Self-Organizing Floor Plans

Carta, Silvio

doi:10.1162/99608f92.e5f9a0c7

Cited by 9 publications

(1 citation statement)

References 36 publications

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“…The resurgence and evolution of neural networks and probabilistic methods have reignited interest in AI as a design assistant, reinstating these technologies at the forefront of architectural innovation. Carta (2021) critically challenges the oversimplified and fundamentally erroneous belief that computers will replace human designers. The study underlines a pivotal concern in the use of algorithmic design tools: while there is an abundance of data that is easily accessible and interpretable, insufficient attention is paid to the architectural merit of the training plans used.…”

Section: Co-designer or Design Assistance?mentioning

confidence: 99%

The Role of AI Design Assistance on the Architectural Design Process: An Empirical Research with Novice Designers

Zeytin,

Öztürk Kösenciğ,

Öner

2024

Journal of Computational Design

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This study explores the integration of Generative Design Assistants (GDAs), specifically machine learning based tools, in the architectural design process. It investigates how these tools, once confined to experimental realms, are now influencing mainstream architectural practice, particularly among novice architects. The research focuses on third and fourth-year architecture students, examining how they adapt to and integrate these advanced AI tools into their design workflows. Through an empirical online workshop, the study collected data of design process recordings, design output success scores of students by an independent jury, and post-experiment surveys. This approach provided insights into the timing, frequency, and sequence of GDA usage, as well as the influence of specific GDA features on design success. The research reveals that three primary strategies emerged in students' GDA usage: continuous use throughout the design process, selective problem-solving use, and initial ideation use followed by traditional methods. However, an over-reliance on GDAs was noted to potentially limit the designer’s interpretive and developmental input. The survey shows that different GDAs have distinct strengths and impacts on the design process. In terms of selected GDAs for the experiment, ArchiGAN aids in discovery and ideation, while HouseGAN excels in reframing design problems. In conclusion, the study underscores the transformative potential and challenges of GDAs in architectural design and highlights the need for balanced GDA integration. The research outputs show that future research should focus on the long-term implications of GDAs in architectural education. This research aims to guide the effective integration of AI in architecture, enhancing the human designer's role rather than overshadowing it.

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Section: Co-designer or Design Assistance?mentioning

confidence: 99%

The Role of AI Design Assistance on the Architectural Design Process: An Empirical Research with Novice Designers

Zeytin,

Öztürk Kösenciğ,

Öner

2024

Journal of Computational Design

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Human-AI Co-creation: Evaluating the Impact of Large-Scale Text-to-Image Generative Models on the Creative Process

Turchi

Carta

Ambrosini³

et al. 2023

End-User Development

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Large-scale Text-to-image Generative Models (LTGMs) are a cutting-edge class of Artificial Intelligence (AI) algorithms specifically designed to generate images from natural language descriptions (prompts). These models have demonstrated impressive capabilities in creating high-quality images from a wide range of inputs, making them powerful tools for nontechnical users to tap into their creativity. The field is advancing rapidly and we are witnessing the emergence of an increasing number of tools, such as DALL-E, MidJourney and StableDiffusion, that are leveraging LTGMs to support creative work across various domains. However, there is a lack of research on how the interaction with these tools might affect the users' creativity and their ability to control the generated outputs. In this paper, we investigate how the interaction with LTGMs-based tools might impact creativity by analyzing the feedback provided by groups of design students developing an architectural project with the help of LTGMs tools. Keywords• Generative AI • Creativity • Human-AI • AI-driven design process Notes 1. 1. https://stability.ai/blog/stable-diffusion-public-release. 2. 2. https://nimblebox.ai/blog/stable-diffusion-ai. 3. 3. https://www.grasshopper3d.com/. 4. 4. The author of the plugin started to develop and share the Toolkit in November 2022 and the current version is v1.1.6 (2023/02/06). The Main AI components are available from this GitHub page: https://github.com/lucianoambrosini. 5. 5.The current version of the tool "LA_OpenAI-GHadv" is the build 111 and that one of the tool "LA_StabilityAI-GHadv" is the build 107. 6. 6.This workshop focused only on text-to-image and image-to-image procedures, so all "masking" mode parameters have been skipped in this description. 7. 7.The engines are different neural models that are developed by the use of a specific pre-trained set of images with different sizes. More info here: https://stability.ai/blog/stable-diffusion-v2-release. 8. 8.Clip guidance mode (ClipG) works only with the "Ancestral Sampler" models, according to StabilityAI's API documentation. Source, https://platform.stability.ai/docs/getting-started/python-sdk.

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Encoding Social Values of Local Communities in Algorithmic-Driven Design Methods

Carta,

Turchi,

Spencer

et al. 2023

Formal Methods in Architecture

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Self-Organizing Floor Plans

Cited by 9 publications

References 36 publications

The Role of AI Design Assistance on the Architectural Design Process: An Empirical Research with Novice Designers

The Role of AI Design Assistance on the Architectural Design Process: An Empirical Research with Novice Designers

Human-AI Co-creation: Evaluating the Impact of Large-Scale Text-to-Image Generative Models on the Creative Process

Encoding Social Values of Local Communities in Algorithmic-Driven Design Methods

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