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.
You can ask a question about the theoretical aspects of the following sub-fields of artificial intelligence.
- Artificial general intelligence (e.g. AIXI)
- Evolutionary computation
- Machine learning (1, 2, 4, 6)
- Reinforcement learning
- Computational learning theory (1, 6, 7)
- Affective computing
- Natural language processing and understanding (6)
- Computer vision (1, 2, 4, 6, 8, 10)
- Knowledge representation and reasoning (6)
- Search (6)
- Problem solving (8)
- Planning (6)
- Robotics (5)
The following philosophical (or theoretical) aspects are on-topic.
- Intelligence definitions and testing
- Emotional intelligence
- Artificial consciousness
The following social aspects are on-topic.
- Ethics (3)
- Explainable artificial intelligence
The following historical aspects are on-topic.
- Timeline (e.g. AI winters)
You can also ask questions about
- Terminology and notation
- Proofs (8)
- Clarifications of certain excerpts from papers, books, etc.
- Reference requests (e.g. "Which paper introduced vanilla RNNs?")
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), preferably, backed up by previous research work or well-known philosophical ideas.
Ask one question per post, otherwise, your post may be closed as too broad.
Don't post content with disinformation or misinformation. Out-of-context answers (or questions) will be deleted and, if this persists, your account will be suspended.
Career path recommendation is off-topic.
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).
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.
- 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.
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.
If your question is not specifically on-topic for Artificial Intelligence Stack Exchange, it may be on-topic for another Stack Exchange site, such as
- Cross Validated
- Data Science
- Stack Overflow
- Computer Science
- Theoretical Computer Science
- Psychology & Neuroscience
- Signal Processing
- Software Recommendations
- Hardware Recommendations
- Open Data
Certain questions are probably on-topic on multiple of these websites. For example, machine learning questions are also on-topic at Cross Validated, which is more statistics-oriented. There are probably other overlapping sites.
If no site currently exists that will accept your question, you may commit to or propose a new site at Area 51, the place where new Stack Exchange communities are democratically created.