Jay McClelland | Neural Networks: Artificial and Biological - The Cartesian Cafe | Lyssna här

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/