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Abstract: Gray box modelling combines first principles -based white box models with a data-driven approach to strike a balance between representation power and controllability. I discuss interactive AI and computational design as application areas for gray box models. So-called light gray models learn psychological parameters from data. Dark gray models, on the other hand, are data-driven models pretrained with white-box models. As a promising approach in intelligent user interfaces, I discuss models of human performance and cognition, which can predict the human consequences of a design decision. These models can 1) represent population and individual characteristics in a psychologically meaningful way and 2) predict the adaptive behavioral response of a person. However, previously their use has been limited because of lack of appropriate likelihood inference methods. I discuss the use of probabilistic machine learning methods for learning model parameters from real world data and taking decisions in the light of confidence levels.

Speaker: Antti Oulasvirta

Affiliation: Professor of Computer Science, Aalto University