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Volume 12, Issue 1-2
  • ISSN: 2040-3682
  • E-ISSN: 2040-3690

Abstract

Borrowing Timothy Morton’s notion of hyperobject, this article explores questions of network and scale in generative adversarial networks (GAN) images. In this context, the term network refers to the omnipresence of algorithmic images today and their significant impact on our lives. Such images are massively distributed in time and space beyond any sensible human-scale. Scale, in this context, denotes the relations between different operational layers of algorithmic images, such as the pictorial layer in contrast to the data layer. An algorithmic image is simultaneously a visual image, a symbol, a data point and part of a mass visual milieu. Its meaning is thus polymorphic and can, arguably, never be exhausted. The article explores these terms through analysis of the website www.thispersondoesnotexist.com.

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2023-01-24
2025-01-15
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References

  1. Barad, Karen Michelle. ( 2007), Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning, Durham, NC:: Duke University Press;.
     [Google Scholar]
  2. Belluz, Julia. ( 2018;), ‘ Selfie face distortion is driving people to get nose jobs. ’, Vox, 1 March, https://www.vox.com/science-and-health/2018/3/1/17059566/plastic-surgery-selfie-distortion. Accessed 15 May 2020.
     [Google Scholar]
  3. Buolamwini, Joy, and Gebru, Timnit. ( 2018;), ‘ Gender shades: Intersectional accuracy disparities in commercial gender classification. ’, Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 25 October, http://proceedings.mlr.press/v81/buolamwini18a.html. Accessed 15 May 2020.
     [Google Scholar]
  4. Chun, Wendy Hui Kyong. ( 2005;), ‘ On software, or the persistence of visual knowledge. ’, Grey Room, 18, pp. 26–51, https://doi.org/10.1162/1526381043320741. Accessed 15 May 2020.
     [Google Scholar]
  5. Chun, Wendy Hui Kyong. ( 2011), Programmed Visions: Software and Memory, Software Studies, Cambridge, MA:: MIT Press;.
     [Google Scholar]
  6. Crawford, Kate, and Joler, Vladar. ( 2018;), ‘ Anatomy of an AI system: The Amazon Echo as an anatomical map of human labor, data and planetary resources. ’, AI Now Institute and Share Lab , 7 September, http://www.anatomyof.ai. Accessed 15 May 2020.
  7. Crawford, Kate, and Paglen, Trevor. ( 2019;), ‘ Excavating AI: The politics of training sets for machine learning. ’, 19 September, https://excavating.ai. Accessed 15 May 2020.
  8. Dahmani, Louisa, and Véronique, D. Bohbot. ( 2020;), ‘ Habitual use of GPS negatively impacts spatial memory during self-guided navigation. ’, Scientific Reports, 10:1, p. 6310, [Crossref] https://doi.org/10.1038/s41598-020-62877-0. Accessed 24 July 2020.
     [Google Scholar]
  9. Elkins, James. ( 1997), The Object Stares Back: On the Nature of Seeing, San Diego, CA, New York and London:: Harcourt Brace;, http://archive.org/details/objectstaresback00elki_0. Accessed 15 May 2020.
     [Google Scholar]
  10. Eveleth, Rose. ( 2015;), ‘ How many photographs of you are out there in the world?. ’, The Atlantic, 2 November, https://wwwtheatlantic.com/technology/archive/2015/11/how-many-photographs-of-you-are-out-there-in-the-world/413389/. Accessed 15 May 2020.
     [Google Scholar]
  11. Fei-Fei, Li. ( 2020;), ‘ Where did ImageNet come from?. ’, lecture delivered at The Photographers’ Gallery, London, 21 September, https://unthinking.photography/articles/where-did-imagenet-come-from. Accessed 4 January 2020.
  12. Fisher, Andrew. ( 2012;), ‘ Photographic scale. ’, Philosophy of Photography, 3:2, pp. 310–29, https://doi.org/10.1386/pop.3.2.310_1. Accessed 24 July 2020.
