for decades. She says they’ll never truly be like ours until they can make analogies.

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.

Advances in brain-computer interface technology are impressive, but we’re not close to anything resembling mind control.

Two new approaches allow deep neural networks to solve entire families of partial differential equations, making it easier to model complicated systems and to do so orders of magnitude faster.

To the surprise of experts in the field, a postdoctoral statistician has solved one of the most important problems in high-dimensional convex geometry.

Even as mathematicians and computer scientists proved big results in computational complexity, number theory and geometry, computers proved themselves increasingly indispensable in mathematics.

A small community of mathematicians is using a software program called Lean to build a new digital repository. They hope it represents the future of their field.

Computer scientists are trying to build an AI system that can win a gold medal at the world’s premier math competition.

AI tools are shaping next-generation theorem provers, and with them the relationship between math and machine.