Bruder, Johannes
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- PublikationThe Rewrite Collaborative Framework Browser Extension(15.02.2022) Bruder, Johannes; Sobecka, Karolina; Granzotto, Alberto; Frei, Fabian; Suess, Solveig; Kolb, LucieA browser extension, based on the open access framework hypothes.is, which allows for collective reading and annotation of complex texts (e.g. policy texts, legal texts, international treaties). The extension can be used for teaching & research, consensus-building and negotiation, reviewing or rewriting. Please consult the manuals before installing and using the extension.09 - Software
- PublikationKI als Medium und ›message‹ und die (Un-)Möglichkeit einer queeren Antwort(Transcript, 2022) Bruder, Johannes; Michael Klipphahn-Karge; Ann-Kathrin Koster; Sara Morais dos Santos Bruss [in: Queere KI. Zum Coming-out smarter Maschinen]Johannes Bruder untersucht in seinem Beitrag die konstitutiven Ein- und Ausschlüsse von autistischer Subjektivität und Kognition im Kontext von künstlicher Intelligenz. Während autistische Kognition in Fantasien von zukünftiger KI als konstitutives Anderes fungiert, waren und sind autistische Individuen essenzieller Bestandteil der kognitiven Infrastruktur von real existierender KI - ob als Testobjekte, Coder, oder Data Worker. Diese Dynamiken von Ein- und Ausschluss sind nicht neu, sondern gesellschaftlich fest verankert; autistische Aktivist*innen haben dementsprechend Strategien entworfen, sich selektiven Ein- und Ausschlüssen performativ zu entziehen. Im Text versucht Johannes Bruder diese Strategien für eine Antwort auf die Medientheorien zeitgenössischer AI fruchtbar zu machen.04A - Beitrag Sammelband
- PublikationThe Algorithms of Mindfulness(SAGE, 22.06.2021) Bruder, Johannes [in: Science, Technology & Human Values]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.01A - Beitrag in wissenschaftlicher Zeitschrift
- PublikationOptimal Brain Damage. Theorizing our Nervous Present(Culture Machine, 2021) Bruder, Johannes; Halpern, Orit [in: Culture Machine]01A - Beitrag in wissenschaftlicher Zeitschrift
- PublikationDonkey Kong's Legacy. About Microprocessors as Model Organisms and the Behavioral Politics of Video Games in AI(Universität Bern, 2021) Bruder, Johannes [in: Tsantsa]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.01A - Beitrag in wissenschaftlicher Zeitschrift