I found that quite a few tags are present at each of these sites in the SE universe:

• Artificial Intelligence (AI)
• Data Science (DS)
• Cross Validated (CV)

For example:

• reinforcement-learning (AI, DS, CV)
• neural-network(s) (AI, DS, CV)
• deep-learning (AI, DS, CV)
• etc

Considering the descriptions of these SE sites doesn't help in drawing clear boundaries:

[Artificial Intelligence] Q&A for people interested in conceptual questions about life and challenges in a world where "cognitive" functions can be mimicked in purely digital environment.

 

[Data Science] Q&A for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.

 

[Cross Validated] Q&A for people interested in statistics, machine learning, data analysis, data mining, and data visualization.

And neither do the tag descriptions, for example reinforcement-learning:

[Artificial Intelligence] For questions related to learning controlled by external positive reinforcement or negative feedback signal or both, where learning and use of what has been thus far learned occur concurrently.

 

[Data Science] Area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.

 

[Cross Validated] A set of dynamic strategies by which an algorithm can learn the structure of an environment online by adaptively taking actions associated with different rewards so as to maximize the rewards earned.

While the site description for AI seems to invite only conceptual questions rather than specific or detailing ones, surveying the posted questions reveals a different trend.

For example tagged reinforcement-learning at Artificial Intelligence:

• DQN it's not working properly
• Why are all the actions converging to the same index?
• Q-Learning fails to converge even after 50K iterations for a simple board game - What could be the reason for this?

These questions are quite specific and not conceptual (in a sense that they ask about implementation details or aspects specific to a problem).

Surveying the first few pages of the reinforcement-learning tag at Data Science on the other hand created the impression of an excess of conceptual questions on that site and the same is observed for Cross Validated.

All these sites support MathJax rendering, in favor of conceptual questions.

Questions

Now the obvious dilemma (actually "trilemma") is, if I have a question about any of these shared-tag topics, ...

1. ... which SE site should I prefer for posting that question?
2. ... is there a preference regarding conceptual / (implementation-) specific questions?
3. ... is cross-posting considered bad manners?

Also are there any attempts in merging these SE sites into a single one, in order to merge the corresponding knowledge and competences?

Note

I posted this questions here, at Cross Validated Meta, because Cross Validated appeared topmost at SE sites, hosting most of the questions, considering the above three candidates.

I found that quite a few tags are present at each of these sites in the SE universe:

• Artificial Intelligence (AI)
• Data Science (DS)
• Cross Validated (CV)

For example:

• reinforcement-learning (AI, DS, CV)
• neural-network(s) (AI, DS, CV)
• deep-learning (AI, DS, CV)
• etc

Considering the descriptions of these SE sites doesn't help in drawing clear boundaries:

[Artificial Intelligence] Q&A for people interested in conceptual questions about life and challenges in a world where "cognitive" functions can be mimicked in purely digital environment.

[Data Science] Q&A for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.

[Cross Validated] Q&A for people interested in statistics, machine learning, data analysis, data mining, and data visualization.

And neither do the tag descriptions, for example reinforcement-learning:

[Artificial Intelligence] For questions related to learning controlled by external positive reinforcement or negative feedback signal or both, where learning and use of what has been thus far learned occur concurrently.

[Data Science] Area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.

[Cross Validated] A set of dynamic strategies by which an algorithm can learn the structure of an environment online by adaptively taking actions associated with different rewards so as to maximize the rewards earned.

While the site description for AI seems to invite only conceptual questions rather than specific or detailing ones, surveying the posted questions reveals a different trend.

For example tagged reinforcement-learning at Artificial Intelligence:

• DQN it's not working properly
• Why are all the actions converging to the same index?
• Q-Learning fails to converge even after 50K iterations for a simple board game - What could be the reason for this?

These questions are quite specific and not conceptual (in a sense that they ask about implementation details or aspects specific to a problem).

Surveying the first few pages of the reinforcement-learning tag at Data Science on the other hand created the impression of an excess of conceptual questions on that site and the same is observed for Cross Validated.

All these sites support MathJax rendering, in favor of conceptual questions.

Questions

Now the obvious dilemma (actually "trilemma") is, if I have a question about any of these shared-tag topics, ...

1. ... which SE site should I prefer for posting that question?
2. ... is there a preference regarding conceptual / (implementation-) specific questions?
3. ... is cross-posting considered bad manners?

Also are there any attempts in merging these SE sites into a single one, in order to merge the corresponding knowledge and competences?

Note

I posted this questions here, at Cross Validated Meta, because Cross Validated appeared topmost at SE sites, hosting most of the questions, considering the above three candidates.