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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

(36:40) Creativity vs Reliability in AI

(39:14) Can AI Achieve Scientific Breakthroughs?

(41:26) Why Reasoning by Analogy Is AI’s Missing Link

(45:10) Evolutionaries: The Most Distant Tribe in AI

(48:41) Will Quantum Computing Help AI Reach AGI?

(53:15) Are We Close to the Master Algorithm?

(57:44) Tech Leaders, Hype & the Reality of AGI

(01:04:06) The AGI Spectrum: Where We Are & What’s Missing

(01:06:18) Pedro’s Research Focus

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In this episode of Eye on AI, Craig Smith sits down with Brice Challamel, Head of AI Products and Innovation at Moderna, to explore how one of the world’s leading biotech companies is embedding artificial intelligence across every layer of its business—from drug discovery to regulatory approval.

Brice breaks down how Moderna treats AI not just as a tool, but as a utility—much like electricity or the internet—designed to empower every employee and drive innovation at scale. With over 1,800 GPTs in production and thousands of AI solutions running on internal platforms like Compute and MChat, Moderna is redefining what it means to be an AI-native company.

Key topics covered in this episode:

• How Moderna operationalizes AI at scale

• GenAI as the new interface for machine learning

• AI’s role in speeding up drug approvals and clinical trials

• The future of personalized cancer treatment (INT)

• Moderna’s platform mindset: AI + mRNA = next-gen medicine

• Collaborating with the FDA using AI-powered systems

Don’t forget to like, comment, and subscribe for more interviews at the intersection of AI and innovation.

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(00:00) Preview

(02:49) Brice Challamel’s Background and Role at Moderna

(05:51) Why AI Is Treated as a Utility at Moderna

(09:01) Moderna's AI Infrastructure

(11:53) GenAI vs Traditional ML

(14:59) Combining mRNA and AI as Dual Platforms

(18:15) AI’s Impact on Regulatory & Clinical Acceleration

(23:46) The Five Core Applications of AI at Moderna

(26:33) How Teams Identify AI Use Cases Across the Business

(29:01) Collaborating with the FDA Using AI Tools

(33:55) How Moderna Is Personalizing Cancer Treatments

(36:59) The Role of GenAI in Medical Care

(40:10) Producing Personalized mRNA Medicines

(42:33) Why Moderna Doesn’t Sell AI Tools

(45:30) The Future: AI and Democratized Biotech

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In this episode, renowned AI researcher Pedro Domingos, author of The Master Algorithm, takes us deep into the world of Connectionism—the AI tribe behind neural networks and the deep learning revolution.

From the birth of neural networks in the 1940s to the explosive rise of transformers and ChatGPT, Pedro unpacks the history, breakthroughs, and limitations of connectionist AI. Along the way, he explores how supervised learning continues to quietly power today’s most impressive AI systems—and why reinforcement learning and unsupervised learning are still lagging behind.

We also dive into:

• The tribal war between Connectionists and Symbolists

• The surprising origins of Backpropagation

• How transformers redefined machine translation

• Why GANs and generative models exploded (and then faded)

• The myth of modern reinforcement learning (DeepSeek, RLHF, etc.)

• The danger of AI research narrowing too soon around one dominant approach
  
  

Whether you're an AI enthusiast, a machine learning practitioner, or just curious about where intelligence is headed, this episode offers a rare deep dive into the ideological foundations of AI—and what’s coming next.

Don’t forget to subscribe for more episodes on AI, data, and the future of tech.

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(00:00) What Are Generative Models?

(03:02) AI Progress and the Local Optimum Trap

(06:30) The Five Tribes of AI and Why They Matter

(09:07) The Rise of Connectionism

(11:14) Rosenblatt’s Perceptron and the First AI Hype Cycle

(13:35) Backpropagation: The Algorithm That Changed Everything

(19:39) How Backpropagation Actually Works

(21:22) AlexNet and the Deep Learning Boom

(23:22) Why the Vision Community Resisted Neural Nets

(25:39) The Expansion of Deep Learning

(28:48) NetTalk and the Baby Steps of Neural Speech

(31:24) How Transformers (and Attention) Transformed AI

(34:36) Why Attention Solved the Bottleneck in Translation

(35:24) The Untold Story of Transformer Invention

(38:35) LSTMs vs. Attention: Solving the Vanishing Gradient Problem

(42:29) GANs: The Evolutionary Arms Race in AI

(48:53) Reinforcement Learning Explained

(52:46) Why RL Is Mostly Just Supervised Learning in Disguise

(54:35) Where AI Research Should Go Next