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Deep-Learned Artificial Intelligence for Semantic Communication and Data Co-processing

  • Nicolay VasilyevEmail author
  • Vladimir Gromyko
  • Stanislav Anosov
Conference paper
  • 16 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1130)

Abstract

Trans-disciplinary activity in system-informational culture (SIC) caused great knowledge sophistication and necessity for any person to have synthesis of true scientific presentations. Complication of informational flows satiated with scientific meanings needs their co-processing with the help of deep-learned artificial intelligence (DL IA). Artificial intelligence (IA) will assist man to identify universalities for third world understanding. Otherwise, it will be impossible to live comfortably in computer instrumental systems and its applications. Arising intellectual difficulties will alter significantly SIC subject armed with DL IA − powerful means of learning, cognition, and world study. Trained rational consciousness allows achieving semantic level of communication in SIC. In its work, DL IA leans on system axiomatic method and personal cogno-ontological knowledge base descript in language of categories. Examples explain contributed technology.

Keywords

Trans-disciplinary activity Meaning Universalities Deep-learned artificial intelligence Cogno-ontological knowledge base Consciousness auto-building Self-reflection Language of categories System axiomatic method 

References

  1. 1.
    Gromyko, V.I., Kazaryan, V.P., Vasilyev, N.S., Simakin, A.G., Anosov, S.S.: Artificial intelligence as tutoring partner for human intellect. J. Adv. Intell. Syst. Comput. 658, 238–247 (2018)Google Scholar
  2. 2.
    Gromyko, V.I., Vasilyev, N.S.: Mathematical modeling of deep-learned artificial intelligence and axiomatic for system-informational culture. Int. J. Robot. Autom. 4(4), 245–246 (2018)Google Scholar
  3. 3.
    Vasilyev, N.S., Gromyko, V.I., Anosov, S.S.: On inverse problem of artificial intelligence in system-informational culture. J. Adv. Intell. Syst. Comput. Hum. Syst. Eng. Des. 876, 627–633 (2019)CrossRefGoogle Scholar
  4. 4.
    Sadique Shaikh, Md.: Defining ultra artificial intelligence (UAI) implementation using bionic (biological-like-electronics) brain engineering insight. MOJ Appl. Bio Biomech. 2(2), 127–128 (2018)Google Scholar
  5. 5.
    Deviatkov, V.V., Lychkov, I.I.: Recognition of dynamical situations on the basis of fuzzy finite state machines. In: International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing and Big Data Analytics, Data Mining and Computational Intelligence, pp. 103–109 (2017)Google Scholar
  6. 6.
    Fedotova, A.V., Davydenko, I.T., Pförtner, A.: Design intelligent lifecycle management systems based on applying of semantic technologies. J. Adv. Intell. Syst. Comput. 450, 251–260 (2016)Google Scholar
  7. 7.
    Volodin, S.Y., Mikhaylov, B.B., Yuschenko, A.S.: Autonomous robot control in partially undetermined world via fuzzy logic. J. Mech. Mach. Sci. 22, 197–203 (2014)CrossRefGoogle Scholar
  8. 8.
    Svyatkina, M.N., Tarassov, V.B., Dolgiy, A.I.: Logical-algebraic methods in constructing cognitive sensors for railway infrastructure intelligent monitoring system. Adv. Intell. Syst. Comput. 450, 191–206 (2016)Google Scholar
  9. 9.
    Hadamer, G.: Actuality of Beautiful. Art, Moscow (1991)Google Scholar
  10. 10.
    Mclane, S.: Categories for Working Mathematician. Phys. Math. Ed., Moscow (2004)Google Scholar
  11. 11.
    Goldblatt, R.: The Categorical Analysis of Logic. North-Holland Publishing Company, Amsterdam (1979)zbMATHGoogle Scholar
  12. 12.
    Husserl, A.: From Idea to Pure Phenomenology and Phenomenological Philosophy: Book 1: General Introduction in Pure Phenomenology. Acad. Project, Moscow (2009)Google Scholar
  13. 13.
    Pinker, S.: Thinking Substance Language as Window in Human Nature. Librokom, Moscow (2013)Google Scholar
  14. 14.
    Kassirer, E.: Philosophy of Symbolical Forms. Language Univ. Book, Saint Petersburg 1 (2000)Google Scholar
  15. 15.
    Courant, R., Robbins, G.: What is Mathematics?. Moscow Center of Continuous Education, Moscow (2017)zbMATHGoogle Scholar
  16. 16.
    Euclid: Elements. GosTechIzd, Leningrad (1949–1951)Google Scholar
  17. 17.
    Hilbert, D.: Grounds of Geometry. Tech.-Teor. Lit., Leningrad (1948)Google Scholar
  18. 18.
    Kirillov, A.: What is the Number?. Nauka, Moscow (1993)zbMATHGoogle Scholar
  19. 19.
    Artin, E.: Geometric Algebra. Nauka, Moscow (1969)zbMATHGoogle Scholar
  20. 20.
    Bachman, F.: Geometry Construction on the Base of Symmetry Notion. Nauka, Moscow (1969)Google Scholar
  21. 21.
    Maltsev, A.: Algebraic Systems. Nauka, Moscow (1970)Google Scholar
  22. 22.
    Maltsev, A.I.: Algorithms and Recursive Functions. Nauka, Moscow (1986)Google Scholar
  23. 23.
    Shafarevich, I.R.: Main Notions of Algebra. Reg. and Chaos Dynam, Izhevck (2001)Google Scholar
  24. 24.
    Kourosh, A.: Lecture Notes on General Algebra. Phys.-Mat., Moscow (1962)Google Scholar
  25. 25.
    Engeler, E.: Metamathematik der Elementarmathematik. MIR, Moscow (1987)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Nicolay Vasilyev
    • 1
    Email author
  • Vladimir Gromyko
    • 2
  • Stanislav Anosov
    • 3
    • 1
  1. 1.Fundamental SciencesBauman Moscow State Technical UniversityMoscowRussia
  2. 2.Computational Mathematics and CyberneticsLomonosov Moscow State UniversityMoscowRussia
  3. 3.Public Company Vozrozhdenie BankMoscowRussia

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