Progress in artificial intelligence

Artificial intelligence applications have been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, scientific discovery, video games, 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][2] "Many thousands of AI applications are deeply embedded in the infrastructure of every industry."[3] In the late 1990s and early 21st century, AI technology became widely used as elements of larger systems,[3][4] but the field is rarely credited for these successes.

See also: History of artificial intelligence and Timeline of artificial intelligence
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Artificial intelligence
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History
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Glossary
Progress in machine classification of images
The error rate of AI by year. Red line - the error rate of a trained human on a particular task

Kaplan and Haenlein structure artificial intelligence along three evolutionary stages: 1) artificial narrow intelligence – applying AI only to specific tasks; 2) artificial general intelligence – applying AI to several areas and able to autonomously solve problems they were never even designed for; and 3) artificial super intelligence – applying AI to any area capable of scientific creativity, social skills, and general wisdom.[2]

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.

Current performance
Game Champion year[5] Legal states (log10)[6] Game tree complexity (log10)[6] Game of perfect information? Ref
Othello (reversi)19972858Perfect[7]
Draughts (checkers)19942131Perfect[8]
Chess199746123Perfect
Scrabble2006[9]
Shogi201771226Perfect[10]
Go2016172360Perfect
2p no-limit hold 'em2017Imperfect[11]
StarCraft-270+Imperfect[12]

In his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis.[13] Yet, there are many other useful abilities that can be described as showing some form of intelligence. This gives better insight into the comparative success of artificial intelligence in different areas.

In what has been called the Feigenbaum test, the inventor of expert systems argued for subject specific expert tests.[14] A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior.[15]

AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at.[16] Some versions of Moravec's paradox observe that humans are more likely to outperform machines in areas such as physical dexterity that have been the direct target of natural selection.[17] While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets.[18][19] Researcher Andrew Ng has suggested, as a "highly imperfect rule of thumb", that "almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI."[20]

Games provide a high-profile benchmark for assessing rates of progress; many games have a large professional player base and a well-established competitive rating system. AlphaGo brought the era of classical board-game benchmarks to a close when Artificial Intelligence proved their competitive edge over humans in 2016. Deep Mind’s AlphaGo AI software program defeated the world’s best professional Go Player Lee Sedol.[21] Games of imperfect knowledge provide new challenges to AI in the area of game theory; the most prominent milestone in this area was brought to a close by Libratus' poker victory in 2017.[22][23] E-sports continue to provide additional benchmarks; Facebook AI, Deepmind, and others have engaged with the popular StarCraft franchise of videogames.[24][25]

Broad classes of outcome for an AI test may be given as:`

  • optimal: it is not possible to perform better (note: some of these entries were solved by humans)
  • super-human: performs better than all humans
  • high-human: performs better than most humans
  • par-human: performs similarly to most humans
  • sub-human: performs worse than most humans

Optimal
See also: Solved game

Super-human

High-human

Par-human

Sub-human

Past and current predictions

An expert poll around 2016, conducted by Katja Grace of the Future of Humanity Institute and associates, gave median estimates of 3 years for championship Angry Birds, 4 years for the World Series of Poker, and 6 years for StarCraft. On more subjective tasks, the poll gave 6 years for folding laundry as well as an average human worker, 7–10 years for expertly answering 'easily Googleable' questions, 8 years for average speech transcription, 9 years for average telephone banking, and 11 years for expert songwriting, but over 30 years for writing a New York Times bestseller or winning the Putnam math competition.[56][57][58]

Chess
Deep Blue at the Computer History Museum

An AI defeated a grandmaster in a regulation tournament game for the first time in 1988; rebranded as Deep Blue, it beat the reigning human world chess champion in 1997 (see Deep Blue versus Garry Kasparov).[59]

