Esther - xenodataset: Difference between revisions

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Xenodatasets

Esther Rizo-Casado

Counterculture has always functioned as a means to envision non-hegemonic futures. However, we have been seeing how phenomena like grunge, techno, or queer movements have succumbed to capitalism's ability to commodify them into commercial content. Is it possible to imagine something non capitalizable? The mediaeval mystic definition of imagination as "thinking with images" has evolved into a contemporary understanding as "the capacity to represent possibilities other than present possibilities" (Picón, et al., 2024). Therefore, a deficiency in imagination is correlated with image generation and a lack of potential alternative futures (Fisher, 2016). In the era of crowd-generated imagery and automated cognition, the question arises: what form would counterculture assume?

AI models, designed to generate images akin to their training dataset, contribute to the homogenization of datasets. This trend is pervasive in the contemporary technological landscape and is identified as a primary cause of cultural biases. Technical and social attempts to rectify biased or incomplete data, such as the unlearning machine (Nguyên, et al. 2024), have proven unsuccessful. Conversely, artists like Mimi Onuoha or Caroline Sinders propose the use of datasets to identify underrepresented identities, resulting in their research: missing datasets or feminist dataset. Acknowledging the impossibility of seeking an impartial data, these two researchers leverage the potential of datasets to an artistic format capable of subverting capitalist practices.

Similarly to the missing datasets, the concept of the Weird serves as an ally in recovering what is absent, repressed, forgotten, or ignored (Fisher, 2018). This includes entities deemed as “monster” originating from "monere," signifying the manifestation of aspects that society tends to avoid, thereby neglecting intrinsic revelations. In its semantic family the term "xeno", referred to the alien, the other, and recovered by the Xenofemisnists (Hester, 2018) gains significance due to its lack of classificatory criteria.

The XenoVisual Studies collective explores images generated by and for cognitive assemblages between humans, machines and the xeno (Hayles, 2017). The xenoimages they generate disrupts normative represented bodies by hacking generative protocols, repurposing tools, gathering images of xenobodies as training data, engaging in algorithmic apophenia, or creating invented languages (XenoVisual Studies, 2024). The whole co-imagining movement occurs in a collaborative environment where communities most affected by biased data meet with artists and technologists.

Subsequently, xenovisuals become operational images (Parikka, 2023), crafted by (machines) cognizers for other (machines) cognizers in the pursuit of creating new imaginaries. Unclassifiable image datasets serve as catalysts for imagining future fictions, interpreting the dataset not merely as a format or data container but as imaginal matter for cognizers.

Example images from the collective project Xenoimage Dataset 2022.

References

Fisher, M. (2016). Capitalist Realism. [Realismo Capitalista]. Buenos Aires, Caja Negra.

Fisher, M. (2018). The Weird and the Eerie. [Lo raro y lo Espeluznante]. Buenos Aires, Caja Negra.

Goh, A. (2019). Appropriating the Alien: A critique of Xenofeminism. Mute. https://www.metamute.org/editorial/articles/appropriating-alien-critique-xenofeminism

Hayles, N. Katherine (2017) Unthought: The Power of the Cognitive Nonconscious. Chicago, IL: Chicago University Press.

Hester, H. (2018). Xenofeminism. John Wiley & Sons.

Nguyên, T. T., Huynh, T. T., Nguyen, P. L., Liew, A. W., Yin, H., & Nguyen, Q. V. H. (2022). A survey of machine unlearning. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2209.02299

Ọnụọha, M. (2024, 1st January). The Library of Missing Datasets. MIMI ỌNỤỌHA. https://mimionuoha.com/the-library-of-missing-datasets

Picón, D., Castro, J., & Rocío, D. (2021, noviembre). La imaginación (temporada ELR171). Recuperado 8 de enero de 2024, de https://open.spotify.com/episode/4hEYgzMJj43jN2j4Hnj8Ue?si=66eef3748744404a

XenoVisual Studies (2024, 11th January). XenoVisualStudies. https://xenovisualstudies.com/

