What Machine Learning Tells Us About the Mathematical Structure of Concepts

• URL: http://arxiv.org/abs/2408.15507v1
• Date: Wed, 28 Aug 2024 03:30:22 GMT
• Title: What Machine Learning Tells Us About the Mathematical Structure of Concepts
• Authors: Jun Otsuka,
• Abstract summary: The study highlights how each framework provides a distinct mathematical perspective for modeling concepts. This work emphasizes the importance of interdisciplinary dialogue, aiming to enrich our understanding of the complex relationship between human cognition and artificial intelligence.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper examines the connections among various approaches to understanding concepts in philosophy, cognitive science, and machine learning, with a particular focus on their mathematical nature. By categorizing these approaches into Abstractionism, the Similarity Approach, the Functional Approach, and the Invariance Approach, the study highlights how each framework provides a distinct mathematical perspective for modeling concepts. The synthesis of these approaches bridges philosophical theories and contemporary machine learning models, providing a comprehensive framework for future research. This work emphasizes the importance of interdisciplinary dialogue, aiming to enrich our understanding of the complex relationship between human cognition and artificial intelligence.

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