     [Google Scholar]
  13. Frosh, Paul. ( 2002;), ‘ Rhetorics of the overlooked: On the communicative modes of stock advertising images. ’, Journal of Consumer Culture, 2:2, July, pp. 171–96, https://doi.org/10.1177/146954050200200202. Accessed 24 July 2020.
     [Google Scholar]
  14. Galloway, Alexander R.. ( 2013;), ‘ The poverty of philosophy: Realism and post-Fordism. ’, Critical Inquiry, 39:2, pp. 347–66, https://doi.org/10.1086/668529. Accessed 24 July 2020.
     [Google Scholar]
  15. Goodfellow, Ian J.,, Pouget-Abadie, J.,, Mirza, M.,, Xu, B.,, Warde-Farley, D.,, Ozair, S.,, Courville, A., and Bengio, Y.. ( 2014;), ‘ Generative adversarial networks. ’, ArXiv:1406.2661 [Cs, Stat] , 10 June, http://arxiv.org/abs/1406.2661. Accessed 15 May 2020.
  16. Goodfellow, Ian J.,, Shlens, Jonathon, and Szegedy, Christian. ( 2015;), ‘ Explaining and harnessing adversarial examples. ’, ArXiv:1412.6572 [Cs, Stat] , 20 March http://arxiv.org/abs/1412.6572. Accessed 15 May 2020.
  17. Harman, Graham. ( 2007), Heidegger Explained: From Phenomenon to Thing, Chicago, IL:: Open Court;.
     [Google Scholar]
  18. Harman, Graham. ( 2018), Object-Oriented Ontology: A New Theory of Everything, London:: Pelican Books;.
     [Google Scholar]
  19. Harvey, Adam. ( n.d.), CV Dazzle , https://cvdazzle.com/. Accessed 20 May 2020.
  20. Harwell, Drew. ( 2019;), ‘ A face-scanning algorithm increasingly decides whether you deserve the job. ’, Washington Post, 6 November, https://www.washingtonpost.com/technology/2019/10/22/ai-hiring-face-scanning-algorithm-Increasingly-decides-whether-you-deserve-job/. Accessed 7 February 2021.
     [Google Scholar]
  21. Hogan, Mél. ( 2018;), ‘ Big data ecologies. ’, Ephemera, 18:3, p. 631.
     [Google Scholar]
  22. Horton, Zachary K.. ( 2020), The Cosmic Zoom: Scale, Knowledge, and Mediation, Chicago, IL and London:: The University of Chicago Press;.
     [Google Scholar]
  23. Huang, Jensen. ( 2020;), ‘ GTC Digital 2020 Keynote Speaker CEO Jensen Huang. ’, NVIDIA , 14 May, https://www.nvidia.com/en-us/gtc/keynote/. Accessed 20 May 2020.
  24. Karras, T.,, Laine, S.,, Aittala, M.,, Hellsten, J.,, Lehtinen, J., and Aila, T.. ( 2020;), ‘ Analyzing and improving the image quality of StyleGAN. ’, ArXiv:1912.04958 [Cs, Eess, Stat] , http://arxiv.org/abs/1912.04958. Accessed 24 July 2021.
  25. Latour, Bruno. ( 2005), Reassembling the Social: An Introduction to Actor-Network-Theory, Oxford and New York:: Oxford University Press;.
     [Google Scholar]
  26. Lemke, Thomas. ( 2017;), ‘ Materialism without matter: The recurrence of subjectivism in object-oriented ontology. ’, Distinktion: Journal of Social Theory, 18:2, pp. 133–52, https://www.tandfonline.com/doi/full/10.1080/1600910X.2017.1373686. Accessed 24 July 2020.
     [Google Scholar]
  27. Lyons, Michael J.,, Akamatsu, Shigeru,, Kamachi, Miyuki, and Gyoba, Jiro. ( 1998;), ‘ Coding facial expressions with Gabor wavelets. ’, in Third IEEE International Conference on Automatic Face and Gesture Recognition, 14–16 August, pp. 200–05, http://doi.org/10.1109/AFGR.1998.670949. Accessed 24 July 2020.
     [Google Scholar]
  28. MacKenzie, Adrian, and Munster, Anna. ( 2019;), ‘ Platform seeing: Image ensembles and their invisualities. ’, Theory, Culture & Society, 36:5, pp. 3–22, https://doi.org/10.1177/0263276419847508. Accessed 24 July 2020.
     [Google Scholar]
  29. Mitchell, William J. T.. ( 1992), The Reconfigured Eye: Visual Truth in the Post-Photographic Era, Cambridge, MA:: MIT Press;.
     [Google Scholar]
  30. Mitchell, William J. T.. ( 2005), What Do Pictures Want? The Lives and Loves of Images, Chicago, IL:: University of Chicago Press;.
