Aktiviteter pr. år
Projektdetaljer
Beskrivelse
The project Reconsidering otherness is funded by the AI cluster at the Department of Communication and Psychology at Aalborg university.
Using machine learning to generate works of art, design and architecture has been an emerging research field in the last decades, and experiments have been made as early as the 1960, inspired by Alan Turing's question ‘can computers think?’.
In architecture, machine learning allows the exploration of large design spaces and optimization of certain aspects which can be expressed in a numerical format, such as areas, volumes, material use or energy consumption. In the last years, much effort has gone in automating floor plan generation and in generative design tools which optimize material use in concert with structural requirements. Additionally, initial work has shown results from training a generative adversarial neural network to ‘hallucinate’, creating images about architecture (del Campo et al., 2021).
In continuation of previous work, Reconsidering otherness is a design-based research project aiming to create conceptual architecture using machine learning tools for generating texts, images and annotated point clouds. This might help to uncover implicit biases in training models and will contribute to current discussions on (creative) authorship in a (post)-digital age (Carpo, 2017). The work builds on Kyle Steinfeld’s (Steinfeld, 2021) categorization of machine learning in art and architecture as (a) actor, (b) as material and (c) as provocateur.
---
Carpo, M. (2017). The Second Digital Turn Design Beyond Intelligence. MIT Press.
del Campo, M., Carlson, A., & Manninger, S. (2021). Towards Hallucinating Machines - Designing with Computational Vision. International Journal of Architectural Computing, 19(1). https://doi.org/10.1177/1478077120963366
Steinfeld, K. (2021). Significant others: Machine learning as actor, material and provocateur in art and design. In I. As & P. Basu (Eds.), The Routledge Companion to Artificial Intelligence in Architecture. Routledge. https://doi.org/10.4324/9780367824259
Using machine learning to generate works of art, design and architecture has been an emerging research field in the last decades, and experiments have been made as early as the 1960, inspired by Alan Turing's question ‘can computers think?’.
In architecture, machine learning allows the exploration of large design spaces and optimization of certain aspects which can be expressed in a numerical format, such as areas, volumes, material use or energy consumption. In the last years, much effort has gone in automating floor plan generation and in generative design tools which optimize material use in concert with structural requirements. Additionally, initial work has shown results from training a generative adversarial neural network to ‘hallucinate’, creating images about architecture (del Campo et al., 2021).
In continuation of previous work, Reconsidering otherness is a design-based research project aiming to create conceptual architecture using machine learning tools for generating texts, images and annotated point clouds. This might help to uncover implicit biases in training models and will contribute to current discussions on (creative) authorship in a (post)-digital age (Carpo, 2017). The work builds on Kyle Steinfeld’s (Steinfeld, 2021) categorization of machine learning in art and architecture as (a) actor, (b) as material and (c) as provocateur.
---
Carpo, M. (2017). The Second Digital Turn Design Beyond Intelligence. MIT Press.
del Campo, M., Carlson, A., & Manninger, S. (2021). Towards Hallucinating Machines - Designing with Computational Vision. International Journal of Architectural Computing, 19(1). https://doi.org/10.1177/1478077120963366
Steinfeld, K. (2021). Significant others: Machine learning as actor, material and provocateur in art and design. In I. As & P. Basu (Eds.), The Routledge Companion to Artificial Intelligence in Architecture. Routledge. https://doi.org/10.4324/9780367824259
Status | Afsluttet |
---|---|
Effektiv start/slut dato | 18/11/2021 → 31/07/2023 |
FN's verdensmål
I 2015 blev FN-landene enige om 17 verdensmål til at bekæmpe fattigdom, beskytte planeten og sikre velstand for alle. Dette projekt bidrager til følgende verdensmål:
Fingerprint
Udforsk forskningsemnerne, som dette projekt berører. Disse etiketter er oprettet på grundlag af de underliggende bevillinger/legater. Sammen danner de et unikt fingerprint.
Aktiviteter
- 1 Gæsteforelæsning
-
Reconsidering the Artificial in Machine Vision Aesthetics for Architecture
Horvath, A.-S. (Foredragsholder)
9 maj 2022Aktivitet: Foredrag og mundtlige bidrag › Gæsteforelæsning
Priser
-
Best paper award: 29th Conference of Computer-Aided Architectural Design in Asia (CAADRIA2024)
Pouliou, P. (Modtager), Palamas, G. (Modtager) & Horvath, A.-S. (Modtager), 2024
Pris: Konferencepriser
Fil
Publikation
- 2 Tidsskriftartikel
-
Speculative hybrids: Investigating the generation of conceptual architectural forms through the use of 3D generative adversarial networks
Pouliou, P., Horvath, A. S. & Palamas, G., jun. 2023, I: International Journal of Architectural Computing. 21, 2, s. 315-336 22 s.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
Åben adgangFil10 Citationer (Scopus)428 Downloads (Pure) -
ComPara: A Corpus Linguistics in English of Computation in Architecture Dataset
Horvath, A.-S., apr. 2022, I: Data in Brief. 42, 108169.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
Åben adgangFil2 Citationer (Scopus)113 Downloads (Pure)
Forskningsdatasæt
-
Annotated point clouds of buildings: a segmented dataset of single-family houses
Pouliou, P. (Ophavsperson), Horvath, A.-S. (Ophavsperson) & Palamas, G. (Ophavsperson), Mendeley Data, 9 nov. 2022
Datasæt
-
ComPara: A Corpus Linguistics Dataset of Computation in Architecture
Horvath, A.-S. (Ophavsperson), Mendeley Data, 21 okt. 2020
DOI: doi: 10.17632/7ktscvmxvg.5
Datasæt