Presense 预感知 Igor Sladoljev, Sveta Gorlatova, Gleb Papyshev, Artem Nikitin Presense is a machine learning prediction platform prototype. Trained on users activity in his/her environment it can produce predictions of a user’s future behavior, not just in one, but multiple urban environments in parallel. Acting as a “refractive lens” between the user-citizen and their native city, Presense creates personalized predictive model we call “the predicted self” – a quantified entity which can be copied and deployed as “synthetic replicas” in any number of foreign cities. By learning from users’ unique urban signature and interacting with a range of contexts, Presense grants the user insight into the lives he or she might be living elsewhere – the experience previously unattainable. We imagine this would lead to, what we call, “a social multiverse of the self”. Since users can’t influence their “synthetic replicas” other than by adjusting their own patterns of behavior and observing the gradual change of machine re-learning the question of “who is training who” becomes apparent. This phenomenon of continuous negotiation between us and “predicted” us we call “the predictive self-sensing”.
In the age of ubiquitous data mining, we want to look at the benefits and hazards of predictive models as they operate at present but also speculate on how they might evolve with a special interest in how it impacts the urban environment. Coming to terms with the fact that whatever we choose to do is only retraining the machine learning algorithms to make a more accurate model of us is a phenomenon that deserves a global pause and a buzzword as big as climate change. The existential change running silently in parallel is relevant to our idea of the self, as arguably the most prized resource is no longer fossil fuel but us, or rather, our data. Because of this, it seems it is not the technology but the human being that must adapt.
As an expanded urban design practice, Presense investigates the intersections of urban planning and synthetic modelling looking for a new understanding of the scale, we, until recently, found useful to call human. In challenging the technology we are building by envisioning its ‘side effects’ we find cinematic language to be the most communicative. For ‘Eyes of the City’ we would like to present several such narratives.
Credits: Igor Sladoljev, Gleb Papyshev, Sveta Gorlatova, Artem Nikitin
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