Estimates when computers would exceed humans at Chess
Year prediction madePredicted yearNumber of YearsPredictorContemporaneous source
1957 1967 or sooner10 or lessHerbert A. Simon, economist[60]
1990 2000 or sooner10 or lessRay Kurzweil, futuristAge of Intelligent Machines[61]

Go

AlphaGo defeated a European Go champion in October 2015, and Lee Sedol in March 2016, one of the world's top players (see AlphaGo versus Lee Sedol). According to Scientific American and other sources, most observers had expected superhuman Computer Go performance to be at least a decade away.[62][63][64]

Estimates when computers would exceed humans at Go
Year prediction madePredicted yearNumber of yearsPredictorAffiliationContemporaneous source
1997 2100 or later100 or morePiet Hutt, physicist and Go fanInstitute for Advanced StudyNew York Times[65][66]
2007 2017 or sooner10 or lessFeng-Hsiung Hsu, Deep Blue leadMicrosoft Research AsiaIEEE Spectrum[67][68]
2014 202410Rémi Coulom, Computer Go programmerCrazyStoneWired[68][69]

Human-level artificial general intelligence (AGI)

AI pioneer and economist Herbert A. Simon inaccurately predicted in 1965: "Machines will be capable, within twenty years, of doing any work a man can do". Similarly, in 1970 Marvin Minsky wrote that "Within a generation... the problem of creating artificial intelligence will substantially be solved."[70]

Four polls conducted in 2012 and 2013 suggested that the median estimate among experts for when AGI would arrive was 2040 to 2050, depending on the poll.[71][72]

The Grace poll around 2016 found results varied depending on how the question was framed. Respondents asked to estimate "when unaided machines can accomplish every task better and more cheaply than human workers" gave an aggregated median answer of 45 years and a 10% chance of it occurring within 9 years. Other respondents asked to estimate "when all occupations are fully automatable. That is, when for any occupation, machines could be built to carry out the task better and more cheaply than human workers" estimated a median of 122 years and a 10% probability of 20 years. The median response for when "AI researcher" could be fully automated was around 90 years. No link was found between seniority and optimism, but Asian researchers were much more optimistic than North American researchers on average; Asians predicted 30 years on average for "accomplish every task", compared with the 74 years predicted by North Americans.[56][57][58]

Estimates of when AGI will arrive
Year prediction madePredicted yearNumber of yearsPredictorContemporaneous source
1965 1985 or sooner20 or lessHerbert A. SimonThe shape of automation for men and management[70][73]
1993 2023 or sooner30 or lessVernor Vinge, science fiction writer"The Coming Technological Singularity"[74]
1995 2040 or sooner45 or lessHans Moravec, robotics researcherWired[75]
2008 Never / Distant future[note 1]Gordon E. Moore, inventor of Moore's LawIEEE Spectrum[76]
2017 202912Ray KurzweilInterview[77]

