strategic game-playing systems
webCredibility Rating
High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: Google DeepMind
This link is broken (404); the actual AlphaGo paper is accessible via Nature at doi:10.1038/nature16961. It is frequently cited in AI safety contexts as evidence of rapid, surprising capability gains and the challenge of forecasting AI progress.
Metadata
Summary
This is a broken link to the original DeepMind publication page for AlphaGo, the landmark 2016 Nature paper demonstrating that deep reinforcement learning combined with Monte Carlo tree search could achieve superhuman performance in Go. The paper represented a major milestone in AI capabilities, showing that complex intuitive reasoning could be learned from data rather than hand-coded heuristics.
Key Points
- •Page is currently returning a 404 error; the canonical paper is published in Nature (2016) by Silver et al.
- •AlphaGo combined policy and value neural networks with Monte Carlo tree search to master Go at superhuman level.
- •Demonstrated that deep learning could handle high-dimensional strategic reasoning previously thought to require human intuition.
- •Significant capabilities milestone: Go had long been considered a benchmark problem beyond near-term AI reach.
- •Relevant to AI safety discussions about rapid capability jumps and the difficulty of predicting AI progress timelines.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Power-Seeking Emergence Conditions Model | Analysis | 63.0 |
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