Intellectual Paradigm of Artificial Vision: From Video-Intelligence to Strong Artificial Intelligence

Тип публикации: статья из журнала

Год издания: 2018

Ключевые слова: Gauge approach, fibration space, informational hypergraph, video-intelligence, strong artificial intelligence

Аннотация: A new (post-Shannon) informational approach is suggested in this paper, which allows to make deep analysis of nature of the information. It was found that information could be presented as an aggregate of quantitative (physical) and qualitative (structural) components to be considered together. It turned out that such full information theory could be efficiently used as the guiding theory at modeling of video-information recognition, perception and understanding. These hierarchical processes are solving the intellectual tasks step-by-step for formation of the corresponding video-information evaluation and also represent a strong interactions-measurements video-information's ensuring adequacy of these assessments. That is why there is a need to build corresponding video information macro-objects (video-thesauruses) on every level of hierarchy of artificial vision system, which are formed by training (self-training) and form together an upward hierarchy of qualitative measuring scales. The top of this hierarchy is video-intelligence. Information theory of artificial intelligence is a logical development of new information approach from analysis to synthesis. Further "analysis through synthesis" allows establishing the informational nature and structure of not only video-intelligence, but also strong artificial intelligence, which for video-intelligence constitute as intellectual suprasystem.

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Издание

Журнал: INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS

Выпуск журнала: Vol. 9, Is. 11

Номера страниц: 16-32

ISSN журнала: 2158107X

Место издания: WEST YORKSHIRE

Издатель: SCIENCE & INFORMATION SAI ORGANIZATION LTD

Авторы

  • Yarichin E.M. (NeocorTek Lab LLC, Lab Brain Technol, Krasnoyarsk, Russia)
  • Gruznov V.M. (Russian Acad Sci, Trofimuk Inst Petr Geol & Geophys, Siberian Branch, Novosibirsk, Russia)
  • Yarichina G.F. (Siberian Fed Univ, Inst Business Proc & Econ Management, Krasnoyarsk, RussiaArticle)

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