Speaker
Lucie Flek
(University of Bonn)
Description
What can we actually conclude when a model gives us the right answer? Did it learn the right correlations? Does it generalize beyond the setting in which we tested it? And what does it mean to validate increasingly complex "scientific AI" models and workflows? I will explore these questions through examples and cautionary tales from language modeling, human modeling, particle physics and astrophysics — from unexpected correlations and hidden systematics to surprising generalization, foundation models and scientific agents producing convincing, but not always plausible, results.