Until very recently, the machines that could trounce champions were at least respectful enough to start by learning from human experience.
To beat Garry Kasparov at chess in 1997, IBM engineers made use of centuries of chess wisdom in their Deep Blue computer. In 2016, Google DeepMind’s AlphaGo thrashed champion Lee Sedol at the ancient board game Go after poring over millions of positions from tens of thousands of human games.
But now artificial-intelligence researchers are rethinking the way their bots incorporate the totality of human knowledge. The current trend is: Don’t bother.
Last October, the DeepMind team published details of a new Go-playing system, AlphaGo Zero, that studied no human games at all. Instead, it started with the game’s rules and played against itself. The first moves it made were completely random. After each game, it folded in new knowledge of what led to a win and what didn’t. At the end of these scrimmages, AlphaGo Zero went head-to-head with the already-superhuman version of AlphaGo that had beaten Lee Sedol. It won 100 games to zero.
The team went on to create what would become another master gamer in the AlphaGo family, this one called simply AlphaZero. In a paper posted to the scientific preprint site ArXiv.org in December, DeepMind researchers revealed that after starting again from scratch, the trained-up AlphaZero outperformed AlphaGo Zero—in other words, it beat the bot that beat the bot that beat the best Go players in the world. And when it was given the rules for chess or the Japanese chess variant shogi, AlphaZero quickly learned to defeat bespoke top-level algorithms for those games, too. Experts marveled at the program’s aggressive, unfamiliar style. “I always wondered how it would be if a superior species landed on Earth and showed us how they played chess,” the Danish grandmaster Peter Heine Nielsen told a BBC interviewer. “Now I know.”