Yes, I was going to say. Taking courses in "ML" is a great way to get to write a hyped-up term on your resume. Actually understanding and being able to write code for statistical learning is something else entirely.
Edit: On the other hand, I think Carmack observed that as long as you get the sign right, you'll get something out of it, even if it won't learn very quickly. So maybe speed of implementation and learning is how skilled people will differentiate themselves?
Research tasks take a long time. If you're going off of speed, it's no longer data science it's software engineering. (Though, to be fair, with all the new software engineers becoming data scientists, the data science title is becoming more like software engineering.)
imho metaphysics and metalearning might be a better way to differentiate.
Right. There's room for research, and then there's room for commoditising existing research. I think the latter is where we currently have the most to gain from expanding our efforts in, and this was the context in which I made my comment.
