AlphaGo (2016): In 2016, DeepMind’s AlphaGo defeated Lee Sedol, one of the world’s top Go players, in a historic five-game match. Go, with its vast search space and reliance on intuition, was considered a grand challenge for AI—unlike chess, it resisted brute-force methods. AlphaGo combined deep neural networks (policy and value nets) with Monte Carlo tree search, trained via supervised learning from human games and reinforced through self-play. Its creative moves, like “Move 37” in Game 2, surprised experts and revealed new strategic insights. The victory demonstrated that AI could master domains requiring pattern recognition, long-term planning, and intuition. AlphaGo’s successors, like AlphaZero, learned without human data, mastering chess and shogi faster and better. This milestone proved reinforcement learning’s potential and inspired applications in drug discovery, materials science, and complex system optimization.
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