Training AI to Recognize Realizable Gauss Diagrams: The Same Instances Confound AI and Human Mathematicians

Abdullah Khan, Alexei Lisitsa, Alexei Vernitski

2022

Abstract

Recent research in computational topology found sets of counterexamples demonstrating that several recent mathematical articles purporting to describe a mathematical concept of realizable Gauss diagrams contain a mistake. In this study we propose several ways of encoding Gauss diagrams as binary matrices, and train several classical ML models to recognise whether a Gauss diagram is realizable or unrealizable. We test their accuracy in general, on the one hand, and on the counterexamples, on the other hand. Intriguingly, accuracy is good in general and surprisingly bad on the counterexamples. Thus, although human mathematicians and AI perceive Gauss diagrams completely differently, they tend to make the same mistake when describing realizable Gauss diagrams.

Paper Citation

in Harvard Style

Khan A., Lisitsa A. and Vernitski A. (2022). Training AI to Recognize Realizable Gauss Diagrams: The Same Instances Confound AI and Human Mathematicians. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 990-995. DOI: 10.5220/0010992700003116

in Bibtex Style

@conference{icaart22,
author={Abdullah Khan and Alexei Lisitsa and Alexei Vernitski},
title={Training AI to Recognize Realizable Gauss Diagrams: The Same Instances Confound AI and Human Mathematicians},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={990-995},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010992700003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Training AI to Recognize Realizable Gauss Diagrams: The Same Instances Confound AI and Human Mathematicians
SN - 978-989-758-547-0
AU - Khan A.
AU - Lisitsa A.
AU - Vernitski A.
PY - 2022
SP - 990
EP - 995
DO - 10.5220/0010992700003116