Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics

• URL: http://arxiv.org/abs/2307.02502v1
• Date: Tue, 4 Jul 2023 20:16:32 GMT
• Title: Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics
• Authors: Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos
• Abstract summary: Beyond human-AI chat, large language models (LLMs) are emerging in programming, algorithm discovery, and theorem proving. This project introduces Math Agents and mathematical embedding as fresh entries to the "Moore's Law of Mathematics" Project aims to use Math Agents and mathematical embeddings to address the ageing issue in information systems biology.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The advancement in generative AI could be boosted with more accessible mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in programming, algorithm discovery, and theorem proving, yet their genomics application is limited. This project introduces Math Agents and mathematical embedding as fresh entries to the "Moore's Law of Mathematics", using a GPT-based workflow to convert equations from literature into LaTeX and Python formats. While many digital equation representations exist, there's a lack of automated large-scale evaluation tools. LLMs are pivotal as linguistic user interfaces, providing natural language access for human-AI chat and formal languages for large-scale AI-assisted computational infrastructure. Given the infinite formal possibility spaces, Math Agents, which interact with math, could potentially shift us from "big data" to "big math". Math, unlike the more flexible natural language, has properties subject to proof, enabling its use beyond traditional applications like high-validation math-certified icons for AI alignment aims. This project aims to use Math Agents and mathematical embeddings to address the ageing issue in information systems biology by applying multiscalar physics mathematics to disease models and genomic data. Generative AI with episodic memory could help analyse causal relations in longitudinal health records, using SIR Precision Health models. Genomic data is suggested for addressing the unsolved Alzheimer's disease problem.

Related papers

• Large Language Models for Mathematical Analysis [3.7325315394927023]
  This work addresses critical gaps in mathematical reasoning and contributes to advancing trustworthy AI. We developed the DEMI-MathAnalysis dataset, comprising proof-based problems from mathematical analysis topics. We also designed a guiding framework to rigorously enhance LLMs' ability to solve these problems.
  arXiv  Detail & Related papers  (2024-12-28T20:37:55Z)
• Formal Mathematical Reasoning: A New Frontier in AI [60.26950681543385]
  We advocate for formal mathematical reasoning and argue that it is indispensable for advancing AI4Math to the next level. We summarize existing progress, discuss open challenges, and envision critical milestones to measure future success.
  arXiv  Detail & Related papers  (2024-12-20T17:19:24Z)
• Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning [85.635988711588]
  We argue that enhancing the capabilities of large language models requires a paradigm shift in the design of mathematical datasets. We advocate for mathematical dataset developers to consider the concept of "motivated proof", introduced by G. P'olya in 1949, which can serve as a blueprint for datasets that offer a better proof learning signal. We provide a questionnaire designed specifically for math datasets that we urge creators to include with their datasets.
  arXiv  Detail & Related papers  (2024-12-19T18:55:17Z)
• FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI [2.0608396919601493]
  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)
• MathLearner: A Large Language Model Agent Framework for Learning to Solve Mathematical Problems [0.936726079405677]
  We propose an agent framework for learning to solve mathematical problems based on inductive reasoning. By emulating the human learning process of generalization of learned information, this framework has great performance in the mathematical reasoning process. Our model can be used as a personalised learning aid, thus reducing the inequality of educational resources.
  arXiv  Detail & Related papers  (2024-08-03T13:28:19Z)
• MathScale: Scaling Instruction Tuning for Mathematical Reasoning [70.89605383298331]
  Large language models (LLMs) have demonstrated remarkable capabilities in problem-solving. However, their proficiency in solving mathematical problems remains inadequate. We propose MathScale, a simple and scalable method to create high-quality mathematical reasoning data.
  arXiv  Detail & Related papers  (2024-03-05T11:42:59Z)
• MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning [2.9104279358536647]
  We present MathSensei, a tool-augmented large language model for mathematical reasoning. We study the complementary benefits of the tools - knowledge retriever (Bing Web Search), program generator + executor (Python), and symbolic equation solver (Wolfram-Alpha API)
  arXiv  Detail & Related papers  (2024-02-27T05:50:35Z)
• Large Language Models for Mathematicians [53.27302720305432]
  Large language models (LLMs) have received immense interest for their general-purpose language understanding and, in particular, their ability to generate high-quality text or computer code. In this note, we discuss to what extent they can aid professional mathematicians.
  arXiv  Detail & Related papers  (2023-12-07T18:59:29Z)
• 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)
• Measuring Mathematical Problem Solving With the MATH Dataset [55.4376028963537]
  We introduce MATH, a dataset of 12,500 challenging competition mathematics problems. Each problem has a full step-by-step solution which can be used to teach models to generate answer derivations and explanations. We also contribute a large auxiliary pretraining dataset which helps teach models the fundamentals of mathematics.
  arXiv  Detail & Related papers  (2021-03-05T18:59:39Z)

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.