The field of machine learning has undergone a revolution: Certain well-known ‘black box’ algorithms have shown their long-suspected potential and now dominate methods delivering the state-of-the-art performance on many benchmark problems. What makes these algorithms ‘black box’ is that we have no rigorous understanding of their success that is also intuitive to humans. As a result, questions at the heart of their application have no clear answers today: What architecture should a neural network have to handle a certain new data type? How can one combine different networks effectively? How can one best add prior or expert knowledge? etc.

At IARAI, we believe that these fundamental questions need to be answered in a theoretically rigorous manner to allow humanity to exploit the full benefits of AI. White-boxing AI will also improve the interpretability of models and results, improve learning efficiency, robustness, and inform us how we best deal with rare yet important events.


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