Nobody's saying this is an impossible problem. The paper shows how much additional work is required beyond a traditional data science workflow. The team behind

Nobody's saying this is an impossible problem. The paper shows how much additional work is required beyond a traditional data science workflow. The team behind the paper built a model that had good performance on training data. They're a smart lot so they knew they needed to cross-validate. The results held up in cross-validation! Hooray, the model works! ...right? That's as far as a lot of data scientists go. This paper points out that you need to have a model that does (at least) three things: 1. Generates good scores with training and testing data 2. Outperforms existing models in the real world 3. Runs really really quickly There are a lot of data scientists who have no idea how to do #2 and #3. This paper says "These parts are really important!!!"