In December 2017, DeepMind published AlphaZero, a system taught only the basic rules of chess, shogi and Go, which reaches superhuman level in all three games in under 24 hours of training via self-play reinforcement learning, without any database of human games or hand-designed heuristics. In its paper, DeepMind directly pits AlphaZero against Stockfish (chess) and other specialized engines, decisively defeating them. Unlike Deep Blue or Stockfish — heirs to the alpha-beta search and hand-crafted evaluation paradigm Shannon formulated in 1950 — AlphaZero entirely replaces that approach with deep neural networks guiding a Monte Carlo tree search, symbolically closing the cycle begun by Torres Quevedo's Ajedrecista: from a fixed decision tree hand-built by an engineer to a system that discovers its own strategies with no prior human knowledge.