Summary
In March 2016, AlphaGo beat Lee Sedol 4-1 at Go — a game long considered a decade or more away from machine mastery. The system combined deep networks, Monte Carlo tree search, and self-play reinforcement learning. AlphaGo Zero (2017) achieved superhuman play from scratch, with no human games.
Objectives
- 01State what AlphaGo demonstrated that prior systems could not.
- 02Explain the difference between AlphaGo and AlphaGo Zero.
- 03Name the pattern (search + neural evaluation + self-play) that generalized beyond Go.
The Lesson
The match
Move 37 of Game 2 — a move human experts initially called a mistake — is the moment the community understood something had changed. AlphaGo won 4-1 against the top-ranked Go player of the previous decade.
The generalization
The self-play RL pattern generalized to chess, shogi (AlphaZero), protein folding (AlphaFold), and matrix multiplication (AlphaTensor). The recipe travels.
Key Ideas
- Self-play RL scales to superhuman performance in closed environments.
- The AlphaGo template — search + neural evaluation + self-play — is the RL pattern that generalized.