Three fundamental misconceptions of Artificial Intelligence

In the discussions on the limitation of Artificial Intelligence (AI), there are three major misconceptions, which identify an AI system with an axiomatic system, a Turing machine, and a system with a model-theoretic semantics, respectively. Though these three notions can be used to describe a computer system for certain purposes, they are not always the proper theoretical notions when an AI system is under consideration. These misconceptions are not only the basis of many criticisms of AI from the outside, but also responsible for many problems within AI research. This paper analyzes these misconceptions, and points out the common root of them, that is, to treat empirical reasoning as mathematical reasoning. Finally, an intelligent system, NARS, is introduced as an example, which is neither an axiomatic system, nor a Turing machine in its problem-solving process, and does not use a model-theoretic semantics, though is still implementable in an ordinary computer.