Challenges and Limitations of Using Artificial Intelligence for Interpreting Physical Concepts

Abstract

Artificial Intelligence (AI) has significantly transformed scientific research, including data analysis, modeling, and simulation in physics. From machine learning–based particle detection to automated theorem discovery, AI systems increasingly contribute to interpreting complex physical phenomena. However, despite remarkable advancements, AI faces serious epistemological, methodological, and conceptual limitations when applied to interpreting foundational physical concepts such as space-time, causality, quantum indeterminacy, entropy, and relativity. This paper critically examines the challenges and limitations of using AI in interpreting physical concepts. It argues that while AI excels in pattern recognition and predictive modeling, it lacks intrinsic conceptual understanding, theoretical intentionality, and philosophical depth necessary for interpreting the meaning of physical laws. The study highlights issues of data dependency, model opacity, reductionism, bias, interpretability crisis, and ethical concerns. The paper concludes that AI should be treated as an augmentative tool rather than an autonomous interpreter in physics research.