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Google’s Chess Experiments Reveal How to Boost the Power of AI
By rewarding computers that combined different approaches to solve chess puzzles, Google created an enhanced AI that could defeat its existing champion, AlphaZero.
So he and his colleagues developed a way to weave together multiple (up to 10) decisionmaking AI systems, each optimized and trained for different strategies, starting with AlphaZero, DeepMind’s powerful chess program. “This is a great example that looking for more than one way to solve a problem—like winning a chess game—provides a lot of benefits,” said Antoine Cully, an AI researcher at Imperial College London who was not involved with the DeepMind project. “What normally tends to happen with reinforcement learning, almost regardless of the method, is that you get the policy that solves the particular instance of the problem you’ve been training on, but it doesn’t generalize,” said Julian Togelius, a computer scientist at New York University and research director at modl.ai.
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