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Per Moderator @BenN's request in this thread to change the site description, we need to open a new thread and vote on new suggestions.

Please propose site description texts exactly as past users did in this older thread, and vote on the suggestions of other users. After a reasonable consensus is reached, the moderators will update the site descriptions to match the top voted answer.

For people new to this process, most of the AI SE suggestions in the past follow the convention of the descriptions of most SE sites and start with something like, "Artificial Intelligence Stack Exchange is a question and answer site for ..."

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  • $\begingroup$ Perhaps we can summon @BenN by mentioning him? It looks as though we did reach a good consensus. $\endgroup$ – John Doucette♦ Oct 11 '18 at 21:33
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The two leaders are ...

people interested in artificial intelligence theory, design, development, practice, research, and policy.

... and ...

people interested in embedded, mathematical, cognitive, and discovery centered artificial intelligence research and development.

... so I propose the union.

people interested in AI theory, mathematics, research, discovery, design, development, practice, embedded uses, cognition, policy, and impact.

This one is inclusive and dodges the terms statistics and data science which are the explicit domains of established SE siblings.

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    $\begingroup$ I like this idea. It's a bit wordy, but that's the not the worst thing to compromise on. $\endgroup$ – John Doucette♦ Oct 4 '18 at 23:22
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    $\begingroup$ This answer seems fine to me, it doesn't seem to exclude anything that should be on-topic, and seems to cover everything that should be on-topic imo. If people think it's too wordy, I think some of the words don't add much and could be removed though: 1) mathematics (I'd say theory and research already imply that AI-related math is on-topic), 2) embedded uses (is it necessary to specify this? I feel like questions about embedded uses of AI will already also always be covered by one of the other terms, like development or practice). $\endgroup$ – Dennis Soemers Oct 5 '18 at 12:05
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    $\begingroup$ I don't feel strongly about removing them though... just feel like they could be removed if it's necessary to reduce the word-count for whatever reason. $\endgroup$ – Dennis Soemers Oct 5 '18 at 12:05
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    $\begingroup$ I appreciate the broad expansion in scope of "cognition" unqualified, but I agree it's on-topic, and no one seems to object. I agree that if we include mathematics, it's with the understanding that we're trying to raise awareness of that aspect of the field, for beginners especially. My thought is including mathematics would encourage mathematical questions, which we definitely want. $\endgroup$ – DukeZhou♦ Oct 5 '18 at 22:52
  • $\begingroup$ @DouglasDaseeco I agree it's in scope and useful. Most importantly, we seem to have some consensus and buy-in on the merged descriptions in this answer, which helps in moving forward confidently with the change. $\endgroup$ – DukeZhou♦ Oct 8 '18 at 20:52
  • $\begingroup$ @DukeZhou in case we need to mention you in a thread that you were on already. Please see comment thread under the main question. $\endgroup$ – John Doucette♦ Oct 11 '18 at 23:35
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    $\begingroup$ I think the first vote can be dismissed. It is probable that the electorate of the previous vote is different from the electorate of this vote because different users are active now. Our process should reflect the will of those who are currently active on, and thus running, the site. $\endgroup$ – John Doucette♦ Oct 12 '18 at 0:50
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I think we should also provide guidance to users about which questions may be more suitable for Data Science, Overflow, etc.

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    $\begingroup$ How would we do that without making the sentence long? Is that for the description or the help guidelines could have that? Maybe we should, as a group with interests in common, think ahead about what should go where as we vote here. $\endgroup$ – FauChristian Sep 29 '18 at 22:40
  • $\begingroup$ @FauChristian I was thinking in terms of a code or appendix with more information. (My sense is we get a lot of question that are more in line with other stacks because OP's aren't aware of those stacks, or their relevance. Only a subset are going to make the connection that Data Science is a place for machine learning questions.) $\endgroup$ – DukeZhou♦ Oct 1 '18 at 19:36
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    $\begingroup$ Reading this entire Q&A, I agree that AI is hard to describe. The meaning of intelligence is still developing. "We'll know it when we see it," is what we used to say about disease before culturing & sequencing. Laxity will only lead to increased inter-site animosity and dysfunctional question closures later. The liquid delineation between AI, CV, and DS is not Franck's delusion. He's right, and, difficult as it is, solidification will be harder later. The title Artificial Intelligence for this site is excellent, but in distinguishing AI from CV/DS via description, it was written too narrowly. $\endgroup$ – FauChristian Oct 3 '18 at 22:21
  • $\begingroup$ @DouglasDaseeco Confirmed that I am unable to make the edit. I'll begin the process via notifying the powers that be, but prepare to be patient. $\endgroup$ – DukeZhou♦ Dec 10 '18 at 21:56
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Artificial Intelligence Stack Exchange is a question and answer site for ...

people interested in artificial intelligence theory, design, development, practice, research, and policy.

