Artificial intelligence

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.

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.

Rediet Abebe uses the tools of theoretical computer science to understand pressing social problems — and try to fix them.

The learning algorithm that enables the runaway success of deep neural networks doesn’t work in biological brains, but researchers are finding alternatives that could.

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

To tame urban traffic, the computer scientist Carlos Gershenson finds that letting transportation systems adapt and self-organize often works better than trying to predict and control them.