AI’s Models of the World, and Ours | Theoretically Speaking

When we see someone performing at the top of their craft, we often marvel at both their observable achievements and the hidden internal expertise that they’ve accumulated. Something similar is true, in a very different way, with generative AI and large language models: their successes involve both powerful observable behavior and deep internal representations of the world that they construct for their own uses. How do these internal representations work, and to what extent are they similar to or different from the representations of the world that we build as humans? In this talk in our Theoretically Speaking public lecture series, Jon Kleinberg (Cornell) explores these questions through the lens of generative AI, drawing on examples from game-playing, geographic navigation, and other complex tasks. Featuring joint work with Ashton Anderson, Karim Hamade, Reid McIlroy-Young, Siddhartha Sen, Justin Chen, Sendhil Mullainathan, Ashesh Rambachan, Keyon Vafa, and Fan Wei.