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A Peer-Reviewed Journal About MACHINE FEELING

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A Peer-Reviewed Journal About MACHINE FEELING A Peer-Reviewed Journal About MACHINE FEELING Mitra Azar Daniel Chávez Heras Michela De Carlo Iain Emsley Malthe Stavning Erslev Tomas Hollanek Rosemary Lee Carleigh Morgan Carman Ng Irina Raskin Tiara Roxanne Rebecca Uliasz Maria Dada Tanja Wiehn Brett Zehner Christian Ulrik Andersen & Geoff Cox (Eds.) Volume 8, Issue 1, 2019 ISSN 2245-7755 1 Contents Christian Ulrik Andersen & Geoff Cox Editorial: Feeling, Failure, Fallacies 4 MAKING SENSE Iain Emsley Iteracies of Feeling 10 Irina Raskin Machine Learning and Technoecological Conditions of Sensing 20 Maike Klein Robotic Affective Abilities 34 (UN)BEING 47 Brett Zehner Machines of Subjection: Notes on A Tactical Approach to Artificial Intelligence 48 Maria Dada Queering Global Information Systems 58 Tiara Roxanne Digital Territory, Digital Flesh: Decoding the Indigenous Body 70 Rebecca Uliasz Assemblages of Desire: Reappropriating Affective Technologies 82 FEELING GENERATORS 95 Carman Ng Affecting Reality: Intersecting Games, Trauma, and Imaginaries 96 Malthe Stavning Erslev I forced a bot to read over 1,000 papers from open-access journals and then asked it to write a paper of its own. Here is the result. Or: A quasi-materialist approach to bot-mimicry 114 Michela De Carlo Synthetic Bodies and Feeling Generators 128 Tanja Wiehn (Un)Predictable Acts of Data in Machine Learning Environments 142 SEEING THINGS 155 Mitra Azar POV-matter, Cinematic POV and Algorithmic POV between Affects and Umwelten 156 Daniel Chávez Heras Spectacular Machinery and Encrypted Spectatorship 170 Tomasz Hollanek Non-user-friendly: Staging Resistance with Interpassive User Experience Design 184 Rosemary Lee Operative Image: Automation and Autonomy 194 Carleigh Morgan Calculated Error: Glitch Art, Compression Artefacts, and Digital Materiality 204 Contributors 218 A Peer-Reviewed Newspaper Journal About_ ISSN: 2245-7755 Editors: Christian Ulrik Andersen and Geoff Cox Published by: Digital Aesthetics Research Centre, Aarhus University Design: Mark Simmonds, Liverpool CC license: ‘Attribution-NonCommercial-ShareAlike’ www.aprja.net EDITORIAL FEELING, FAILURE, FALLACIES Christian Ulrik Andersen & Geoff Cox APRJA Volume 8, Issue 1, 2019 ISSN 2245-7755 CC license: ‘Attribution-NonCommercial-ShareAlike’. EDITORIAL capture of everyday styles, expressions, Feeling preferences, sentiments, and so forth – the very means that Williams alludes to. Digital culture has become instrumental for If, in general, machine learning appears capturing and managing what Raymond to lack an affective dimension, then in what Williams would once have called “struc- ways are we to understand its resolute and tures of feeling”. The journal issue A Peer- concerted pursuit of this? What old registers Reviewed Journal About Machine Feeling of processing culture and organizing time, alludes to this, and points to a material space and power does it build on? What analysis of aesthetics and culture, including potential new sensibilities and structures of its technical and social forms, and in the way feeling may arise in such normalized registers that this concept was originally employed of our habits? What new cultural and social as an acknowledgment of the importance of forms and practices emerge in the coming the hard to capture dimensions of everyday together of machine learning and structures life. Styles, expressions and sentiments are of feeling? In each their own way, the authors always in flux, yet Williams, and others after in this journal explore these questions. him, have with this term argued that they are grounded in cultural history and specific eve- ryday situations. In developing a critical and analytic understanding we should therefore Failure turn our attention to changes in language, style, aesthetics and those social forms To capture moments when new patterns of which are active in the present, but not yet experience emerge, when people start to fully formed or captured by a conceptual or think differently, when new sensibilities arise scientific knowledge framework. Taking their will first and foremost depend on a large set point of departure from Williams, Devika of training data — sound, text, biological Sharma and Frederik Tygstrup write: data, and more that can be used for image recognition, sentiment analysis and more. At We recognise the facts of cultural a more general level, these datasets absorb life once they are established and all kinds of social and cultural production; institutionalised, but we tend to miss they seek to absorb every moment that peo- those moments when new patterns of ple start to think, act, sense, and experience experience emerge, when people start phenomena in new ways. to think differently, when new