In physics it just recently became mainstream to try experimenting with/incorporating ML into thesis projects. Most stuff ive seen it used for is signal processing related. An example might be particle track reconstruction in a time projection chamber with ML instead of a hough transform. I think it's inevitable that these methods will grow in application, but the two biggest problems right now in my opinion are reproducability and quantification of uncertainties. It's much easier to believe someone's stated uncertainties when you can see the analytic functions they were propagated through. There are ways to kindof work around this, but in my mind those two points are the main things holding back ML from broader applications in science. The article talks about ML tools closer to proof assistants / tools for experimentally driven mathematics. Less of a problem in that domain since the ML model only need make an interesting conjecture which can then be examined the traditional way.