Similarities and Differences Between Machine Learning and Human Understanding

This article explores the capabilities and limitations of artificial intelligence (AI) and machine learning (ML) in simulating human cognition. The article first reviews the research on the process of human understanding in philosophy, especially Heidegger and Gadamer's theory of hermeneutics, explaining the cyclic structure in the process of understanding and how preconceived ideas affect our cognition. Furthermore, by comparing the basic models of machine learning with the dynamic processes of human understanding, this article reveals that despite some similarities, such as the role of preconceived notions and the process of continuous information integration, machine learning still has significant shortcomings in handling emotional intelligence, creative problem-solving, and intuitive reasoning. The article also discusses the fundamental differences between the human brain and machine algorithms, as well as their impact on the future role of AI in society. This article aims to deepen our understanding of AI technology capabilities through a philosophical perspective, emphasizing the importance of considering its cognitive limitations when further developing AI technology.