AR-302(k) / 12 credits

Teacher: Huang Jeffrey

Language: English

Withdrawal: It is not allowed to withdraw from this subject after the registration deadline.

Remark: Inscription faite par la section


Summary

The studio examines the effects of artificial intelligence on architecture and cities. We explore data-driven design processes by the use of algorithmic and parametric tools that take into consideration geographical, economical, personal, image, political, ecological parameters.

Content

Keywords

  • Architectural form
  • Data-driven design
  • Artificial intelligence
  • Urban design

Learning Outcomes

By the end of the course, the student must be able to:

  • Critique a specific project brief and a specific context and respond with a meaningful data-driven design concept.
  • Translate a data-driven design concept into meaningful architectural and/or urban propositions at appropriate scales and levels of granularity.
  • Produce coherent architectural representations and models at sufficient levels of detail.
  • Formulate Formulate the morphogenetic narrative and create convincing arguments for the design propositions.
  • Develop convincing final diagrams, drawings, renderings, simulations, physical and digital models.

Transversal skills

  • Collect data.
  • Design and present a poster.
  • Make an oral presentation.
  • Demonstrate the capacity for critical thinking
  • Demonstrate a capacity for creativity.

Teaching methods

  • Presentations
  • Mapping exercises
  • Hands-on design activities
  • Design reviews
  • Group projects.

 

Expected student activities

  • Architectural projects will be developed individually (or exceptionally in groups of 2).
  • Some group work may occur in the analysis stages.

Assessment methods

Grading will be based upon the quality of the projects in the preliminary stages, intermediary reviews, and in the final review. Projects will be 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.

 

 

Supervision

Office hours Yes
Assistants Yes

Resources

Bibliography

On GANs, NLP and Architecture: Combining Human and Machine Intelligences for the Generation and Evaluation of Meaningful Designs, J Huang, M Johanes, F Kim, C Doumpioti, and C Holz. In Technology, Architecture + Design 5 (2): 207-24, 2021

Growth Typologies, Localities and Defamiliarisation: Experiments with Artificial Urbanism in Sichuan, Guangzhou and Beijing, J Huang. In: Archit. Design, 85: 70-75, 2015

Ressources en bibliothèque

Websites

In the programs

  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Studio BA6 (Huang)
  • Lecture: 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 BA6 (Huang)
  • Lecture: 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 BA6 (Huang)
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 4 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Studio BA6 (Huang)
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Project: 4 Hour(s) per week x 14 weeks
  • Type: mandatory

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

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