Summary of Unsupervised Discovery Of Formulas For Mathematical Constants, by Michael Shalyt et al.
Unsupervised Discovery of Formulas for Mathematical Constants
by Michael Shalyt, Uri Seligmann, Itay Beit Halachmi, Ofir David, Rotem Elimelech, Ido Kaminer
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Number Theory (math.NT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper tackles the challenge of accelerating scientific discovery in mathematics using AI methods. Despite decades of efforts in applying AI for scientific acceleration, the field has yet to yield effective solutions for discovering formulas that accurately represent mathematical constants. The problem lies in the requirement for infinite precision, making “near-true” formulas useless for gaining insights into the correct ones. To address this issue, a clear distance metric is needed to guide automated discovery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to use AI to help scientists discover new formulas that accurately represent mathematical constants. Right now, it’s hard for computers to find these formulas because they need to be perfect forever. This makes “almost right” formulas not very helpful. To make progress, we need a way to measure how close an idea is to the correct answer. |
Keywords
» Artificial intelligence » Precision
