Using Information Theory to Observe Natural Intelligence and Artificial Intelligence

This paper takes a philosophical view as axiom, and
reveals the relationship between information theory and Natural
Intelligence and Artificial Intelligence under real world conditions.
This paper also derives the relationship between natural intelligence
and nature. According to communication principle of information
theory, Natural Intelligence can be divided into real part and virtual
part. Based on information theory principle that Information does not
increase, the restriction mechanism of Natural Intelligence creativity is
conducted. The restriction mechanism of creativity reveals the limit of
natural intelligence and artificial intelligence. The paper provides a
new angle to observe natural intelligence and artificial intelligence.

Authors:

• Lipeng Zhang
• Limei Li
• Yanming Pearl Zhang

Keywords:

• Natural intelligence
• artificial intelligence
• creativity
• information theory.

References:

[1] Shannon, Claude E. (July–October 1948). "A Mathematical Theory of
Communication". Bell System Technical Journal 27 (3): 379–423.
[2] Borda, Monica (2011). Fundamentals in Information Theory and Coding.
Springer. p. 11. ISBN 978-3-642-20346-6.
[3] Han, Te Sun & Kobayashi, Kingo (2002). Mathematics of Information
and Coding. American Mathematical Society. pp. 19–20. ISBN
978-0-8218-4256-0.
[4] Schneider, T.D, Information theory primer with an appendix on
logarithms, National Cancer Institute, 14 April 2007.
[5] Carter, Tom (March 2014). An introduction to information theory and
entropy (PDF). Santa Fe. Retrieved Aug 2014.Young, “Synthetic
structure of industrial plastics (Book style with paper title and editor),” in
Plastics, 2nd ed. vol. 3, J. Peters, Ed. New York: McGraw-Hill, 1964, pp.
15–64.
[6] Tian BaoYu edited Zhou JiongPan and Wu WeiLing review
<Engineering Information Theory> Beijing University of Posts and
Telecommunications Press
[7] Turing, Alan (October 1950), "Computing Machinery and Intelligence",
Mind LIX (236): 433–460, doi:10.1093/mind/LIX.236.433, ISSN
0026-4423, retrieved 2008-08-18.
[8] Poole, David; Mackworth, Alan; Goebel, Randy (1998). Computational
Intelligence: A Logical Approach. New York: Oxford University Press.
ISBN 0-19-510270-3.
[9] Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence Artificial
Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New
Jersey: Prentice Hall, ISBN 0-13-790395-2.
[10] McCarthy, John; Minsky, Marvin; Rochester, Nathan; Shannon, Claude
(1955). "A Proposal for the Dartmouth Summer Research Project on
Artificial Intelligence Artificial Intelligence". Archived from the original
on 26 August 2007. Retrieved 30 August 2007\
[11] Loehlin, J. C.; Perloff, R.; Sternberg, R. J.; Urbina, S. (1996).
"Intelligence: Knowns and unknowns". American Psychologist 51 (2):
77. doi:10.1037/0003-
[12] Maich, Aloysius (1995). "A Hobbes Dictionary". Blackwell. p. 305
[13] Nidditch, Peter. "Foreword". An Essay Concerning Human
Understanding. Oxford University Press. p. xxii
[14] Gottfredson, Linda S. (1997). "Mainstream Science on Intelligence.
Intelligence 24: 13–23. doi:10.1016/s0160-2896(97)90011-8. ISSN
0160-2896.
[15] Neisser, Ulrich; Boodoo, Gwyneth; Bouchard, Thomas J.; Boykin, A.
Wade; Brody, Nathan; Ceci, Stephen J.; Halpern, Diane F.; Loehlin, John
C.; Perloff, Robert; Sternberg, Robert J.; Urbina, Susana (1996).
"Intelligence: Knowns and unknowns". American Psychologist 51:
77–101. ISSN 0003-066X. Retrieved9 October 2014.
[16] Legg, Shane and Hutter, Marcus (2006) A Formal Measure of Machine
Intelligence. Technical Report. UNSPECIFIED. Proceedings of the 16th
Annual Machine Learning Conference of Belgium and The Netherlands,
Belelearn (2006)