Properties (53)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
computer program
|
| gptkbp:advertising |
widely covered in media
|
| gptkbp:architect |
neural network
|
| gptkbp:area |
over 50
|
| gptkbp:defeated |
gptkb:Lee_Sedol
Fan Hui Ke Jie |
| gptkbp:developedBy |
DeepMind
|
| gptkbp:equipment |
gptkb:TPU
|
| gptkbp:evaluates |
Monte Carlo Tree Search
human feedback |
| gptkbp:filmography |
AlphaGo_(2017)
|
| gptkbp:firstMatch |
4
October 2015 May 2017 |
| gptkbp:hasVersion |
2015
2017 |
| https://www.w3.org/2000/01/rdf-schema#label |
AlphaGo
|
| gptkbp:impact |
increased interest in AI
increased interest in Go |
| gptkbp:inspiration |
gptkb:AlphaZero
|
| gptkbp:language |
Python
|
| gptkbp:lost_and_found |
1
|
| gptkbp:notableFeature |
achieved high win rates
achieved superhuman performance adapted to opponents' strategies analyzed millions of positions demonstrated strategic thinking exhibited creativity in gameplay learned from experience led to advancements in AI technology. played against top players set benchmarks for AI in games showcased potential of AI in strategic games used advanced algorithms utilized large datasets influenced_AI_research_directions pioneered_AI_in_complex_board_games |
| gptkbp:operatingHours |
Linux
|
| gptkbp:rank |
top Go player
|
| gptkbp:released |
2015
|
| gptkbp:researchField |
artificial intelligence
machine learning deep learning |
| gptkbp:significance |
first AI to defeat a human professional Go player
|
| gptkbp:successor |
gptkb:AlphaGo_Zero
AlphaGo Master |
| gptkbp:training |
several months
self-play human expert games |
| gptkbp:type |
Go
|
| gptkbp:uses |
reinforcement learning
supervised learning |