---
title: Move 37
type: vocabulary
url: "https://www.envisioning.com/vocab/move-37"
summary: "AlphaGo's shocking, creative move that signaled AI's capacity for superhuman strategic reasoning."
year: 2016
generality: 0.09
---

# Move 37

AlphaGo's shocking, creative move that signaled AI's capacity for superhuman strategic reasoning.
Move 37 refers to a specific play made by DeepMind's AlphaGo during the second game of its five-game match against world Go champion Lee Sedol in March 2016. On the 37th turn, AlphaGo placed a stone on the fifth row near the board's edge, a position so unusual that professional commentators initially assumed it was a mistake. Human Go tradition, built over thousands of years, strongly disfavored such placements at that stage of the game. The move contributed to AlphaGo's victory in that game and the match overall.

What made Move 37 technically remarkable was that it emerged from AlphaGo's combination of deep convolutional neural networks and Monte Carlo tree search, trained through supervised learning on human expert games and self-play reinforcement learning. AlphaGo's policy network estimated the move had roughly a one-in-ten-thousand probability of being chosen by a human player. The system had departed from the distribution of known human strategies. Rather than interpolating between human moves, AlphaGo had found a strategic concept through its own simulated experience that humans had not converged on.

The broader significance of Move 37 extends beyond the game of Go. It demonstrated publicly that modern AI systems trained with deep reinforcement learning can explore strategy spaces in ways that go beyond their training data and surface new knowledge. This pushed back against a common assumption that AI would always be bounded by the ceiling of human expertise it was trained on.

Move 37 has since become a cultural and technical touchstone in AI discourse, frequently cited when discussing creativity, superhuman performance, and the limits of human intuition in complex domains. It accelerated interest in applying similar reinforcement learning techniques to other high-dimensional decision-making problems, from protein folding to chip design.

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Source: Envisioning — Technology Research Institute (https://www.envisioning.com/vocab/move-37)