URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 -URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站 - URBAN CURATING 城市策展 - FACE TRACKING 人脸追踪 - VIRTUAL REALITY 虚拟现实 - RELAX AREA 休闲区 - AUGMENTED REALITY 增强现实 - RAILWAY STATION 火车站

Presense


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.

“预感知”是一个机器学习和预测平台,它可以在用户环境中进行用户行为训练,并对用户未来的行为做出预测,且不仅在单一环境,而是多个城市环境中并行。作为用户与其所在城市之间的“折射镜”,该项目创建了个性化的预测模型,我们称之为“预测的自我”。这是一个量化的实体,可以作为“合成复制品”在任意数量的外国城市中复制和展开。通过学习用户独特的都市行为并与一系列环境进行互动,“预感知”可以让用户预见他们可能在其他地方的生活,这是一种前所未有的体验。这将会导致“自我的社会性多元化”。用户只能通过调整自己的行为方式并观察机器再学习的过程变化,来影响他们的“合成复制品”,“谁在训练谁”就成为一个问题。我们和“预测”的我们之间不断协商的现象,我们称之为“预测性自我感知”。

在数据挖掘普遍存在的时代,我们希望了解预测模型在当前运作时的利弊,同时推测它们可能会如何演变,并特别关注其如何影响城市环境。我们所做的事情是为了重新训练机器学习算法,以使我们的模型更准确,这是一个像气候变化这样的流行语一样值得全球关注的现象。生存条件的变化无声无息,且与我们的自我观念息息相关。可以说,最珍贵的资源不再是化石燃料,而是我们,或者更准确地说是我们的数据。正因为如此,必须适应的似乎不是技术,而是人类。

作为一个扩展的城市设计实践,“预感知”项目研究了城市规划和综合建模的交叉领域,寻求对尺度的新理解,而直到最近,我们才发现其对呼吁人类有作用。通过设想其“副作用”来挑战我们正在构建的技术,我们发现电影语言是最具沟通性的。我们想在“城市之眼”板块展开这样的叙述。

Credits:

Igor Sladoljev, Gleb Papyshev, Sveta Gorlatova, Artem Nikitin