Despite its great progress so far, artificial intelligence (AI) is facing a serious challenge in the availability of high-quality Big Data. In many practical applications, data are in the form of isolated islands. Efforts to integrate the data are increasingly difficult partly due to serious concerns over user privacy and data security. The problem is exacerbated by strict government regulations such as Europe's General Data Privacy Regulations (GDPR).
In this talk, I will review these challenges and describe possible technical solutions to address them. In particular, I will give an overview of recent advances in transfer learning and show how it can alleviate the problems of data shortage. I will also give an overview of recent efforts in federated learning and transfer learning, which aims to bridge data repositories without compromising data security and privacy.
Qiang Yang is the chief AI officer of WeBank, China's first internet only bank with more than 100 million customers. He is also a chair professor at Computer Science and Engineering Department at Hong Kong University of Science and Technology (HKUST). His research interests include artificial intelligence, machine learning, especially transfer learning and federated machine learning. He is a fellow of AAAI, ACM, IEEE, AAAS, and the founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST) and the founding Editor in Chief of IEEE Transactions on Big Data (IEEE TBD). He received his PhD from the University of Maryland, College Park in 1989 and has taught at the University of Waterloo and Simon Fraser University. He received the ACM SIGKDD Distinguished Service Award in 2017, AAAI Distinguished Applications Award in 2018, Best Paper Award of ACM TiiS in 2017, and the championship of ACM KDDCUP in 2004 and 2005. He is the current President of IJCAI (2017-2019) and an executive council member of AAAI.