I think the problem with these words is that the vernacular has different meanings with the common lexicon. But this is quite true for any field. "Field" is even a good example of this as mathematicians use it in a drastically different way than I just used it now. This can make people think they understand technicals more than they do. But if you're making the argument that ML needs to learn more math and needs more rigor, then I'd defend that claim. It is a personal pet peeve of min (fuck man, how often I have to explain to my research group what a covariance matrix is and why it is essential to diffusion is absurd).
A lot of what we see is cargo cult engineering and not fundamental research in ML. Most of it is applied research or engineering - there is a little bit of fundamental research that actually expands our own knowledge about how things work and what their limits are, while applied science keeps marching on (maybe towards funamentally impossible goals).
