Machine Learning Street Talk (MLST)

#65 Prof. PEDRO DOMINGOS [Unplugged]

Feb 26, 2022

Pedro Domingos, a renowned professor of computer science and author of "The Master Algorithm," dives deep into the fundamentals of machine learning. He emphasizes the need for a solid understanding of AI for both professionals and the public, likening it to learning to drive. Domingos discusses the evolution of generative and discriminative models, critiques existing algorithms, and explores the interplay of entropy and reality. He also proposes a unifying 'master algorithm' while questioning the complexities of causality in learning systems, ultimately advocating for a broader understanding of AI.

01:28:27

Creator website

Episode guests

Pedro Domingos

Highlights

AI Chapters

Episode notes

The Role of Instinct and Time in Computational Processes

01:27

Jonathan Frankl: The Role of Sparsity in Neural Networks

01:39

The Importance of Understanding Generative Models

01:25

The Symmetric Markov Chain and Convergence to the Uniform Distribution

01:26

Intro

2min

Understanding AI: Myths and Methodologies

15min

Neural Insights: Rethinking Machine Learning

28min

Generative vs Discriminative Models in AI

14min

Navigating the Spectrum of AI Theories: Emergentism vs. Symbolism

5min

Causality in Brain Function and Deep Learning

7min

Entropy and the Nature of Reality

18min

Note: there are no politics discussed in this show and please do not interpret this show as any kind of a political statement from us. We have decided not to discuss politics on MLST anymore due to its divisive nature.

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Discord: https://discord.gg/HNnAwSduud

[00:00:00] Intro

[00:01:36] What we all need to understand about machine learning

[00:06:05] The Master Algorithm Target Audience

[00:09:50] Deeply Connected Algorithms seen from Divergent Frames of Reference

[00:12:49] There is a Master Algorithm; and it's mine!

[00:14:59] The Tribe of Evolution

[00:17:17] Biological Inspirations and Predictive Coding

[00:22:09] Shoe-Horning Gradient Descent

[00:27:12] Sparsity at Training Time vs Prediction Time

[00:30:00] World Models and Predictive Coding

[00:33:24] The Cartoons of System 1 and System 2

[00:40:37] AlphaGo Searching vs Learning

[00:45:56] Discriminative Models evolve into Generative Models

[00:50:36] Generative Models, Predictive Coding, GFlowNets

[00:55:50] Sympathy for a Thousand Brains

[00:59:05] A Spectrum of Tribes

[01:04:29] Causal Structure and Modelling

[01:09:39] Entropy and The Duality of Past vs Future, Knowledge vs Control

[01:16:14] A Discrete Universe?

[01:19:49] And yet continuous models work so well

[01:23:31] Finding a Discretised Theory of Everything

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