Computer science

Computational neuroscientists taught an artificial neural network to imitate a biological neuron. The result offers a new way to think about the complexity of single brain cells.

Researchers are turning to the mathematics of higher-order interactions to better model the complex connections within their data.

The most widely used technique for finding the largest or smallest values of a math function turns out to be a fundamentally difficult computational problem.

Mathematicians using the computer program Lean have verified the accuracy of a difficult theorem at the cutting edge of research mathematics.

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 understand what quantum computers can do — and what they can’t — avoid falling for overly simple explanations.

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.