The Evolution of Artificial Intelligence: From Symbolic Logic to Deep Learning

This paper explores the historical and theoretical evolution of Artificial Intelligence (AI), tracing its development from symbolic logic to the modern era of deep learning. It examines how early symbolic reasoning shaped the conceptual foundations of AI and how neural and statistical methods redefined the idea of intelligence. The study highlights the philosophical shift from rule-based systems to data-driven architectures, emphasizing the transition from logical deduction to emergent learning. By situating this evolution within both scientific and philosophical contexts, the paper aims to provide a comprehensive understanding of AI’s intellectual trajectory and its implications for the theory of knowledge, reasoning, and cognition.