AlphaGo Defeats Lee Sedol
DeepMind's Go-playing AI that, in March 2016, defeated 18-time world champion Lee Sedol 4 games to 1. Go had been considered decades away from AI mastery — its branching factor (250+ legal moves per turn vs chess's ~35) had stymied decades of classical AI approaches. AlphaGo combined deep neural networks with Monte Carlo Tree Search to leap past those limits in two years of focused work.
Why it matters
AlphaGo was the first AI system whose capability was undeniable to non-technical audiences worldwide. The cultural moment accelerated AI awareness, funding, and policy attention by perhaps 5 years. Without AlphaGo, the GPT-3 / ChatGPT receptions would have happened against a much less prepared public.
Core Capabilities
Context Window
Context window not disclosed.
Availability
Pricing Model
Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
What it feels like
- Beat Lee Sedol 4-1 in March 2016 — the moment AI 'solved' Go a decade earlier than experts predicted
- Watched live by 280 million people; Move 37 in Game 2 became one of the most analysed moves in Go history
- Combined CNN policy/value nets with Monte Carlo Tree Search — the template later refined into AlphaGo Zero, AlphaZero, MuZero
- AlphaGo Zero (2017) eliminated human game data — pure self-play beat AlphaGo 100-0 in 40 days
- Triggered Korea's national AI investment programme and reset global expectations about AI capability
- Lineage feeds directly into AlphaFold (DeepMind's biology breakthrough) via shared search-and-evaluate machinery
Best use cases
- Inspired the entire deep-RL research wave 2016-2020 (DeepMind, OpenAI, others)
- AlphaFold (same lab, similar compute substrate) (DeepMind)
- MuZero, AlphaStar, AlphaCode (DeepMind lineage) (DeepMind)
Tools to try
Not ideal for
- Tasks far outside the modalities listed in this model's spec
- Workflows where a more recent successor in the same family scores higher