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AI effect

(Redirected from AI paradox)
For the magnitude of effect of other things, see Active ingredient, Micro-encapsulation, Pesticide application, and Agricultural intensification.

The AI effect is the discounting of the behavior of an artificial intelligence program as not "real" intelligence.[1]

The author Pamela McCorduck writes: "It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, 'that's not thinking'."[2]

Researcher Rodney Brooks complains: "Every time we figure out a piece of it, it stops being magical; we say, 'Oh, that's just a computation.'"[3]

Definition

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"The AI effect" refers to a phenomenon where either the definition of AI or the concept of intelligence is adjusted to exclude capabilities that AI systems have mastered. This often manifests as tasks that AI can now perform successfully no longer being considered part of AI, or as the notion of intelligence itself being redefined to exclude AI achievements.[4][2][1] Edward Geist credits John McCarthy for coining the term "AI effect" to describe this phenomenon.[4] The earliest known expression of this notion (as identified by Quote Investigator) is a statement from 1971, "AI is a collective name for problems which we do not yet know how to solve properly by computer", attributed to computer scientist Bertram Raphael.[5]

McCorduck calls it an "odd paradox" that "practical AI successes, computational programs that actually achieved intelligent behavior were soon assimilated into whatever application ___domain they were found to be useful in, and became silent partners alongside other problem-solving approaches, which left AI researchers to deal only with the 'failures', the tough nuts that couldn't yet be cracked."[6] It is an example of moving the goalposts.[7]

Tesler's Theorem is:

AI is whatever hasn't been done yet.

— Larry Tesler

Douglas Hofstadter quotes this[8] as do many other commentators.[9][page needed]

When problems have not yet been formalised, they can still be characterised by a model of computation that includes human computation. The computational burden of a problem is split between a computer and a human: one part is solved by computer and the other part solved by a human. This formalisation is referred to as a human-assisted Turing machine.[10]

AI applications become mainstream

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Software and algorithms developed by AI researchers are now integrated into many applications throughout the world, without really being called AI. This underappreciation is known from such diverse fields as computer chess,[11] marketing,[12] agricultural automation,[9][page needed] hospitality[13] and optical character recognition.[14]

Michael Swaine reports "AI advances are not trumpeted as artificial intelligence so much these days, but are often seen as advances in some other field". "AI has become more important as it has become less conspicuous", Patrick Winston says. "These days, it is hard to find a big system that does not work, in part, because of ideas developed or matured in the AI world."[15]

According to Stottler Henke, "The great practical benefits of AI applications and even the existence of AI in many software products go largely unnoticed by many despite the already widespread use of AI techniques in software. This is the AI effect. Many marketing people don't use the term 'artificial intelligence' even when their company's products rely on some AI techniques. Why not?"[12]

Marvin Minsky writes "This paradox resulted from the fact that whenever an AI research project made a useful new discovery, that product usually quickly spun off to form a new scientific or commercial specialty with its own distinctive name. These changes in name led outsiders to ask, Why do we see so little progress in the central field of artificial intelligence?"[16]

Nick Bostrom observes that "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 labelled AI anymore."[17]

Some experts think that the AI effect will continue, with advances in AI continually producing objections and redefinitions of public expectations.[18][19][20]

Legacy of the AI winter

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Main article: AI winter

In the early 1990s, during the second "AI winter" many AI researchers found that they could get more funding and sell more software if they avoided the bad name of "artificial intelligence" and instead pretended their work had nothing to do with intelligence.[citation needed]

Patty Tascarella wrote in 2006: "Some believe the word 'robotics' actually carries a stigma that hurts a company's chances at funding."[21]

Saving a place for humanity at the top of the chain of being

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Michael Kearns suggests that "people subconsciously are trying to preserve for themselves some special role in the universe".[22] By discounting artificial intelligence people can continue to feel unique and special. Kearns argues that the change in perception known as the AI effect can be traced to the mystery being removed from the system. In being able to trace the cause of events implies that it's a form of automation rather than intelligence.[citation needed]

A related effect has been noted in the history of animal cognition and in consciousness studies, where every time a capacity formerly thought of as uniquely human is discovered in animals (e.g. the ability to make tools, or passing the mirror test), the overall importance of that capacity is deprecated.[citation needed]

Herbert A. Simon, when asked about the lack of AI's press coverage at the time, said, "What made AI different was that the very idea of it arouses a real fear and hostility in some human breasts. So you are getting very strong emotional reactions. But that's okay. We'll live with that."[23]

 
Game 6

Deep Blue defeats Kasparov

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When IBM's chess-playing computer Deep Blue succeeded in defeating Garry Kasparov in 1997, there were complains that Deep Blue had only used "brute force methods" and it wasn't real intelligence.[11] Notably, John McCarthy, an AI pioneer and founder of the term "artificial intelligence", was disappointed by Deep Blue. He described it as a mere brute force machine that did not have any deep understanding of the game. McCarthy would also criticize how widespread the AI effect is ("As soon as it works, no one calls it AI anymore"[24][25]: 12 ), but in this case did not think that Deep Blue was a good example.[24]

On the other side, Fred A. Reed writes:[26]

A problem that proponents of AI regularly face is this: When we know how a machine does something "intelligent", it ceases to be regarded as intelligent. If I beat the world's chess champion, I'd be regarded as highly bright.

