Léon-Yves Bottou

(Ex-Research Scientist, Facebook AI Research)

Bio: Léon Bottou received the Diplôme d’Ingénieur from l’École Polytechnique (X84) in 1987, the Magistère de Mathématiques Fondamentales et Appliquées et d’Informatique from École Normale Supérieure in 1988, and a Ph.D. in Computer Science from Université Paris-Sud (now Université Paris-Saclay) in 1991. His research career has spanned leading research institutions: he held positions at AT&T Bell Laboratories, AT&T Labs Research, NEC Labs America, Microsoft Research, Facebook AI Research, and, since February 2026, the Flatiron Institute.. He received the 2007 Blavatnik Award for Young Scientists in 2007 and the Lagrange Prize in Continuous Optimization in 2021. 

The unifying theme of Léon Bottou’s research is the pursuit of a deeper understanding of intelligence through theoretical and applied contributions to machine learning since the late 1980s. His work encompasses early deep learning systems, stochastic gradient learning algorithms, statistical analyses of learning architectures, structured-output computer vision, and the theory of large-scale learning. Today, he seeks to understand the interplay between learning, reasoning, and intelligence, a challenging topic with growing importance and urgency.

Title of talk: The Fiction Machine

Abstract: Imagine a machine that can read a story and generate a meaningful continuation, that is, one that complies with the narrative demands of the story and makes sense to a meaningful fraction of the readers. Because what is true in the world of the story needs not be true in our world, this machine cannot be expected to say the truth. It only knows narrative necessity. This machine is of course an idealized model of modern AI systems, from language models and chatbots to movie generation. It is also an opportunity to formulate important questions and sometimes catch a glimpse of their answers. How can we define such a machine more rigorously? What can it compute? How does it compare to logic and mathematical reasoning? Can it be used to make inferences about our world even though its output is not constrained by what is true in our world? Such questions are not only relevant to artificial intelligence, but also useful to understand certain aspects of human intelligence and society.