In this episode, we explore one of the most important turning points in the history of artificial intelligence: the shift from rule-based symbolic systems to machine learning approaches that rely on patterns in data instead of hand-crafted logic.

Symbolic AI dominated the early decades of AI research. It was built on the idea that intelligence could be expressed through explicit rules, logical reasoning, and structured knowledge provided by experts. But as real-world problems grew more complex, researchers began to see the limits of this approach — especially in situations filled with ambiguity, uncertainty, or enormous variability.

This episode walks through how those limitations led to a new idea: instead of programming intelligence, what if machines could learn it? We explore how early statistical methods, neural networks, ...