I think that's an insightful point. Personally, I believe an important part of ML architecture engineering is figuring out architectures that make giving "the w

I think that's an insightful point. Personally, I believe an important part of ML architecture engineering is figuring out architectures that make giving "the wrong answer" to a problem impossible, or at least difficult. But sometimes just throwing a bunch of data at a generic architecture and hoping it can learn the "rules" is easier. And of course, as alluded in the article, sometimes we don't really know the "rules", or don't know how to articulate them in a way we can feasibly synthesize data according to those rules. Then people might reach for ML to figure out those rules, implicitly or explicitly. So it becomes a bit of an ouroboros situation. But still somewhere in that loop there's some domain knowledge injected somewhere by whoever is engineering the pipeline.