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Rethinking BICA’s R&D Challenges: Grief Revelations of an Upset Revisionist

  • Emanuel Diamant
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 636)

Abstract

Biologically Inspired Cognitive Architectures (BICA) is a subfield of Artificial Intelligence aimed at creating machines that emulate human cognitive abilities. What distinguish BICA from other AI approaches is that it based on principles drawn from biology and neuroscience. There is a widespread conviction that nature has a solution for almost all problems we are faced with today. We have only to pick up the solution and replicate it in our design. However, Nature does not easily give up her secrets. Especially, when it is about human brain deciphering. For that reason, large Brain Research Initiatives have been launched around the world. They will provide us with knowledge about brain workflow activity in neuron assemblies and their interconnections. But what is being “flown” (conveyed) via the interconnections the research programme does not disclose. It is implied that what flows in the interconnections is information. But what is information? – that remains undefined. Having in mind BICA’s interest in the matters, the paper will try to clarify the issues.

Keywords

Biological inspiration Brain research programs Cognitive modeling Information duality Cognitive information processing 

References

  1. 1.
    Kotseruba, I., Gonzalez, O., Tsotsos, J.: A Review of 40 Years of Cognitive Architecture Research (2016). https://arxiv.org/ftp/arxiv/papers/1610/1610.08602.pdf
  2. 2.
    Poo, M., et al.: China brain project: basic neuroscience, brain diseases, and brain-inspired computing. Neuron 92 (2016). Elsevier Inc. http://www.cell.com/neuron/pdf/S0896-6273(16)30800-5.pdfCrossRefGoogle Scholar
  3. 3.
    Jeong, S.-J., et al.: Korea brain initiative: integration and control of brain functions. Neuron 92, 607–611 (2016). Elsevier Inc. http://www.cell.com/neuron/pdf/S0896-6273(16)30805-4.pdfCrossRefGoogle Scholar
  4. 4.
    Schierwagen, A.: The Way We Get Bio-Inspired: A Critical Analysis (2010). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.188.6007&rep=rep1&type=pdf
  5. 5.
    Shannon, C.: A Mathematical Theory of Communication. Published by the Board of Trustees of the University of Illinois, Used with the permission of University of Illinois Press (1948). http://www.mast.queensu.ca/~math474/shannon1948.pdf
  6. 6.
    Information: Stanford Encyclopedia of Philosophy. First published 26 October 2012. http://plato.stanford.edu/entries/information/
  7. 7.
    Kolmogorov, A.: Three approaches to the quantitative definition of information. Probl. Inf. Transm. 1(1), 1–7 (1965). http://alexander.shen.free.fr/library/Kolmogorov65_Three-Approaches-to-Information.pdfMathSciNetGoogle Scholar
  8. 8.
    Grunwald, P., Vitanyi, P.: Algorithmic information theory (2008). http://arxiv.org/pdf/0809.2754.pdfCrossRefGoogle Scholar
  9. 9.
    Grunwald, P., Vitanyi, P.: Shannon Information and Kolmogorov Complexity (2004). http://arxiv.org/pdf/cs/0410002.pdf
  10. 10.
    Diamant, E.: Brain, Vision, Robotics and Artificial Intelligence. http://www.vidia-mant.info

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.VIDIA-mantKiriat OnoIsrael

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