Mathematics, word problems, common sense, and artificial intelligence

• URL: http://arxiv.org/abs/2301.09723v2
• Date: Wed, 25 Jan 2023 01:24:25 GMT
• Title: Mathematics, word problems, common sense, and artificial intelligence
• Authors: Ernest Davis
• Abstract summary: We discuss the capacities and limitations of current artificial intelligence (AI) technology to solve word problems that combine elementary knowledge with commonsense reasoning. We review three approaches that have been developed, using AI natural language technology. We argue that it is not clear whether these kinds of limitations will be important in developing AI technology for pure mathematical research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The paper discusses the capacities and limitations of current artificial intelligence (AI) technology to solve word problems that combine elementary knowledge with commonsense reasoning. No existing AI systems can solve these reliably. We review three approaches that have been developed, using AI natural language technology: outputting the answer directly, outputting a computer program that solves the problem, and outputting a formalized representation that can be input to an automated theorem verifier. We review some benchmarks that have been developed to evaluate these systems and some experimental studies. We discuss the limitations of the existing technology at solving these kinds of problems. We argue that it is not clear whether these kinds of limitations will be important in developing AI technology for pure mathematical research, but that they will be important in applications of mathematics, and may well be important in developing programs capable of reading and understanding mathematical content written by humans.

Related papers

• The Mathematics of Artificial Intelligence [23.03787751696068]
  This overview article highlights the critical role of mathematics in artificial intelligence (AI) It emphasizes that mathematics provides tools to better understand and enhance AI systems. Conversely, AI raises new problems and drives the development of new mathematics at the intersection of various fields.
  arXiv  Detail & Related papers  (2025-01-15T15:00:23Z)
• Mathematics and Machine Creativity: A Survey on Bridging Mathematics with AI [3.7721193149333634]
  This paper presents a comprehensive survey on the applications of artificial intelligence (AI) in mathematical research. Recent developments in AI, particularly in reinforcement learning (RL) and large language models (LLMs), have demonstrated the potential for AI to contribute back to mathematics. This survey aims to establish a bridge between AI and mathematics, providing insights into the mutual benefits and fostering deeper interdisciplinary understanding.
  arXiv  Detail & Related papers  (2024-12-21T08:58:36Z)
• 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)
• Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
  We argue that shortcomings stem from one overarching failure: AI systems lack wisdom. While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
  arXiv  Detail & Related papers  (2024-11-04T18:10:10Z)
• Cognition is All You Need -- The Next Layer of AI Above Large Language Models [0.0]
  We present Cognitive AI, a framework for neurosymbolic cognition outside of large language models. We propose that Cognitive AI is a necessary precursor for the evolution of the forms of AI, such as AGI, and specifically claim that AGI cannot be achieved by probabilistic approaches on their own. We conclude with a discussion of the implications for large language models, adoption cycles in AI, and commercial Cognitive AI development.
  arXiv  Detail & Related papers  (2024-03-04T16:11:57Z)
• AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
  Mathematics is one of the most powerful conceptual systems developed and used by the human species. Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
  arXiv  Detail & Related papers  (2023-10-19T02:00:31Z)
• A Survey on Brain-Inspired Deep Learning via Predictive Coding [85.93245078403875]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks. PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• Reliable AI: Does the Next Generation Require Quantum Computing? [71.84486326350338]
  We show that digital hardware is inherently constrained in solving problems about optimization, deep learning, or differential equations. In contrast, analog computing models, such as the Blum-Shub-Smale machine, exhibit the potential to surmount these limitations.
  arXiv  Detail & Related papers  (2023-07-03T19:10:45Z)
• 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)
• A Classification of Artificial Intelligence Systems for Mathematics Education [3.718476964451589]
  This chapter provides an overview of the different Artificial Intelligence (AI) systems that are being used in digital tools for Mathematics Education (ME) It is aimed at researchers in AI and Machine Learning (ML), for whom we shed some light on the specific technologies that are being used in educational applications.
  arXiv  Detail & Related papers  (2021-07-13T12:09:10Z)

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.