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Human Brain Computer/Machine Interface System Feasibility Study for Independent Core Observer Model Based Artificial General Intelligence Collective Intelligence Systems

  • David J. KelleyEmail author
  • Kyrtin Atreides
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 948)

Abstract

This paper is primarily designed to help address the feasibility of building optimized mediation clients for the Independent Core Observer Model (ICOM) cognitive architecture for Artificial General Intelligence (AGI) mediated Artificial Super Intelligence (mASI) research program where this client is focused on collecting contextual information and the feasibility of various hardware methods for building that client on, including Brain Computer Interface (BCI), Augmented Reality (AR), Mobile and related technologies. The key criteria looked at is designing for the most optimized process for mediation services in the client as a key factor in overall mASI system performance with human mediation services is the flow of contextual information via various interfaces.

Keywords

ICOM mASI Cognitive architecture AGI AI BCI AR 

References

  1. 1.
    Jaffen D Optimizing brain performance, Center for Brain Health, Brain Performance Institute, University of TexasGoogle Scholar
  2. 2.
    Kelley D (2018/2019) Architectural overview of a ‘mediated’ artificial super intelligence systems based on the independent core observer model cognitive architecture. Informatica (Summitted and pending)Google Scholar
  3. 3.
    Kelley D, Twymen M (2019) Independent core observer model (ICOM) theory of consciousness as implemented in the ICOM cognitive architecture and associated consciousness measures. In: AAAI spring symposia 2019, Stanford University. (under review)Google Scholar
  4. 4.
    Kelley D, Waser M (2018) Human-like emotional responses in a simplified independent core observer model system. Procedia Comput Sci 123:221–227CrossRefGoogle Scholar
  5. 5.
    Kelley D (2016) Part V - artificial general intelligence. (3 Chapters in book titled: Google-It). Springer Scientific 2016, New York. ISBN: 978-1-4939-6413-0Google Scholar
  6. 6.
    Li G, Zhang D (2016) Brain-computer interface controlled cyborg: establishing a functional information transfer pathway from human brain to cockroach brain.  https://doi.org/10.1371/journal.pone.0150667CrossRefGoogle Scholar
  7. 7.
    Tovée M (1994) Neuronal processing: how fast is the speed of thought?  https://doi.org/10.1016/s0960-9822(00)00253-0CrossRefGoogle Scholar
  8. 8.
    Wang X, Yu K, Wu S, Gu J, Liu Y, Dong C, Loy C, Quio Y, Tang X (2018) ESRGAN: enhanced super-resolution generative adversarial networks, Cornell University, arXiv:1809.00219v2
  9. 9.
    Wikipedia Foundation (2019) Emotiv. https://en.wikipedia.org/wiki/Emotiv
  10. 10.
    Wikipedia Foundation (2019) OpenBCI. https://en.wikipedia.org/wiki/OpenBCI
  11. 11.
    Wikipedia Foundation (2019) Google Glass. https://en.wikipedia.org/wiki/Google_Glass
  12. 12.
    Wikipedia Foundation (2019) Microsoft HoloLens. https://en.wikipedia.org/wiki/Microsoft_HoloLens
  13. 13.
    Wikipedia Foundation (2019) Magic Leap. https://en.wikipedia.org/wiki/Magic_Leap
  14. 14.
    Wikipedia Foundation (2019) Android (operating system). https://en.wikipedia.org/wiki/Android_(operating_system)
  15. 15.
    Wikipedia Foundation (2019) iOS. https://en.wikipedia.org/wiki/IOS
  16. 16.
    Wikipedia Foundation (2019) Windows 10. https://en.wikipedia.org/wiki/Windows_10
  17. 17.
    Wikipedia Foundation (2019) Kernel (neurotechnology company). https://en.wikipedia.org/wiki/Kernel_(neurotechnology_company)
  18. 18.
    Wikipedia Foundation (2019) Neuralink. https://en.wikipedia.org/wiki/Neuralink
  19. 19.
    Wikipedia Foundation (2019) Mary Lou Jepsen. https://en.wikipedia.org/wiki/Mary_Lou_Jepsen
  20. 20.
    Zhou B, Wu X, Lv Z, Guo X (2016) A fully automated trail selection method for optimization of motor imagery based brain-computer interface. PLoS ONE. https://dol.org/10.1371/journal.pone.0162657

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.AGI LaboratoryProvoUSA
  2. 2.SeattleUSA

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