this is kind of where in the last few years there's been a lot of great work starting to really quantify these concepts of how robust our models how well do they work kind of distribution kind of pressuring and challenging the standards supervised learning paradigm of you know training on an iid training set and evaluating on another ID split held out data and basically showing that that's no longer sufficient and something's going wrong somewhere in supervised learning that means this is a being too fortunate to algorithms and