I like @DouglasDaseeco's answer, but I'm among the users who think that practice, and even code, have a place here. Presently users post questions containing code, and I and others answer them, so I think this description is more accurate.

While the founding moderators' intent was to exclude questions that overlapped with other sites (notably Data Science & Programmers.SE), the boundaries are quite porous in practice, and if we want to claim to be a useful place for AI related Q&A on the web, I think we need to accept practical questions as well.

Some examples of coding questions with no other place to go include:

Keeping track of visited states in Breadth-first Search, which is about the proper data structures to use in a search algorithm. It doesn't belong in Data Science, since it is related to GOFAI and not machine learning. It doesn't really belong in Programmers.SE, because it isn't a generic question about programming, it's related to understanding the algorithm. It seems to clearly belong on this site, and yet it includes code and is about practice.

Snake game: snake converges to going in the same direction every time This question was about the implementation of a reinforcement learning algorithm. The question again has nothing to do with Data Science. It involves programming, but the users' problems were not related to understanding how to program, but to understanding the algorithm (and, as it turned out, the exact behaviour of a particular algorithm for training neural networks). This user is not likely to get useful answers on Programmers.SE. It seems to clearly belong on this site, and yet it also includes code and is about practice.

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    $\begingroup$ maybe also add research to the list? $\endgroup$ – Dennis Soemers Sep 25 '18 at 8:12
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    $\begingroup$ @DennisSoemers Good idea. $\endgroup$ – John Doucette♦ Sep 25 '18 at 12:01
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    $\begingroup$ I would like practice questions to include code as relates to e.g. A*, RL and other high-level AI constructs, but would like exclude code (and questions) that gets bogged down in minutiae of how to implement neural networks, other supervised/unsupervised learners, or Python syntax etc. Likewise, "why does my NN only get a loss of X on this data" type question. This is already a key area of confusion between Cross Validated, Data Science and Stack Overflow. Bundling AI in with those three will further dilute things. The difficulty is phrasing things, because AI is a superset including ML. $\endgroup$ – Neil Slater Sep 26 '18 at 8:55
  • $\begingroup$ Strongly agree that we have to include practice. I also think Neil's points are important, as we do need find the right boundaries with the stacks where there is overlap. PS I wonder if "history" might also have a place. (There is a history of science & mathematics stack, but I feel that the history of AI is most usefully covered here.) $\endgroup$ – DukeZhou♦ Sep 26 '18 at 23:37
  • $\begingroup$ @NeilSlater & JohnDoucette What are your thoughts on providing a little guidance on questions that would be more suitable for Data Science, Overflow, etc. $\endgroup$ – DukeZhou♦ Sep 28 '18 at 18:27
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    $\begingroup$ @DukeZhou: I am torn on that. Although there is clearly a difference between the sites that seem relevant, even experts on multiples of those sites find it hard to express what the actual differences are. It is next to impossible to word a short enough clear statement that a new question asker could follow, even assuming they take the time. The current trend to conflate "Deep X" using NNs with AI is going to cause a lot of misplaced questions. I think the guidance is going to be best focussed on the experts at each site so we can gently push questions around . . . $\endgroup$ – Neil Slater Sep 28 '18 at 19:01
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    $\begingroup$ . . . but the first barrier to that is getting a bunch of experts to agree on any course of action when there is no clear consensus. My default position right now is to answer questions wherever they land, and only comment on suitability of another site when a question would very clearly be welcomed on an alternative according to history of similar questions that I have seen. That's definitely how I have been treating technical questions in Reinforcement Learning. $\endgroup$ – Neil Slater Sep 28 '18 at 19:03
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    $\begingroup$ @DukeZhou I agree with Neil. I think that exactly which technical questions are "on-topic" will be very hard to define, but that moderators can "know it when they see it". A question that is primarily about how to program something is probably (though not always) off topic. A question about what to program (i.e. what the steps in an algorithm actually mean) is probably on topic. But there are lots that fall in between those questions. $\endgroup$ – John Doucette♦ Sep 28 '18 at 19:36
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    $\begingroup$ @NeilSlater "Answer where you see it" seems to best serve the mission of Stack. (Pedantic adherence to guideline minutiae, especially as there is legitimate fuzziness, has definitely not been helpful.) Thanks for sharing your thinking on this and your approach. $\endgroup$ – DukeZhou♦ Sep 28 '18 at 20:11
  • $\begingroup$ Snake game: snake converges to going in the same direction every time is on-topic on both CV and DS. $\endgroup$ – Franck Dernoncourt Sep 30 '18 at 18:30
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    $\begingroup$ It may be on topic in CV or DS (I suspect it would be closed on either of those, despite the site policies). A bigger question is: why would any person expect it to be on topic there? The user certainly isn't doing anything I'd recognize as data science. They are doing a kind of machine learning, but it's not anything a regular statistician would expect to see. If I were going to ask about it, I'd expect to get an answer on the "AI" stack, and if I was searching for an answer, that's where I'd expect to find one too. $\endgroup$ – John Doucette♦ Sep 30 '18 at 23:55
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Realities of Overlap

There are two sub-sites of stackexchange.com with overlapping topic space with Artificial Intelligence.

