Standard model for AI

Standard model for AI is human specifies the objective like here’s the discounted sum of rewards machine says okay I’m on it right now we know this model doesn’t work because we can’t specify the objective correctly and I should mention it’s not just our model this is the same model as control theory where you minimize the cost function in statistics you minimize risk in operations research in fact this is borrowed from operations research you maximize the discount of some rewards and economics you maximize a welfare function or GDP or profit or whatever objective it is all of these are the same basic standard model and it’s a bad model and King Midas could tell you that because he said okay here’s my objective I want everything I touch the turn to gold the machine said righty-ho in fact it was the gods in his case.

Deep Learning for System 2 Processing

Slides: https://photos.app.goo.gl/KfQjX5FqcGEsVEs49

1:09:37 [Talk: Deep Learning for System 2 Processing by Yoshua Bengio] 1:10:10 No-Free-Lunch Theorem, Inductive Biases Human-Level AI 1:15:03 Missing to Extend Deep Learning to Reach Human-Level AI 1:16:48 Hypotheses for Conscious Processing by Agents, Systematic Generalization 1:22:02 Dealing with Changes in Distribution 1:25:13 Contrast with the Symbolic AI Program 1:28:07 System 2 Basics: Attention and Conscious Processing 1:28:19 Core Ingredient for Conscious Processing: Attention 1:29:16 From Attention to Indirection 1:30:35 From Attention to Consciousness 1:31:59 Why a Consciousness Bottleneck? 1:33:07 Meta-Learning: End-to-End OOD Generalization, Sparse Change Prior 1:33:21 What Causes Changes in Distribution? 1:34:56 Meta-Learning Knowledge Representation for Good OOD Performance 1:35:14 Example: Discovering Cause and Effect 1:36:49 Operating on Sets of Pointable Objects with Dynamically Recombined 1:37:36 RIMS: Modularize Computation and Operate on Sets of Named and Typed Objects 1:39:42 Results with Recurrent Independent Mechanisms 1:40:17 Hypotheses for Conscious Processing by Agents, Systematic Generalization 1:40:46 Conclusions