The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. But there is a problem. Today that changes thanks to the work of Henry Lin at Harvard University and Max Tegmark at MIT. First, let’s set up the problem using the example of classifying a megabit grayscale image to determine whether it shows a cat or a dog. Such an image consists of a million pixels that can each take one of 256 grayscale values. In the language of mathematics, neural networks work by approximating complex mathematical functions with simpler ones. Now Lin and Tegmark say they’ve worked out why.