AlphaGo's Ten-Year Legacy: The Main Road to AGI
*An AI Decade Retrospective (translated infographic essay)*
The Copernican Moment: Move 37
Seoul, 2016. While Lee Sedol's "divine move" in Game 4 defended human dignity, Move 37 in Game 2 was the true turning point. Placed where human intuition would never look, it proved the machine had developed an intuition beyond human cognitive paradigms—marking the birth of non-human intelligence.
> "We no longer need to worry about whether machines can play games; the new question is how to use such tools to answer fundamental scientific problems."
The Machine's "Intuition" and "Big Picture"
AlphaGo was not brute-force computation; it simulated human decision-making:
- Policy network — intuition filtering, "fast thinking"
- Value network — position evaluation, "big-picture view"
- Tree search — forward simulation, "slow thinking"
- AlphaFold — predicted protein 3D structures, solving a 50-year biology problem
- AlphaTensor — discovered matrix multiplication algorithms better than known human ones
- AlphaDev — optimized fundamental algorithm libraries, improving global computing efficiency
AlphaZero's Ultimate Leap
AlphaZero eliminated dependence on human data, learning purely from self-play:
1. Rediscovery — independently derived centuries of human openings (joseki) 2. Surpass and discard — found flaws in human "optimal" play and explored new strategies 3. New frontiers — found globally optimal solutions in humanity's cognitive blind spots
Gamifying Scientific Discovery
Complex scientific problems can be converted into well-defined "games," with the AlphaGo paradigm searching the solution space for optimal paths:
The Fork in the Road: Shortcut vs. Main Road
| LLMs (the "shortcut") | AlphaGo paradigm (the "main road") | |---|---| | Compresses and recombines existing human data | Autonomously explores optimal solutions in the solution space | | Struggles to produce disruptive original knowledge | Has a strict verification mechanism (win/lose) | | Open-domain verification is hard (hallucination) | Can transcend the limits of human prior knowledge |
The Path of Fusion: Conjecture and Refutation
The future lies in merging the two roads: LLMs handle conjecture (generating novel hypotheses), while agents handle refutation (verification and filtering through the environment).
*Reference: "10 years of AlphaGo: The turning point for AI" — Thore Graepel & Pushmeet Kohli*