I think yummyfajita's point is that a traditional statistics approach begins with some understanding of the system being modeled, and that you create a model using that understanding. There is usually a high focus on parsimoniousness and explainability, while in ML/AI, you don't really care what the underlying model is or how the model comes to a particular conclusion. The focus is on accuracy at the expense of explainability.