Sub-symbolic AI focuses on enabling computers to learn directly from data by identifying complex patterns, contrasting with symbolic AI's reliance on explicit rules. This approach, inspired by the human brain, underpins advancements in areas like image and language processing through machine learning techniques, including supervised, unsupervised, and reinforcement learning. While powerful in pattern recognition and handling complex problems, sub-symbolic AI faces challenges in explainability, potential bias, and high computational demands, yet it drives numerous real-world applications and continues to evolve towards more transparent and efficient systems.

Symbolic AI is an approach to artificial intelligence that simulates human intelligence by manipulating symbols and explicitly programmed knowledge. This episode explains key aspects like knowledge representation using symbols and logical reasoning, including deductive, inductive, and abductive methods, and various types of logic. Expert systems are highlighted as a real-world practical application, demonstrating how rules and facts can emulate human expertise in specific domains

This dialogue discusses Symbolic AI's logic-based reasoning and knowledge representation as compared to Sub-Symbolic AI's machine learning and neural networks representation, and wraps up by discussing some recent advancements in AI with Large Language Models