SmartData pp 27-38 | Cite as

Perspectives on Artificial Intelligence: Three Ways to Be Smart

Conference paper


Three different styles of achieving Artificial Intelligence (AI) are discussed and compared. The earliest, and best-known to the general public, is the computational approach to AI that takes the brain to be some form of computer. This focuses on a narrow form of intelligence, abstract reasoning. By contrast, Artificial Neural Networks sees the brain as a brain rather than computer. This focuses on categorization and pattern recognition. The newest perspective is Evolutionary Robotics. This takes the broadest view of intelligence and cognition, seeing it as adaptive behaviour in a physical world; cognition and the mind are not centred in the brain at all. Rather than three different methods of achieving similar goals, these perspectives are aimed in very different directions. The implications of this for developing artificially intelligent SmartData agents are discussed.


Artificial Neural Network Turing Machine Human Intelligence Context Sensitivity Chess Player 
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. 1.
    Arbib, M., (1995). Handbook of Brain Theory and Neural Networks. MIT Press.Google Scholar
  2. 2.
    Feigenbaum, E. A., (1986). Handbook of AI. Addison-Wesley.Google Scholar
  3. 3.
    Harvey, I., Di Paolo, E., Wood, R., Quinn, M, and E. A., Tuci, (2005). Evolutionary Robotics: A new scientific tool for studying cognition. Artificial Life, 11(1-2), pp. 79–98.CrossRefGoogle Scholar
  4. 4.
    Harvey, I., Husbands, P., Cliff, D., Thompson, A. and Jakobi, N., (1997): Evolutionary Robotics: the Sussex Approach. Robotics and Autonomous Systems, v. 20 pp. 205–224.Google Scholar
  5. 5.
    Kuhn, T. S., (1962). The Structure of Scientific Revolutions. University of Chicago Press.Google Scholar
  6. 6.
    McClelland, J.L., D.E. Rumelhart and the PDP Research Group (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 2: Psychological and Biological Models, Cambridge, MA: MIT PressGoogle Scholar
  7. 7.
    Nolfi S. & Floreano D. (2000). Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. Cambridge, MA: MIT Press/Bradford Books.Google Scholar
  8. 8.
    Rumelhart, D.E., J.L. McClelland and the PDP Research Group (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, Cambridge, MA: MIT PressGoogle Scholar
  9. 9.
    Sahami, M., Dumais, S., Heckerman, D. and Horvitz, E., (1998). A Bayesian approach to filtering junk e-mail. AAAI'98 Workshop on Learning for Text Categorization.Google Scholar
  10. 10.
    Varela, F. J., Thompson, E., and Rosch, E. (1991). The embodied mind: cognitive science and human experience. Cambridge, Mass.: MIT Press.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of InformaticsUniversity of SussexBrightonUK

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