A Common Logic To Seeing Cats and the Cosmos

An anonymous reader sends this excerpt from Quanta Magazine: "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.

"They actually wrote down on paper, with exact proofs, something that people only dreamed existed," said Ilya Nemenman, a biophysicist at Emory University.

Comments

The arxiv paper

[vikingpower]

offers an interesting look upon what generalizes, and what does not generalize, when you "zoom out" from a system built up of neighbouring spins, replacing groups of neighbouring spins by single-spin blocks. The interesting link with CS is the fact that the arxiv paper considers binary spins. Thinking this through, the paper might indeed offer some explanation for large-scale behaviour ( read: macroscopic ) as composed of small-scale ( read: microscopic ) interactions. Quite interesting, indeed.