Having analysed,reviewed quite a number of user questions inline with answers concerning AI,sometimes I understand nor take note that AI community does not try much to avoid the term computational Intelligence,the feeling I get is that there's need to put some distance between AI and CI. However, there is a little bit confusion especially when it comes to computational intelligence application topics,for instance;

According to IEEE computational intelligence society ,

it defines it's subjects of interest as Neural networks, Fuzzy systems,Evolutionary Computational and Swarm Intelligence,chess programs based on heuristic search..,etc..

Now this makes an impression that computational intelligence could be the umbrella under which AI falls into.

Also,according to computational intelligence, an international journal (Blackwell publishing since 1948 in association with Atlantis Press

It states that computational intelligence is just another name for artificial intelligence.

However, new scientists, engineers nor researchers on board could not as well get it right, when the two terms come into play,this has come to my notice due to some of questions migrated to cross validated community,others being too broad due to the question problem solutions needed, besides scientific projects,and lastly utopian AI questions.

Hint Since we are on the level of artificial narrow intelligence, but when you try to critically figure out its real world applications, they fall under the umbrella of computational intelligence.

I have some hints nor glimpse on the two subjects and I would be very interested in the ideas of others, besides that, the definition of AI,here everyone has a point on it,but what about when we try to state the real description of CI,so that in future,new scientists, machine learning engineers/reseachers get it right away.

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    As far as I am concerned, CI is just a sub-field of AI which focuses on certain topics or certain approaches. – nbro Aug 7 at 22:00
  • @nbro ,we are quite on the same,just a little bit.So this sounds like an assumption nor opinionated idea,some facts to prove it scientifically.However,the only answer so far we have, just elaborates the breakthrough timelines of these two fundamental aspects of computer science.Im still doing my own research via IEEE Computational intelligence society & Oxford artificial intelligence society ,simply because,these two aspects,have no consensus insights inline with the clear definitions. we have been asleep all ever since! special thanks to Oxford university and IEEE. So let's brainstorm. – quintumnia Aug 8 at 17:26
  • I took the liberty of adding some tags for future search (and tweaked the title to say "vs.", which I think captures the spirit of the question and also adds a little zing;) – DukeZhou Aug 9 at 21:40
  • ps- it's a good question so you might want to give it a quick proof read and edit – DukeZhou Aug 9 at 21:42
  • @DukeZhou , Sometimes we know what we are doing here, so before we do post,we take time to analyse, reflect then re-read again! – quintumnia Aug 10 at 4:20

I think your question is "What is the distinction between AI and CI?"

The short answer is that they are two parallel research efforts working on similar problems, but with different methodologies and histories. Essentially, they study similar things, but with different tools. In the modern context, computational intelligence tends to use bio-inspired computing, like evolutionary and genetic algorithms. AI tends to prefer techniques with stronger theoretical guarantees, and still has a significant community focused on purely deductive reasoning. The main area of overlap is in machine learning, especially neural networks.

The longer answer is that your source from 1948 says they are synonyms in part because it predates the split in the research community, which took place later.

The two communities have always some overlap in topics, but in my experience, mostly are skeptical of each other's methodologies, and mostly publish in separate journals. Some authors consider CI to be a subset of AI however, particularly those writing in the 1990s.

Example topics that are solidly in AI but definitely not in CI are logical and expert systems, and statistical approaches to machine learning like regression.

Example topics that are solidly in CI but perhaps not in AI (depending on whether one views CI as a subset of AI or not) are genetic programming, fuzzy logic, and ant colony optimization.

As a rule, AI-rooted techniques have better theoretical guarantees, and better developed theory in general (there are exceptions though). For example, Fuzzy Logic has been strongly criticized for the lack of a solid theoretical foundation (good modern summary here), as have genetic and evolutionary approaches (most famously, both lack a proof of convergence within finite time to a global optima on a smooth surface, even though they do quite well in practice).

CI-rooted techniques nonetheless often see major performance advantages in specific problems (see, for instance, deep learning results), and tend to have a strong experimental and engineering tradition. The No Free Lunch theorems are often used to justify their use when theoretical certainty is missing. Basically the theorems say that, in learning and optimization problems, a technique can only perform well on a problem by performing poorly on some other problem. CI authors argue that there are some problem domains in which their techniques work well (which must be true, because simpler algorithms like hill-climbing outperform them on simple problems).

Check out this paper for lots more references on CI, or this book for a list of core topics in the field.

  • You have twice "Example topics that are solidly in AI but definitely not in CI", I guess one of those is supposed to be switched around. That said, in my experience there isn't really such a clear distinction between the two (anymore)? Isn't it mostly a historical thing? I've seen all of those examples topics being put under the "AI" umbrella plenty of times, and would put them all in AI myself too, and have seen them all when studying AI. In my experience, there isn't really much of a distinction other than that... AI seems to be a more commonly-used term. – Dennis Soemers Aug 7 at 16:05
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    @DennisSoemers Thanks, I've switched the appropriate one. Some authors have said that CI is a subset of AI. In my experience, there are parts of CI (especially fuzzy logic) that are way outside the norm in AI though. I think most of the modern split is in publication venues. For example, there are no papers in the last 2 years on evolutionary or genetic algorithms (which are a core CI topic) published in AIJ or JMLR (I just double checked this). In contrast, you can't publish a paper on deductive logic systems in GPEM or at GECCO. – John Doucette Aug 7 at 18:01
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    @nbro Insofar as Fuzzy logic is part of CI, and some people consider CI a subset of AI, it could be viewed as part of AI. However, it's pretty fringe. See Cheeseman's "In Defence of Probability" from the 1980's for why. If you go to any mainstream AI conference (not CI conference), you won't see a track on fuzzy logic. If you go to a CI conference, you'll probably see one. – John Doucette Aug 7 at 22:02
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    @nbro Thanks for your comment though. You're right that this answer could be improved by sourcing my statements. I've added some links. – John Doucette Aug 7 at 22:16
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    Thanks. A good example topic is in computer vision. Symbolic systems never managed to perform well outside of very artificially simplified environments. CI approaches like deep learning did far better without using explicit symbolic reasoning. NLP is in a similar place right now. – John Doucette Aug 10 at 14:23

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