This is an aside to the main point of the article, but I have to take issue with a couple of quotes: > For a long time scientific computing has kept machine le

This is an aside to the main point of the article, but I have to take issue with a couple of quotes: > For a long time scientific computing has kept machine learning at an arm's length because its lack of interpretability and structure mean that, despite it’s tremendous predicitve success, it is almost useless for answering these kinds of central scientific questions. > However, the recent trend has been to merge the two disciplines, allowing explainable models that are data-driven, require less data than traditional machine learning, and utilize the knowledge encapsulated in centuries of scientific literature. Recent trend?? This is the norm in every scientific field I've even slightly worked in... This quote just described what we call "inverse theory" in the geosciences. It's the root of pretty much everything we do and has been for many decades. Those of in the scientific computing world have hardly been keeping machine learning at arm's length... A lot of machine learning methods come from scientific fields (e.g. gaussian processes). There's more to machine learning than CNNs, and many fields have been using machine learning much longer terms like "data science" and "AI" have been around. We just trend towards parametric approaches or approaches that can be reasoned about more clearly. (E.g. lots of convex optimization problems and interpolation/"super-resolution" problems.) Those are machine learning methods as well. All that said, most of what they're talking about in this article is about approximating solutions to differential equations with purely non-parametric approaches. That is somewhat new and is a much narrower topic, which this article does a nice job of describing. (I don't really take objection to the article as a whole, but a few sentences in it strike me as presumptuous.)