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FCAI research focuses on tackling three challenges, which are key bottleneck problems for a wider adoption of AI systems:



Huge labeled data sets made the AI revolution possible. However, most of the value in big data is in the enormous number of small questions it could answer but at this resolution data becomes a scarce resource. We will widen this bottleneck and make AI applicable to a significantly wider scope of questions by developing data efficient methods, by bringing in prior knowledge in the form of models, and by enabling privacy-preserving sharing of data.




AI systems are not dependable, i.e., they are vulnerable to manipulation and information stealing, and it is not certain whether the outputs are trustworthy. We will develop the required privacy-preserving and secure AI, and methods that can take into account uncertainty in data and decision-making. We will provide new resilient deep learning approaches for the currently popular and successful deep neural networks. Societal trust stemming from dependable AI enables wide applicability in the society.




AI does not understand the user. We will provide AI with the capability to understand the user, which is a prerequisite for making AI understandable. Modeling the user and the interaction will help AI understand the user and vice versa. The outcome is AIs that are able to augment human capabilities in a multitude of tasks.