Following our studio last semester (Refuge 2.0 – Artificial Swissness), where we looked at Generative Adversarial Networks (GAN) and their potential and limits for creating (in silico) a primitive hut in the Swiss alpine context, this semester we will investigate urban typologies in Zürich and explore the idea of the “post-2020 home”, as our studio focus.
We are interested in the architecture of home not only as the shelter for domestic life, but also in the notion of „Heimat“ and the identity a house can produce. Images are powerful vehicles for these ideas, and we will use AI to process images to capture, collect, and learn the architectural DNA of the local identity and the visual characteristics that constitute the genius loci, in our case the identity of Zürich Altstadt. What is Zürichness? What is it for us, what is it for the machine?
What is important for us, and for this “deep city” studio, is that AI has the potential to not only capture the essence of a set of images, i.e., accurately depicting the mood and style, but also uncover subtle details that would otherwise be overlooked by human eyes.
While images of house exteriors (street-views, facades, axons) convey the identity of a place and neighborhood, they also reflect the functions inside the house, such as domestic life-styles, societal status, internal organizational hierarchies. Here architecture provides the structural framework.
The recent confinement during the Covid -19 crisis has uncovered the limits of today’s housing typologies, highlighting an increased misalignment between their affordances and emerging life styles where living and working converge, and everyday social activities, such as shopping, learning, entertainment have moved from the city into the intimate confines of home. This studio asks students to rethink a typical housing unit in the Altstadt of Zürich and explore the possible transformation of the urban house as a typology where increasingly (almost) everything takes place.
The first part of the semester will consist of curating data and manipulating a GAN to generate variations of housing units by learning from the Altstadt Zurich. The collection of façades will serve as samples for the algorithms. Students will select and interpret the output generated by the algorithm, and, through a process of inverse geometric projection, transform the machine-generated images into 3D architectural forms.
During the second part of the semester, students will confront the architectural forms generated by the GAN algorithms with imagined scenarios of domestic life post 2020, emphasizing spatial and temporal flexibility. This creative confrontation will result in a series of proposed Zurich Altstadt housing “proto-typologies”, presented as schematic, axonometric drawings and Zurich-blue 3D prints.
The studio will culminate in a personal interpretation of the post 2020 house on a precise, specific site in Altstadt Zurich, as a final project: a privileged, virtually public and flexible, yet physically private and intimate space, amidst a changing world and confused ecology.