I'd agree with this assessment 100% - ML can achieve some very impressive results - in my domain (computer graphics for movie VFX) with regards to image generat

I'd agree with this assessment 100% - ML can achieve some very impressive results - in my domain (computer graphics for movie VFX) with regards to image generation / identification / modification, e.g. denoising for the latter - however the result is almost always effectively a black box of functionality which: A: isn't always understood what it is actually doing or how. B: often needs to be retrained when input data varies slightly outside original training data - this is expected at a technical level, but is annoying in practice and often then starts deviating and losing its effectiveness or gets inconsistent results with larger less homogeneous input data sets. C: Can't really (at least easily) be used as a stand-alone / packaged component/library to re-use nicely in other places as a conventional algorithm would be.