Foundations of AI: Search, Reasoning, and the First AI Boom

Artificial intelligence may seem like a recent breakthrough, but its foundational concepts were established in the mid-twentieth century during the first AI boom. Understanding these origins provides essential insight into how modern intelligent systems evolved from basic symbolic logic and structured problem-solving rules. This written course guides you through the fundamental theories and historical milestones of early artificial intelligence. You will explore how pioneer researchers attempted to simulate human thought using symbolic reasoning, logic, and state-space search techniques, while discovering the technical bottlenecks that ultimately paused progress. What you'll learn: - Learn key AI terminology, foundational definitions, and early computer science milestones. - Understand the Turing Test and the mechanics behind classic conversational programs like ELIZA. - Apply concepts of search trees, state spaces, and symbolic logic to structured decision problems. - Examine the computational limitations and real-world complexity challenges that triggered the first AI winter. - Compare classical rule-based approaches with contemporary data-driven and generative paradigms. Content starts with core definitions and historical context before moving into theoretical problem-solving frameworks and symbolic reasoning models. You will conclude with a comparative look at how these early concepts connect to contemporary AI workflows. This course is tailored for beginners, tech enthusiasts, and students seeking a clear conceptual grounding in AI history. No prior programming or mathematical background is required. Begin reading today to build a strong foundational understanding of artificial intelligence history and core principles.