Embracing Competitive Balance: The Case for Substrate-Independent Minds and Whole Brain Emulation

  • Randal A. Koene
Part of the The Frontiers Collection book series (FRONTCOLL)


More important than debates about the nature of a possible singularity is that we successfully navigate the balance of opportunities and risks that our species is faced with. In this context, we present the objective to upload to substrate-independent minds (SIM). We emphasize our leverage along this route, which distinguishes it from proposals that are mired in debates about optimal solutions that are unclear and unfeasible. We present a theorem of cosmic dominance for intelligence species based on principles of universal Darwinism, or simply, on the observation that selection takes place everywhere at every scale. We show that SIM embraces and works with these facts of the physical world. And we consider the existential risks of a singularity, particularly where we may be surpassed by artificial intelligence (AI). It is unrealistic to assume the means of global cooperation needed to the create a putative “friendly” super-intelligent AI. Besides, no one knows how to implement such a thing. The very reasons that motivate us to build AI lead to machines that learn and adapt. An artificial general intelligence (AGI) that is plastic and at the same time implements an unchangeable “friendly” utility function is an oxymoron. By contrast, we note that we are living in a real world example of a Balance of Intelligence between members of a dominant intelligent species. We outline a concrete route to SIM through a set of projects on whole brain emulation (WBE). The projects can be completed in the next few decades. So, when we compare this with plans to “cure aging” in human biology, SIM is clearly as feasible in the foreseeable future—or more so. In fact, we explain that even in the near term life extension will require mind augmentation. Rationality is a wonderful tool that helps us find effective paths to our goals, but the goals arise from a combination of evolved drives and interests developed through experience. The route to a new Balance of Intelligence by SIM has this additional benefit, that it does acknowledges our emancipation and does not run counter to our desire to participate in advances and influence future directions.


Technological Singularity Intelligent Species Existential Risk Slippery Concept Artificial General Intelligence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Bock, D. D., et al. (2011). Network anatomy and in vivo physiology of visual cortical neurons. Nature, 471, 177–182.MathSciNetCrossRefGoogle Scholar
  2. Boyden, E. S., Zhang, F., Bamberg, E., Nagel, G., & Deisseroth, K. (2005). Millisecond-timescale, genetically targeted optical control of neural activity. Nature Neuroscince, 8(9), 1263–1268.CrossRefGoogle Scholar
  3. Briggman, K. L., Helmstaedter, M., & Denk, W. (2011). Wiring specificity in the direction-selectivity circuit of the retina. Nature, 471, 183–188.CrossRefGoogle Scholar
  4. Chalmers, D. (2010). A philosophical analysis of the possibility of a technological singularity or “intelligence explosion” resulting from recursively self-improving AI. John Locke Lecture, 10 May, Exam Schools, Oxford.Google Scholar
  5. de Grey, A., & Rae, M. (2007). Ending aging: The rejuvenation breakthroughs that could reverse human aging in our lifetime. New York: St. Martin’s Press.Google Scholar
  6. Dennett, D. (2005). Darwin’s dangerous idea (pp. 352–360). New York: Touchstone Press.Google Scholar
  7. Eldredge, N., & Gould, S. J. (1972). Punctuated equilibria: An alternative to phyletic gradualism. In T. J. M. Schopf (Ed.), Models in Paleobiology (pp. 82–115). San Francisco: Freeman Cooper.Google Scholar
  8. Friedland and Bernard. (2005). Control system design: An introduction to state space methods. Dover. (ISBN 0-486-44278-0).Google Scholar
  9. Gildert, S. (2010). Pavlov’s AI: What do super intelligences really want? Humanity + @ Caltech, Pasadena.Google Scholar
  10. Goertzel, B. (2010). AI for increased human healthspan. Next big future, August 14.Google Scholar
  11. Good, I. J. (1965). Speculations concerning the first ultraintelligent machine. In Franz L. Alt, Morris Rubinoff, (Ed.), Advances in Computers, 6 (pp. 31–88). Academic Press.Google Scholar
  12. Hayworth, K. J., Kasthuri, N., Hartwieg, E. Lichtman, J. W. et al. (2007). Automating the collection of ultrathin brain sections for electron microscopic volume imaging. Program No. 534.6, Neuroscience Meeting, San Diego.Google Scholar
  13. Hayworth, K. J. (2011). Lossless thick sectioning of plastic-embedded brain tissue to enable parallelizing of SBFSEM and FIBSEM imaging. High resolution circuit reconstruction conference 2011. Janelia Farms, Ashburn.Google Scholar
  14. Koene, R. A. (2011). Pattern survival versus gene survival,, February 11, 2011.
  15. Kurzweil, R. (2005). The singularity is near. (pp. 135–136). Penguin Group.Google Scholar
  16. Markram, H. (2006). The blue brain project. Nature Reviews Neuroscience, 7, 153–160.CrossRefGoogle Scholar
  17. McCormick, B. Mayerich, D. M. (2004). Three-dimensional imaging using knife-edge scanning microscopy. In proceedings of the microscopy and micro-analysis conference 2004, Savannah.Google Scholar
  18. Moravec, H. (1998). Robot: Mere Machine to Transcendent Mind. Oxford University Press.Google Scholar
  19. Paredis, J. (1997). Coevolving cellular automata: Be aware of the red queen! In proceedings of the seventh international conference on genetic algorithms ICGA97.Google Scholar
  20. Ridley, M. (1995). The red queen: Sex and the evolution of human nature. Penguin books, ISBN 0-14-024548-0.Google Scholar
  21. Sandberg, A. Bostrom, N. (2008). Global catastrophic risks survey. Technical Report 2008/1, Future of humanity institute, Oxford University.Google Scholar
  22. Simpson, G. G. (1944). Tempo and mode in evolution. New York.: Columbia Univ. Press.Google Scholar
  23. Solomonoff, R. J. (1985). The time scale of artificial intelligence: reflections on social effects. Human Systems Management, 5, 149–153.Google Scholar
  24. Tegmark, M. (2011). The future of life: A cosmic perspective, presented at the singularity summit 2011, Oct. 15, New York.Google Scholar
  25. Vinge, V. (1981). True names and other dangers, Baen books, ISBN-13: 978-0671653637.Google Scholar
  26. Vinge, V. (1993). The coming technological singularity, Vision-21: Interdisciplinary science and engineering in the era of cyberspace, proceedings of a symposium held at NASA lewis research center (NASA Conference Publication CP-10129).Google Scholar
  27. Zador, A. (2011). Sequencing the connectome: A fundamentally new way of determining the brain’s wiring diagram, Project proposal, Paul G. Allen foundation awards grants.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.NeuraLink Co. and Carboncopies.orgSan FranciscoUSA

Personalised recommendations