Yep! There can be. But if you want concrete examples, I used Xgboost to identify people within a population at risk for an adverse event. This is strictly #1. If I optimized Xgboost code to make it faster, that's also probably firmly #1. If I improved Xgboost with a better understanding of gradient boosting to provide more accurate results, that's probably a firm case of overlap. When Leo Breiman [0] did his work that led to gradient boosting and tools like Xgboost, that was firmly #2.
