Intelligent User Interfaces – HCI meets AI
Summer Term 2017 > Start Monday, March, 6, 2017, 14:00 IDEG 134 Seminarraum Inffeldgasse 16c
Take part in the 2017 Challenge: Intelligent User Interface
We implemented an iML algorithm based on the Travelling Salesman Problem (TSP). For the evaluation of the algorithm we designed a simple GUI. The result of the evaluation was, that we are able to improve the output of the algorithm with human in the loop. To test the results in a better way, we want to analyze it with more people and to gamify the approach, so that the user has both a greater experience and a higher motivation to try out the algorithm. It is also panned to use the game for real data, to solve problems including protein folding with a game based approach.
Design a user interface for our iML Algorithm in a group of 1-3 people. In a first step the students have to design a draft oft the game, after the presentation of the game to our group, the students will receive feedback about their ideas. After improving the draft, they have to implement the game. In a final presentation at the end of the term the winner of the competition will be awarded.
All groups with working games will get the ECTS of the Lecture and aditionally the first 3 places (Gold, Silver, Bronze) will awarded a prize.
Human-Computer Interaction meets Artificial Intelligence
[AK HCI, 16S, 706.046, 3 VU, 4,5 ECTS > TUG-Online]
Intelligent User Interfaces (IUI) is where the Human-computer interaction (HCI) meet Artificial Intelligence (AI), often defined as the design of intelligent agents – the core essence in Machine Learning (ML). In interactive Machine Learning (iML) this agents can also be humans:
Holzinger, A. 2016. Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop? Springer Brain Informatics (BRIN), 3, (2), 119-131, doi:10.1007/s40708-016-0042-6.
Holzinger, A. 2016. Interactive Machine Learning (iML). Informatik Spektrum, 39, (1), 64-68, doi:10.1007/s00287-015-0941-6.
In this practically oriented course, Software Engineering is seen as dynamic, interactive and cooperative process which facilitate an optimal mixture of standardization and tailor-made solutions.
Previous knowledge expected
Interest in experimental Software Engineering in the sense of:
Science is to test crazy ideas – Engineering is to put these ideas into Business.
Interest in cross-disciplinary work, particularly in the HCI-KDD approach: Many novel discoveries and insights are found at the intersection of two domains, see: A. Holzinger, “Human–Computer Interaction and Knowledge Discovery (HCI-KDD): What is the benefit of bringing those two fields to work together?“, in Multidisciplinary Research and Practice for Information Systems, Springer Lecture Notes in Computer Science LNCS 8127, A. Cuzzocrea, C. Kittl, D. E. Simos, E. Weippl, and L. Xu, Eds., Heidelberg, Berlin, New York: Springer, 2013, pp. 319-328. [DOI] [Download pdf]
After successful completion of this course:
- Students are autonomously able to apply a selection of the most important scientific HCI methods and practical methods of UE
- Students understand the most essential problems which End-Users are faced in our modern, complex and dynamic environment
- Students are able to apply the most important experimental designs
- Students learn to deal with the problems in modern user interface design
- Students are able to conduct elementary research experiments and carry out solid evaluations in HCI research
This year, the Holzinger group offers a selection of interesting topics for mini projects:
- Tumor-Growth Simulation and Visualization (Supervisor Fleur JEANQUARTIER)
- Privacy and Open Data with the doctor-in-the-loop (Supervisor Peter KIESEBERG)
- Eye-Tracking and applications (Supervisor Markus FASSOLD)
- Image Augmentation for Machine Learning (Supervisor Marcus BLOICE)
- 3D->2D data modelling in Archaeology (Supervisor Tobias SCHRECK, CGV & Paul BAYER, KFUG Archeology)
- Towards experimental evidence for interactive ML (Supervisor Andreas HOLZINGER)
1. One scientific paper per group (50 %)
2. Project presentations during the semester – EVERY student of a group has to present one part of the work! (50%)
Basically this VU is a very practice-led course, and therefore the majority of the work will take place at home or in the field (field work with end-users). The room is reserved from 14:00 to 18:00, but that does not mean that we always need the full time! We start on Monday 07.03.2016 at 14:00 (Room IDEG134, Inffeldgasse 16c). Please make sure you are on time that day, as we will be presenting the projects (first come, first served!).
|Mo 06.03.2017||14:00 to 18:00||IDEG134||01 Introduction and presentation of cool mini-projects by the tutors|
|Mo 13.03.2017||14:00 to 18:00||IDEG134||02 Presenting the mini-project goals by the groups – to ensure mutual understanding|
|Mo 03.04.2017||14:00 to 18:00||IDEG134||03 Progress Meeting – presenting the mini project status|
|Mo 29.05.2017||14:00 to 18:00||IDEG134||04 Progress Report presentation – mid term review|
|Mo 20.06.2017||14:00 to 18:00||IDEG134||05 Mini Conference – final presentation|
General guidelines for the scientific paper
Holzinger, A. (2010). Process Guide for Students for Interdisciplinary Work in Computer Science/Informatics. Second Edition. Norderstedt: BoD (128 pages, ISBN 978-3-8423-2457-2)
also available at Fachbibliothek Inffeldgasse.
Scientific paper templates
Please use the following templates for your scientific paper:
(new) A general LaTeX template can be found on overleaf > https://www.overleaf.com/4525628ngbpmv
Further information and templates available at: Springer Lecture Notes in Computer Science (LNCS)
Some pointers to interesting sources in intelligent HCI:
- Visual Turing Test, see: Lake, B. M., Salakhutdinov, R. [expertise] & Tenenbaum, J. B. 2015. Human-level concept learning through probabilistic program induction. Science, 350, (6266), 1332-1338. [http://web.mit.edu/cocosci/Papers/Science-2015-Lake-1332-8.pdf]
You can try out some online experiments (“visual Turing tests”) to see if you can find out the difference between human and computer behavior. The code and images for running these experiments are available on github.
- The Human Kernel, see: Wilson, A. G. , Dann, C., Lucas, C. & Xing, E. P. The Human Kernel. Advances in Neural Information Processing Systems, 2015. 2836-2844. [papers.nips.cc/paper/5765-the-human-kernel.pdf]
You can try out some online experiements for the Human Kernel here: