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Thinking Machines versus Thinking Organisms

  • Conference paper
Engineering Applications of Neural Networks (EANN 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 383))

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Abstract

The recent hypothesis of concurrent infinity and a phenomenology formalization based on it allow us to mathematically define meaning, subjectivity and super-Turing machines that implement them. Using these results, in this paper, at mathematical level of rigor, the phenomena of life and thinking are defined and perspectives of their artificial reproduction are considered. It is demonstrated that machines built of inorganic raw materials can never think. Natural and artificial living organisms built of organic raw materials are the physically implemented partial universal super-Turing machines (tied collections of many super-Turing machines) that can think. The necessary and sufficient “Mowgli’s test” for thinking, solving simultaneously the problem of other minds, is proposed and discussed. It is concluded that cyborgs, the products of evolutionary symbiosis of humans and human artifacts, seem to be our evolutionary perspective.

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References

  1. Turing, A.M.: Computing Machinery and Intelligence. Mind 59, 433–460 (1950)

    Article  MathSciNet  Google Scholar 

  2. Gopych, P.: Beyond the Zermelo-Fraenkel Axiomatic System: BSDT Primary Language and its Perspective Applications. Int. J. Advances Intelligent Systems 5, 493–517 (2012)

    Google Scholar 

  3. Gopych, P.M.: Elements of the Binary Signal Detection Theory, BSDT. In: Yoshida, M., Sato, H. (eds.) New Research in Neural Networks, pp. 55–63. Nova Science, NY (2008)

    Google Scholar 

  4. Gopych, P.: BSDT Multi-valued Coding in Discrete Spaces. In: Corchado, E., Zunino, R., Gastaldo, P., Herrero, Á. (eds.) CISIS 2008. ASC, vol. 53, pp. 258–265. Springer, Heidelberg (2009)

    Google Scholar 

  5. Gopych, P.: Minimal BSDT Abstract Selectional Machines and Their Selectional and Computational Performance. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds.) IDEAL 2007. LNCS, vol. 4881, pp. 198–208. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  6. Gopych, P.: Biologically Plausible BSDT Recognition of Complex Images: The Case of Human Faces. Int. J. Neural Systems 18, 527–545 (2008)

    Article  Google Scholar 

  7. Rizzolatti, G., Craighero, L.: The Mirror-neuron System. Ann. Rev. Neurosci. 27, 169–192 (2004)

    Article  Google Scholar 

  8. Copeland, B.J.: Hypercomputation. Minds and Machines 12, 461–502 (2002)

    Article  MATH  Google Scholar 

  9. Penny, D., Poole, A.: The Nature of the Last Common Ancestor. Curr. Opin. Genet. Dev. 9, 672–677 (1999)

    Article  Google Scholar 

  10. Woese, C.R.: On the Evolution of Cells. Proc. Natl. Acad. Sci. USA 99, 8742–8747 (2002)

    Article  Google Scholar 

  11. Glansdorff, N., Xu, Y., Labeda, B.: The Last Universal Common Ancestor: Emergence, Constitution and Genetic Legacy of an Elusive Forerunner. Biology Direct 3, 29 (2008)

    Article  Google Scholar 

  12. Gopych, P.: BSDT Atom of Consciousness Model, AOCM: The Unity and Modularity of Consciousness. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009, Part II. LNCS, vol. 5769, pp. 54–64. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  13. Ullman, S.: Object Recognition and Segmentation by a Fragment-based Hierarchy. Trends Cogn. Sci. 11, 58–64 (2007)

    Article  Google Scholar 

  14. Gopych, P., Gopych, I.: BSDT ROC and Cognitive Learning Hypothesis. In: Herrero, Á., Corchado, E., Redondo, C., Alonso, Á. (eds.) CISIS 2010. AISC, vol. 85, pp. 13–23. Springer, Heidelberg (2010)

    Google Scholar 

  15. Ferrucci, D., Brown, E., Chu-Carroll, J., et al.: Building Watson: an Overview of DeepQA Project. AI Magazin 31, 59–79 (2010)

    Google Scholar 

  16. Mateu, M.G.: Virus Engineering: Functionalization and Stabilization. Protein Engineering, Design, and Selection 24, 53–63 (2011)

    Article  Google Scholar 

  17. Joy, J.B.: Symbiosis Catalyses Niche Expansion and Diversification. Proc. R. Soc. B 280, 20122820 (2013)

    Article  Google Scholar 

  18. Hyslop, A.: Other Minds. Kluwer, Dordrecht (1995)

    Book  Google Scholar 

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Author information

Authors and Affiliations

  1. Universal Power Systems USA-Ukraine LLC, 3 Kotsarskaya Street, Kharkiv, 61012, Ukraine

    Petro Gopych

Authors
  1. Petro Gopych
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Editor information

Editors and Affiliations

  1. Department of Forestry & Management of the Environment and Natural Resources, Democritus University of Thrace, GR-68200, Orestiada, Hellas

    Lazaros Iliadis

  2. Frederick University of Cyprus, Cyprus

    Harris Papadopoulos

  3. Faculty of Engineering and Computing, Coventry University, UK

    Chrisina Jayne

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Gopych, P. (2013). Thinking Machines versus Thinking Organisms. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_8

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  • DOI: https://doi.org/10.1007/978-3-642-41013-0_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41012-3

  • Online ISBN: 978-3-642-41013-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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