Bruder, Johannes
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Teaching the Radical Catalogue, Nr. 8: Friendly Peer Review
Für die letzte Lektion von «Teaching the Radical Catalogue – A Syllabus», ein Studienprogramm das im Rahmen der Ausstellung «Reading the Library» im Sitterwerk St.Gallen entwickelt wurde werden Bibliothekar:innen, Künstler:innen, Programmierer:innen, Forschende und Dozierende eine «friendly peer review» des Lehrplans vornehmen. Was fehlt im Lehrplan, der im während der Ausstellung entwickelt wurde? Wo sind eventuelle blinde Flecken der Verfasser:innen? Wie sollte oder könnte das Studienprogramm erweitert werden? Nicht zuletzt soll zudem gefragt werden, wie es weitergeht? Wie und wo kann der Lehrplan angewandt werden?
Don't waste a good crisis! Finance, Machine Learning, and the Pursuit of Productive Paranoia
The last decade has seen a number of flash and hack crashes that exposed how already volatile stock markets are increasingly integrated with other social domains, resulting in entirely unstable media ecologies. For instance, the AP hack crash exposed how entwined technologies that mine twitter data and high frequency trading systems are, and what effects the increasing automation of information processing and trading has already wrought (Karppi and Crawford 2016). This development has infinitely complicated prediction and aggravated uncertainties that characterized markets ever since. In environments where volatility is the source of productivity and growth as well as of catastrophe and trauma, secret information, hidden patterns, affect and contagious processes now take center stage—and so do practices and technologies conceived to manage and exploit the related, mutating uncertainties. My paper is of conceptual nature and zooms in on the pursuit of paranoid ideation as a technology in both contemporary machine learning and finance. I draw on sociologies and anthropologies of finance and trading (Arnoldi and Borch 2007; Maurer 2002; Zaloom 2007) to carve out the significance of quasi-paranoid ideation in the market. I argue that paranoid tendencies among traders and in markets more generally are exacerbated in cutting-edge machine learning systems that trade. Mechanisms designed to prevent the becoming pathological of information processing therefore make for an object of study that has implications beyond the narrow confines of computing. I will provide some insights into the design of cutting-edge machine learning systems and work towards a technology-based approach to the libidinal economies of contemporary capitalism.
The Algorithms of Mindfulness
This paper analyzes notions and models of optimized cognition emerging at the intersections of psychology, neuroscience, and computing. What I somewhat polemically call the algorithms of mindfulness describes an ideal that determines algorithmic techniques of the self, geared at emotional resilience and creative cognition. A reframing of rest, exemplified in corporate mindfulness programs and the design of experimental artificial neural networks sits at the heart of this process. Mindfulness trainings provide cues as to this reframing, for they detail each in their own way how intermittent periods of rest are to be recruited to augment our cognitive capacities and combat the effects of stress and information overload. They typically rely on and co-opt neuroscience knowledge about what the brains of North Americans and Europeans do when we rest. Current designs for artificial neural networks draw on the same neuroscience research and incorporate coarse principles of cognition in brains to make machine learning systems more resilient and creative. These algorithmic techniques are primarily conceived to prevent psychopathologies where stress is considered the driving force of success. Against this backdrop, I ask how machine learning systems could be employed to unsettle the concept of pathological cognition itself.
The Cognitive Agent
Zooming in on the design of machine learning algorithms, he discusses characteristics of the post-anthropocentric figure that is “the cognitive agent” and elaborates on leakages and cross-contaminations between North American social science, psychology and computing.
Mapping Algorithmic Assumptions: Reflections from a Society for Psychological Anthropology roundtable
Milieux postdocs on pedagogy, collaboration, and the work of the (post)humanities
This year, 10 postdoctoral fellows have made Milieux the home for their continued research and practice. Each fellow embodies a dedication to interdisciplinary thought and critical inquiry, pursued in ways that rethink the idea of clear boundary between theory and practice. Their presence is indicative of the active state of research at the institute this year, and we are very happy to welcome them to the Milieux community. To learn more about their individual projects and histories, check out their detailed bios here. We are also excited to share an interview with two of our 2021 fellows, Dr. Marcelina Piotrowski and Dr. Johannes Bruder. This conversation, conducted by Concordia Philosophy MA Candidate, Matthew Raymond, examines questions of contemporary pedagogy, the uncertain and necessary role of the “humanities”, and the infrastructural conditions of knowledge-production. This reflection on the place and possibility of research is a recurrent theme across the work of all our 2021 fellows and is demonstrative of the translation and experimentation, unfettered by thematic or methodological boundaries, that makes Milieux such an exciting space to think and work. Enjoy!
Donkey Kong's Legacy. About Microprocessors as Model Organisms and the Behavioral Politics of Video Games in AI
The article discusses forms of contamination between human and artificial intelligence in computational neuroscience and machine learning research. I begin with a deep dive into an experiment with the legacy microprocessor MOS 6502, conducted by two engineers working in computational neuroscience, to explain why and how machine learning algorithms are increasingly employed to simulate human cognition and behavior. Through the strategic use of the microprocessor as “model organism” and references to biological and psychological lab research, the authors draw attention to speculative research in machine learning, where arcade video games designed in the 1980s provide test beds for artificial intelligences under development. I elaborate on the politics of these test beds and suggest alternative avenues for machine learning research to avoid that artificial intelligence merely reproduces settler-colonialist politics in silico.