sensibili- There is a certain paradox in this. As ties arise, when habits swerve. (4) pointed out by Matteo Pasquinelli, machine learning is a paradigm of intelligence that This journal issue further explores fails to provide a methodology of failure. this line of thinking, and more specifically What people generally refer to as artificial responds to the current developments in intelligence and machine learning is merely machine learning and the ability of technolo- a statistical mapping of correlations in the gies to capture and structure feelings and dataset. Because of this, machine learning experiences that are active, in flux, and in will reduce the least common structures in the present; for example, in the ways that the dataset, simply in order to reduce calcu- automated experiences of seeing and read- lation costs. Consequently, machine learning ing begin to produce knowledge through the is not a sign of cognition, but of compression 5 APRJA Volume 8, Issue 1, 2019 as a means to efficiency, which on the other Zehner); feeling generators (Malthe Stavning side is also a loss of diversity; failure does Erslev, Michela De Carlo, Carman Ng, Tanja not exist. In this, he claims, machine learning Wiehn); and seeing things (Mitra Azar, Daniel seems much more aligned with a history of Chavez Heras, Tomasz Hollanek, Rosemary optical lenses who operate by resolutions Lee, Carleigh Morgan). and diffraction. This is what he calls statisti- There is more than a hint of Williams cal cinema. This problem of generalization or (and his cultural materialism) across these “regression towards the mean” is mathemati- positions in recognition of the ways that cal but not without political consequences. certain ideas (such as affect theory and ma- chine learning) achieve hegemonic status. We, as contributors to this journal issue, all feel/felt the weight of history and privilege Fallacies here, not least as the workshop leading to the publication was held at the University What then is the role of researching digital of Cambridge where Williams himself once culture and machine feeling? On the one taught. The setting for our (and his) work is hand, to follow Williams and capture the clearly an important issue if we take struc- “habits that swerve” seems to be relegated tures of feeling seriously and recognise that to corporate research institutions that seek to the contents of a journal such as this are a align calculation costs and statistical resolu- consequence of a wider factors that include tion; institutions that perform the statistical actual work, social relations, and place of spectacle of contemporary digital culture. On production: “it is a trivial fantasy to suppose the other, could researching machine feeling that these general and pressing conditions be regarded as an interrogation of the fail- are for long or even at all separable from the ures of machine learning; or, even providing immediate and the personal”, as Williams a methodology of failure that machine learn- puts it (Culture and Materialism 222). Herein ing otherwise lacks? lies the tension between received forms and This kind of research could take differ- lived experience, of structures of feeling. ent shapes. For one, it might address the implied inclusions and exclusions that are at Thanks to all authors as well as further play in the politics of research, such as the contributors to the workshop (Anne intersectional feelings of race, gender, and Alexander, Alan Blackwell, Anja Breljak, class. It might address the emotionalisation Jennifer Gabrys, Kristoffer Gansing, of not only politics and a people born to Leonardo Impett, Matteo Pasquinelli, Søren feel (which seems to be intrinsically related Pold, Winnie Soon, Magda Tyzlik-Carver, to the statistical spectacle), but also of re- Martin Zeilinger), a collaboration between search itself and how it links to subjective transmediale festival, Aarhus University, patterns of experience. The contributions and Cambridge Digital Humanities Learning to the journal resonate with this approach Programme. and expose some of the fallacies at work in research processes once feelings are en- We dedicate this issue to the memory of gaged. The subsections of this journal reflect Sascha Pohflepp. this problem: making sense (Iain Emsley, Maike Klein, Irina Raskin); (un)being (Maria Dada, Tiara Roxanne, Rebecca Uliasz, Brett 6 EDITORIAL Works cited Pasquinelli, Matteo. “The Undetection of the New.” Machine Feeling. University of Cambridge, 14 Jan. 2019. Keynote at workshop. Sharma, Devika and Frederik Tygstrup. “Introduction.” Structures of Feeling: Affectivity and the Study of Culture, edited by Devika Sharma and Frederik Tygstrup. Berlin: De Gruyter, 2015. Print. Williams, Raymond. “Structures of Feeling.” Marxism and Literature, Oxford: Oxford University Press, 1977, pp. 128-135. Print.
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