Detecting Intelligence - The Turing Test and Other Design Detection Methodologies

George D. Montañez

Abstract

“Can machines think?” When faced with this “meaningless” question, Alan Turing suggested we ask a different, more precise question: can a machine reliably fool a human interviewer into believing the machine is human? To answer this question, Turing outlined what came to be known as the Turing Test for artificial intelligence, namely, an imitation game where machines and humans interacted from remote locations and human judges had to distinguish between the human and machine participants. According to the test, machines that consistently fool human judges are to be viewed as intelligent. While popular culture champions the Turing Test as a scientific procedure for detecting artificial intelligence, doing so raises significant issues. First, a simple argument establishes the equivalence of the Turing Test to intelligent design methodology in several fundamental respects. Constructed with similar goals, shared assumptions and identical observational models, both projects attempt to detect intelligent agents through the examination of generated artifacts of uncertain origin. Second, if the Turing Test rests on scientifically defensible assumptions then design inferences become possible and cannot, in general, be wholly unscientific. Third, if passing the Turing Test reliably indicates intelligence, this implies the likely existence of a designing intelligence in nature.

References

  1. Auerbach, D. (2014). A computer program finally passed the Turing Test? slate.com/articles/technology/ bitwise/2014/06/turing test reading university did eugene oostman finally make the grade.html/. Accessed: 2015-11-10.
  2. Baker, M. (2015). First results from psychology's largest reproducibility test. http://bit.ly/1bk6qDF. Accessed: 2015-05-13, nature.com/news.
  3. Burrows, M. and Sutton, G. (2013). Interacting gears synchronize propulsive leg movements in a jumping insect. Science, 341(6151):1254-1256.
  4. Cicero, M. T. (45BC). Of the Nature of the Gods. Book II, chapters XXXVII, XLIV, and XLVII.
  5. Copeland, B. J. (2012). Turing: pioneer of the information age. Oxford University Press.
  6. Cover, T. M. and Thomas, J. A. (2006). Elements of information theory. Wiley & Sons. Section 2.8.
  7. Egnor, M. (2014). Steven Novella Doesn't Trust His Computer One Bit. evolutionnews.org/2014/12/ steven novella092181.html. Accessed: 2015-05-11.
  8. French, R. M. (2000). The Turing Test: the first 50 years. Trends in cognitive sciences, 4(3):115-122.
  9. Hood, L. and Galas, D. (2003). The digital code of DNA. Nature, 421(6921):444-448.
  10. Ioannidis, J. P. (2005). Why most published research findings are false. PLoS medicine, 2(8):e124.
  11. Kurzweil, R., Gilder, G. F., and Richards, J. W. (2002). Are We Spiritual Machines?: Ray Kurzweil vs. the Critics of Strong AI. Discovery Institute.
  12. Larson, E. J. (2015). Group Delusions Aside, Sentient Robots Aren't on the Way. evolutionnews.org/2015/ 03/sorry nature gr094741.html. Accessed: 2015-05- 11.
  13. Qin, S., Yin, H., Yang, C., Dou, Y., Liu, Z., Zhang, P., Yu, H., Huang, Y., Feng, J., Hao, J., Hao, J., Deng, L., Yan, X., Dong, X., Zhao, Z., Jiang, T., Wang, H.-W., Luo, S.-J., and Xie, C. (2015). A magnetic protein biocompass. Nat Mater, advance online publication. Article.
  14. Richards, J. (2011). I, for one, welcome our new robot overlords. aei.org/publication/i-for-one-welcome-ournew-robot-overlords/. Accessed: 2015-05-19.
  15. Robinson, R. (2006). Mutations change the boolean logic of gene regulation. PLoS biology, 4(4).
  16. Saygin, A. P., Cicekli, I., and Akman, V. (2003). Turing test: 50 years later. In The Turing Test, pages 23-78. Springer.
  17. Shin, J. H. and Tam, B. (2007). Unleashing of a biological spring. In Magjarevic, R. and Nagel, J., editors, World Congress on Medical Physics and Biomedical Engineering 2006, volume 14 of IFMBE Proceedings, pages 2824-2827. Springer Berlin Heidelberg.
  18. Smith, W. J. (2015). Machines Will Always Be Things, Never “Persons”. evolutionnews.org/2015/04/ machines will a095201.html. Accessed: 2015-05-11.
  19. Smolin, L. (2004). Cosmological natural selection as the explanation for the complexity of the universe. Physica A: Statistical Mechanics and its Applications, 340(4):705-713.
  20. Torley, V. (2014). Why a simulated brain is not conscious. http://www.uncommondescent.com/intelligent-design/ why-a-simulated-brain-is-not-conscious/. Accessed: 2015-05-19.
  21. Turing, A. M. (1950). Computing machinery and intelligence. Mind, pages 433-460.
  22. Wigner, E. P. (1995). The unreasonable effectiveness of mathematics in the natural sciences. In Philosophical Reflections and Syntheses, pages 534-549. Springer.
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Paper Citation


in Harvard Style

Montañez G. (2016). Detecting Intelligence - The Turing Test and Other Design Detection Methodologies . In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-172-4, pages 517-523. DOI: 10.5220/0005823705170523


in Bibtex Style

@conference{icaart16,
author={George D. Montañez},
title={Detecting Intelligence - The Turing Test and Other Design Detection Methodologies},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2016},
pages={517-523},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005823705170523},
isbn={978-989-758-172-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Detecting Intelligence - The Turing Test and Other Design Detection Methodologies
SN - 978-989-758-172-4
AU - Montañez G.
PY - 2016
SP - 517
EP - 523
DO - 10.5220/0005823705170523