Hochschule für Gestaltung und Kunst Basel FHNW
Dauerhafte URI für den Bereichhttps://irf.fhnw.ch/handle/11654/11
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Publikation Donkey Kong's Legacy. About Microprocessors as Model Organisms and the Behavioral Politics of Video Games in AI(Universität Bern, 2021) Bruder, JohannesThe 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.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Distracted Attention: Mindful Algorithms and the Algorithms of Mindfulness(05.09.2019) Bruder, Johannes06 - PräsentationPublikation Donkey Kong is not a circuit. A psychogram of contemporary neural media(16.03.2019) Bruder, Johannes06 - PräsentationPublikation The Cognitive Agent(17.02.2021) Bruder, JohannesZooming 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.06 - PräsentationPublikation The Algorithms of Mindfulness(SAGE, 22.06.2021) Bruder, JohannesThis 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.01A - Beitrag in wissenschaftlicher Zeitschrift