     [Google Scholar]
  31. Morton, Timothy. ( 2013), Hyperobjects: Philosophy and Ecology after the End of the World, Minneapolis, MN:: University of Minnesota Press;.
     [Google Scholar]
  32. Morton, Timothy. ( 2018;), ‘ Being Ecological: Timothy Morton, RSA Replay. ’, RSA, YouTube, 29 January, https://www.youtube.com/watch?v=d_5UWI-SEVE. Accessed 20 May 2020.
     [Google Scholar]
  33. Nvidia ( 2020;), ‘ Deep learning glossary. ’, NVIDIA, https://www.nvidia.com/en-us/data-center/resources/deep-learning-glossary/. Accessed 4 March 2020.
     [Google Scholar]
  34. NVlabs/ffhq-dataset ( n.d.), GitHub, https://github.com/NVlabs/ffhq-dataset. Accessed 24 July 2020.
     [Google Scholar]
  35. Paglen, Trevor. ( 2014;), ‘ Operational images. ’, E-Flux, 59, November, https://www.e-flux.com/journal/59/61130/operational-images/. Accessed 20 May 2020.
     [Google Scholar]
  36. Paglen, Trevor, and Crawford, Kate. ( 2019;), ‘ Excavating AI: The politics of training sets for machine learning. , Excavating AI , 19 September, https://www.excavating.ai. Accessed 24 July 2020.
  37. Pasquinelli, Matteo. ( 2019;), ‘ How a machine learns and fails: A grammar of error for artificial intelligence. ’, Spheres, 20 November, http://spheres-journal.org/how-a-machine-learns-and-fails-a-grammar-of-error-for-artificial-intelligence/. Accessed 20 May 2020.
     [Google Scholar]
  38. Pasquinelli, Matteo, and Joler, Vladan. ( 2020;), ‘ The Nooscope manifested: Artificial intelligence as instrument of knowledge extractivism. ’, Nooscope Manifested , 1 May, https://link.springer.com/article/10.1007/s00146-020-01097-6. Accessed 20 May 2020.
  39. Ritchin, Fred. ( 2010), After Photography, New York:: Norton Co;.
     [Google Scholar]
  40. Rose, Todd. ( 2015), The End of Average: How We Succeed in a World That Values Sameness, New York:: HarperOne;.
     [Google Scholar]
  41. Rubinstein, Daniel. (ed.) ( 2020;), ‘ Fragmentation of the photographic image in the digital age. ’, Routledge History of Photography, New York:: Routledge;, https://www.tandfonline.com/doi/abs/10.1080/17540760701785842. Accessed 24 July 2020.
     [Google Scholar]
  42. Rubinstein, Daniel, and Sluis, Katrina. ( 2008;), ‘ A life more photographic: Mapping the networked image. ’, Photographies, 1:1, pp. 9–28, https://doi.org/10.1080/17540760701785842. Accessed 24 July 2020.
     [Google Scholar]
  43. Virilio, Paul. ( 1994), The Vision Machine, Bloomington, IN:: Indiana University Press;.
     [Google Scholar]
  44. Yang, Kaiyu,, Qinami, Klint,, Fei-Fei, Li,, Deng, Jia, and Russakovsky, Olga. ( 2019;), ‘ Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the ImageNet hierarchy. ’, ArXiv:1912.07726 [Cs] , 16 December, https://doi.org/10.1145/3351095.3375709. Accessed 24 July 2020.
  45. Zylinska, Joanna. ( 2017), Nonhuman Photography, Cambridge, MA:: MIT Press;.
     [Google Scholar]
  46. Yiu, Sheung. ( 2022 [2021];), ‘ One face, millions of faces: Computer vision as hyperobject. ’, Philosophy of Photography, 12:1&2, pp. 71–91, https://doi.org/10.1386/pop_00048_1
     [Google Scholar]
/content/journals/10.1386/pop_00048_1
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One face, millions of faces: Computer vision as hyperobject
Philosophy of Photography 12, 71 (2021); https://doi.org/10.1386/pop_00048_1
/content/journals/10.1386/pop_00048_1
/content/journals/10.1386/pop_00048_1
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  • Article Type: Article
Keyword(s): algorithmic images; distributed seeing; generative adversarial networks (GAN); hyperimage; hyperobject; network scale

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