Last year, AI accomplished a task many people thought impossible: DeepMind, Google’s deep learning AI system, defeated the world’s best Go player after trouncing the European Go champion. The feat stunned the world because the number of potential Go moves exceeds the number of atoms in the universe, and past Go-playing robots performed only as well as a mediocre human player.
But even more astonishing than DeepMind’s utter rout of its opponents was how it accomplished the task.
“The big mystery behind neural networks is why they work so well,” said study co-author Henry Lin, a physicist at Harvard University. “Almost every problem we throw at them, they crack.”
For instance, DeepMind was not explicitly taught Go strategy and was not trained to recognize classic sequences of moves. Instead, it simply “watched” millions of games, and then played many, many more against itself and other players.
Like newborn babies, these deep-learning algorithms start out “clueless,” yet typically outperform other AI algorithms that are given some of the rules of the game in advance, Tegmark said.
Another long-held mystery is why these deep networks are so much better than so-called shallow ones, which contain as little as one layer, Tegmark said. Deep networks have a hierarchy and look a bit like connections between neurons in the brain, with lower-level data from many neurons feeding into another “higher” group of neurons, repeated over many layers. In a similar way, deep layers of these neural networks make some calculations, and then feed those results to a higher layer of the program, and so on, he said.