What is recorded here?
Used learned patterns and attribution to guide human research in knot theory and representation theory, leading to new mathematical relationships and conjectures.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The package contains distinct proven and conjectural outcomes. Model predictions are not proofs; human mathematicians developed the arguments and corrected an initial knot conjecture.
Read the original work
- Advancing mathematics by guiding human intuition with AI
- Publisher-deposited byline: Advancing mathematics by guiding human intuition with AI
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Alex Davies; Petar Veličković; Lars Buesing; Sam Blackwell; Daniel Zheng; Nenad Tomašev; Richard Tanburn; Peter Battaglia; Charles Blundell; András Juhász; Marc Lackenby; Geordie Williamson; Demis Hassabis; Pushmeet Kohli
Full byline of the specified publication; original laboratory roles remain in the source.
AI-guided mathematical intuition · Google DeepMind · Supervised neural networks described in the paper
Pattern discovery and feature attribution; human conjecture development and proof. Source-reported attribution; provider attestation is unavailable.
Research locations and affiliations
Documented mapping · Included in country comparison
Documented countries / regions: United Kingdom, Australia
1 grid.498210.6 0000 0004 5999 1726 DeepMind, London, UK; 2 grid.4991.5 0000 0004 1936 8948 University of Oxford, Oxford, UK; 3 grid.1013.3 0000 0004 1936 834X University of Sydney, Sydney, New South Wales Australia
English catalogue narrative
AI guides new mathematical intuition
Used learned patterns and attribution to guide human research in knot theory and representation theory, leading to new mathematical relationships and conjectures.
Scientific scope
The package contains distinct proven and conjectural outcomes. Model predictions are not proofs; human mathematicians developed the arguments and corrected an initial knot conjecture.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Alex Davies; Petar Veličković; Lars Buesing; Sam Blackwell; Daniel Zheng; Nenad Tomašev; Richard Tanburn; Peter Battaglia; Charles Blundell; András Juhász; Marc Lackenby; Geordie Williamson; Demis Hassabis; Pushmeet Kohli
Full byline of the specified publication; original laboratory roles remain in the source.
AI-guided mathematical intuition / Google DeepMind / Supervised neural networks described in the paper: Pattern discovery and feature attribution; human conjecture development and proof (source reported).