Artificial intelligence (AI) is a concept in data science that refers to the intelligent-like data that is exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving" (known as machine learning).  As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition. For instance, optical character recognition is no longer perceived as an example of "artificial intelligence", having become a routine technology.[3] Capabilities currently classified as AI include successfully understanding human speech, competing at a high level in strategic game systems (such as chess and Go), self-driving cars, intelligent routing in content delivery networks, and interpreting complex data.


Artificial Intelligence measures an agent's ability to achieve goals in a wide range of environments. (Universal Intelligence: A Definition of Machine Intelligence by Shane Legg and Marcus Hutter, December 2007)

It means that a more intelligent agent is supposed to perform better on average. Formally, the complexity of the environment is also considered. The intelligent agent should assume that simple environments are more often. That is the Occam's razor principle.

The Strong AI tries to build the really universal intelligent machines.

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