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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?

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In this episode of Eye on AI, Mark Bailey, Associate Professor at the National Intelligence University, joins Craig Smith to explore the rapidly evolving role of AI in modern warfare—its promises, risks, and the ethical dilemmas it presents.

Mark shares his expertise on AI autonomy in military strategy, breaking down the differences between automation and true autonomy. We discuss how AI-driven systems could revolutionize combat by reducing human casualties, improving precision, and enhancing battlefield decision-making. But with these advancements come serious concerns—how do we prevent automation bias? Can we trust AI to make life-or-death decisions? And will AI-driven warfare lower the threshold for conflict, making war more frequent?

We also examine the global AI arms race, the impact of AI on defense policies, and the ethical implications of fully autonomous weapons. Mark unpacks key challenges like the black box problem, AI alignment issues, and the long-term consequences of integrating AI into military operations. He also shares insights from his latest book, where he calls for international AI regulations to prevent an uncontrolled escalation of AI warfare.

With AI-driven drone swarms, autonomous targeting systems, and defense innovations shaping the future of global security, this conversation is a must-watch for anyone interested in AI, defense technology, and the moral questions of war in the digital age.

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(00:00) AI’s Role in Warfare

(02:02) Introducing Dr. Mark Bailey

(04:02) Automation vs. Autonomy in Military AI

(12:02) AI Warfare: A Threat to Global Stability?

(17:10) Inside Dr. Bailey’s Book: Ethics & AI in War

(20:05) AI Reliability in Warfare

(23:28) The Future of AI Swarms & Autonomous Warfare

(24:17) Who Decides How AI is Used in War?

(28:05) The Future of AI & Military Ethics

This episode is sponsored by Thuma.

Thuma is a modern design company that specializes in timeless home essentials that are mindfully made with premium materials and intentional details.

To get $100 towards your first bed purchase, go to http://thuma.co/eyeonai

In this episode of the Eye on AI podcast, Pedro Domingos, renowned AI researcher and author of The Master Algorithm, joins Craig Smith to explore the evolution of machine learning, the resurgence of Bayesian AI, and the future of artificial intelligence.

Pedro unpacks the ongoing battle between Bayesian and Frequentist approaches, explaining why probability is one of the most misunderstood concepts in AI. He delves into Bayesian networks, their role in AI decision-making, and how they powered Google’s ad system before deep learning. We also discuss how Bayesian learning is still outperforming humans in medical diagnosis, search & rescue, and predictive modeling, despite its computational challenges.

The conversation shifts to deep learning’s limitations, with Pedro revealing how neural networks might be just a disguised form of nearest-neighbor learning. He challenges conventional wisdom on AGI, AI regulation, and the scalability of deep learning, offering insights into why Bayesian reasoning and analogical learning might be the future of AI.

We also dive into analogical learning—a field championed by Douglas Hofstadter—exploring its impact on pattern recognition, case-based reasoning, and support vector machines (SVMs). Pedro highlights how AI has cycled through different paradigms, from symbolic AI in the '80s to SVMs in the 2000s, and why the next big breakthrough may not come from neural networks at all.

From theoretical AI debates to real-world applications, this episode offers a deep dive into the science behind AI learning methods, their limitations, and what’s next for machine intelligence.

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Craig Smith Twitter: https://twitter.com/craigss

Eye on A.I. Twitter: https://twitter.com/EyeOn_AI

(00:00) Introduction

(02:55) The Five Tribes of Machine Learning Explained

(06:34) Bayesian vs. Frequentist: The Probability Debate

(08:27) What is Bayes' Theorem & How AI Uses It

(12:46) The Power & Limitations of Bayesian Networks

(16:43) How Bayesian Inference Works in AI

(18:56) The Rise & Fall of Bayesian Machine Learning

(20:31) Bayesian AI in Medical Diagnosis & Search and Rescue

(25:07) How Google Used Bayesian Networks for Ads

(28:56) The Role of Uncertainty in AI Decision-Making

(30:34) Why Bayesian Learning is Computationally Hard

(34:18) Analogical Learning – The Overlooked AI Paradigm

(38:09) Support Vector Machines vs. Neural Networks

(41:29) How SVMs Once Dominated Machine Learning

(45:30) The Future of AI – Bayesian, Neural, or Hybrid?

(50:38) Where AI is Heading Next