index.php?title=Category:Content form

@@ Line 1: / Line 1: @@
 <div class="metadata">
-== Xenodatasets as format, content & counterculture ==
+== Xenodatasets ==
 '''Esther Rizo-Casado'''
 </div>
-We are facing a cultural hole created by a compilation of lacks:
+Counterculture has always functioned as a means to envision non-hegemonic futures. However, we have been seeing how phenomena like grunge, techno, or queer movements have succumbed to capitalism's ability to commodify them into commercial content. Is it possible to imagine something non capitalizable? The mediaeval mystic definition of imagination as "thinking with images" has evolved into a contemporary understanding as "the capacity to represent possibilities other than present possibilities" (Picón, et al., 2024). Therefore, a deficiency in imagination is correlated with image generation and a lack of potential alternative futures (Fisher, 2016). In the era of crowd-generated imagery and automated cognition, the question arises: what form would counterculture assume?
-The lack of counterculture. Until today counterculture served as a means to envision alternative futures. However, as it happens with phenomena like grunge, techno, or queer movements, capitalism has demonstrated a remarkable ability to commodify them as commercial content. Living in the times of crowd generated imagery and automated cognition (Hayles, 2017): what form could the cultural avant-garde take?
+AI models, designed to generate images akin to their training dataset, contribute to the homogenization of datasets. This trend is pervasive in the contemporary technological landscape and is identified as a primary cause of cultural biases. Technical and social attempts to rectify biased or incomplete data, such as the unlearning machine (Nguyên, et al. 2024), have proven unsuccessful. Conversely, artists like Mimi Onuoha or Caroline Sinders propose the use of datasets to identify underrepresented identities, resulting in their research: missing datasets or feminist dataset. Acknowledging the impossibility of seeking an impartial data, these two researchers leverage the potential of datasets to an artistic format capable of subverting capitalist practices.
-The lack of imagination. We can not think without imagining since imagination is the mediation from the outside world to the inside (Picón, et al., 2021). The definition of imagination in medieval mysticism was "thinking with images". The contemporary imagination definition has mutated to “the capacity to represent possibilities other than present possibilities” (Picón. D, et al., 2024). Therefore a lack of imagination connects with a lack of other possible futures (Fisher, 2016).
+Similarly to the missing datasets, the concept of the Weird serves as an ally in recovering what is absent, repressed, forgotten, or ignored (Fisher, 2018). This includes entities deemed as “monster” originating from "monere," signifying the manifestation of aspects that society tends to avoid, thereby neglecting intrinsic revelations. In its semantic family the term "xeno", referred to the alien, the other, and recovered by the Xenofemisnists (Hester, 2018) gains significance due to its lack of classificatory criteria.
-Lack of perspective. In one of his letters Socrates expressed his concern about the impact of writing on human memory and dialectical capacities (Bonde, 2023). Then came the concerns around the printed press: it will end with architecture (Hugo, 1840). Meanwhile writing and printing gave birth to imaginative pleasures like reading science fiction books. How should we understand that in 2023 The Center for Artistic Inquiry and Reporting is extremely concerned about AI ending with the work of book illustrators?
+The XenoVisual Studies collective explores images generated by and for cognitive assemblages between humans, machines and the xeno (Hayles, 2017). The xenoimages they generate disrupts normative represented bodies by hacking generative protocols, repurposing tools, gathering images of xenobodies as training data, engaging in algorithmic apophenia, or creating invented languages (XenoVisual Studies, 2024). The whole co-imagining movement occurs in a collaborative environment where communities most affected by biased data meet with artists and technologists.
-Lack of something new. The contemporary technological corpus tends toward the trend. This has been identified in most cases as very boring and in some cases as cultural bias. If you ask an AI to generate an image of a futuristic world it will give you the all time cliches. This is because AI models are trained to generate content similar to their training dataset, and datasets are boring. What if we stop this procedural animism of treating tools as entities (Anikina, 2022)? What if we stop understanding bias as something we can unlearn? (Nguyên, 2024).
+Subsequently, xenovisuals become operational images (Parikka, 2023), crafted by  (''machines)'' cognizers for other (''machines) cognizers'' in the pursuit of creating new imaginaries. Unclassifiable image datasets serve as catalysts for imagining future fictions, interpreting the dataset not merely as a format or data container but as imaginal matter for cognizers.
-In our search for other imaginaries, we will need “other images”. There are plenty invisibilised images, different from what we normally see. Some aesthetic approaches are proposing images of the Weird as alternatives to the sterile nature of current content. Understanding Weird as an indicator of the absence, that is to say, of that which has been repressed, forgotten, or ignored (Fisher, 2018). The aesthetics of the Weird are letting us rescue concepts like “xeno” from the pejorative and moralistic visions where they were sinking (Goh, 2019). The represented identities in the weird imaginaries will then be monsters since the term “monster”, derived from "monere", refers to the manifestation of aspects that society tends to avoid, thereby neglecting intrinsic revelations.
+Example images from the collective project [https://www.medialab-matadero.es/proyectos/xenoimage-dataset Xenoimage Dataset] 2022.
-The real value of the term xeno is another lack: the lack of classifying criteria. In a resetting approach to biased data, the only real ''machine unlearning'' method (Nguyên, et al. 2022) will need datasets of the Weird (Ọnụọha, 2024). These alterations of normative imagery into [https://www.xenovisualstudies.com/ xenovisualities] is being done by hacking generative protocols, collecting xenoimages as training data, allowing algorithmic apophenia or prompting with invented languages (XenoVisual Studies, 2024).
-Unclassifiable datasets are the trigger to other possible futures.
-Some images from the collective project [https://www.medialab-matadero.es/proyectos/xenoimage-dataset Xenoimage Dataset] 2022 (Mar Osés, Miguel Rangil, Inna Mart, Pilar del Puerto, Mon Cano, Levi Jose Jiménez Rufes, Esther Rizo, Claudix Vanesa Figueroa Muro)
 [[File:Xenoimage Examples.png|thumb|TSNE Xenoimage Dataset aligned visualisation.]]
   [[File:Xenoimage Dataset examples close up.png|thumb|TSNE Xenoimages close up.]]
@@ Line 44: / Line 38: @@
 '''References'''
-Anikina, A. (2022). Procedural animism. A peer-reviewed journal about --, 11(1), 134-151. <nowiki>https://doi.org/10.7146/aprja.v11i1.134311</nowiki>
-Bonde, N. (2023) Sociedades Algorítmicas. Conferencia CCCB (citar bien)
 Fisher, M. (2016). Capitalist Realism. [Realismo Capitalista]. Buenos Aires, Caja Negra.
@@ Line 57: / Line 47: @@
 Hayles, N. Katherine (2017) Unthought: The Power of the Cognitive Nonconscious. Chicago, IL: Chicago University Press.
-Hugo, V. (1840). Nuestra señora de París [Notre-dame de Paris]. Edelvives.
+Hester, H. (2018). ''Xenofeminism''. John Wiley & Sons.
 Nguyên, T. T., Huynh, T. T., Nguyen, P. L., Liew, A. W., Yin, H., & Nguyen, Q. V. H. (2022). A survey of machine unlearning. arXiv (Cornell University). <nowiki>https://doi.org/10.48550/arxiv.2209.02299</nowiki>
@@ Line 67: / Line 57: @@
 XenoVisual Studies (2024, 11th January). XenoVisualStudies. <nowiki>https://xenovisualstudies.com/</nowiki>
-[[Category:Content form]]
+[[index.php?title=Category:Content form]]