See also

References
  1. AI set to exceed human brain power CNN.com (July 26, 2006)
  2. Kaplan, Andreas; Haenlein, Michael (2019). "Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence". Business Horizons. 62: 15–25. doi:10.1016/j.bushor.2018.08.004.
  3. Kurtzweil 2005, p. 264
  4. National Research Council (1999), "Developments in Artificial Intelligence", Funding a Revolution: Government Support for Computing Research, National Academy Press, ISBN 978-0-309-06278-7, OCLC 246584055 under "Artificial Intelligence in the 90s"
  5. Approximate year AI started beating top human experts
  6. van den Herik, H.Jaap; Uiterwijk, Jos W.H.M.; van Rijswijck, Jack (January 2002). "Games solved: Now and in the future". Artificial Intelligence. 134 (1–2): 277–311. doi:10.1016/S0004-3702(01)00152-7.
  7. "www.othello-club.de". berg.earthlingz.de. Retrieved 2018-07-15.
  8. Madrigal, Alexis C. (2017). "How Checkers Was Solved". The Atlantic. Retrieved 6 May 2018.
  9. Webley, Kayla (15 February 2011). "Top 10 Man-vs.-Machine Moments". Time. Retrieved 28 December 2017.
  10. "Shogi prodigy breathes new life into the game | The Japan Times". The Japan Times. Retrieved 2018-07-15.
  11. Brown, Noam; Sandholm, Tuomas (2017). "Superhuman AI for heads-up no-limit poker: Libratus beats top professionals". Science. 359 (6374): 418–424. Bibcode:2018Sci...359..418B. doi:10.1126/science.aao1733. PMID 29249696.
  12. "Facebook Quietly Enters StarCraft War for AI Bots, and Loses". WIRED. 2017. Retrieved 6 May 2018.
  13. Turing, Alan (October 1950), "Computing Machinery and Intelligence", Mind, LIX (236): 433–460, doi:10.1093/mind/LIX.236.433, ISSN 0026-4423
  14. Feigenbaum, Edward A. (2003). "Some challenges and grand challenges for computational intelligence". Journal of the ACM. 50 (1): 32–40. doi:10.1145/602382.602400. S2CID 15379263.
  15. Gray, Jim (2003). "What Next? A Dozen Information-Technology Research Goals". Journal of the ACM. 50 (1): 41–57. arXiv:cs/9911005. Bibcode:1999cs.......11005G. doi:10.1145/602382.602401. S2CID 10336312.
  16. Brynjolfsson, Erik; Mitchell, Tom (22 December 2017). "What can machine learning do? Workforce implications". Science. pp. 1530–1534. Bibcode:2017Sci...358.1530B. doi:10.1126/science.aap8062. Retrieved 7 May 2018.
  17. "IKEA furniture and the limits of AI". The Economist. 2018. Retrieved 24 April 2018.
  18. Sample, Ian (18 October 2017). "'It's able to create knowledge itself': Google unveils AI that learns on its own". the Guardian. Retrieved 7 May 2018.
  19. "The AI revolution in science". Science | AAAS. 5 July 2017. Retrieved 7 May 2018.
  20. "Will your job still exist in 10 years when the robots arrive?". South China Morning Post. 2017. Retrieved 7 May 2018.
  21. Mokyr, Joel (2019-11-01). "The Technology Trap: Capital Labor, and Power in the Age of Automation. By Carl Benedikt Frey. Princeton: Princeton University Press, 2019. Pp. 480. $29.95, hardcover". The Journal of Economic History. 79 (4): 1183–1189. doi:10.1017/s0022050719000639. ISSN 0022-0507.
  22. Borowiec, Tracey Lien, Steven (2016). "AlphaGo beats human Go champ in milestone for artificial intelligence". latimes.com. Retrieved 7 May 2018.
  23. Brown, Noam; Sandholm, Tuomas (26 January 2018). "Superhuman AI for heads-up no-limit poker: Libratus beats top professionals". Science. pp. 418–424. doi:10.1126/science.aao1733. Retrieved 7 May 2018.
  24. Ontanon, Santiago; Synnaeve, Gabriel; Uriarte, Alberto; Richoux, Florian; Churchill, David; Preuss, Mike (December 2013). "A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft". IEEE Transactions on Computational Intelligence and AI in Games. 5 (4): 293–311. CiteSeerX 10.1.1.406.2524. doi:10.1109/TCIAIG.2013.2286295. S2CID 5014732.
