If you have a question about theoretical, philosophical, social, historical, and certain developmental and academic aspects of artificial intelligence, then you are probably in the right place to ask your question!

Below you can find a non-exhaustive list of specific topics that are considered on-topic here. Next to each topic, you have links to other stacks where the corresponding topics may also be on-topic.

### Specific topics

You can ask a question about the theoretical aspects of the following sub-fields of artificial intelligence.

• Artificial general intelligence (e.g. AIXI)
• Affective computing
• Swarm intelligence (e.g. ant colony optimization)
• Evolutionary algorithms (1, 4, 6)
• Machine learning (1, 2, 4, 6)
• Computational learning theory (1, 6, 7)
• Natural language processing and understanding (6)
• Computer vision (1, 2, 4, 6, 8, 10)
• Knowledge representation and reasoning (6)
• Ontology (3)
• Search (6)
• Planning (6)
• Robotics (5)

The following philosophical (or theoretical) aspects are on-topic.

• Intelligence definitions and testing
• Superintelligence
• Emotional intelligence
• Artificial consciousness

The following social aspects are on-topic.

• Ethics (3)
• Explainable artificial intelligence
• Applications

The following historical aspects are on-topic.

• Timeline (e.g. AI winters)
• Progress

• Terminology and notation
• Proofs (8)
• Clarifications of certain excerpts from papers, books, etc.
• Reference requests (e.g. "Which paper introduced vanilla RNNs?")

### Notes

• Before posting, please, look around to see if your question has been asked before. Your question could be closed as a duplicate of another, if you don't do it.

• You should put some effort into writing your question. If your question is unclear, it could be flagged as unclear, your question could be closed, and you will not receive help. Furthermore, we expect users to do a little bit of research before asking a question.

• You should try asking one question or address a single problem per post, unless the questions are really very related to each other. If you ask multiple questions per post, your post may be closed as too broad.

• Ideally, we are looking for questions that can be answered objectively. More precisely, do not ask for advice (such as career path recommendation or a tool, which are, in general, off-topic here anyway) but for facts (including references) and arguments. If you have a philosophical question, you should demand a logical, rational and reasonable answer that argues the philosophical perspective (and not just an opinion).

• Implementation questions in the context of understanding the theoretical topics are on-topic. For example, if a theoretical topic is described by a certain mathematical formula and you want to understand how a certain implementation is related to the formula, then your question is on-topic. Here's an example of an implementation-related question that would be on-topic. However, as a rule of thumb, if you can describe your problem without the source code and if you think that a solution to your problem can be given without the source code, then your question is more likely on-topic. The source code can be provided to further clarify the issue, but you should provide a Minimal, Reproducible Example.

• General programming questions are off-topic. For example, if you have a question like "Why am I getting this exception?", "How do I merge two Pandas' data frames?" or "How can I use this Keras API?", then your question is off-topic (and you should probably ask it on Stack Overflow).

• Answers with cut-and-paste content and no additional context or explanation will be deleted, if you don't even cite/quote the source & provide a link. We encourage citation from reliable sources, but answerers are expected to (at least) describe in their own words the reason for the excerpt, and why it answers or comments on the question.

• Don't post content to primarily promote yourself, blogs, articles, or source code. Stack Exchange is a Q&A site, where good questions ideally receive more than one well-founded answer.

• Don't post content with disinformation or misinformation. Out-of-context answers (or questions) will be deleted. After an initial warning, if you don't stop posting such content, your account will be suspended. See A Day in the Penalty Box for more details.

• Questions related to specific hardware, software, or datasets are off-topic, including

• the comparison of two specific pieces of hardware or software, and
• asking for an API, library, or dataset (to solve a specific problem).

For example, questions like "Is CPU $$X$$ better than CPU $$Y$$ for training deep learning models?" or "What are the differences between TensorFlow and PyTorch" are off-topic here. These questions are more appropriate for Data Science SE, because these are more engineering/programming issues.

However, a question like "Why do people use GPUs to train neural networks?" is more acceptable here because it's more general and theoretical.

If you are looking for software or hardware recommendations, the sites Software Recommendations SE and Hardware Recommendations SE are more appropriate, respectively.

If you are looking for datasets, the site Open Data SE may be the right place to ask your question.

Moreover, note that, as stated above, you can ask for "references" (such as books, articles, papers, blog posts, courses, etc.).

• Questions seeking pre-trained models for a specific problem or problem domain are off-topic here, although questions about how such models are made, how they perform, or when one might want to use one are on-topic.