Artificial intelligence and machine learning generated conjectures with TxGraffiti

• URL: http://arxiv.org/abs/2407.02731v1
• Date: Wed, 3 Jul 2024 01:03:09 GMT
• Title: Artificial intelligence and machine learning generated conjectures with TxGraffiti
• Authors: Randy Davila,
• Abstract summary: We outline the machine learning and techniques implemented by TxGraffiti. We also announce a new online version of the program available for anyone curious to explore conjectures in graph theory.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: \emph{TxGraffiti} is a machine learning and heuristic based artificial intelligence designed to automate the task of conjecturing in mathematics. Since its inception, TxGraffiti has generated many surprising conjectures leading to publication in respectable mathematical journals. In this paper we outline the machine learning and heuristic techniques implemented by TxGraffiti. We also recall its contributions to the mathematical literature and announce a new online version of the program available for anyone curious to explore conjectures in graph theory.

Related papers

• FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI [2.1061205911958876]
  FrontierMath is a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians. Current state-of-the-art AI models solve under 2% of problems, revealing a vast gap between AI capabilities and the prowess of the mathematical community. As AI systems advance toward expert-level mathematical abilities, FrontierMath offers a rigorous testbed that quantifies their progress.
  arXiv  Detail & Related papers  (2024-11-07T17:07:35Z)
• MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code [38.127313175508746]
  We introduce a novel method for generating mathematical code accompanied with corresponding reasoning steps for continued pretraining. Our approach begins with the construction of a high-quality mathematical continued pretraining dataset. Appending the generated code to each reasoning step results in data consisting of paired natural language reasoning steps and their corresponding code.
  arXiv  Detail & Related papers  (2024-10-10T17:58:40Z)
• Automated conjecturing in mathematics with \emph{TxGraffiti} [0.0]
  emphTxGraffiti is a data-driven computer program developed to automate the process of generating conjectures. We present the design and core principles of emphTxGraffiti, including its roots in the original emphGraffiti program.
  arXiv  Detail & Related papers  (2024-09-28T15:06:31Z)
• Apriori Knowledge in an Era of Computational Opacity: The Role of AI in Mathematical Discovery [0.7673339435080445]
  We argue that if a proof-checker is attached to such machines, then we can obtain apriori mathematical knowledge from them. Many accept that we can acquire genuine mathematical knowledge of the Four Color Theorem from Appel and Haken's program.
  arXiv  Detail & Related papers  (2024-03-15T21:38:26Z)
• Machine learning and information theory concepts towards an AI Mathematician [77.63761356203105]
  The current state-of-the-art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms of mathematical reasoning. This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities. It takes an information-theoretical posture to ask questions about what constitutes an interesting mathematical statement.
  arXiv  Detail & Related papers  (2024-03-07T15:12:06Z)
• A Survey of Deep Learning for Mathematical Reasoning [71.88150173381153]
  We review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade. Recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning.
  arXiv  Detail & Related papers  (2022-12-20T18:46:16Z)
• Self-Supervised Pretraining of Graph Neural Network for the Retrieval of Related Mathematical Expressions in Scientific Articles [8.942112181408156]
  We propose a new approach for retrieval of mathematical expressions based on machine learning. We design an unsupervised representation learning task that combines embedding learning with self-supervised learning. We collect a huge dataset with over 29 million mathematical expressions from over 900,000 publications published on arXiv.org.
  arXiv  Detail & Related papers  (2022-08-22T12:11:30Z)
• JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem Understanding [74.12405417718054]
  This paper aims to advance the mathematical intelligence of machines by presenting the first Chinese mathematical pre-trained language model(PLM) Unlike other standard NLP tasks, mathematical texts are difficult to understand, since they involve mathematical terminology, symbols and formulas in the problem statement. We design a novel curriculum pre-training approach for improving the learning of mathematical PLMs, consisting of both basic and advanced courses.
  arXiv  Detail & Related papers  (2022-06-13T17:03:52Z)
• Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions [58.220137936626315]
  This paper extensively discusses automated graph machine learning approaches. We introduce AutoGL, our dedicated and the world's first open-source library for automated graph machine learning. Also, we describe a tailored benchmark that supports unified, reproducible, and efficient evaluations.
  arXiv  Detail & Related papers  (2022-01-04T18:31:31Z)
• Generative Language Modeling for Automated Theorem Proving [94.01137612934842]
  This work is motivated by the possibility that a major limitation of automated theorem provers compared to humans might be addressable via generation from language models. We present an automated prover and proof assistant, GPT-f, for the Metamath formalization language, and analyze its performance.
  arXiv  Detail & Related papers  (2020-09-07T19:50:10Z)
• Learning to Prove Theorems by Learning to Generate Theorems [71.46963489866596]
  We learn a neural generator that automatically synthesizes theorems and proofs for the purpose of training a theorem prover. Experiments on real-world tasks demonstrate that synthetic data from our approach improves the theorem prover.
  arXiv  Detail & Related papers  (2020-02-17T16:06:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.