Neural networks

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.

Glycans, the complex sugars that stud cellular surfaces, are like a language that life uses to mediate vital interactions. Researchers are learning how to read their meaning.

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

While the study of the SARS-CoV-2 virus was the most urgent priority, biologists also learned more about how brains process information, how to define individuality and why sleep deprivation kills.

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

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.