The spread of data-centrism1 throughout multiple Stack Exchange sub-sites should not be surprising given its rise in funding, and some have noted that overlap is inevitable. Still, Franck Dernoncourt and Robert Cartaino expressed concern about this overlap in the past Q&A posts referenced in this question text.

Distinctions

However data intelligence is a subset of smaller proportion in relation to the entire field of artificial intelligence than public media and SE discourse suggest. The flow of funding and actual research is not all public.

Also of note is that others have suggested throughout the Artificial Intelligence Q&A that machine learning is distinct from artificial intelligence. Although there is obvious overlap in the literature and in practice, one can easily argue that point.

  • There are things machines learn that to use the term intelligence would be a gross stretch of the meaning of the term
  • There are elements of artificial intelligence systems that are deliberately and effectively not mechanical
  • There are elements of both biological and artificial systems that have inherited intelligence, DNA based in biology, hard coded learning software, and likely VLSI algorithm realization in the near future

AI Foundations Rest in Theory and Action Rather Than Data

The foundation of AI is not data-centrism but rather cybernetics, kick started by cold war funding of ICBM countermeasures under Reagan. Concurrent with data-centrism is the serious investment into real time embedded AI for transportation, factory defect reduction, and smart home robotics.

Automated mathematics (Leibniz) is also well funded & lacks data in the conventional sense.

In the (by far) most popular answer on this topic, Robert Cartaino wrote,

"With autonomous cars, smart surveillance, and 'the next big thing' capturing the headlines, this isn't a terrible idea for a subject. Draping it in the popular AI label gives it a better focus, and it completely disambiguate[s] that this is not a technical implementation or programming site. We already have that."

Possible Shift May be Indicated

For the above reasons and the plethora of truly distinctive questions dating to the initial proposal of this site2, it is important to also list the following categorically different (and therefore probably unpopular) possible direction in the site description.

Artificial Intelligence Stack Exchange is a question and answer site for ...

people interested in embedded, mathematical, cognitive, and discovery centered artificial intelligence research and development.

There would be much less overlap and, in case the data-centrism craze dies down, which many in the absence of any historical perspective assume would never occur, this AI sub-site would have much stronger long term viability.

Footnote

[1] Data-centrism, the belief that in data lies the truth, strongly supported by statistics and probability and criticized as pointing human activity randomly and without progressive objective, is increasingly the dominant perspective of the cultures of developed regions. Success with mining, ranking, and indexing and the power that practitioners such as Google Inc. have gained globally through the realization of what was once a card catalog in the local library drive this philosophic paradigm shift.

[2] An example of one of the many early questions that are not data-centric but classical and of enduring importance to the development of artificial intelligence is this one from the proposal phase of the AI sub-site: Is the Turing Test, or any of its variants, a reliable test of artificial intelligence?

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    $\begingroup$ I actually don't mind your suggested wording, but I don't see how it's related to the rest of your post. Someone interested in mathematical-centered AI could easily be a machine learning practitioner. The same applies to "discovery" and "embedded". I also disagree with the characterization your post makes about the roots of AI. AI is older than Regan. Even in the Regan years, there were AI projects in other countries (most notably Canada) that were data-centric. Finally, if you go to a modern AI conference, you will be hard-pressed to find a person under 40 who thinks AI is all based in theory $\endgroup$ – John Doucette♦ Sep 30 '18 at 13:50
  • $\begingroup$ @JohnDoucette, The connection between this answer's proposed description in section 4 and the prior three sections is the addressing of what Franck's critique correctly outlines. The AI site's long term viability with SE staff and attractiveness to researchers may heavily rely on developing less ambiguous topical delimitation. Although this answer's proposal has some common ground with CV and DS sites, it would tend to route stats questions to CV and Big Data questions to DS. AI should be the go-to place for Q&A that is, even if vaguely related to data and stats, clearly distinct topically. $\endgroup$ – FauChristian Oct 3 '18 at 21:56
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    $\begingroup$ My concern with the wording is... it's unnecessarily complex. You pretty much have to be a domain expert to be able to accurately judge whether or not the question you want to ask is about AI R&D that is "embedded, mathematical, cognitive, and discovery centered". Which parts of AI R&D do and do not fit those words? And why even would some other parts of AI R&D (whatever those other parts would be) excluded? Very few people (especially newcomers, towards whom the description should be tailored) are going to understand what this really means. $\endgroup$ – Dennis Soemers Oct 4 '18 at 12:27
  • $\begingroup$ In contrast, the description suggested by @JohnDoucette is much better. Everyone, even beginners in the field of AI, will have a reasonable idea of what theory, design, development, practice, research, and policy mean. John's description also doesn't appear to be excluding anything that should be excluded, whereas... I think this answer does. I'm not sure since it's so difficult to judge exactly what is meant due to the unnecessary complexity, but it looks like some parts of AI R&D are excluded by the wording. $\endgroup$ – Dennis Soemers Oct 4 '18 at 12:29
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Artificial Intelligence Stack Exchange is a question and answer site for ...

