Machine learning

Mathematicians and computer scientists answered major questions in topology, set theory and even physics, even as computers continued to grow more capable.

It’s simple enough for AI to seem to comprehend data, but devising a true test of a machine’s knowledge has proved difficult.

Tiny amounts of artificial noise can fool neural networks, but not humans. Some researchers are looking to neuroscience for a fix.

The computational biologist Anne Carpenter creates software that brings the power of machine learning to researchers seeking answers in mountains of cell images.

Algorithms that zero in on solutions to optimization problems are the beating heart of machine reasoning. New results reveal surprising limits.

Today’s powerful but little-understood artificial intelligence breakthroughs echo past examples of unexpected scientific progress.

A new model of learning centers on bursts of neural activity that act as teaching signals — approximating backpropagation, the algorithm behind learning in AI.

To help them explain the shocking success of deep neural networks, researchers are turning to older but better-understood models of machine learning.

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.