Published Mar 13, 2023

#107 - Dr. RAPHAËL MILLIÈRE - Linguistics, Theory of Mind, Grounding

Dr. Raphaël Millière delves into the potential and limitations of AI in understanding human cognition, examining theory of mind, symbol grounding, and semantic competence, while contemplating the ethical implications of AI advancements and the challenges of achieving human-like language capabilities.

Episode Highlights

Topics covered

Episode Highlights

Related Episodes

#83 Dr. ANDREW LAMPINEN (Deepmind) - Natural Language, Symbols and Grounding [NEURIPS2022 UNPLUGGED]
Answers 383 questions
#062 - Dr. Guy Emerson - Linguistics, Distributional Semantics
Answers 383 questions
#103 - Prof. Edward Grefenstette - Language, Semantics, Philosophy
Answers 383 questions
MLST #78 - Prof. NOAM CHOMSKY (Special Edition)
Answers 383 questions
Mahault Albarracin - Cognitive Science
Answers 383 questions
#93 Prof. MURRAY SHANAHAN - Consciousness, Embodiment, Language Models
Answers 383 questions
#68 DR. WALID SABA 2.0 - Natural Language Understanding [UNPLUGGED]
Answers 383 questions
#65 Prof. PEDRO DOMINGOS [Unplugged]
Answers 383 questions
#84 LAURA RUIS - Large language models are not zero-shot communicators [NEURIPS UNPLUGGED]
Answers 383 questions
#53 Quantum Natural Language Processing - Prof. Bob Coecke (Oxford)
Answers 383 questions
#90 - Prof. DAVID CHALMERS - Consciousness in LLMs [Special Edition]
Answers 383 questions
#70 - LETITIA PARCALABESCU - Symbolics, Linguistics [UNPLUGGED]
Answers 383 questions
How Do AI Models Actually Think? - Laura Ruis
Answers 383 questions
Robert Lange on NN Pruning and Collective Intelligence
Answers 383 questions
#54 Gary Marcus and Luis Lamb - Neurosymbolic models
Answers 383 questions

Dexa/Machine Learning Street Talk (MLST)

#107 - Dr. RAPHAËL MILLIÈRE - Linguistics, Theory of Mind, Grounding

Topics covered

Popular Clips

Color Space and Language Models

Simulating Agents

Neural Networks Compositionality Debate

Uncovering Model Competence

Neural Networks and Cognition

Language Model Capacities

Symbol Grounding Discussion

Intrinsic Grounding Debate

Novel Conceptual Combinations

Language Model Generalization

Unraveling Language Models

Language Models' Grounding

Theory of Mind

Language Models and Real World Representation

Episode Highlights

Theory of Mind

Symbol Grounding

Semantic Competence

Compositionality in AI

Related Episodes

#83 Dr. ANDREW LAMPINEN (Deepmind) - Natural Language, Symbols and Grounding [NEURIPS2022 UNPLUGGED]

#062 - Dr. Guy Emerson - Linguistics, Distributional Semantics

#103 - Prof. Edward Grefenstette - Language, Semantics, Philosophy

MLST #78 - Prof. NOAM CHOMSKY (Special Edition)

Mahault Albarracin - Cognitive Science

#93 Prof. MURRAY SHANAHAN - Consciousness, Embodiment, Language Models

#68 DR. WALID SABA 2.0 - Natural Language Understanding [UNPLUGGED]

#65 Prof. PEDRO DOMINGOS [Unplugged]

#84 LAURA RUIS - Large language models are not zero-shot communicators [NEURIPS UNPLUGGED]

#53 Quantum Natural Language Processing - Prof. Bob Coecke (Oxford)

#90 - Prof. DAVID CHALMERS - Consciousness in LLMs [Special Edition]

#70 - LETITIA PARCALABESCU - Symbolics, Linguistics [UNPLUGGED]

How Do AI Models Actually Think? - Laura Ruis

Robert Lange on NN Pruning and Collective Intelligence

#54 Gary Marcus and Luis Lamb - Neurosymbolic models

#107 - Dr. RAPHAËL MILLIÈRE - Linguistics, Theory of Mind, Grounding

Topics covered

Popular Clips

Episode Highlights

Theory of MindDr. Raphaël Millière examines the potential for AI, particularly language models, to develop a Theory of Mind. He questions whether these models can truly understand human cognition or if they merely mimic it, highlighting the ethical implications of such advancements.

Theory of Mind

Symbol GroundingThe discussion explores the complexities of grounding in AI, focusing on referential and sensory-motor grounding. Raphaël Millière examines how these concepts apply to AI models and the challenges they face in achieving meaningful grounding.

Symbol Grounding

Semantic Competence

Compositionality in AIThe challenges of compositionality in AI models and their ability to generate novel conceptual combinations are explored by Raphaël Millière. He examines how recent advancements in deep learning challenge traditional views on AI's capacity for human-like cognition.

Compositionality in AI

Related Episodes