Anexact Building

Yuchun Huang and Yuxin Lin

The goal of Anexact Building is to demonstrate the ability of a Neural Network to integrate the architectural language of form through texts, and its potential to interpret the texts into a building under the topic of the coexistence between technologies and humans. The form of Anexact Building is not exact, or wholly to be simplified, but it is able to be reduced on a local level. It is through anexact forms that architecture takes a step towards becoming more heterogeneous: more like writing.

Since Machines are destined to compensate each other with humans, Anexact Building is motivated by future building patterns based on human and machine logic - Swarm Intelligence. It is also an experiment on exploring the purpose of design by bringing together all parts of a design problem, breaking them apart along the joints to better understand and recombine them using the power of the complexity of language.

Anexact Building is based on the texts describing architecture space design (160 Pattern Lists, 4720 Sentences) and nest-building behavior within Swarm Intelligence (36 Article Lists, 2250 Sentences). Looking for inter-relationship and connecting the same symbols to organize the compositions as a whole, all the texts in the dataset are arranged in the format of A Pattern Language1 and then trained with GPT-2 Algorithm2 to merge the swarm intelligence with architecture by inter-relating them together. To trigger the conversation with the machine after training, System Prompts referring to the interacting elements are composed to establish the holistic system within the context. The interacting elements are generated by mixing texts dataset of 4 architecture space types with 9 SI space types. Attentional Generative Adversarial Networks3 (AttnGAN) is the other algorithm to visualize the dialogues from GPT-2. Every sentence can be related to an image representing itself by using the algorithm, and every image will be interpreted by human intelligence into assembly unit models.  The unit models will be pieced together into the Anexact Building. Finally, Anexact Building will include multiple functions of shopping, hotel, gardens, and apartments as a mixed-use building.

The output of Anexact Building is an experiment of combining texts, visualizing machine logic and artificial intelligence communicating with human intelligence. The process of designing Anexact Building provides the possibility to include the need from innate and intuitively derived feelings about spaces and places. In the aspects of language including semantic, allegory, and poetics that generate feelings and emotions, there is a sense of order inside. They could extend beyond the structural, topological, and syntactic aspects of the whole process. The next generation of the algorithm could be able to interpret output sentences into images automatically and then it could generate dialogue within the image database. Image discrimination can be automated by semantic segmentation or object detection in the future.

Faculty Advisor: Matias del Campo, Visionary Machines

1 Alexander, Christopher, Ishikawa, Sara, and Silverstein, Murray. “A Pattern Language : Towns, Buildings, Construction.” Book. New York: Oxford University Press, 1977.
2 Radford, A., Jeffrey Wu, R. Child, David Luan, Dario Amodei and Ilya Sutskever. “Language Models are Unsupervised Multitask Learners.” (2019).
3 Xu, T., Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang and X. He. “AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks.” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018): 1316-1324.