Meet DeepHimmelblau

Meet Deep Himmelb(l)au. Our algorithm learns CHBL’s semantic characteristics to generate new interpretations and new worlds.

Great improvement of the DeepHimmelb(l)au network that I’ve been building recently at Wolf D. Prix / Coop Himmelb(l)au. DeepHimmelblau is now able to learn an enormous amount of semantic characteristics to create more detailed interpretations #computervision #deephimmelblau #deeplearning

(Video: Our algorithm leanrs CHBL’s semantic charateristics to generate new interpretations and create a new language of architecture)

What is DeepHimmelblau?

DeepHimmelb(l)au is the result of the cumulative research effort undertaken by Coop Himmelb(l)au which operates at the intersection between architecture research, practice and Ai/deep learning.

DeepHimmeb(l)au is an experimental research project led by Design Principal Wolf D. Prix, Design Partner Karolin Schmidbaur and Chbl’s Computational Design Specialist Daniel Bolojan, which explores the potential of teaching machines to perceive, interpret and generate new designs of buildings, augment design workflows and augment the architect’s / designer’s creativity.

DeepHimmelb(l)au is currently the most advanced research dealing with the design potential of AI/deep learning undertaken by any architectural office.

What is DeepHimmelblau’s main aim?

Marshall McLuhan had a very interesting comment about the relationship between the creator / designer and his operating medium / tools – ”First we shape our tools, thereafter they shape us”. Similarly, the research inquires about the possible impact of Ai on the role of architects / designers and the relationship between new technologies / tools and designers. What role can Ai play in the design process? Should the role of Ai be to replace architects/designers? Or should it have a design assistant role interacting with designers/architects to augments design workflows and creativity?
For decades it has been the design methodology employed by Coop Himmelb(l)au to work within an open process, driven by an architectural idea and utilizing a multitude of available tools, that finds precisely in the productive translation from one medium to another inspiration for further steps in the development of the project. The individual outputs are often used in a similar fashion like a found object, an ‘objet trouvé’, to inspire new ways of perception and articulation through their interpretation and translation into architectural vocabulary.
One of the main focuses of Coop Himmelb(l)au’s current research on artificial intelligence is the concept of Augmented Creativity, where Ai is used as a new medium within that overall design methodology. We are examining the disruptive paradigm shift driven by the introduction of generative Ai methods in architectural design. Could Ai support the designer’s creativity?
We are currently developing a DeepHimmelb(l)au network, not as a means to automate a designer’s creativity, but rather to augment it per the methodology described above. DeepHimmelb(l)au is designed to interact with designers and inspire creativity. It is designed to facilitate a medium of constant interaction / feedback loops between designer interpretations and its own interpretations, between designer perceptions and its own perceptions. In that sense we are aiming – through augmentation – to strengthen our capabilities as creators – a collaboration between machines and humans.
While developments in Ai mean computers can be trained on certain creativity criteria, the degree to which Ai can develop its own sense of creativity is still something to investigate. Can Ai be taught how to create without guidance? Can Ai be taught how to interpret things? Can we, with help of AI, build the intelligence of the hand into an all digital design process? Can Ai be taught how to reinterpret representations from one domain to another, similar to how architects are inspired by concepts outside their architectural domain? Teaching computers to be creative is inherently different from how people create, but we do not yet know much about our own creative methodology.
Our perceptions and our conscious visual representations of reality are not a direct mapping of the real world. Humans interpret reality through reconstructions and interpretations based on past experiences. Our past experiences act as a frame / filter on our way of interpreting, understanding and perceiving the real world. Our training as architects operates as a filter / frame in the way we perceive the world, the way we interpret it and the way we draw inspiration from it.
One very common practice in design and architecture is that a designer learns, consciously or unconsciously, semantic representations of one domain, reinterprets that representation through a particular filter e.g. architectural style, architectural culture etc., and translates it to a different domain.
While humans unconsciously are capable of recognizing and disentangling various semantic features of what they perceive, neural networks are capable of having similar behavior after learning from a large enough set of samples. Some Networks learn automatically to separate/disentangle various semantic features of a dataset and afterwards enable specific features to be separated and managed on a particular level. In addition, machines exposed to large sample sets can discover perceptual deficiencies in human recognition capabilities. Can this innate capacity augment the creativity and interpretation of the designer?