Séminaire au DIC: «The Debate Over “Understanding” in AI’s Large Language Models» par Melanie Mitchell

Séminaire ayant lieu dans le cadre du doctorat en informatique cognitive, en collaboration avec le centre de recherche CRIA et l'ISC  

 

Melanie MITCHELL

Jeudi le 19 octobre 2023 à 10h30

PK-5115 (aussi possible d'y assister à distance, pour ce faire, vous devez vous inscrire ici)         

 

Titre :  The Debate Over “Understanding” in AI’s Large Language Models

 

Résumé

I will survey a current, heated debate in the AI research community on whether large pre-trained language models can be said -- in any important sense -- to "understand" language and the physical and social situations language encodes. I will describe arguments that have been made for and against such understanding, and, more generally, will discuss what methods can be used to fairly evaluate understanding and intelligence in AI systems.  I will conclude with key questions for the broader sciences of intelligence that have arisen in light of these discussions. 

 

Biographie

Melanie Mitchell is Professor at the Santa Fe Institute. Her current research focuses on conceptual abstraction and analogy-making in artificial intelligence systems.  Melanie is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her 2009 book Complexity: A Guided Tour (Oxford University Press) won the 2010 Phi Beta Kappa Science Book Award, and her 2019 book Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux) is a finalist for the 2023 Cosmos Prize for Scientific Writing. 

 

References 

Mitchell, M. (2023). How do we know how smart AI systems are? Science, 381(6654), adj5957.

Mitchell, M., & Krakauer, D. C. (2023). The debate over understanding in AI’s large language models. Proceedings of the National Academy of Sciences, 120(13), e2215907120.

Millhouse, T., Moses, M., & Mitchell, M. (2022). Embodied, Situated, and Grounded Intelligence: Implications for AI. arXiv preprint arXiv:2210.13589.

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jeudi 19 octobre 2023
10 h 30

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UQAM - Pavillon Président-Kennedy (PK)
PK-5115 et en ligne
201, avenue du Président-Kennedy
Montréal (QC)

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