美式英语

Deep Himmelblau

While developments in artificial intelligence (AI) mean computers can be trained on certain creativity criteria, the degree to which AI can develop its own sense of creativity it’s still something to enquire about. Can AI be taught without guidance how to create?

Cloud augmentation of Deep Himmelblau universe

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Coop Himmelb(l)au

引言

Much like feathers were developed by nature without dinosaurs ever thinking about flying, AI - which I like to call "Architectural Intelligence" - is a tool that will one day allow us, architects, to fly.

Wolf dPrix
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Coop Himmelb(l)au

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 it’s still something to enquire about. Can AI be taught without guidance how to create? Can AI be taught how to interpret things? 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 the 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 representation of one domain, reinterpret that representation through a particular filter e.g. architectural style, architectural culture etc, and translate it to a different domain.

While humans unconsciously are capable to recognize and disentangle 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?

Representation learning on the field of various 3D geometries

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Coop Himmelb(l)au

Representation learning on single 3D form - zoom in

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Coop Himmelb(l)au

Deep Himmelblau Universe

Domain Transfer

#augmentation #unsupervisedlearning #explorativecreativity

Domain Transfer 
#augmentation #unsupervisedlearning #explorativecreativity

Representation Learning

#gestalt #augmentation #representationlearning

Representation Learning 
#gestalt #augmentation #representationlearning

Representation Learning

#gestalt #augmentation #representationlearning

Representation Learning 
#gestalt #augmentation #representationlearning

Representation Learning

#gestalt #augmentation #representationlearning

Representation Learning 
#gestalt #augmentation #representationlearning

Representation Learning

#gestalt #augmentation #representationlearning

Representation Learning 
#gestalt #augmentation #representationlearning

Representation Learning

#gestalt #augmentation #representationlearning

Representation Learning 
#gestalt #augmentation #representationlearning

Representation Learning

#gestalt #augmentation #representationlearning

Representation Learning 
#gestalt #augmentation #representationlearning

Representation Learning

#gestalt #augmentation #representationlearning

Representation Learning 
#gestalt #augmentation #representationlearning

Augmentation_

#chbluniverse

Augmentation_ 
#chbluniverse

Deep Himmelblau

#deeplearning

Deep Himmelblau 
#deeplearning

Daniel Bolojan

Senior Architect, Computational Design Specialist

Efilena Baseta

Senior Architect, Computational Designer

Index 

CHBL Universe

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Coop Himmelb(l)au

What is Deep Himmelblau?

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

Deep Himmeblau is an experimental research project led by Design Principal Wolf dPrix, Design Partner Karolin Schmidbaur and Chbl’s Computational Design Specialist Daniel Bolojan, which explores the potential of teaching machines to interpret, perceive, to be creative, propose new designs of buildings, augment design workflows and augment architect’s / designer’s creativity. Deep Himmelblau is currently the most advanced research dealing with the design potential of AI/​deep learning undertaken by any architectural office.

What is Deep Himmelblau’s main aim?

There is 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 enquires about the future impact of AI on the role of architects/​designers and the relationship between new technologies / tools and designers. What role should AI play in the design process? Should the role of AI be to replace architects/​designers? Or should it have a design assistant role to interacting with designers/​architects to augments design workflows and creativity?

Presentation time laps - DigitalFUTURES

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Coop Himmelb(l)au

Cloud augmentation

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Coop Himmelb(l)au