What Is Machine Intelligence? A Case Study: Stock Market Forecasting Agents

Chapter
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 342)

Abstract

This chapter is a summary of my talk given during the 19th International Multiconference on Advanced Computer Systems held in Miedzyzdroje, Poland in October 2014. The talk started with the Legg and Hutter’s formal definition of what they call Universal Machine Intelligence which aims to measure intelligence of almighty robot, where agents are given an infinitely large number of different types of task to measure its universal intelligence. We take it a consideration what if we apply the formula to only one specific task. We claim the measurement of performance for each of those tasks given in the Legg and Hutter’s definition is not sufficient to represent agent’s intelligence. Then, we present our ongoing definition of machine intelligence for a specific one task such as forecasting stock market price.

Keywords

Stock Market Emotional Intelligence Machine Intelligence Bayesian Belief Network Intelligent People 
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.
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Notes

Acknowledgments

The author greatly thanks Professor Jerzy Pejaś and the other organizing committee members of this conference for constantly inviting me to a series of this conference with wonderful atmosphere in a beautiful small town in Poland along the Baltic Sea. To take part in this conference has always been giving me a big motivation to proceed my reflection to what is machine intelligence.

References

  1. 1.
    Chaitin, G.J.: Gödel’s theorem and information. Theor. Phys. 21(12), 941–954 (1982)CrossRefMATHMathSciNetGoogle Scholar
  2. 2.
    Dowd, M.: Silicon valley Sharknado. Editorial in NYT on 08 July 2014Google Scholar
  3. 3.
    Ehrentreich, N.: A corrected version of the Santa Fe institute artificial stock market model (2004)Google Scholar
  4. 4.
    Floreano, D., et al.: Evolutionary robots with on-line self-organization and behavioral fitness. Neural Netw. 13(4–5), 431–443 (2000)CrossRefGoogle Scholar
  5. 5.
    Hasanhodzic, J. et al.: Is It Real, or Is It Randomized?: A Financial Turing Test, Cornell University Library (2010). arXiv:1002.4592
  6. 6.
    Hofstadter, D.: Gödel, Escher, Bach: An Eternal Golden Braid. Basic Books (1979)Google Scholar
  7. 7.
  8. 8.
    Imada, A.: Intelligent machine? Then how intelligent? In: International Multi-Conference: Advanced Computer Systems (2012)Google Scholar
  9. 9.
    Ke, J., et al.: Modeling and simulation of the artificial stock market trading system. Appl. Math. Inf. Sci. 7(4), 1599–1607 (2013)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Kim, H.-J., et al.: Weighted scale-free network in financial correlations. J. Phys. Soc. Jpn. 71(9), 2131–2136 (2002)Google Scholar
  11. 11.
    Kluger, J.: Inside The Minds of Animals–TIME. Also available at http://content.time.com/time/magazine/article/0,9171/2008867,00.html (2010)
  12. 12.
    Kobos, M. et al.: Artificial intelligence methods in stock index prediction with the use of newspaper articles. In: Foundations of Control and Management Sciences, vol. 9, pp. 67–77. Publishing House of Poznan, University of Technology (2008)Google Scholar
  13. 13.
    Kurzweil, R.: Exponential Finance Conference. New York (2014)Google Scholar
  14. 14.
    Kurzweil, R.: The Observer of London. Also available at http://news.genius.com/Ray-kurzweil-on-google-and-the-singularity-annotated#note-2835513 (2014)
  15. 15.
    Kwon, Y-K.: Stock prediction based on financial correlation. In: Proceedings of the Annual Conference on Genetic and Evolutionary Computation, pp. 2061–2066 (2005)Google Scholar
  16. 16.
    Laloux, L., et al.: Noise dressing of financial correlation matrices. Phys. Rev. Lett. 83, 1467–1469 (1999)Google Scholar
  17. 17.
    LeBaron, B.: Building the Santa Fe artificial stock market (2002)Google Scholar
  18. 18.
    Legg, S., Hutter, M.: Universal intelligence: a definition of machine intelligence. Minds. Mach. 17(4), 391–444 (2007)CrossRefGoogle Scholar
  19. 19.
    Malkiel, B.G.: A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing. W. W. Norton & Company (2007)Google Scholar
  20. 20.
    Page, L.: Silicon Valley Summit (2014)Google Scholar
  21. 21.
    Plerou, V., et al.: Universal and nonuniversal properties of cross correlations in financial time series. Phys. Rev. Lett. 83(7), 1471–1474 (1999)CrossRefGoogle Scholar
  22. 22.
    Smith, W. D.: Mathematical definition of intelligence (and consequences). Available at http://math.temple.edu/wds/homepage/works.html (2006)
  23. 23.
    Turing, A.M.: Computing machinery and intelligence. Mind 59(236), 433–460 (1948). Also available at http://www.loebner.net/Prizef/TuringArticle.html
  24. 24.
    Yixian, F. et al.: The stock index forecast based on dynamic recurrent neural network trained with GA. In: Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation, pp. 319–323 (2006)Google Scholar
  25. 25.
    Zhang, J.: Modeling the influence of social networks in an artificial stock market. In: International Workshop on Agent-Mediated Electronic Commerce and Trading Agents Design and Analysis (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Intelligent Information TechnologyBrest State Technical UniversityBrestBelarus

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