Understanding AI: Myths and Methodologies

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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[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