MSc. J.E. (Jordi) Bieger
I'm a researcher at TU Delft, currently working on the Massive Open Online Deliberation (MOOD) platform. I spent 4 years at Reykjavik University, Iceland to pursue a PhD in the area of Artificial General Intelligence on the topic of Artificial Pedagogy ("how to teach your AI"). Before that I obtained bachelor's and master's degrees in Artificial Intelligence from the Radboud University Nijmegen, and researched Computer Vision, Brain-Computer Interfacing and Reinforcement Learning in industry jobs.
I'm currently mainly working on the implementation of the Massive Open Online Deliberation (MOOD) platform, and wrapping up my PhD. MOOD is meant to enhance critical thinking and reflection among discussion participants by providing a formalized and guided process of moral deliberation. The platform’s emphasis on values rather than (conflicting) interests helps different stakeholders come together, understand each other, and foster more productive and less adversarial discussion.
- J. Bieger, K. R. Thórisson, and D. Garrett (2014), Raising AI: Tutoring Matters, In: B. Goertzel, L. Orseau and J. Snaider (eds.), Proceedings of Artificial General Intelligence (AGI-14), 1–10, Springer-Verlag, Quebec, Canada
- K. R. Thórisson, J. Bieger, T. Thorarensen, J. S. Sigurðardóttir, and B. R. Steunebrink (2016), Why Artificial Intelligence Needs a Task Theory—And What It Might Look Like, In: B. S. Steunebrink, P. Wang, and B. Goertzel (eds.), Proceedings of Artificial General Intelligence (AGI-16), 118–128, Springer-Verlag, New York, USA
- J. Bieger, K. R. Thórisson, and B. R. Steunebrink (2017), The Pedagogical Pentagon: A Conceptual Framework for Artificial Pedagogy, In: T. Everitt, B. Goertzel, and A. Potapov (eds.), Proceedings of Artificial General Intelligence (AGI-17), 212–222, Springer-Verlag, Melbourne, Australia
- + 31 15 27 81043
Faculty of Technology, Policy and Management
Room number: B3.280
Engineering Systems and Services
Information and Communication Technology
Artificial General Intelligence
(Cooperative Inverse) Reinforcement learning