Computational_tools_for_artificial_intelligence

Outline of artificial intelligence

Outline of artificial intelligence

Overview of and topical guide to artificial intelligence

The following outline is provided as an overview of and topical guide to artificial intelligence:

Artificial intelligence (AI) is intelligence exhibited by machines or software. It is also the name of the scientific field which studies how to create computers and computer software that are capable of intelligent behaviour.

AI algorithms and techniques

Main article: Artificial intelligence § Tools

Logic

Other symbolic knowledge and reasoning tools

Symbolic representations of knowledge

Unsolved problems in knowledge representation

Probabilistic methods for uncertain reasoning

Classifiers and statistical learning methods

Artificial neural networks

Biologically based or embodied

Cognitive architecture and multi-agent systems

Philosophy

Main articles: Artificial intelligence § Philosophy, and Philosophy of AI

Definition of AI

Classifying AI

Goals and applications

Main articles: Applications of artificial intelligence and Artificial intelligence § Goals

General intelligence

Reasoning and Problem Solving

Knowledge representation

Planning

Learning

Natural language processing

Perception

Robotics

Control

Social intelligence

Game playing

Creativity, art and entertainment

Integrated AI systems

  • AIBO – Sony's robot dog. It integrates vision, hearing and motorskills.
  • Asimo (2000 to present) – humanoid robot developed by Honda, capable of walking, running, negotiating through pedestrian traffic, climbing and descending stairs, recognizing speech commands and the faces of specific individuals, among a growing set of capabilities.
  • MIRAGE – A.I. embodied humanoid in an augmented reality environment.
  • Cog – M.I.T. humanoid robot project under the direction of Rodney Brooks.
  • QRIO – Sony's version of a humanoid robot.
  • TOPIO, TOSY's humanoid robot that can play ping-pong with humans.
  • Watson (2011) – computer developed by IBM that played and won the game show Jeopardy! It is now being used to guide nurses in medical procedures.
  • Project Debater (2018) – artificially intelligent computer system, designed to make coherent arguments, developed at IBM's lab in Haifa, Israel.

Intelligent personal assistants

Intelligent personal assistant –

Other applications

History

History by subject

Future

Fiction

Artificial intelligence in fiction – Some examples of artificially intelligent entities depicted in science fiction include:

AI community

Open-source AI development tools

Projects

List of artificial intelligence projects

Competitions and awards

Competitions and prizes in artificial intelligence

Publications

List of important publications in computer science

Organizations

Companies

Artificial intelligence researchers and scholars

1930s and 40s (generation 0)

1950s (the founders)

1960s (their students)

1970s

1980s

1990s

  • Yoshua Bengio –
  • Hugo de Garis – known for his research on the use of genetic algorithms to evolve neural networks using three-dimensional cellular automata inside field programmable gate arrays.
  • Geoffrey Hinton
  • Yann LeCun – Chief AI Scientist at Facebook AI Research and founding director of the NYU Center for Data Science
  • Ray Kurzweil – developed optical character recognition (OCR), text-to-speech synthesis, and speech recognition systems. He has also authored multiple books on artificial intelligence and its potential promise and peril. In December 2012 Kurzweil was hired by Google in a full-time director of engineering position to "work on new projects involving machine learning and language processing".[54] Google co-founder Larry Page and Kurzweil agreed on a one-sentence job description: "to bring natural language understanding to Google".

