The Debate Over “Understanding” in AI’s Large Language Models
Abstract: Mitchell will survey a current, heated debate in the AI research community on whether large pre-trained language models can be said to "understand" language—and the physical and social situations language encodes—in any important sense. She 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. Mitchell will conclude with key questions for the broader sciences of intelligence that have arisen in light of these discussions.
Bio: Melanie Mitchell is a Professor at the Santa Fe Institute. Her current research focuses on conceptual abstraction, analogy-making, and visual recognition 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 book Complexity: A Guided Tour (Oxford University Press) won the 2010 Phi Beta Kappa Science Book Award and was named by Amazon.com as one of the ten best science books of 2009. Her latest book is Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux).