The Epistemology of Neural Networks: Understanding vs. Correlation

This paper examines the epistemological foundations of neural networks and their relation to human understanding. While neural architectures excel at identifying correlations in massive datasets, they lack the semantic depth and contextual awareness that characterize human cognition. The study explores whether statistical generalization can ever amount to genuine knowledge, situating neural epistemology within broader debates on representation, meaning, and inference. By connecting machine learning to classical epistemological theories—from empiricism and rationalism to contemporary naturalized epistemology—this paper questions whether current AI systems understand or merely approximate understanding through correlation.