See also

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References

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  1. ^ a b Haenlein, Michael; Kaplan, Andreas (2019). "A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence". California Management Review. 61 (4): 5–14. doi:10.1177/0008125619864925. S2CID 199866730.
  2. ^ a b McCorduck 2004, p. 204
  3. ^ Kahn, Jennifer (March 2002). "It's Alive". Wired. Vol. 10, no. 30. Retrieved 24 Aug 2008.
  4. ^ a b Geist, Edward (2016). "It's already too late to stop the AI arms race—We must manage it instead". Bulletin of the Atomic Scientists. 72 (5: The psychology of doom). Taylor & Francis: 318–321. Bibcode:2016BuAtS..72e.318G. doi:10.1080/00963402.2016.1216672. S2CID 151967826. Bulletin of the Atomic Scientists.
  5. ^ "Quote Origin: As Soon As It Works, No One Calls It AI Anymore". Quote Investigator. 2024-06-20. Retrieved 2025-08-19.
  6. ^ McCorduck 2004, p. 423.
  7. ^ Nadin, Mihai (2023). "Intelligence at any price? A criterion for defining AI". AI & Society. Springer Science and Business Media LLC. doi:10.1007/s00146-023-01695-0. ISSN 0951-5666. S2CID 259041703.
  8. ^ As quoted by Hofstadter (1980, p. 601). Larry Tesler actually feels he was misquoted: see his note in the "Adages" section of [1].
  9. ^ a b Bhatnagar, Roheet; Tripathi, Kumar; Bhatnagar, Nitu; Panda, Chandan (2022). The Digital Agricultural Revolution : Innovations and Challenges in Agriculture Through Technology Disruptions. Hoboken, NJ, US: Scrivener Publishing LLC (John Wiley & Sons, Inc.). pp. 143–170. doi:10.1002/9781119823469. ISBN 978-1-119-82346-9. OCLC 1314054445. ISBN 9781119823339.
  10. ^ Dafna Shahaf and Eyal Amir (2007). Towards a theory of AI completeness. Commonsense 2007, 8th International Symposium on Logical Formalizations of Commonsense Reasoning.
  11. ^ a b McCorduck 2004, p. 433
  12. ^ a b Henke, Stottler. "AI Glossary". Archived from the original on 2008-05-09. Retrieved 2009-02-23.
  13. ^ Xiang, Zheng; Fuchs, Matthias; Gretzel, Ulrike; Höpken, Wolfram, eds. (2020). Handbook of e-Tourism (PDF). Cham, Switzerland: Springer International Publishing. p. 1945. doi:10.1007/978-3-030-05324-6. ISBN 978-3-030-05324-6. S2CID 242136095.
  14. ^ Jaffe, Peter (17 September 2020). "Are we being too hard on HAL? Some thoughts on the legal need for 'explainable' artificial intelligence". Freshfields Bruckhaus Deringer. Retrieved 25 October 2024.
  15. ^ Swaine, Michael (September 5, 2007). "AI – It's OK Again! Is AI on the rise again?". Dr. Dobbs.
  16. ^ Minsky, Marvin. "The Age of Intelligent Machines: Thoughts About Artificial Intelligence". Archived from the original on 2009-06-28.
  17. ^ Quoted in "AI set to exceed human brain power". CNN. July 26, 2006. Archived from the original on 2006-07-25.
  18. ^ Stone, Peter; Brooks, Rodney; Brynjolfsson, Erik; Calo, Ryan; Etzioni, Oren; Hager, Greg; Hirschberg, Julia; Kalyanakrishnan, Shivaram; Kamar, Ece; Kraus, Sarit; Leyton-Brown, Kevin; Parkes, David; Press, William; Saxenian, AnnaLee; Shah, Julie; Tambe, Milind; Teller, Astro. "Defining AI". 'Artificial Intelligence and Life in 2030.' One Hundred Year Study on Artificial Intelligence: Report of the 2015–2016 Study Panel. Stanford, CA: Stanford University. Retrieved September 6, 2016.
  19. ^ Press, Gil (2022). "The Trouble With AI: Human Intelligence". Forbes Magazine.
  20. ^ Bjola, Corneliu (2022). "AI for development: implications for theory and practice". Oxford Development Studies. 50 (1). Routledge: 78–90. doi:10.1080/13600818.2021.1960960. S2CID 238851395.
  21. ^ Tascarella, Patty (August 11, 2006). "Robotics firms find fundraising struggle, with venture capital shy". Pittsburgh Business Times.
  22. ^ Flam, Faye (January 15, 2004). "A new robot makes a leap in brainpower". Philadelphia Inquirer. available from Philly.com
  23. ^ Hann, Reuben L. (1998). "A Conversation with Herbert Simon". Gateway. IX (2): 12–13. Archived from the original on February 25, 2015. (Gateway is published by the Crew System Ergonomics Information Analysis Center, Wright-Patterson AFB.)
  24. ^ a b Vardi, Moshe (2012). "Artificial intelligence: past and future". Communications of the ACM. 55 (1): 5. doi:10.1145/2063176.2063177. S2CID 21144816.
  25. ^ Bostrom, Nick (2014). Superintelligence: Paths, Dangers, Strategies (1 ed.). Oxford University Press (OUP). ISBN 978-0-19-967811-2. LCCN 2013955152.
  26. ^ Reed, Fred (2006-04-14). "Promise of AI not so bright". The Washington Times.

Further reading

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