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Is (Artificial) Intelligence Possible Without Mathematics?
8 June 2019

The last decade saw spectacular progress in AI applications, with deep learning and neural networks outperforming many traditional approaches. However, the theoretical part of this revolution remains incomplete in many ways, with limited understanding of when and why machine-learning works, how reliable its decisions are, and what its limitations are. Mathematics has historically underpinned most of the progress in science and engineering. How could mathematical methods advance the state of AI and help uncover why it works in some cases and fails in others?

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Congress Centre, conference hall B1
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