| id |
architectural_intelligence2022_12 |
| authors |
Matias del Campo |
| year |
2022 |
| title |
Deep House - datasets, estrangement, and the problem of the new |
| doi |
https://doi.org/https://doi.org/10.1007/s44223-022-00013-w
|
| source |
Architectural Intelligence Journal |
| summary |
The purpose of this article is to discuss the application of artificial intelligence (AI) in the design of the Deep House project (Fig. 1), an attempt to use estrangement as a method to emancipate a house from a canonical approach to the progressive design of a one-family house project. The main argument in this text is that the results created by Artificial Neural Networks (ANNs), whether in the form of GANs, CNNs, or other networks, generate results that fall into the category of Estranged objects. In this article, I would like to offer a possible definition of what architecture in this plateau of thinking represents and how it differentiates from previous attempts to use estrangement to explain the phenomena observed when working with NNs in architecture design. A potpourri of thoughts that demonstrate the intellectual tradition of exploring estrangement, especially in theater and literature, that ultimately circles back to its implications for architecture, particularly in light of the application of AI. |
| series |
Architectural Intelligence |
| email |
|
| full text |
 file.pdf (4,982,066 bytes) |
| references |
Content-type: text/plain
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| last changed |
2025/01/09 15:00 |
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