2016/12/14 by Emanuel Diamant, Diamant, Emanuel
Computer Science · #Cognitive Computing and Networks #Computability, Logic, AI Algorithms #FOS: Computer and information sciences #Neural Networks and Applications #Other Computer Science (cs.OH) #cs.OH
paper · pdf · doi:10.48550/arxiv.1612.05087
Presented at the 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI2016), December 6-9, 2016, Athens, Greece. arXiv admin note: text overlap with arXiv:1607.05810, arXiv:1505.05186
arxiv created 2016/12/14 · openalex publication_date 2016/12/14 · arxiv updated 2016/12/16 · openalex created_date 2018/03/29 · openalex updated_date 2026/07/28
Computational Intelligence is a dead-end attempt to recreate human-like intelligence in a computing machine. The goal is unattainable because the means chosen for its accomplishment are mutually inconsistent and contradictory: "Computational" implies data processing ability while "Intelligence" implies the ability to process information. In the research community, there is a lack of interest in data versus information divergence. The cause of this indifference is the Shannon's Information theory, which has dominated the scientific community since the early 1950s. However, today it is clear that Shannon's theory is applicable only to a specific case of data communication and is inapplicable to the majority of other occasions, where information about semantic properties of a message must be taken into account. The paper will try to explain the devastating results of overlooking some of these very important issues - what is intelligence, what is semantic information, how they are interrelated and what happens when the relationship is disregarded.