[YeGoblynQueenne]

I think your comment does what the OP complains about, regarding gatekeeping etc.

I don't know about OP, whose comment I find a little harsh, but personally I'm always frustrated a bit and despairing a bit when I realise how poor the background is of the average machine learning researcher today, i.e. of my generation. Sometimes it's like nothing matters other than the chance that Google or Facebook will want to hire someone with a certain skillset and any knowledge that isn't absolutely essential to getting that skillset, is irrelevant.

Who said "Those who do not know their history are doomed to repeat it"? In research that means being oblivious of the trials and tribulations of previous generations of researchers and then falling down the same pits that they did. See for example how deep learning models today are criticised for being "brittle", a criticism that was last levelled against expert systems, and for similar, although superficially different, reasons. Why can't we ever learn?

[jstx1]

> I think your comment does what the OP complains about, regarding gatekeeping etc.

Oh absolutely, that's how I intended it. I don't think that preemptively calling out people's reaction gives the parent comment a pass on gatekeeping.

Your concern about poor background... it's only a problem for people who are jumping into things without the prerequisite background and they aren't learning fast enough. But modern deep learning is much more empirical - there are a few building blocks and people are trying out different things to see how they perform. I don't get why we need to look down on people for not knowing things that they don't need to know. If there was some magic that comes from knowing much more statistics, then the researchers who do would be outperforming the rest of the field by a lot but I don't think that's the case.

[YeGoblynQueenne]

That certainly is the case. Not for statistics specifically, but all the people at the top of the field, Bengio, LeCunn, Schmidhuber, Hinton, and so on, all have deep backgrounds in computer science, maths, psychology, statistics, physics, AI, etc. You don't get to make progress in a field as saturated as deep learning when all you know how to do is throw stuff at the wall to see what sticks.

I never said anything about needing to look down on anyone. Where did that come from?

My concern is that without a solid background in AI, no innovation can happen, because innovation means doing something entirely new and one cannot figure out what "entirely new" means, without knowing what has been done before. The people who "are trying out different things to see how they perform" as you say, are forced to do that because that's all you can do when you don't understand what you're doing.