Published April 12, 2023 | Version v1
Working paper Open

Unhype Artificial 'Intelligence'! A proposal to replace the deceiving terminology of AI.

Creators

Description

Artificial Intelligence as a field of research and also its criticism is dominated by notions such as ‘intelligence’, ‘learning’ or ‘neuronal’. This paper discusses how the use of anthropomorphising language is fueling AI hype. AI hype involves many promises, such as that ‘AI can be creative’, or ‘AI can solve world hunger’. This hype is problematic since it covers up the negative consequences of AI use. Instead, the author proposes to use alternative terminology such as: ‘Automated Pattern Recognition’, ‘Machine Conditioning’, or ‘Weighted Network’.

Files

6 Unhype AI EN.pdf

Files (480.4 kB)

Name Size Download all
md5:758b06d73223ec7f83bbb6050e088611
480.4 kB Preview Download

Additional details

References

  • Abdurahman, J. Khadijah. 2021. "A Body of Work That Cannot Be Ignored." Logic Magazine, December 25, 2021. https://logicmag.io/beacons/a-body-of-work-that-cannot-be-ignored
  • Agre, Philip E. 1997. "Toward a Critical Technical Practice Lessons Learned in Trying to Reform AI." In: Bridging the Great Divide – Social Science, Technical Systems, and Cooperative Work, edited by Bowker, Geoffrey C, Susan Leigh Star, and Bill Turner, 131–58. Hillsdale, NJ: Erlbaum
  • Amaro, Ramon, 2020. Fake It till You Make It – AI and Hype. YouTube, UCL ALGO Conference 2020. https://www.youtube.com/watch?v=cNKwH6wBpnI
  • Bender, Emily M. 2022. "On NYT Magazine on AI: Resist the Urge to Be Impressed." Medium (blog). May 2, 2022. https://medium.com/@emilymenonbender/on-nyt-magazine-on-ai-resist-the-urge-to-be-impressed-3d92fd9a0edd
  • Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. "On the Dangers of Stochastic Parrots – Can Language Models Be Too Big?" In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–23. Virtual Event Canada: ACM. https://doi.org/10.1145/3442188.3445922
  • Bickerton, Derek. 1996. Language and Human Behavior. The Jessie and John Danz Lectures. Seattle: Univ. of Washington Press
  • Birhane, Abeba, Pratyusha Kalluri, Dallas Card, William Agnew, Ravit Dotan, and Michelle Bao. 2022. "The Values Encoded in Machine Learning Research [V2]." arXiv. https://doi.org/10.48550/arXiv.2106.15590
  • Bostrom, Nick. 2014. Superintelligence – Paths, Dangers, Strategies. Oxford: Oxford University Press
  • Broeckmann, Andreas. 2020. "Optical Calculus." Lecture, Prague. http://abroeck.in-berlin.de/wp-content/uploads/ 2020/11/Broeckmann_Optical_Calculus_Prague_20201106.pdf
  • Cardon, Dominique, Jean-Philippe Cointet, and Antoine Mazières. 2018. "Neurons Spike Back: The Invention of Inductive Machines and the Artificial Intelligence Controversy." Réseaux 36 (211): 173–220. https://doi.org/ 10.3917/res.211.0173
  • Chollet, François [@fchollet]. 2023. "When It Comes to Similarities between the Brain and Deep Learning …." Twitter. https://twitter.com/fchollet/status/1611286048084041728
  • Chun, Wendy Hui Kyong. 2021. Discriminating Data – Correlation, Neighborhoods, and the New Politics of Recogni-tion. Cambridge, MA: MIT Press
  • Crick, Francis. 1989. "The Recent Excitement about Neural Networks." Nature 337 (6203): 129–32. https://doi.org/ 10/dqnmkm
  • Daub, Adrian. 2020. What Tech Calls Thinking. New York: FSG Originals, Farrar, Straus and Giroux
  • D'Ignazio, Catherine, and Lauren F. Klein. 2020. Data Feminism. Cambridge, MA: The MIT Press
  • Good, Irving John. 1966. "Speculations Concerning the First Ultraintelligent." In Advances in Computers, 6:31–88. Elsevier. https://doi.org/10.1016/S0065-2458(08)60418-0
