Machine learning has a reproducibility crisis

Machine learning is often characterized as much an "art" as a "science" and in at least one regard, that's true: its practitioners are prone to working under loosely controlled conditions, using training data that is being continuously tweaked with no versioning; modifying parameters during runs (because it takes too long to wait for the whole run before making changes); squashing bugs mid-run; these and other common practices mean that researchers often can't replicate their own results -- and virtually no one else can, either. Read the rest

Machine learning models keep getting spoofed by adversarial attacks and it's not clear if this can ever be fixed

Machine learning models use statistical analysis of historical data to predict future events: whether you are a good candidate for a loan, whether you will violate parole, or whether the thing in the road ahead is a stop sign or a moose. Read the rest