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A similar question than this one has been asked there: Difference between the Cross Validated and Data Science SE sites?

People, including myself, seems to be confused about the distinction of all these sub-websites.

"Data Science" generally refers to everything that process data and tries to infer or deduce something from it. Machine learning falls under this category, since it process data to train an algorithm and some parameters from the data. Machine Learning is one part of the family "Artificial Intelligence". But according to the description, it also enters in "Cross Validated".

We can easily distinct these sites from Stack Overflow, since the questions raised there often requires programming, but that is not the core part of the discussion. But what about these three: Data science, Cross Validated and Artificial Intelligence?

Just to compare, all these websites contains references to RPN (Region Proposal Network), which is a neural network method, meaning an AI/Machine Learning technique.

  1. How to train the RPN in Faster R CNN?
  2. How does region proposal network (RPN) and R-CNN works?
  3. Binary score in RPN (Faster-RCNN)

Should we do something about it? I feel that people that could have valuable answer, can't provide them, because they are not in the same community and miss the opportunity.

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I contribute on all three sites.

There is a large amount of overlap in subject matter, especially between Data Science and Cross Validated regarding machine learning algorithms. When it comes to ML questions, those two sites mainly differ in where the practical lines are drawn between subjectively good/bad and on-topic/off-topic. This is a murky area, and neither site is 100% consistent, because it is driven by individual behaviour and there is not strong consensus yet.

However, my feeling is that Data Science is more accepting of practical implementation issues - whether for code, specific data issues, or specific technologies. Whilst Cross Validated is the go to place for underlying mathematics and theory. Often a question could be valid on both sites at its core, but may need to be phrased differently to fit the culture. Again, my feeling is that CV, as the largest of the three sites, has the strongest internal culture here, and is driving how a lot of the edge cases work.

Meanwhile Artificial Intelligence is still quite a new site and trying to find its voice and community. That means that topics that should logically be very strong for it, such as Reinforcement Learning, often get asked on DS and CV sites, because even though a low fraction of experts on those sites know RL well, a low fraction of CV or DS users is still very competitive with the available experts on AI.


If I were to offer some opinion on the questions you have posted:

  • How to train the RPN in Faster R CNN? - should be on Data Science, as it is asking for practical advice working with a specific neural network based on reading a paper.

  • How does region proposal network (RPN) and R-CNN works? despite the upvotes is IMO off-topic on any Stack Exchange network, as it is just asking for external resources and with very little context

  • Binary score in RPN (Faster-RCNN) could be on any of the three Stack Exchanges, as it could be answered from different perspectives. By posting on AI, the user can currently expect a descriptive, qualitative answer, without maths (because no LaTex implemented yet) or large amounts of detail. Or, sadly, no answer at all, because not enough experts available to cover all possible technical subjects.


Outside of machine learning, I think the sites have some clearer differences, and these can in part be seen by looking at the top 20 tags on each one. The following are all tags in the top 20 of each site, and not on the first page of the other two:

Cross Validated https://stats.stackexchange.com/tags

probability, hypothesis-testing, self-study, distributions, bayesian, correlation, statistical-significance, mathematical-statistics, anova, normal-distribution,

Data Science https://datascience.stackexchange.com/tags

data-mining, scikit-learn, keras, dataset, text-mining, bigdata, pandas

Artificial Intelligence https://ai.stackexchange.com/tags

ai-design, reinforcement-learning, philosophy, genetic-algorithms, ai-basics, game-ai, computer-vision

Of course, that is not to say that Data Science would not consider probability questions, or Cross Validated does not deal with deep learning issues. But I think the differences between the tags do show that a different focus for each community is evident.

  • Indeed. For the Data Science post, "datascience.stackexchange.com/questions/27277/…" would have been a better example. But if you have to be on these three sub-websites as probably a lot of us, doesn't it mean that they gather the same community? And then, that the separation is a bit superfluous? – Emile D. Jun 21 '18 at 14:16
  • I also mean by that: some interesting people may not be aware of the demarcation and subtleties between these websites (as I was before your explanation and doing some research about it). Thus, some people may miss a community only because they are not aware of another similar-topic website and also their ranking/privilege disappear from one another, etc. And people that don't do the research will always post on the wrong place... – Emile D. Jun 21 '18 at 14:22
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    @EmileD. Yes, I think that is a time-old problem, not limited to Q&A sites though: Is a larger, more varied, community better or worse at achieving its goals than multiple smaller more focussed communities? I would be interested to know how much the regular contributors on the three sites overlap. Another interesting question is whether the tags in Data Science would be considered suitable topics in CV, because tags are another way to sub-divide a larger community and have differences co-exist. I would suspect the AI tags are too far removed from CV's core purpose. – Neil Slater Jun 21 '18 at 19:29

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