After successful completion of the course, students are able to...
... define research questions
... select apropriate research methods
... use/apply research methods
... extract from scientific text/papers
... interpret research findings/results
In addition, students will be familiar with and understand:
... essential AI concepts
... grasp the significance of bias, fairness and ethics
... ability to pose appropriate inquiries
... develop effective questioning strategies
The course aims to provide students with a platform to conduct experiments, research, and make informed decisions while learning the principles of AI, conducting experiments, documenting findings, and presenting results. The course will focus on various topics closely related to the Turing test and the unexpected capabilities of AI that can be harnessed by individuals. Students will engage in profound yet simple experiments to gain a deeper understanding of research exploring AI's capabilities, potential and limitations.
The course will consist of lecture, workshops, research exercises, and presentation + group discussions.
Students will select and read scientific texts related to AI and Language Generation Models, as well as their chosen topic and summarize the key points.
Workshops will provide guidance on research approaches, methods, and the documentation of findings.
Participatory observation will be employed to analyze and document research results. The collected data will be further analyzed in workshops, and suitable forms of research documentation will be explored and developed. At the end of the cource a presentation and discussion will take place.
Course topics to be chosen to research and conduct experiments with Language generation models, such as GPT and/or others; or Generative AI Models, such as Stable Diffusion, Midjourney and/or others:
1. Simplification and Abstraction (NLG)
2. Humor (NLG/CGV)
3. Emotion Generation (NLG/CGV)
4. Ethical Decision-Making (NLG)
5. Bias and Fairness (NLG/CGV)
6. Personalized Education (NLG)
7. Cooperative Design (NLG+CGV)
8. Image Style Transfer (CGV)
9. Historic Reconstruction and Preservation (CGV)
10. AI and architecture landscape research (desk)
Other topics may be considered if students raise them or if they are brought up during the discussion.
Course Schedule:
1.04.10 . | 9-12 Uhr | Input, among others, on the elective seminar, Generative Models (AI), Research approach, Research topics. Selection process of the topics
2. 11.10. | 9-12 Uhr | Workshop 1: Input on AI, its principles of work and research topics selected previosly.
With the support of the instructors, you will develop research approaches and learn about scientific methods in a workshop. Individual work. Appointments with the lecturers: to be arranged. Hybrid: on-spot and online
3. 18.10. | 9-12 Uhr | Workshop 2: First findings, Presentations on the research. Experimentation, iterations.
With the support of the instructors, you will analyze the collected research results in a workshop and develop suitable forms of research documentation. You will document your research findings. Appointments with the lecturers: to be arranged, hybrid: on-spot and online
4. 22.11. | 9-12 Uhr | Workshop 3: Experimentation Documentation. Final Preparation. Presentation on the research. Discussion of the results of participant observation. Discussion of research documentation, etc. You will document the research findings
24.11 15:00-21:00 SYMPOSIUM ON AI:
Keynote speaker : Patrik Schumacher, Zaha Hadid Principle
With: Matias del Campo, SPAN architecture, University of Michigan
and others to be announced
- Legal & ethical aspects of using AI and evolving, what to expect
- Application of AI in architecture.
29.11 | 9:00-12:00 Seminar and Symposium conclusion. Presentation of the work results. Submission of the documentation of research findings by the end of the semester.