Robinson's Podcast

124 - Jay McClelland: Deep Learning, Neural Networks, and Artificial Intelligence

Aug 6, 2023
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Episode notes
1
Introduction
00:00 • 3min
2
Why I Chose a Career in Psychology to Pursue My Research Interests
03:19 • 5min
3
Parallel Distributed Processing Explains Cognitive Function
08:08 • 4min
4
The Perception of Letters in Words
12:24 • 3min
5
The Neural Network's Ability to Recognize Letters
15:02 • 5min
6
The Conspiracy of Mental Agents
19:39 • 2min
7
The Connectionist Model of Parallel Distributed Processing
21:35 • 2min
8
The Bidirectionality and Mutuality of Cognitive Networks
23:06 • 2min
9
The Cognitive Phenomenon of Learning
25:01 • 1min
10
How to Teach a Neural Network to Complete a Pattern Like a Word
26:19 • 5min
11
The Motivation Behind the Interactive Activation Model
31:04 • 5min
12
The Importance of Parallel Distributed Processing in Intelligence
36:34 • 2min
13
The Photo and Pollution Argument for the Parallel Distributed Processing Model Never Capturing Human Intelligence
38:07 • 6min
14
The Importance of Exploiting Context Effectively
44:00 • 3min
15
The Origins of AI in Psychology
47:00 • 4min
16
The Limits of Affective Reasoning
51:25 • 5min
17
How Artificial Neural Networks Capture Advanced Human Cognitive Ability
56:33 • 6min
18
The Importance of Goal-Directedness
01:02:11 • 4min
19
The Future of Artificial Intelligence
01:06:32 • 4min
20
The Importance of Perforation in Science
01:10:41 • 2min
21
The Importance of Intention
01:13:04 • 2min
22
The Power of Neural Networks for Providing Insight
01:14:56 • 5min
23
The Importance of the Macro Structure of the Neural Network
01:19:26 • 5min
24
The Transformer Based Revolution in Language Models
01:24:12 • 2min
25
The Power of the Transformer
01:25:59 • 3min
26
The Importance of Context in Translation
01:29:05 • 4min
27
Consolidation: A New Principle for Reasoning
01:33:03 • 2min
28
CHATG-EPT's Knowledge in Weights
01:35:26 • 2min
29
How to Use a Word in a Sentence
01:37:22 • 5min
30
The Emergence of a Connectionist Model
01:42:18 • 2min
31
The Gap Between Neural Networks and Our Pioneering Scientists
01:44:13 • 4min