Progress in artificial intelligence

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Progress in machine classification of images
The error rate of AI by year. Red line - the error rate of a trained human

Artificial intelligence applications have been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, scientific discovery and toys. However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore."[1] "Many thousands of AI applications are deeply embedded in the infrastructure of every industry."[2] In the late 90s and early 21st century, AI technology became widely used as elements of larger systems,[2][3] but the field is rarely credited for these successes.

To allow comparison with human performance, artificial intelligence can be evaluated on constrained and well-defined problems. Such tests have been termed subject matter expert Turing tests. Also, smaller problems provide more achievable goals and there are an ever-increasing number of positive results.

Performance evaluation[edit]

The broad classes of outcome for an AI test are:

  • optimal: it is not possible to perform better
  • strong super-human: performs better than all humans
  • super-human: performs better than most humans
  • par-human: performs similarly to most humans
  • sub-human: performs worse than most humans


See also solved games.




See also[edit]

External links[edit]


  1. ^ AI set to exceed human brain power (July 26, 2006)
  2. ^ a b Kurtzweil 2005, p. 264
  3. ^ NRC 1999[verification needed] under "Artificial Intelligence in the 90s"
  4. ^ Schaeffer, J.; Burch, N.; Bjornsson, Y.; Kishimoto, A.; Muller, M.; Lake, R.; Lu, P.; Sutphen, S. (2007). "Checkers is solved". Science 317 (5844): 1518–1522. Bibcode:2007Sci...317.1518S. doi:10.1126/science.1144079. PMID 17641166. CiteSeerX:  edit
  5. ^ "God's Number is 20"
  6. ^ Rubin, Jonathan; Watson, Ian (2011). "Computer poker: A review". Artificial Intelligence. doi:10.1016/j.artint.2010.12.005.  edit
  7. ^ Bowling, M.; Burch, N.; Johanson, M.; Tammelin, O. (2015). "Heads-up limit hold'em poker is solved". Science 347 (6218): 145. doi:10.1126/science.1259433.  edit
  8. ^ Tesauro, Gerald (March 1995). "Temporal difference learning and TD-Gammon". Communications of the ACM 38 (3): 58–68. doi:10.1145/203330.203343. 
  9. ^ Computer bridge#Computers versus humans
  10. ^ Computer Chess#Computers versus humans
  11. ^ Proverb: The probabilistic cruciverbalist. By Greg A. Keim, Noam Shazeer, Michael L. Littman, Sushant Agarwal, Catherine M. Cheves, Joseph Fitzgerald, Jason Grosland, Fan Jiang, Shannon Pollard, and Karl Weinmeister. 1999. In Proceedings of the Sixteenth National Conference on Artificial Intelligence, 710-717. Menlo Park, Calif.: AAAI Press.
  12. ^
  13. ^
  14. ^ Reversi#Computer opponents
  15. ^ Sheppard, B. (2002). "World-championship-caliber Scrabble". Artificial Intelligence 134: 241–275. doi:10.1016/S0004-3702(01)00166-7.  edit
  16. ^ Watson beats Jeopardy grand-champions.
  17. ^ Jackson, Joab. "IBM Watson Vanquishes Human Jeopardy Foes". PC World. IDG News. Retrieved 2011-02-17. 
  18. ^ According to, "The Arimaa Challenge was won on April 18, 2015 and is no longer available."
  19. ^
  20. ^ There are several ways of evaluating machine translation systems. People competent in a second language frequently outperform machine translation systems but the average person is often less capable. Some machine translation systems are capable of a large number of languages, like google translate, and as a result have a broader competence than most humans. For example, very few humans can translate from Arabic to Polish and French to Swahili and Armenian to Vietnamese. When comparing over several languages machine translation systems will tend to outperform humans.