Artificial Intelligence (AI) is an advance branch of science that studies the process of human thinking and attempts to apply the knowledge to simulate the same process in machines.
As computers are far ahead in the marathon of processing machines, AI is considered to be the branch of Computer Science than that of General Science. There have been many research and development in the field of Artificial Intelligence. The area of research include speech and pattern recognition, natural language processing, learning from previous experiences (learning by making and correcting mistakes!), reasoning under the situations providing limited or incomplete information etc. AI is practically applied in the field of computer games, expert systems, neural networks, robotics and many other fields of science and technology.
By "common sense", AI researchers mean that large corpus of worldly knowledge that human beings use to get along in daily life. A moment's reflection reveals that even the simplest activities and transactions presuppose a mass of trivial-seeming knowledge: to get to a place one should (on the whole) move in its direction; one can pass by an object by moving first towards it and then away from it; one can pull with a string, but not push; pushing something usually affects its position; an object resting on a pushed object usually but not always moves with the pushed object; water flows downhill; city dwellers do not usually go outside undressed; causes generally precede their effects; time constantly passes and future events become past events ... and so on and so on. A computer that is to get along intelligently in the real world must somehow be given access to millions of such facts. Winograd, the creator of SHRDLU, has remarked "It has long been recognised that it is much easier to write a program to carry out abstruse formal operations than to capture the common sense of a dog".
The CYC project involves "hand-coding" many millions of assertions. By the end of the first six years, over one million assertions had been entered manually into the KB. The Cyc KB is divided into many (currently thousands of) "microtheories", each of which is essentially a bundle of assertions that share a common set of assumptions; some microtheories are focused on a particular domain of knowledge, a particular level of detail, a particular interval in time, etc. The microtheory mechanism allows Cyc to independently maintain assertions which are prima facie contradictory, and enhances the performance of the Cyc system by focusing the inferencing process. Lenat describes CYC as "the complement of an encyclopaedia": the primary goal of the project is to encode the knowledge that any person or machine must have before they can begin to understand an encyclopaedia. At the present time, the Cyc KB contains nearly five hundred thousand terms, including about fifteen thousand types of relations, and about five million facts (assertions) relating these terms. New assertions are continually added to the KB through a combination of automated and manual means. Additionally, term-denoting functions allow for the automatic creation of millions of non-atomic terms and CYC adds a vast number of assertions to the KB by itself as a product of the inferencing process.
CYC uses its common-sense knowledge to draw inferences that would defeat simpler systems. For example, CYC can infer "Shaun is wet" from the statement "Shaun is finishing a marathon run", employing its knowledge that running a marathon entails high exertion, that people sweat at high levels of exertion, and that when something sweats it is wet.
One of the major problems CYC are facing is issues in knowledge representation, for example how basic concepts such as those of substance and causation are to be analyzed and represented within the KB.
AI engineers might learn more about biological processes of relevance to understanding the nature of knowledge, they ultimately will be able to develop a machine with human capability that is not biological or organic. This strategy has considerable support, but unfortunately, the thrust has been to ignore the differences between human knowledge and computer programs and instead to tout existing programs as "intelligent." Emphasizing the similarities between people and computer models, rather than the differences, is an ironic strategy for AI researchers to adopt, given that one of the central accomplishments of AI has been the formalization of means-ends analysis as a problem- solving method: Progress in solving a problem can be made by describing the difference between the current state and a goal state and then making a move that attempts to bridge that gap.