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.

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

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.

The computer scientist Rediet Abebe’s passion for applied mathematics closely aligns with her passion to solve problems with poverty and social inequality.

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.

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