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In this episode of the Eye on AI podcast, Pedro Domingos—renowned AI researcher and author of The Master Algorithm—joins Craig Smith to break down the Symbolist approach to artificial intelligence, one of the Five Tribes of Machine Learning.

Pedro explains how Symbolic AI dominated the field for decades, from the 1950s to the early 2000s, and why it’s still playing a crucial role in modern AI. He dives into the Physical Symbol System Hypothesis, the idea that intelligence can emerge purely from symbol manipulation, and how AI pioneers like Marvin Minsky and John McCarthy built the foundation for rule-based AI systems.

The conversation unpacks inverse deduction—the Symbolists' "Master Algorithm"—and how it allows AI to infer general rules from specific examples. Pedro also explores how decision trees, random forests, and boosting methods remain some of the most powerful AI techniques today, often outperforming deep learning in real-world applications.

We also discuss why expert systems failed, the knowledge acquisition bottleneck, and how machine learning helped solve Symbolic AI’s biggest challenges. Pedro shares insights on the heated debate between Symbolists and Connectionists, the ongoing battle between logic-based reasoning and neural networks, and why the future of AI lies in combining these paradigms.

From AlphaGo’s hybrid approach to modern AI models integrating logic and reasoning, this episode is a deep dive into the past, present, and future of Symbolic AI—and why it might be making a comeback.

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(00:00) Pedro Domingos onThe Five Tribes of Machine Learning  

(02:23) What is Symbolic AI?  

(04:46) The Physical Symbol System Hypothesis Explained  

(07:05) Understanding Symbols in AI  

(11:51) What is Inverse Deduction?  

(15:10) Symbolic AI in Medical Diagnosis  

(17:35) The Knowledge Acquisition Bottleneck  

(19:05) Why Symbolic AI Struggled with Uncertainty  

(20:40) Machine Learning in Symbolic AI – More Than Just Connectionism  

(24:08) Decision Trees & Their Role in Symbolic Learning  

(26:55) The Myth of Feature Engineering in Deep Learning  

(30:18) How Symbolic AI Invents Its Own Rules  

(31:54) The Rise and Fall of Expert Systems – The CYCL Project  

(38:53) Symbolic AI vs. Connectionism  

(41:53) Is Symbolic AI Still Relevant Today?  

(43:29) How AlphaGo Combined Symbolic AI & Neural Networks  

(45:07) What Symbolic AI is Best At – System 2 Thinking  

(47:18) Is GPT-4o Using Symbolic AI?