Name
Abstract | ||
|---|---|---|
In discussions on the limitations of Artificial Intelligence (AI), there are three major misconceptions, identifying an AI system with an axiomatic system, a Turing machine, or a system with a model-theoretic semantics. 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 analyses these misconceptions, and points out the common root of them: treating empirical reasoning as mathematical reasoning. Finally, an example intelligent system called NARS is introduced, which is neither an axiomatic system nor a Turing machine in its problem-solving process, and does not use model-theoretic semantics, but is still implementable in an ordinary computer. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1080/09528130601143109 | J. Exp. Theor. Artif. Intell. |
Keywords | Field | DocType |
model-theoretic semantics,axiomatic system,formalization,axiomatization,limitation of ai,ai system,fundamental misconception,turing machine,computer system,artificial intelligence,algorithmization,empirical reasoning,mathematical reasoning,ordinary computer,ai research,example intelligent system,artificial intelligent,empirical | Axiomatic system,Computer science,Turing machine,Artificial intelligence,Artificial intelligence, situated approach,Mathematical reasoning,Semantics,Machine learning | Journal |
Volume | Issue | ISSN |
19 | 3 | 0952-813X |
Citations | PageRank | References |
4 | 0.43 | 16 |
Authors | ||
1 | ||