Statements (111)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Artificial_Intelligence
|
| gptkbp:bfsLayer |
3
|
| gptkbp:bfsParent |
gptkb:philosopher
|
| gptkbp:adapted_into |
different games
|
| gptkbp:architectural_style |
gptkb:microprocessor
|
| gptkbp:based_on |
gptkb:Alpha_Go_Zero
gptkb:Monte_Carlo_Tree_Search |
| gptkbp:competes_with |
human players
traditional AI programs |
| gptkbp:defeated |
gptkb:Alpha_Go
gptkb:Stockfish gptkb:Elmo |
| gptkbp:developed_by |
gptkb:philosopher
gptkb:Graphics_Processing_Unit advanced algorithms cutting-edge technology machine learning techniques strategic games |
| gptkbp:example |
general-purpose AI
|
| gptkbp:exhibited_at |
generalization ability
the power of self-learning AI |
| gptkbp:first_released |
December 2017
|
| gptkbp:has |
no human knowledge
|
| gptkbp:has_achievements |
superhuman performance
|
| gptkbp:has_feature |
playing multiple games simultaneously
|
| gptkbp:has_influence_on |
AI research community
AI game playing strategies future AI research directions |
| gptkbp:has_programs |
decision making
game playing strategy optimization |
| https://www.w3.org/2000/01/rdf-schema#label |
Alpha Zero
|
| gptkbp:innovation |
gptkb:software_framework
|
| gptkbp:input_output |
game state
move probabilities |
| gptkbp:inspired |
subsequent AI systems
|
| gptkbp:is_a_tool_for |
understanding AI capabilities
|
| gptkbp:is_analyzed_in |
its decision-making process
|
| gptkbp:is_capable_of |
learning from scratch
|
| gptkbp:is_cited_in |
AI literature
|
| gptkbp:is_compared_to |
human players
traditional AI other AI systems human intuition in games |
| gptkbp:is_considered |
a landmark achievement
a pioneer in AI research |
| gptkbp:is_considered_as |
breakthrough in AI
|
| gptkbp:is_discussed_in |
AI conferences
|
| gptkbp:is_evaluated_by |
gptkb:sports_event
performance metrics peer reviews match results |
| gptkbp:is_featured_in |
gptkb:documentaries
|
| gptkbp:is_influenced_by |
gptkb:battle
previous AI models |
| gptkbp:is_known_for |
adapt strategies
its versatility generalized learning approach playing multiple games |
| gptkbp:is_part_of |
gptkb:Deep_Mind's_AI_portfolio
AI advancements AI evolution Deep Mind's research portfolio AI game playing systems Deep Mind's innovations Deep Mind's research the evolution of game AI. |
| gptkbp:is_played_by |
gptkb:Go
gptkb:chess_match shogi |
| gptkbp:is_recognized_by |
AI experts
AI researchers worldwide |
| gptkbp:is_recognized_for |
its efficiency
its innovative approach self-learning capabilities innovative algorithms |
| gptkbp:is_supported_by |
high-performance computing
research funding AI community |
| gptkbp:is_tested_for |
controlled environments
various opponents various strategies human champions various AI opponents |
| gptkbp:is_used_in |
AI competitions
improve AI algorithms |
| gptkbp:is_utilized_in |
research projects
|
| gptkbp:notable_achievement |
AI history
|
| gptkbp:notable_for |
fast learning capabilities
|
| gptkbp:number_of_games |
millions
|
| gptkbp:outcome |
AI ethics
|
| gptkbp:performance |
gptkb:Alpha_Go
gptkb:Stockfish gptkb:Elmo AI development |
| gptkbp:products |
Deep Mind's research team
|
| gptkbp:publishes |
gptkb:academic_journal
|
| gptkbp:rank |
world champion level
|
| gptkbp:released_in |
gptkb:2017
|
| gptkbp:research |
academic institutions
|
| gptkbp:result |
gptkb:Nature
|
| gptkbp:significance |
gptkb:Research_Institute
|
| gptkbp:successor |
gptkb:Alpha_Go
|
| gptkbp:training |
gptkb:Monte_Carlo_Tree_Search
several hours game data self-play requires no human intervention |
| gptkbp:uses |
reinforcement learning
|
| gptkbp:utilizes |
gptkb:microprocessor
GPU acceleration |