Kadish Workshop in Law, Philosophy, and Political Theory: Melanie Mitchell, Santa Fe Institute

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) was shortlisted for the 2023 Cosmos Prize for Scientific Writing.

Paper Titles and Abstracts:

The Debate Over Understanding in AI’s Large Language Models

I 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. 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. 

Why AI is Harder Than We Think

Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment (“AI spring”) and periods of disappointment, loss of confidence, and reduced funding (“AI winter”). Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I conclude by discussing the open questions spurred by these fallacies, including the age-old challenge of imbuing machines with humanlike common sense.

About the Workshop:

A workshop for presenting and discussing work in progress in moral, political, and legal theory. The central aim is to provide an opportunity for students to engage with philosophers, political theorists, and legal scholars working on normative questions. Another aim is to bring together people from different disciplines who have strong normative interests or who speak to issues of potential interest to philosophers and political theorists.

The theme for the Spring 2024 workshop is “Intelligence: Human, Animal, Artificial,” and we will host scholars working in Philosophy, Biology, Psychology, Law, and Engineering. Our underlying concern will be the normative implications of different ideas of what intelligence is and can do.

This semester the workshop is co-taught by Christopher Kutz and Josh Cohen.