Progress in artificial intelligence

Artificial intelligence
Major goals
Knowledge reasoning
Planning
Machine learning
Natural language processing
Computer vision
Robotics
Artificial general intelligence
Approaches
Symbolic
Deep learning
Recurrent neural networks
Bayesian networks
Evolutionary algorithms
Philosophy
Ethics
Existential risk
Turing test
Chinese room
Friendly AI
History
Timeline
Progress
AI winter
Technology
Applications
Projects
Programming languages
Glossary
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 1990s 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

The broad classes of outcome for an AI test are:

Optimal

See also: Solved game

Strong super-human

Super-human

Par-human

Sub-human

See also

References

  1. AI set to exceed human brain power CNN.com (July 26, 2006)
  2. 1 2 Kurtzweil 2005, p. 264
  3. NRC 1999 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. CiteSeerX 10.1.1.95.5393Freely accessible. doi:10.1126/science.1144079. PMID 17641166.
  5. "God's Number is 20".
  6. Bowling, M.; Burch, N.; Johanson, M.; Tammelin, O. (2015). "Heads-up limit hold'em poker is solved". Science. 347 (6218): 145–9. doi:10.1126/science.1259433. PMID 25574016.
  7. Rubin, Jonathan; Watson, Ian (2011). "Computer poker: A review". Artificial Intelligence. 175: 958–987. doi:10.1016/j.artint.2010.12.005.
  8. Computer bridge#Computers versus humans
  9. see for example: https://www.chess.com/news/komodo-beats-nakamura-in-final-battle-1331
  10. AlphaGo versus Lee Sedol
  11. "Computer software sets new record for solving jigsaw puzzle".
  12. Reversi#Computer opponents
  13. Sheppard, B. (2002). "World-championship-caliber Scrabble". Artificial Intelligence. 134: 241–275. doi:10.1016/S0004-3702(01)00166-7.
  14. Watson beats Jeopardy grand-champions. http://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html
  15. Jackson, Joab. "IBM Watson Vanquishes Human Jeopardy Foes". PC World. IDG News. Retrieved 2011-02-17.
  16. Tesauro, Gerald (March 1995). "Temporal difference learning and TD-Gammon". Communications of the ACM. 38 (3): 58–68. doi:10.1145/203330.203343.
  17. 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.
  18. http://www.technologyreview.com/news/520746/data-shows-googles-robot-cars-are-smoother-safer-drivers-than-you-or-i/
  19. According to http://arimaa.com/arimaa/challenge/, "The Arimaa Challenge was won on April 18, 2015 and is no longer available."
  20. "Microsoft researchers say their newest deep learning system beats humans -- and Google - VentureBeat - Big Data - by Jordan Novet". VentureBeat.
  21. 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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