Studio MA2 (Huang)

AR-402(k) / 12 credits

Teacher: Huang Jeffrey

Language: French/English

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Résumé

The studio critically examines the effects of artificial intelligence on architecture and cities. Generative tools are approached as cultural and political instruments, shaping design through data grounded in territory, economy, identity, imagery, and ecology.

Contenu

In the wake of rogue LLMs, autonomous agents, opaque algorithms, and AI slop, architectural tools are undergoing a profound reconfiguration. Generative design workflows and computational surrogates offer immense modeling power but also contribute to homogenization, "slopification," and erosion of critical authorship. The goals of the studio are threefold: (1) to critically explore the use of generative AI in architectural design, tracing its risks and potentials beyond aesthetic automation; (2) to invent new architectural typologies that counters the tendency toward isolated, opaque, extractive techno-capitalist systems; (3) to articulate a new architectural process and language for intelligence where form is shaped as much by functional demands, as by the need for commons, symbolic resonance, public trust, and poetry. This experimental studio will employ advanced digital tools and generative AI for "vibecoding" architecture. Experimental and remote LLMs and MCP agents will be introduced as exploratory digital modeling tools. A range of software, scripts, and plugins for mapping and open geodata analysis (e.g., Rhino & Grasshopper, QGIS), along with GenAI tools for representation (such as Stable Diffusion, Midjourney, and DALL-E), may act as co-design agents throughout the studio's successive phases. No prior programming or software knowledge is required, but curiosity and strong motivation to learn are essential.

Mots-clés

  • Architectural elements
  • New Typologies
  • Data-driven design
  • Artificial intelligence
  • Vibecoding
  • Urban Design

Acquis de formation

A la fin de ce cours l'étudiant.e doit être capable de:

Compétences transversales

  • Réaliser et présenter un poster.
  • Recueillir des données.
  • Faire une présentation orale.
  • Faire preuve d'esprit critique
  • Faire preuve d'inventivité

Méthode d'évaluation

Projects will be reviewed and assessed based on:

(1) their conceptual strength and innovation,

(2) the coherence and resolution of their architectural translation,

(3) their representative clarity and expressive power, and

(4) the persuasiveness of their communication, both orally, and through the physical and digital artifacts.

 

Encadrement

Office hours Oui
Assistant.e.s Oui
Forum électronique Non

Ressources

Bibliographie

  1. Huang, Jeffrey, Mikhael Johanes, Frederick Chando Kim, Christina Doumpioti, and Georg-Christoph Holz. On GANs, NLP and Architecture: Combining Human and Machine Intelligences for the Generation and Evaluation of Meaningful Designs. Technology | Architecture + Design 5, no. 2 (2021): 207-224. https://doi.org/10.1080/24751448.2021.1967060.
  2. Huang, Jeffrey, Dieter Dietz, Laura Trazic, and Korinna Zinovia Weber, eds. Transcalar Prospects in Climate Crisis: Architectural Research in re/Action. Zurich: Lars Müller Publishers, 2024.
  3. Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21),  610-623. New York: ACM, 2021. https://doi.org/10.1145/3442188.3445922.
  4. Carpo, Mario. Beyond Digital: Design and Automation at the End of Modernity. Cambridge, MA: MIT Press, 2023.
  5. Crawford, Kate. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven: Yale University Press, 2021.
  6. Del Campo, Matias, Stefan Manninger, and Andre Carlson. Archiitectural Hallucinations: Diffusion Models and New Aesthetics. International Journal of Architectural Computing 20, no. 4 (2022): 493-509. https://doi.org/10.1177/14780771221135036.
  7. Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, et al. Generative Adversarial Nets. In Advances in Neural Information Processing Systems 27 (NeurIPS 2014). https://papers.nips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html.
  8. Mattern, Shannon. A City Is Not a Computer: Other Urban Intelligences. Princeton, NJ: Princeton University Press, 2021.
  9. Zuboff, Shoshana. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York: PublicAffairs, 2019.

Ressources en bibliothèque

Sites web

In the programs

  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Studio MA2 (Huang)
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 4 Hour(s) per week x 14 weeks
  • Type: mandatory
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Studio MA2 (Huang)
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 4 Hour(s) per week x 14 weeks
  • Type: mandatory
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Studio MA2 (Huang)
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 4 Hour(s) per week x 14 weeks
  • Type: optional

Reference week

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