Deep Green (2020)
article⁄Deep Green (2020)
abstract⁄Ubiquitous computing enables us to decipher the biosphere’s anthropogenic dimension, what we call the Urbansphere Pasquero and Poletto 2020. This machinic perspective unveils a new postanthropocentric reality, where the impact of artificial systems on the natural biosphere is indeed global, but their agency is no longer entirely human. This paper explores a protocol to design the Urbansphere, or what we may call the urbanization of the nonhuman, titled DeepGreen. With the development of DeepGreen, we are testing the potential to bring the interdependence of digital and biological intelligence to the core of architectural and urban design research. This is achieved by developing a new biocomputational design workflow that enables the pairing of what is algorithmically drawn with what is biologically grown Pasquero and Poletto 2016. In other words, and more in detail, the paper will illustrate how generative adversarial network GAN algorithms Radford, Metz, and Soumith 2015 can be trained to ‘behave’ like a Physarum polycephalum, a unicellular organism endowed with surprising computational abilities and selforganizing behaviors that have made it popular among scientist and engineers alike Adamatzky 2010 Fig. 1. The trained GANPhysarum is deployed as an urban design technique to test the potential of polycephalum intelligence in solving problems of urban remetabolization and in computing scenarios of urban morphogenesis within a nonhuman conceptual framework.
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| Year |
2020 |
| Authors |
Pasquero, Claudia; Poletto, Marco. |
| Issue |
ACADIA 2020: Distributed Proximities / Volume I: Technical Papers |
| Pages |
668-677. |
| Library link |
N/A |
| Entry filename |
deep-green |
