2022
DOI: 10.1002/ad.2809
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Creative AI: Augmenting Design Potency

Abstract: Assistant Professor at Florida Atlantic University School of Architecture, founder of Nonstandardstudio and a PhD student in Vienna, Daniel Bolojan researches AI‐augmented systems that have the potential to liberate architects’ and designers’ creativity. This is not to say that human imagination and machine learning should be equated; they are very different in the way they function and respond to the world, but when combined they offer new creative opportunities.

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Cited by 9 publications
(4 citation statements)
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“…Rather than relying on a human-designed algorithm to solve a problem or create specific forms, asking machines to analyze a data set and extract the rules from it is possible (Leach, 2018). This has allowed for moving beyond expert systems and toward learning systems (Bolojan, 2022). For instance, generative adversarial networks (GANs) consist of two neural networks that compete with each other: the generator and the discriminator.…”
Section: Computations In Architecturementioning
confidence: 99%
“…Rather than relying on a human-designed algorithm to solve a problem or create specific forms, asking machines to analyze a data set and extract the rules from it is possible (Leach, 2018). This has allowed for moving beyond expert systems and toward learning systems (Bolojan, 2022). For instance, generative adversarial networks (GANs) consist of two neural networks that compete with each other: the generator and the discriminator.…”
Section: Computations In Architecturementioning
confidence: 99%
“…(Del Campo, 2022). Similarly, Bolojan's research into non-standard and computational design strategies has illuminated the augmentative capacity of AI in enhancing human creativity and efficiency (Bolojan, 2022). By investigating the intersections between AI, computational design, and digital fabrication, Bolojan has contributed to a deeper understanding of how these technologies can be harmoniously integrated into architectural practice to foster innovative solutions and methodologies.…”
Section: Shout-out To Pioneering!mentioning
confidence: 99%
“…However, with the rapid advancements in deep generative models in the 2010s (Isola, 2017), particularly conditional generative models, much of the limitations inherent in traditional research methodologies, specifically in the realm of prior knowledge, have been removed for architectural style analysis and style-based design creation (Huang, 2021;Meng, 2021;Bolojan, 2022;Huang et al, 2023). The advancement in methodology signifies a paradigmatic transition in the comprehension and categorisation of architectural styles, rooted in computational formal analyses, notably façade analysis employing emergent AI techniques.…”
Section: History and Previous Studies Of Shophousesmentioning
confidence: 99%