Incremental Advances in Machine Learning: A Critical Look at Supervised Learning

This chapter explores the progression of machine learning, emphasizing the significance of supervised learning. The speakers discuss the incremental nature of AI advancements and the skepticism surrounding newer methodologies like reinforcement learning, which often echo foundational concepts in supervised learning.

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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.

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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