How to distinguish between 'programming' and 'conceptual' questions?

Question

There's been some comment discussion as to whether a couple of questions e.g. this one and this one have been on topic.

In my opinion:

1. We should take care not to readily dismiss technical questions as being 'programming related'.

2. It's worth asking whether (even if the question mentions a specific technique) it could be answered with reference to open issues in AI.

For example, quite a number of questions (most of which have, in my opinion rightly, been left open without issue) are concerned with how to choose features for learning. In one respect, this is the single biggest issue facing AI: the current vogue for DL approaches is precisely because of the progress they claim in this area.

In particular: the data science community has not solved this problem - they are in general consumers of relatively stable research, rather than at the cutting edge, as is the case for AI.

Hence, we maybe shouldn't dismiss these things as implementation if they can usefully be treated conceptually.

Perhaps we can use "Is this a solved problem (in research terms)" as a heuristic to help us here. There's certainly precident for this: it is precisely the distinction between the 'Mathematics' and 'Math Overflow' SE sites.

Answer 1

I would say that it's a judgment call on a case by case basis. I don't think there's a simple rule you can implement that can capture all of the nuance involved here. My feeling is, unless you say with pretty close to absolute certainty that a question which includes code would get a better answer somewhere else, it's better to err on the side of leaving it alone.

That a question might contain math is, to me, nearly completely irrelevant to whether a question belongs here or not. Irrelevant in that it's orthogonal to the issue of whether something is "conceptual" or "implementation". After all, math is the language of science.

Answer 2

When I think of "implementation", things like math and code come to mind, while the larger components of AI construction don't fall under that category. Selecting features to build an AI for a certain purpose would therefore be on-topic, though they could easily be too broad. Your first example approaches "how do I solve this important problem with AI?", which possibly requires a deep knowledge of that field.

Questions tangentially related to programming, but not actually about the coding of the AI itself, are also OK. Your second example asks how to represent part of an AI's state for debugging visualization. It's a pretty neat question in my opinion, landing squarely in the science part of artificial intelligence.

I would be a little wary of allowing questions about the fine details (i.e. the mathematical/statistical mechanics) of yet-to-be-solved research problems, as those are likely to be much better served at one of the math-heavy sites. Conceptual questions about what kinds of things they work on are interesting and well-suited to our site.

Executive summary: if a question has mathematical formulae or computer code as critical elements, the best home for it is possibly a different site. This answer contains a lot of weasel words to emphasize that's it's not at all a rulebook that applies everywhere. Such an answer would be a tome.