AlphaGo Beats Lee Sedol, 2016

A game thought to be a decade away for AI fell in Seoul, and move 37 rewrote what humans knew about Go.

25 min · Reviewed 2026

Seoul, March 2016

Go had resisted computers for decades. The board has 19 by 19 intersections and a branching factor roughly ten times chess. Experts had predicted a decade more before a machine could beat a top human.

DeepMind's AlphaGo, led by David Silver and team, defeated Lee Sedol 4 to 1 in a televised five-game match in Seoul in March 2016. Over 200 million people watched online. Lee won game four with a move now called the divine move, a rare reminder that humans still had tricks.

What AlphaGo combined

Policy network trained on millions of human professional games
Value network that estimated the probability of winning from a position
Monte Carlo tree search that looked ahead thousands of moves
Self-play reinforcement learning to improve beyond human games

The following year, AlphaGo Zero started from random play, used only self-play and the rules of Go, and surpassed the Lee Sedol version in a matter of days. It proved that a system could reach superhuman play without imitating human games at all.

It's not a human move. I've never seen a human play this move.
— Fan Hui, after move 37

The big idea: reinforcement learning plus deep networks plus self-play produced superhuman play in domains humans had studied for millennia. The technique generalizes far beyond games.

End-of-lesson check

8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-history-alphago-2016-builders

What is the main idea of "AlphaGo Beats Lee Sedol, 2016"?
1. A game thought to be a decade away for AI fell in Seoul, and move 37 rewrote what humans knew about Go.
2. Use AI as the final authority for the whole decision
3. Avoid checking the answer once it sounds polished
4. Focus only on speed instead of judgment
Which concept is most central to "AlphaGo Beats Lee Sedol, 2016"?
1. DeepMind
2. AlphaGo
3. reinforcement learning
4. Monte Carlo tree search
Which use of AI fits this topic best?
1. Let the AI decide what matters without your review
2. Use the answer before checking whether it fits the situation
3. Policy network trained on millions of human professional games
4. Use the first answer without checking it
What should a careful learner remember about "Move 37"?
1. Use "Move 37" as a reminder to verify the AI output before anyone relies on it.
2. Skip the context so the tool can guess faster
3. Treat the output as private even after sharing it online
4. Use the answer without checking the source
You want to use AI after this lesson. What is the safest next step?
1. Act immediately because the AI answer is written clearly
2. Use the AI answer as a draft, then check it against a reliable source.
3. Hide uncertainty so the final answer looks cleaner
4. Use private or sensitive details before checking permission
How should AI output about AlphaGo be treated?
1. As proof that no other source is needed
2. As a replacement for context, consent, or expert review
3. As a draft or helper output that still needs human judgment and verification
4. As something that becomes correct when it sounds confident
Name one way to verify an AI answer about AlphaGo.
Which action would help you apply "AlphaGo Beats Lee Sedol, 2016" responsibly?
1. Use the tool to avoid thinking through the tradeoff
2. Keep going even if the output conflicts with a trusted source
3. Use the first answer without checking it
4. Value network that estimated the probability of winning from a position

← Back to interactive lesson

Tendril · Builders · AI Foundations

AlphaGo Beats Lee Sedol, 2016

A game thought to be a decade away for AI fell in Seoul, and move 37 rewrote what humans knew about Go.

25 min · Reviewed 2026

Seoul, March 2016

What AlphaGo combined

Policy network trained on millions of human professional games
Value network that estimated the probability of winning from a position
Monte Carlo tree search that looked ahead thousands of moves
Self-play reinforcement learning to improve beyond human games

It's not a human move. I've never seen a human play this move.
— Fan Hui, after move 37

The big idea: reinforcement learning plus deep networks plus self-play produced superhuman play in domains humans had studied for millennia. The technique generalizes far beyond games.

End-of-lesson check

8 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-history-alphago-2016-builders

What is the main idea of "AlphaGo Beats Lee Sedol, 2016"?
1. A game thought to be a decade away for AI fell in Seoul, and move 37 rewrote what humans knew about Go.
2. Use AI as the final authority for the whole decision
3. Avoid checking the answer once it sounds polished
4. Focus only on speed instead of judgment
Which concept is most central to "AlphaGo Beats Lee Sedol, 2016"?
1. DeepMind
2. AlphaGo
3. reinforcement learning
4. Monte Carlo tree search
Which use of AI fits this topic best?
1. Let the AI decide what matters without your review
2. Use the answer before checking whether it fits the situation
3. Policy network trained on millions of human professional games
4. Use the first answer without checking it
What should a careful learner remember about "Move 37"?
1. Use "Move 37" as a reminder to verify the AI output before anyone relies on it.
2. Skip the context so the tool can guess faster
3. Treat the output as private even after sharing it online
4. Use the answer without checking the source
You want to use AI after this lesson. What is the safest next step?
1. Act immediately because the AI answer is written clearly
2. Use the AI answer as a draft, then check it against a reliable source.
3. Hide uncertainty so the final answer looks cleaner
4. Use private or sensitive details before checking permission
How should AI output about AlphaGo be treated?
1. As proof that no other source is needed
2. As a replacement for context, consent, or expert review
3. As a draft or helper output that still needs human judgment and verification
4. As something that becomes correct when it sounds confident
Name one way to verify an AI answer about AlphaGo.
Which action would help you apply "AlphaGo Beats Lee Sedol, 2016" responsibly?
1. Use the tool to avoid thinking through the tradeoff
2. Keep going even if the output conflicts with a trusted source
3. Use the first answer without checking it
4. Value network that estimated the probability of winning from a position

← Back to interactive lesson