FOUNDATIONAL THEORIES AND DEVELOPMENTAL PATHWAYS OF ARTIFICIAL INTELLIGENCE

Abstract

This paper explores the foundational theories and developmental pathways that
have shaped the evolution of Artificial Intelligence (AI) as a multidisciplinary field.
It begins by examining the core theoretical frameworks, including symbolic AI,
connectionism, and machine learning, that laid the groundwork for AI research and
development. The study then traces the chronological development of AI through key
milestones, from early rule-based systems to modern deep learning architectures and
generative models. Emphasis is placed on how advancements in computational
power, data availability, and algorithmic design have accelerated AI progress.