2000s on

See also

References

  1. [1]
    Russell & Norvig 2003, pp. 59–189; Luger & Stubblefield 2004, pp. 79–164, 193–219
  2. [2]
    Russell & Norvig 2003, pp. 59–93; Luger & Stubblefield 2004, pp. 79–121
  3. [3]
    Russell & Norvig 2003, pp. 94–109; Luger & Stubblefield 2004, pp. 133–150
  4. [4]
    Russell & Norvig 2003, pp. 217–225, 280–294; Luger & Stubblefield 2004, pp. 62–73
  5. [5]
    Russell & Norvig 2003, pp. 382–387.
  6. [6]
    Russell & Norvig 2003, pp. 110–116, 120–129;Luger & Stubblefield 2004, pp. 127–133
  7. [7]
    Luger & Stubblefield 2004, pp. 509–530.
  8. [8]
    Holland, John H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. ISBN 978-0-262-58111-0.
  9. [9]
    Koza, John R. (1992). Genetic Programming (On the Programming of Computers by Means of Natural Selection). MIT Press. Bibcode:1992gppc.book.....K. ISBN 978-0-262-11170-6.
  10. [10]
    Poli, R.; Langdon, W. B.; McPhee, N. F. (2008). A Field Guide to Genetic Programming. Lulu.com. ISBN 978-1-4092-0073-4 – via gp-field-guide.org.uk.
  11. [11]
    Luger & Stubblefield 2004, pp. 530–541.
  12. [12]
    Daniel Merkle; Martin Middendorf (2013). "Swarm Intelligence". In Burke, Edmund K.; Kendall, Graham (eds.). Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques. Springer Science & Business Media. ISBN 978-1-4614-6940-7.
  13. [13]
    Russell & Norvig 2003, pp. 194–310; Luger & Stubblefield 2004, pp. 35–77
  14. [14]
    Russell & Norvig 2003, pp. 204–233; Luger & Stubblefield 2004, pp. 45–50
  15. [15]
    Russell & Norvig 2003, pp. 240–310; vLuger & Stubblefield 2004, pp. 50–62
  16. [16]
    Russell & Norvig 2003, pp. 526–527
  17. [17]
    "What is 'fuzzy logic'? Are there computers that are inherently fuzzy and do not apply the usual binary logic?". Scientific American. Retrieved 5 May 2018.
  18. [18]
    Russell & Norvig 2003, pp. 354–360; Luger & Stubblefield 2004, pp. 335–363
  19. [19]
    Luger & Stubblefield (2004, pp. 335–363) places this under "uncertain reasoning"
  20. [20]
    Russell & Norvig 2003, pp. 349–354; Luger & Stubblefield 2004, pp. 248–258
  21. [21]
    Russell & Norvig 2003, pp. 328–341.
  22. [22]
    Poole, David; Mackworth, Alan; Goebel, Randy (1998). Computational Intelligence: A Logical Approach. New York: Oxford University Press. pp. 335–337. ISBN 978-0-19-510270-3.
  23. [23]
    Russell & Norvig 2003, pp. 341–344.
  24. [24]
    Russell & Norvig 2003, pp. 402–407.
  25. [25]
    Russell & Norvig 2003, pp. 678–710; Luger & Stubblefield 2004, pp. ~422–442
  26. [26]
    Breadth of commonsense knowledge:
  27. [27]
    Russell & Norvig 2003, pp. 462–644; Luger & Stubblefield 2004, pp. 165–191, 333–381
  28. [28]
    Russell & Norvig 2003, pp. 492–523; Luger & Stubblefield 2004, pp. ~182–190, ≈363–379
  29. [29]
    Russell & Norvig 2003, pp. 504–519; Luger & Stubblefield 2004, pp. ~363–379
  30. [30]
    Russell & Norvig 2003, pp. 712–724.
  31. [31]
    Russell & Norvig 2003, pp. 597–600.
  32. [32]
    Russell & Norvig 2003, pp. 551–557.
  33. [33]
    Russell & Norvig 2003, pp. 549–551.
  34. [34]
    Russell & Norvig 2003, pp. 584–597.
  35. [35]
    Russell & Norvig 2003, pp. 600–604.
  36. [36]
    Russell & Norvig 2003, pp. 613–631.
  37. [37]
    Russell & Norvig 2003, pp. 631–643.
  38. [38]
    Russell & Norvig 2003, pp. 712–754; Luger & Stubblefield 2004, pp. 453–541
  39. [39]
    Russell & Norvig 2003, pp. 653–664; Luger & Stubblefield 2004, pp. 408–417
  40. [40]
    Russell & Norvig 2003, pp. 736–748; Luger & Stubblefield 2004, pp. 453–505
  41. [41]
    Russell & Norvig 2003, pp. 733–736.
  42. [42]
    Russell & Norvig 2003, pp. 749–752.
  43. [43]
    Russell & Norvig 2003, p. 718.
  44. [44]
    Russell & Norvig 2003, pp. 739–748, 758; Luger & Stubblefield 2004, pp. 458–467
  45. [45]
    Russell & Norvig 2003, p. 758; Luger & Stubblefield 2004, pp. 474–505
  46. [46]
    Hochreiter, Sepp; and Schmidhuber, Jürgen; Long Short-Term Memory, Neural Computation, 9(8):1735–1780, 1997
  47. [47]
    Luger & Stubblefield 2004, pp. 474–505.
  48. [48]
    Russell & Norvig 2003, pp. 744–748; Luger & Stubblefield 2004, pp. 467–474
  49. [49]
    Hinton, G. E. (2007). "Learning multiple layers of representation". Trends in Cognitive Sciences. 11 (10): 428–434. doi:10.1016/j.tics.2007.09.004. PMID 17921042. S2CID 15066318.
  50. [50]
    "Artificial intelligence can 'evolve' to solve problems". Science | AAAS. 10 January 2018. Retrieved 7 February 2018.
  51. [51]
    Hinton 2007.
  52. [52]
    Developmental robotics:
  53. [53]
    "The 6 craziest robots Google has acquired". Business Insider. Retrieved 2018-06-13.
  54. [54]
    Letzing, John (2012-12-14). "Google Hires Famed Futurist Ray Kurzweil". The Wall Street Journal. Retrieved 2013-02-13.
  55. [55]
    Claire Miller and Nick Bilton (3 November 2011). "Google's Lab of Wildest Dreams". New York Times.

Bibliography

The two most widely used textbooks in 2008

Further reading
Share this article:

This article uses material from the Wikipedia article Computational_tools_for_artificial_intelligence, and is written by contributors. Text is available under a CC BY-SA 4.0 International License; additional terms may apply. Images, videos and audio are available under their respective licenses.