Over the last few decades, computational mechanics has rigorously investigated the ultimate limits of prediction, together with the resources and internal representations required to predict optimally. In just the last few years, AI models trained to predict have developed impressive and surprising general capabilities. This workshop explores how computational mechanics can be applied and adapted to understand current and future AI models, with the aim of anticipating emerging capabilities and enabling alignment with human values. The program consists of a mix of tutorials, talks, and ample time for small-group discussions.
Lead organizers: Alexander Gietelink Oldenziel, Paul Riechers, Nora Ammann
