Creativity and aesthetics of data & AI

The course is structured along three lines. The first two lines can run in parallel, with the following activities:

• lectures and individual readings on different learning models for artificial intelligence (e.g. learning by example, conditioning, mirroring, reinforcement)
• group discussions on the relevance of these models for creativity and aesthetics in the real world
• lectures and individual readings of different philosophies, theories, design frameworks and design cases related to creativity and everyday aesthetics.
• group discussions on the relevance of creativity and aesthetics for artificial intelligence

The third line will combine and integrate the knowledge of the first two lines to give ‘form’, beauty, expression and meaning to user experiences as they are shaped by data and artificial intelligence.
The activities in the third line are: envisioning, designing, prototyping, and design critiques.

Course Deliverables:
Annotated Portfolio, Visionary Prototype(s), Personal Reflection
The assessment will be based on Rubrics

• Understanding the opportunities for the ‘form giving’ of learning, adaptation, personalization (“human”) and de-personalization (“post-human”) strategies for Artificial Intelligence.\
• Understanding the opportunities of Data and Artificial Intelligence for Creativity and Aesthetics.
• Identifying opportunities and requirements of AI specific for design.
• Developing a critical perspective on the limitations and implications of Artificial Intelligence for the creation and aesthetics of everyday products, systems, and services.
• Realizing visionary high-definition scenarios to direct the future of Industrial Design.

You must meet the following requirements

• Research papers for recommended reading and course preparation will be provided closer to the course scheduling.
• Literature and other media on: ‘everyday aesthetics’, pragmatist aesthetics, post-phenomenology, perceptual crossing. Literature and other media on: Artificial Intelligence techniques, machine-learning models, HCI and design literature concerning in the embedding of AI/ML in applications, critical literature on the influence of AI/ML technologies on design and society.