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Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

Participation: AI and Data Science

 

AI and Data Science One Day Workshop

 

21.11.2019. Daniel Jacob, PhD student of the IRTG 1792, participated in a one day workshop on AI and Data Science held at the Department of Statistics, National Cheng Kung University Tainan.

The theme of the workshop revolved around the idea that AI and Data Science promise to produce solutions to many problems once provided with many data. This might not (yet) be true and one needs to understand the underlying statistics and econometric assumptions in order to evaluate such models. To shed some light into these models and put them into prospective, speakers from statistics, applied mathematics and computer science where invited to speak about deep learning, complex network analysis, fintech and machine learning in econometrics.

This one-day workshop, featured a broad range of topics where every speaker gave its insights into research and where they use AI and Data Science. About 60 listeners discussed the topics and asked questions.

We would like to thank the organizers, Ying Chen (Department of Mathematics, National University of Singapore) and Ray-Bing Chen (Department of Statistics, National Cheng Kung University) for this great workshop. It is very important that in an era of machine learning, big data and AI, everybody understands what these models and algorithms can be used for and what they can not do. We also need to rethink our understanding of “data” and on the same time always be critical about the meaning of causation.

Especially for clinical workflows, presented by Weichung Wang from National Taiwan University, such novel AI algorithms can be used to analyze images and detect e.g. early stages of prostate cancers. This leads to a higher rate of treatment and can reshape the medical examination as we know it. However, we still need and want to understand why a person shows such results and what we can do to prevent such outcomes.