DeepMind and mathematicians use machine learning to guide new theorems in knot theory and representation theory
Result confirmed
The takeaway
Davies et al. (Nature, Dec 2021) used supervised learning plus attribution to point mathematicians to hidden relationships.
Status
- Claim
Result confirmed
- Our reporting
- High confidence
- Verification
- Peer-reviewed
- Importance
- 3 of 5
- Last verified
- 29 September 2026
Your AI and this story
- GPT-6 AstraIn its training data
- Claude Opus 5.5In its training data
- Gemini 4 HydrogenIn its training data
- Grok 4.7In its training data
It happened before the cutoffs of all four assistants, so it can be in their training data.
Key facts
- Nature 600:70–74 (2021)
- Knot theory: new relation between signature and the ‘natural slope’ (Lackenby, Juhász); follow-up in Geometry & Topology (2024)
- Representation theory: progress towards the combinatorial invariance conjecture (Williamson)
- Humans stated and proved the theorems; ML highlighted which features mattered
What happened
DeepMind trained models to predict one mathematical quantity from others, then used attribution to show which inputs mattered, prompting expert mathematicians to formulate and prove new results.
Why it matters
It was the first Nature-level demonstration of AI contributing to pure maths research, as an intuition aid rather than a prover.
Sources
2 sources from 2 sites. Numbers match the chips in the text.
2 sources: 1 primary, 1 reaction
Primary
- Advancing mathematics by guiding human intuition with AI (Nature)nature.com, paper
Reactions
- Critical review of the paper (arXiv 2112.04324)arxiv.org, discussion
Changes
- Filed