The problem that CI is trying to solve is to how to create an intelligent system. As a partial answer to the question ‘what is intelligence’, CI looks to nature for inspiration and tries to mimic, through algorithms, a degree of that observable intelligence 1. If one definition of intelligence includes making a decision in the face of uncertainty, or making inferences based on past experiences, then with some confidence we can say that process resembles ‘intelligence’. The end result is a program that can generate decisions more like a human or animal. The reason this is desirable is because there are certain problems that can be better solved by nature, like how to evolve, how to self-organize, or for a predator, how to spot the weakest in the herd. Whether or not we want computers to be better at spotting the weakest in the heard is a good question, but the difference between how computers and humans process information makes it a challenging problem to implement. To deal with that challenge, there is a group of nature-inspired algorithms: Fuzzy logic, evolutionary computation, neural networks (also swarm optimization and genetic algorithms) — together these research fields make up what is known to be CI. 2
CI concerns itself with mimicking what intelligence can be observed in nature (natural sciences), and AI concerns itself with what can be mimicked from humans (social sciences) (Schmutter, P. 2002). The distinction between these approaches isn’t a ‘crisp’ distinction; humans being a part of of nature is the most obvious area of overlap. Another one is that neural networks (CI) are modeled after the structure of human brain cells. But knowing the ultimate goal of AI is the best way I understand the distinction from CI. At the end of the road for AI, you would have computer systems that think, learn, store and process information like a human. At the end of the road for CI you would have systems that can optimize data, ‘learn’ from data, or output predictions or probable outcomes. This benefits AI but the goals of CI are different.
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- Schmutter, P. 2002. A taxonomy for artificial and computational intelligence. In Object-Oriented Ontogenetic Programming: Breeding Computer Programs That Work Like Multicellular Creatures. MSc Thesis, University of Dortmund, 72–78. http://www.ooop.org/publications/thesis/node18.html
- Hall, L.O. 2008. Computational intelligence. In AccessScience, © McGraw-Hill Companies. http://dx.doi.org/10.1036/1097-8542.801910