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Listen "Jay McClelland | Neural Networks: Artificial and Biological"
Episode Synopsis
Jay McClelland is a pioneer in the field of artificial intelligence and is a cognitive psychologist and professor at Stanford University in the psychology, linguistics, and computer science departments. Together with David Rumelhart, Jay published the two volume work Parallel Distributed Processing, which has led to the flourishing of the connectionist approach to understanding cognition.
In this conversation, Jay gives us a crash course in how neurons and biological brains work. This sets the stage for how psychologists such as Jay, David Rumelhart, and Geoffrey Hinton historically approached the development of models of cognition and ultimately artificial intelligence. We also discuss alternative approaches to neural computation such as symbolic and neuroscientific ones.
Patreon (bonus materials + video chat):
https://www.patreon.com/timothynguyen
Part I. Introduction
00:00 : Preview
01:10 : Cognitive psychology
07:14 : Interdisciplinary work and Jay's academic journey
12:39 : Context affects perception
13:05 : Chomsky and psycholinguists
8:03 : Technical outline
Part II. The Brain
00:20:20 : Structure of neurons
00:25:26 : Action potentials
00:27:00 : Synaptic processes and neuron firing
00:29:18 : Inhibitory neurons
00:33:10 : Feedforward neural networks
00:34:57 : Visual system
00:39:46 : Various parts of the visual cortex
00:45:31 : Columnar organization in the cortex
00:47:04 : Colocation in artificial vs biological networks
00:53:03 : Sensory systems and brain maps
Part III. Approaches to AI, PDP, and Learning Rules
01:12:35 : Chomsky, symbolic rules, universal grammar
01:28:28 : Neuroscience, Francis Crick, vision vs language
01:32:36 : Neuroscience = bottom up
01:37:20 : Jay’s path to AI
01:43:51 : James Anderson
01:44:51 : Geoff Hinton
01:54:25 : Parallel Distributed Processing (PDP)
02:03:40 : McClelland & Rumelhart’s reading model
02:31:25 : Theories of learning
02:35:52 : Hebbian learning
02:43:23 : Rumelhart’s Delta rule
02:44:45 : Gradient descent
02:47:04 : Backpropagation
02:54:52 : Outro: Retrospective and looking ahead
Image credits:
http://timothynguyen.org/image-credits/
Further reading:
Rumelhart, McClelland. Parallel Distributed Processing.
McClelland, J. L. (2013). Integrating probabilistic models of perception and interactive neural networks: A historical and tutorial review
Twitter: @iamtimnguyen
Webpage: http://www.timothynguyen.org
In this conversation, Jay gives us a crash course in how neurons and biological brains work. This sets the stage for how psychologists such as Jay, David Rumelhart, and Geoffrey Hinton historically approached the development of models of cognition and ultimately artificial intelligence. We also discuss alternative approaches to neural computation such as symbolic and neuroscientific ones.
Patreon (bonus materials + video chat):
https://www.patreon.com/timothynguyen
Part I. Introduction
00:00 : Preview
01:10 : Cognitive psychology
07:14 : Interdisciplinary work and Jay's academic journey
12:39 : Context affects perception
13:05 : Chomsky and psycholinguists
8:03 : Technical outline
Part II. The Brain
00:20:20 : Structure of neurons
00:25:26 : Action potentials
00:27:00 : Synaptic processes and neuron firing
00:29:18 : Inhibitory neurons
00:33:10 : Feedforward neural networks
00:34:57 : Visual system
00:39:46 : Various parts of the visual cortex
00:45:31 : Columnar organization in the cortex
00:47:04 : Colocation in artificial vs biological networks
00:53:03 : Sensory systems and brain maps
Part III. Approaches to AI, PDP, and Learning Rules
01:12:35 : Chomsky, symbolic rules, universal grammar
01:28:28 : Neuroscience, Francis Crick, vision vs language
01:32:36 : Neuroscience = bottom up
01:37:20 : Jay’s path to AI
01:43:51 : James Anderson
01:44:51 : Geoff Hinton
01:54:25 : Parallel Distributed Processing (PDP)
02:03:40 : McClelland & Rumelhart’s reading model
02:31:25 : Theories of learning
02:35:52 : Hebbian learning
02:43:23 : Rumelhart’s Delta rule
02:44:45 : Gradient descent
02:47:04 : Backpropagation
02:54:52 : Outro: Retrospective and looking ahead
Image credits:
http://timothynguyen.org/image-credits/
Further reading:
Rumelhart, McClelland. Parallel Distributed Processing.
McClelland, J. L. (2013). Integrating probabilistic models of perception and interactive neural networks: A historical and tutorial review
Twitter: @iamtimnguyen
Webpage: http://www.timothynguyen.org
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