  • Hayles, Katherine. 1999. How We Became Posthuman – Virtual Bodies in Cybernetics, Literature, and Informatics. Chicago, Ill.: University of Chicago Press
  • Haynes, Suyin. 2019. "Meet the Robot Artist Who Just Became the First to Stage a Solo Exhibition." Time. June 17, 2019. https://time.com/5607191/robot-artist-ai-da-artificial-intelligence-creativity
  • Hunger, Francis. 2021. "Artificial Intelligence …." Twitter. September 7, 2021. https://twitter.com/databaseculture/status/1413462059291975680
  • Hunger, Francis.2022. "Data Workers of All Countries, End It!" In Hamburg Maschine – Digitalität, Kunst Und Urbane Öffentlichkeiten., edited by Isabella Kohlhuber and Oliver Leistert, 98–139. Hamburg: Adocs
  • Kaltheuner, Frederike. 2021. Fake AI. Manchester: Meatspace Press
  • Katz, Yarden. 2020. Artificial Whiteness – Politics and Ideology in Artificial Intelligence. New York: Columbia University Press
  • König, Dénes. 1936. Theorie Der Endlichen Und Unendlichen Graphen. Edited by Horst Sachs and H. Sachs. 1936 Reprint. Teubner-Archiv Zur Mathematik 6. Leipzig: Teubner
  • Krämer, Sybille. 2010. "Zwischen Anschauung und Denken – Zur Epistemologischen Bedeutung des Graphismus." In Was Sich Nicht Sagen Lässt. Das Nicht-Begriffliche in Wissenschaft, Kunst und Religion, edited by Joachim Bromand, 173–92. Berlin: Akademie Verlag
  • Kurzweil, Ray. 2005. The Singularity Is near – When Humans Transcend Biology. New York: Viking
  • LeCun, Y., B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. 1989. "Backpropaga-tion Applied to Handwritten Zip Code Recognition." Neural Computation 1 (4): 541–51. https://doi.org/10/bknd8g
  • Legg, Shane, and Marcus Hutter. 2007. "Universal Intelligence – A Definition of Machine Intelligence." ArXiv:0712.3329 [Cs], December. http://arxiv.org/abs/0712.3329
  • Leroi-Gourhan, André. 1964. Gesture and Speech. Cambridge, MA: MIT Press
  • Lettvin, J., H. Maturana, W. McCulloch, and W. Pitts. 1959. "What the Frog's Eye Tells the Frog's Brain." Proceedings of the IRE 47 (11): 1940–51. https://doi.org/10.1109/JRPROC.1959.287207
  • Mackenzie, Adrian. 2017. Machine Learners – Archaeology of a Data Practice. Cambridge, MA: The MIT Press
  • McCorduck, Pamela. 2004. Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intel-ligence. Natick, MA: A.K. Peters
  • Mendon-Plasek, Aaron. 2021. "Mechanized Significance and Machine Learning – Why It Became Thinkable and Preferable to Teach Machines to Judge the World." In The Cultural Life of Machine Learning, edited by Jonathan Roberge and Michael Castelle, 31–78. Cham: Palgrave Macmillan. https://doi.org/10.1007/978-3-030-56286-1_2
  • Milne, Gemma. 2021. "Uses (and Abuses) of Hype." In Fake AI, edited by Frederike Kaltheuner, 115–23. Manchester: Meatspace Press
  • Peters, Benjamin. 2018. "The Computer Never Was a Brain, or the Curious Death and Designs of John von Neumann." In Verhaltensdesign – Technologische Und Ästhetische Programme Der 1960er Und 1970er Jahre, edited by Jeannie Moser and Christina Vagt, 113–23. Bielefeld: Transcipt. https://doi.org/10.25969/mediarep/12448
  • Rosenblatt, Frank. 1957. "The Perceptron – A Perceiving and Recognizing Automation." 85-460–1. Buffalo, NY: Cornell Aeronautical Laboratory
  • Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. 1986. "Learning Representations by Back- Propagating Errors." Nature 323 (6088): 533–36. https://doi.org/10/cvjdpk
  • Stark, Luke. 2019. "Facial Recognition Is the Plutonium of AI." XRDS: Crossroads, The ACM Magazine for Students 25 (3): 50–55. https://doi.org/10.1145/3313129
  • Wiener, Norbert. 1948. Cybernetics, or, Control and Communication in the Animal and in the Machine. New York, NY: Wiley
  • Žižek, Slavoj. 1997. The Plague of Fantasies. London, New York, NY: Verso