DBM130 - Designing Intelligence in Interaction

Learning objectives

The mission of the Industrial Design Department at the Eindhoven University of Technology is to perform research on and provide education in: Creating intelligent systems, products, and related services in a societal context. Industrial Design of the future is very much the design of intelligent products. In this course, we introduce several perspectives into the keyword “intelligence”.

We as a group of teachers and researchers are intrigued by the inspiring relation between DESIGN and TECHNOLOGY. The relation works in two directions: Technology inspires Design. Design needs technology, yet design-scaled technology. We as the members of the DI group believe that time is ready for a new design profession where new artifacts are designed which are formless, digital and intangible. Systems, products, and services will be based on large-scale connectivity and behave as Complex Adaptive Systems. Design goals include various types of techno-social sustainability such as social sustainability and digital sustainability. Important design qualities are beauty, comfort, and support of human values. In this vision, "intelligence" can be seen as not only an important "technology" but also an inspiration for the design. In this course "intelligence" is the central theme.

Content

The course consists of two parts

week 1-4: Theoretical background.
- Week 1
  - Session 1
    - Computer science and history of AI by Loe Feijs;
    - Human computer interaction and future of AI by Matthias Rauterberg;
    - Forming groups;
    - Homework: paper reading
  - Session 2
    - Machine Learning by Emilia Barakova and Jun Hu
- Week 2
  - Session 1
    - Sensing and data collection by Rong-Hao Liang
  - Session 2
    - Groups presenting the papers;
    - Discussions led by Loe Feijs and Matthias Rauterberg
- Week 3
  - Session 1
    - Actuation and Feedback Loop by Erik van der Spek
  - Session 2
    - Social aspects and connectivity by Mathias Funk
- Week 4
  - Session 1
    - Teams presenting project proposals and concept feasibility studies
    - Discussions led by Emilia Barakova and Jun Hu
  - Session 2
    - Teams working on project proposals and concept feasibility studies
Week 5-8: Teams working on the projects
- Week 5-8
  - During the contact hours, on-demand coaching by Mathias Funk, Emilia Barakova, Rong-Hao Liang, Erik van der Spek and Jun Hu
- Week 8
  - Session 2
    - Final presentations

Entrance requirements

This course will give examples and provide support in Java or Processing.
Experience in Java or Processing is preferred.
Experience in any other programming language is accepted.
Students with no experience in programming will find this course hard to follow.

Assumed previous knowledge

Programming experience is needed.

Previous knowledge can be gained by

Following online tutorials at Processing.org (https://processing.org/tutorials/)
Following the book "Getting Started with Processing" (https://processing.org/books/)

Deliverables

By the end of Week 4:
- Project proposals by the teams, including the individual parts done by the individual members of the group.
  - The overall proposal should address the context, the related work and the proposed concept.
  - Individual parts address the feasibility of the concept. For the feasibility analysis of the concept, four aspects need to be considered, each student individually doing one aspect. Make sure the team covers all four aspects: (1) sensing and data collection, (2) learning, (3) actuation and feedback loop, (4) social aspects and connectivity.
  - Max four pages in the format of ACM extended abstract.
By the end of week 9 (one week after the final presentation):
- A youtube video of two minutes showing the concept and the working prototype;
- Team report of max ten pages in the format of format of ACM extended abstract plus code plus other attachments;
- Individual reflection, max two pages in the format of format of ACM extended abstract

Rubrics

Information about lecturers

prof.dr.ir. Loe Feijs (l.m.g.feijs@tue.nl). He has a background in computer science and interaction design.
prof.dr. Matthias Rauterberg (g.w.m.rauterberg@tue.nl). He has a background in computer science and psychology.
dr.ir. Emilia Barakova (e.i.barakova@tue.nl). She has a background in computer science and human-robot interaction.
dr. Rong-Hao Liang (j.liang@tue.nl). He has a background in computer science and electrical engineering
dr. Mathias Funk (m.funk@tue.nl). He has a background in data analytics.
dr.PDEng.MEng. Jun Hu (j.hu@tue.nl). He has a background in computer science and interaction design.
dr. E.D. van der Spek (e.d.v.d.spek@tue.nl). He has a background in computer science and game design.