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dc.contributor.authorAuthorPortal, Fernando
dc.date.accessionedDate Accessioned2024-09-03T19:12:30Z
dc.date.availableDate Available2024-09-03T19:12:30Z
dc.date.issuedDate Issued2022
dc.identifier.citationReferencia BibliográficaARQ, 2022(112), 10 p.
dc.identifier.issnISSN0716-0852
dc.identifier.uriURIhttp://repositorio.udla.cl/xmlui/handle/udla/1228
dc.identifier.uriURIhttps://edicionesarq.com/Revista-ARQ
dc.description.abstractAbstractDeconstruction questions not only the solidity of structures to detect their weakest points but also whether what we understand by structure is actually structural. If we ask, for example, what is structural in a tradition? we can observe – as this research does – that the structure is based on the reiteration of images. But when processing this repetition ad infinitum with artificial intelligence tools, the structural failure appears: an architectural image as disturbing as those that evoke deconstruction.
dc.format.extentdc.format.extent10 páginas
dc.format.extentdc.format.extent3.766Mb
dc.format.mimetypedc.format.mimetypePDF
dc.language.isoLanguage ISOspa
dc.publisherPublisherPontificia Universidad Católica de Chile
dc.sourceSourcesARQ
dc.subject.lcshdc.subject.lcshArquitectura
dc.titleTitleGolden generation generator: visualization, reproduction, and closure of structural biases in recent architectural production in Chile, through machine learning
dc.title.alternativeAlternative TitleGolden Generation Generator. Visualización, reproducción y clausura de sesgos estructurales en la producción arquitectónica reciente en Chile, a través del aprendizaje maquínico
dc.typeDocument TypeArtículo
dc.udla.catalogadordc.udla.catalogadorCBM
dc.udla.indexdc.udla.indexScopus
dc.identifier.doidc.identifier.doi10.4067/S0717-69962022000300128
dc.facultaddc.facultadFacultad de Arquitectura, Animación, Diseño y Construcción


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