Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Introduction
00:00 • 2min
Three Perspectives for Deep Learning and Neural Nets
02:26 • 3min
How to Design and Train Models That Have Practical Utility
05:52 • 2min
The Expressiveness of a Network
07:36 • 3min
The Problem With the Back Propagation Algorithm
10:25 • 1min
The Problem With Glasses and Physics
11:51 • 3min
Large Width Expansion and Neural Tangent Kernels
15:09 • 5min
The Future of Large Language Models
19:48 • 2min
The Dimensionality of the Vector Space of Our Concepts
22:01 • 4min
How to Train an Embedding Machine
26:16 • 5min
The Transformer Architecture: How Word Order Matters
31:40 • 2min
Attention Is All You Need
33:24 • 3min
The Importance of Attention in Open AI
36:41 • 2min
The Structure of Attention Heads
38:46 • 5min
The Empirical Structure of the Neural Net
43:23 • 6min
The Geometry of Thought
48:56 • 4min
The Theory of Mind
53:22 • 4min
How to Train a Language Model to Translate Natural Language Instructions
57:37 • 5min
The Problem With Human Natural Language Models
01:02:20 • 2min
The Hallucination of the Models
01:03:59 • 2min
The Importance of Ground Truth in AIs
01:06:23 • 5min
Wolfram's Corpus and the Hallucination Problem
01:11:17 • 3min
How Watson Applies Pavlov's Principles to Learn
01:14:39 • 4min
The Future of AI
01:18:29 • 3min
The Future of LLMs
01:21:13 • 3min