  25. "Facebook Quietly Enters StarCraft War for AI Bots, and Loses". WIRED. 2017. Retrieved 7 May 2018.
  26. 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 10.1.1.95.5393. doi:10.1126/science.1144079. PMID 17641166. S2CID 10274228.
  27. "God's Number is 20".
  28. Bowling, M.; Burch, N.; Johanson, M.; Tammelin, O. (2015). "Heads-up limit hold'em poker is solved". Science. 347 (6218): 145–9. Bibcode:2015Sci...347..145B. CiteSeerX 10.1.1.697.72. doi:10.1126/science.1259433. PMID 25574016. S2CID 3796371.
  29. "In Major AI Breakthrough, Google System Secretly Beats Top Player at the Ancient Game of Go". WIRED. Retrieved 28 December 2017.
  30. Sheppard, B. (2002). "World-championship-caliber Scrabble". Artificial Intelligence. 134 (1–2): 241–275. doi:10.1016/S0004-3702(01)00166-7.
  31. Tesauro, Gerald (March 1995). "Temporal difference learning and TD-Gammon". Communications of the ACM. 38 (3): 58–68. doi:10.1145/203330.203343. S2CID 8763243.
  32. Tesauro, Gerald (January 2002). "Programming backgammon using self-teaching neural nets". Artificial Intelligence. 134 (1–2): 181–199. doi:10.1016/S0004-3702(01)00110-2. ...at least two other neural net programs also appear to be capable of superhuman play
  33. "Kramnik vs Deep Fritz: Computer wins match by 4:2". Chess News. 2006-12-05. Retrieved 2018-07-15.
  34. "The Week in Chess 771". theweekinchess.com. Retrieved 2018-07-15.
  35. Nickel, Arno (May 2017). "Zor Winner in an Exciting Photo Finish". www.infinitychess.com. Innovative Solutions. Retrieved 2018-07-17. ... on third place the best centaur ...
  36. Watson beats Jeopardy grand-champions. https://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html
  37. Jackson, Joab. "IBM Watson Vanquishes Human Jeopardy Foes". PC World. IDG News. Retrieved 2011-02-17.
  38. "The Arimaa Challenge". arimaa.com. Retrieved 2018-07-15.
  39. Roeder, Oliver (10 July 2017). "The Bots Beat Us. Now What?". FiveThirtyEight. Retrieved 28 December 2017.
  40. "AlphaGo beats Ke Jie again to wrap up three-part match". The Verge. Retrieved 2018-07-15.
  41. Blair, Alan; Saffidine, Abdallah (30 August 2019). "AI surpasses humans at six-player poker". Science. 365 (6456): 864–865. doi:https://science.sciencemag.org/content/365/6456/864 Check |doi= value (help).
  42. 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.
  43. Wernick, Adam (24 Sep 2014). "'Dr. Fill' vies for crossword solving supremacy, but still comes up short". Public Radio International. Retrieved Dec 27, 2017. The first year, Dr. Fill came in 141st out of about 600 competitors. It did a little better the second-year; last year it was 65th
  44. "AI bots trained for 180 years a day to beat humans at Dota 2". The Verge. 25 June 2018. Retrieved 17 July 2018.
  45. Bethe, P. M. (2009). The state of automated bridge play.
  46. "AlphaStar: Mastering the Real-Time Strategy Game StarCraft II".
  47. "Microsoft researchers say their newest deep learning system beats humans -- and Google - VentureBeat - Big Data - by Jordan Novet". VentureBeat. 2015-02-10.
  48. Santoro, Adam; Bartunov, Sergey; Botvinick, Matthew; Wierstra, Daan; Lillicrap, Timothy (19 May 2016). "One-shot Learning with Memory-Augmented Neural Networks". p. 5, Table 1. arXiv:1605.06065 [cs.LG]. 4.2. Omniglot Classification: "The network exhibited high classification accuracy on just the second presentation of a sample from a class within an episode (82.8%), reaching up to 94.9% accuracy by the fifth instance and 98.1% accuracy by the tenth.
  49. Meyer, Robinson (2015). "How Worried Should We Be About Facial Recognition?". The Atlantic. Retrieved 2 March 2018.
