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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.
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.
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.
At the molecular level, glass looks like a liquid. But an artificial neural network has picked up on hidden structure in its molecules that may explain why glass is rigid like a solid.
Neural networks have been taught to quickly read the surfaces of proteins — molecules critical to many biological processes.
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