The Gradient
The Gradient: Perspectives on AI
Stevan Harnad: AI's Symbol Grounding Problem
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Stevan Harnad: AI's Symbol Grounding Problem

On Symbol Grounding in AI systems, the Turing Test, the Chinese Room Argument, and category formation.
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In episode 88 of The Gradient Podcast, Daniel Bashir speaks to Professor Stevan Harnad.

Stevan Harnad is professor of psychology and cognitive science at Université du Québec à Montréal, adjunct professor of cognitive science at McGill University, and professor emeritus of cognitive science at the University of Southampton. His research is on category learning, categorical perception, symbol grounding, the evolution of language, and animal and human sentience (otherwise known as “consciousness”). He is also an advocate for open access and an activist for animal rights.

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Outline:

  • (00:00) Intro

  • (05:20) Professor Harnad’s background: interests in cognitive psychobiology, editing Behavioral and Brain Sciences

    • (07:40) John Searle submits the Chinese Room article

    • (09:20) Early reactions to Searle and Prof. Harnad’s role

  • (13:38) The core of Searle’s argument and the generator of the Symbol Grounding Problem, “strong AI”

  • (19:00) Ways to ground symbols

  • (20:26) The acquisition of categories

  • (25:00) Pantomiming, non-linguistic category formation

  • (27:45) Mathematics, abstraction, and grounding

  • (36:20) Symbol manipulation and interpretation language

  • (40:40) On the Whorf Hypothesis

  • (48:39) Defining “grounding” and introducing the “T3” Turing Test

  • (53:22) Turing’s concerns, AI and reverse-engineering cognition

  • (59:25) Other Minds, T4 and zombies

  • (1:05:48) Degrees of freedom in solutions to the Turing Test, the easy and hard problems of cognition

  • (1:14:33) Over-interepretation of AI systems’ behavior, sentience concerns, T3 and evidence sentience

  • (1:24:35) Prof. Harnad’s commentary on claims in The Vector Grounding Problem

  • (1:28:05) RLHF and grounding, LLMs’ (ungrounded) capabilities, syntactic structure and propositions

  • (1:35:30) Multimodal AI systems (image-text and robotic) and grounding, compositionality

  • (1:42:50) Chomsky’s Universal Grammar, LLMs and T2

  • (1:50:55) T3 and cognitive simulation

  • (1:57:34) Outro

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