  50. Antol, Stanislaw, et al. "Vqa: Visual question answering." Proceedings of the IEEE International Conference on Computer Vision. 2015.
  51. "Artificial Intelligence Index 2017 Annual Report" (PDF). Stanford 100 Year Study on AI. Retrieved 28 December 2017.
  52. "Robots with legs are getting ready to walk among us". The Verge. Retrieved 28 December 2017.
  53. Hurst, Nathan. "Why Funny, Falling, Soccer-Playing Robots Matter". Smithsonian. Retrieved 28 December 2017.
  54. "The Business of Artificial Intelligence". Harvard Business Review. 18 July 2017. Retrieved 28 December 2017.
  55. Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530-1534.
  56. Gray, Richard (2018). "How long will it take for your job to be automated?". BBC. Retrieved 31 January 2018.
  57. "AI will be able to beat us at everything by 2060, say experts". New Scientist. 2018. Retrieved 31 January 2018.
  58. Grace, K., Salvatier, J., Dafoe, A., Zhang, B., & Evans, O. (2017). When will AI exceed human performance? Evidence from AI experts. arXiv preprint arXiv:1705.08807.
  59. McClain, Dylan Loeb (11 September 2010). "Bent Larsen, Chess Grandmaster, Dies at 75". The New York Times. Retrieved 31 January 2018.
  60. "The Business of Artificial Intelligence". Harvard Business Review. 18 July 2017. Retrieved 31 January 2018.
  61. "4 Crazy Predictions About the Future of Art". Inc.com. 2017. Retrieved 31 January 2018.
  62. Koch, Christof (2016). "How the Computer Beat the Go Master". Scientific American. Retrieved 31 January 2018.
  63. "'I'm in shock!' How an AI beat the world's best human at Go". New Scientist. 2016. Retrieved 31 January 2018.
  64. Moyer, Christopher (2016). "How Google's AlphaGo Beat a Go World Champion". The Atlantic. Retrieved 31 January 2018.
  65. Johnson, George (29 July 1997). "To Test a Powerful Computer, Play an Ancient Game". The New York Times. Retrieved 31 January 2018.
  66. Johnson, George (4 April 2016). "To Beat Go Champion, Google's Program Needed a Human Army". The New York Times. Retrieved 31 January 2018.
  67. "Cracking GO". IEEE Spectrum: Technology, Engineering, and Science News. 2007. Retrieved 31 January 2018.
  68. "The Mystery of Go, the Ancient Game That Computers Still Can't Win". WIRED. 2014. Retrieved 31 January 2018.
  69. Gibney, Elizabeth (28 January 2016). "Google AI algorithm masters ancient game of Go". Nature. pp. 445–446. Bibcode:2016Natur.529..445G. doi:10.1038/529445a. Retrieved 31 January 2018.
  70. Bostrom, Nick (2013). Superintelligence. Oxford: Oxford University Press. ISBN 978-0199678112.
  71. Khatchadourian, Raffi (16 November 2015). "The Doomsday Invention". The New Yorker. Retrieved 31 January 2018.
  72. Müller, V. C., & Bostrom, N. (2016). Future progress in artificial intelligence: A survey of expert opinion. In Fundamental issues of artificial intelligence (pp. 555-572). Springer, Cham.
  73. Muehlhauser, L., & Salamon, A. (2012). Intelligence explosion: Evidence and import. In Singularity Hypotheses (pp. 15-42). Springer, Berlin, Heidelberg.
  74. Tierney, John (25 August 2008). "Vernor Vinge's View of the Future - Is Technology That Outthinks Us a Partner or a Master ?". The New York Times. Retrieved 31 January 2018.
  75. "Superhumanism". WIRED. 1995. Retrieved 31 January 2018.
  76. "Tech Luminaries Address Singularity". IEEE Spectrum: Technology, Engineering, and Science News. 2008. Retrieved 31 January 2018.
  77. Molloy, Mark (17 March 2017). "Expert predicts date when 'sexier and funnier' humans will merge with AI machines". The Telegraph. Retrieved 31 January 2018.

Notes
  1. IEEE Spectrum attributes to Moore both "Never" and "I don't believe this kind of thing is likely to happen, at least for a long time"
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