Yes, but I also think it's a lot easier to get it wrong in finance without even realizing it than in many other domains. Relatively more difficult to screw up t

Yes, but I also think it's a lot easier to get it wrong in finance without even realizing it than in many other domains. Relatively more difficult to screw up the methodology on MNIST. Many mistakenly assume that the methodology from MNIST transfers directly to finance, which makes the problem worse. ALl of that means that when talking about applying ML to finance, discussing the methodology in detail is a must. If it is not talked about or only briefly mentioned, from my experience that usually means that the methodology used is rubbish. Whereas you don't need to focus as much on the methodology when talking about MNIST-like problems - one can usually assume a reasonable one is used.