How computers learn to recognise patterns

When in 2012 a computer learned to recognize cats in YouTube videos and just last month another correctly captioned a photo of “a group of young people playing a game of Frisbee,” artificial intelligence researchers hailed yet more triumphs in “deep learning,” the wildly successful set of algorithms loosely modeled on the way brains grow sensitive to features of the real world simply through exposure.

Using the latest deep-learning protocols, computer models consisting of networks of artificial neurons are becoming increasingly adept at image, speech and pattern recognition — core technologies in robotic personal assistants, complex data analysis and self-driving cars. But for all their progress training computers to pick out salient features from other, irrelevant bits of data, researchers have never fully understood why the algorithms or biological learning work.

Now, two physicists have shown that one form of deep learning works exactly like one of the most important and ubiquitous mathematical techniques in physics, a procedure for calculating the large-scale behavior of physical systems such as elementary particles, fluids and the cosmos.

The new work, completed by Pankaj Mehta of Boston University and David Schwab of Northwestern University, demonstrates that a statistical technique called “renormalization,” which allows physicists to accurately describe systems without knowing the exact state of all their component parts, also enables the artificial neural networks to categorize data as, say, “a cat” regardless of its color, size or posture in a given video.

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Author: Natalie Wolchover is a staff writer at Quanta Magazine covering the physical sciences.

Image: A man types on a computer keyboard REUTERS/Kacper Pempel/Files