people interested in artificial intelligence theory, design, development and policy.

 

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GENERAL INFO FROM PAST DISCUSSION

Description Writing and Selection Criteria

Some good points from others during 2016 through 2018 discussions.

  1. Keep the description concise (short but descriptive)
  2. Capture the real interests of members
  3. Consider new trends in Q&A content (below)
  4. Don't step into Data Sciences territory
  5. Incorporate knowledge of tag use (below)

Top Tags

  • neural-networks
  • machine-learning
  • deep-learning
  • convolutional-neural-networks
  • reinforcement-learning
  • ai-design
  • image-recognition
  • algorithm
  • classification
  • training
  • natural-language-processing
  • game-ai

Fast Growing Newer Tags

  • neuromorphic-engineering
  • natural-language
  • lstm

Some AI Trends in Recent Q&A

  • Hardware parallelism with clusters, GPUs, and AI cores
  • Embedded AI for robotics
  • Neuromorphic and spiking network libraries and VLSI chips
  • Smart surveillance
  • Autonomous vehicles
  • Policy is forming around labor concerns and AI deployment
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    $\begingroup$ I like how short this it's. I suggest adding "practice" as well, perhaps as the second last item. We do both receive and answer many practice oriented questions now. $\endgroup$ – John Doucette♦ Sep 24 '18 at 9:51
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    $\begingroup$ @DouglasDaseeco I wouldn't go by anything from 2016 as SE:AI has evolved. (Don't forget, when this incarnation of SE:AI was launched, nobody thought we'd need math formatting.) PS- I really like how you incorporate the scope of the stack via tags. $\endgroup$ – DukeZhou♦ Sep 26 '18 at 23:58
  • $\begingroup$ The word 'development' includes continuous integration, agile methods, version control, and full life cycle, all desperately needed in government, corporate, and academic laboratory settings. It also includes the concept of 'practice'. The issue with the word practice is that it will encourage posts of buggy code with insufficient bug reports, with the expectation that we will replicate the bug and fix it for free. This answer lacks the words application, algorithms, application, evaluation, and impact. The word 'development' may cover the first 4 and 'policy' may covers the last. Maybe not. $\endgroup$ – FauChristian Sep 27 '18 at 22:24
  • $\begingroup$ Pretty much all the tags you mentioned are already on-topic on stats.stackexchange.com and datascience.stackexchange.com I don't think we need a third Stack Exchange website covering them. $\endgroup$ – Franck Dernoncourt Sep 30 '18 at 22:39
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Artificial Intelligence Stack Exchange is a question and answer site for ...

people interested in artificial intelligence research, theory, algorithms, application, evaluation, policy, and global impact.

The earlier answer that led to this option had "Those involved with," in place of, "People interested in." The difference is how much involvement we imply members have. There would be no actual restriction. The only actual difference between these is whether we encourage Q&A to be contributed by people that are somehow involved with AI or just watching the news about it and have naturally become curious. It relates to the quality of content and the care in posting, not policy or exclusiveness.

  • Those involved with
  • People interested in
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I'd like it short and simple:

Artificial Intelligence Stack Exchange is a question and answer site for people interested in thinking machines. Join them; it only takes a minute:

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  • $\begingroup$ Most of those in academia, those working in the AI field, and those with a keen interested in AI would not consider an artificial network a thinking machine. Thinking implies cognition, and the research into that, although alive, is currently a small sliver of legitimate AI work. For instance, GAN generated images of interior designs are not thought about. GANs were ground breaking, but only as an example of how two networks can be employed in a pattern recognition gaming trick. Even DRools and other production systems do not simulate thought. $\endgroup$ – FauChristian Sep 27 '18 at 22:01
  • $\begingroup$ I agree with you, that Neural networks are different from AI and perhaps this is the answer how to exclude datascience questions from SE.AI. As far as I know, we had a similar question previously about the difference between Machine Learning and Artificial Intelligence: ai.stackexchange.com/questions/35/… $\endgroup$ – Manuel Rodriguez Sep 28 '18 at 6:27

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