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Progress in artificial intelligence

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Title: Progress in artificial intelligence  
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Progress in artificial intelligence

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.

Contents

  • Performance evaluation 1
  • Optimal 2
  • Super-human 3
  • Par-human 4
  • Sub-human 5
  • See also 6
  • External links 7
  • References 8

Performance evaluation

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

Optimal

See also solved games.

Super-human

Par-human

Sub-human

See also

External links

  • http://www.human-competitive.org/

References

  1. ^ AI set to exceed human brain power CNN.com (July 26, 2006)
  2. ^ a b Kurtzweil 2005, p. 264
  3. ^ NRC 1999 under "Artificial Intelligence in the 90s"
  4. ^
  5. ^ "God's Number is 20"
  6. ^
  7. ^
  8. ^
  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. ^ http://www.technologyreview.com/news/520746/data-shows-googles-robot-cars-are-smoother-safer-drivers-than-you-or-i/
  13. ^ http://www.physorg.com/news193908598.html
  14. ^ Reversi#Computer opponents
  15. ^
  16. ^ Watson beats Jeopardy grand-champions. http://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html
  17. ^
  18. ^ According to http://arimaa.com/arimaa/challenge/ "The Arimaa Challenge was won on April 18, 2015 and is no longer available."
  19. ^ http://venturebeat.com/2015/02/09/microsoft-researchers-say-their-newest-deep-learning-system-beats-humans-and-google/
  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.
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