AlphaGo took many AI researchers by surprise. An even bigger surprise came next year, with AlphaZero:
"AlphaZero was a reinforcement learning system that was able to master three different perfect information games - chess, shogi (Japanese chess), and Go - at superhuman levels by just learning from self-play, without using any human expert games or domain knowledge crafted by programmers.
Its predecessor AlphaGo, which defeated the world champion Go player in 2016, was revolutionary but relied on human expert games and domain-specific rules coded by the DeepMind researchers.
AlphaZero started from random play and used a general-purpose reinforcement learning algorithm to iteratively improve its gameplay through self-play, ending up with superior performance compared to the best human players and previous game-specific AI systems.
Many experts were stunned that a general algorithm could rediscover from scratch the millennia-old principles and strategies for these highly complex games, often discovering novel and counterintuitive moves along the way."
"AlphaZero was a reinforcement learning system that was able to master three different perfect information games - chess, shogi (Japanese chess), and Go - at superhuman levels by just learning from self-play, without using any human expert games or domain knowledge crafted by programmers.
Its predecessor AlphaGo, which defeated the world champion Go player in 2016, was revolutionary but relied on human expert games and domain-specific rules coded by the DeepMind researchers.
AlphaZero started from random play and used a general-purpose reinforcement learning algorithm to iteratively improve its gameplay through self-play, ending up with superior performance compared to the best human players and previous game-specific AI systems.
Many experts were stunned that a general algorithm could rediscover from scratch the millennia-old principles and strategies for these highly complex games, often discovering novel and counterintuitive moves along the way."