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This chapter delves into the five paradigms of artificial intelligence, tracing their roots back to the 1950s and highlighting their enduring relevance. Additionally, it touches on a minimalist furniture design company that exemplifies modern creativity and marketing strategies.

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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

Can AI Ever Reach AGI? Pedro Domingos Explains the Missing Link

In this episode of Eye on AI, renowned computer scientist and author of The Master Algorithm, Pedro Domingos, breaks down what’s still missing in our race toward Artificial General Intelligence (AGI) — and why the path forward requires a radical unification of AI's five foundational paradigms: Symbolists, Connectionists, Bayesians, Evolutionaries, and Analogizers.

Topics covered:

Why deep learning alone won’t achieve AGI
How reasoning by analogy could unlock true machine creativity
The role of evolutionary algorithms in building intelligent systems
Why transformers like GPT-4 are impressive—but incomplete
The danger of hype from tech leaders vs. the real science behind AGI
What the Master Algorithm truly means — and why we haven’t found it yet

Pedro argues that creativity is easy, reliability is hard, and that reasoning by analogy — not just scaling LLMs — may be the key to Einstein-level breakthroughs in AI.

Whether you're an AI researcher, machine learning engineer, or just curious about the future of artificial intelligence, this is one of the most important conversations on how to actually reach AGI.

📚 About Pedro Domingos: Pedro is a professor at the University of Washington and author of the bestselling book The Master Algorithm, which explores how the unification of AI's "five tribes" could produce the ultimate learning algorithm.

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(00:00) The Five Tribes of AI Explained

(02:23) The Origins of The Master Algorithm

(08:22) Designing with Bit Strings: Radios, Robots & More

(10:46) Fitness Functions vs Reward Functions in AI

(15:51) What Is Reasoning by Analogy in AI?

(18:38) Kernel Machines and Support Vector Machines Explained

(22:23) Case-Based Reasoning and Real-World Use Cases

(27:38) Are AI Tribes Still Siloed or Finally Collaborating?

(32:42) Why AI Needs a Deeply Unified Master Algorithm