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AGI and Neuroscience: Open Sourcing the Brain

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Abstract

Can research into artificial general intelligence actually benefit from neuroscience and vice-versa? Many AGI researchers are interested in the human mind. Within reasonable limits, we can posit that the human mind is a working general intelligence. There is also a strong connection between work on human enhancement and AGI. Here, we note that there are serious limitations to the use of cognitive models as inspiration for the components deemed necessary to produce general intelligence. A closer examination of the neuroscience may reveal missing functions and hidden interactions. This is possible by making explicit the map of brain circuitry at a scope and a resolution that is required to emulate brain functions.

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References

  1. Goertzel, B., Pennachin, C.: Artificial General Intelligence. Springer, New York (2007)

    Book  Google Scholar 

  2. Markram, H.: The Blue Brain Project. Nature Reviews Neuroscience 7, 153–160 (2006)

    Article  Google Scholar 

  3. Hutter, M.: Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability. Springer, Berlin (2004)

    Google Scholar 

  4. Wang, P.: Artificial General Intelligence: A Gentle Introduction, http://sites.google.com/site/narswang/home/agi-introduction

  5. Gildert, S.: Pavlov’s AI: What do superintelligences REALLY want? At: Humanity+ @Caltech, Pasadena, CA (2010)

    Google Scholar 

  6. Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6th edn. Addison-Wesley, New York (2008)

    Google Scholar 

  7. Burns, N.R., Lee, M.D., Vickers, D.: Individual Differences in Problem Solving and Intelligence. Journal of Problem Solving (2006)

    Google Scholar 

  8. Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psychological Review, 1036–1060 (2004)

    Google Scholar 

  9. Laird, J., Newell, A., Rosenbloom, P.: SOAR: an architecture for general intelligence. Journal of Artificial Intelligence 33(1), 1–63 (1987)

    Article  Google Scholar 

  10. Lehman, J.F., Laird, J., Rosenbloom, P.: A Gentle Introduction to SOAR: An Architecture for Human Cognition: 2006 Update (2006)

    Google Scholar 

  11. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)

    Google Scholar 

  12. Marr, D., Ullman, S., Poggio, T.: Vision. In: A Computational Investigation into the Human Representation and Processing of Visual Information. MIT Press, Cambridge (2010)

    Google Scholar 

  13. Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology 160, 106–154 (1962)

    Google Scholar 

  14. Op de Beek, H.P., Haushofer, J., Kanwisher, N.G.: Interpreting fMRI data: maps, modules and dimensions. Nature Reviews Neuroscience 9, 123–135 (2008)

    Article  Google Scholar 

  15. Geissler, H.-G., Link, S.W., Townsend, J.T. (eds.): Cognition, Information Processing, and Psychophysics: Basic Issues, Erlbaum, Hillsdale, NJ (1992)

    Google Scholar 

  16. Saltelli, A., Tarantola, S., Chan, K.: Quantitative model-independent method for global sensitivity analysis of model output. Technometrics 41(1), 39–56 (1999)

    Article  Google Scholar 

  17. Winsberg, E.: Simulations, models and theories: Complex physical systems and their representations. Philosophy of Science 68(3); Supplement: Proceedings of the 2000 Biennial Meeting of the Philosophy of Science Association. Part I: Contributed Papers (September 2001), pp. S442-S454 (2000)

    Google Scholar 

  18. Sporns, O., Tononi, G., Kötter, R.: The Human Connectome: A Structural Description of the Human Brain. PloS Computational Biology 1(4), e42 (2005)

    Article  Google Scholar 

  19. Hassabis, D.: Combining systems neuroscience and machine learning: a new approach to AGI. At: The Singularity Summit 2010, San Francisco, CA (2010)

    Google Scholar 

  20. Koene, R.A.: The 25 Watt bio-computer: Lessons for Artificial Human Intelligence and Substrate-Independent Minds. At: Humanity+ @Caltech, Pasadena, CA (2010)

    Google Scholar 

  21. Koene, R.A.: Functional requirements determine relevant ingredients to model for on-line acquisition of context dependent memory. Ph.D. Dissertation, McGill University, Montreal, Canada (2001)

    Google Scholar 

  22. Koene, R.A., Hasselmo, M.E.: First-in-first-out item replacement in a model of short-term memory based on persistent spiking. Cerebral Cortex 17(8), 1766–1781 (2007)

    Article  Google Scholar 

  23. Koene, R.A., Hasselmo, M.E.: Reversed and forward buffering of behavioral spike sequences enables retrospective and prospective retrieval in hippocampal regions CA3 and CA1. Neural Networks 21(2-3), 276–288 (2008)

    Article  Google Scholar 

  24. Gorelik, D.: Reducing AGI complexity: copy only high level brain design, http://aidevelopment.blogspot.com/2007/12/reducing-agi-complexity-copy-only-high.html

  25. Fodor, J.: The Mind Doesn’t Work That Way: The Scope and Limits of Computational Psychology. MIT Press, Cambridge (2000)

    Google Scholar 

  26. Strong AI, Wikipedia, http://en.wikipedia.org/wiki/Strong_AI#Whole_brain_emulation

  27. AI is NOT part of transhumanism, Human Enhancement and Biopolitics, http://hplusbiopolitics.wordpress.com/2008/06/13/ai-is-not-part-of-transhumanism/

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© 2011 Springer-Verlag Berlin Heidelberg

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Koene, R.A. (2011). AGI and Neuroscience: Open Sourcing the Brain. In: Schmidhuber, J., Thórisson, K.R., Looks, M. (eds) Artificial General Intelligence. AGI 2011. Lecture Notes in Computer Science(), vol 6830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22887-2_50

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  • DOI: https://doi.org/10.1007/978-3-642-22887-2_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22886-5

  • Online ISBN: 978-3-642-22887-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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