Abstract:

The markets for data and AI models are rapidly emerging and increasingly significant in the realm and the practices of data science and artificial intelligence. These markets are being studied from diverse perspectives, such as e-commerce, economics, machine learning, and data management. This talk aims to provide a brief interdisciplinary introduction to data and AI model markets. Instead of concentrating only on the economics aspect, we take a novel perspective and examine data and AI model markets as grand opportunities to address the long-standing problem of data and model sharing, discovery, and integration. We will motivate the importance of data and model markets using practical examples, present the current industry landscape of such markets, explore the modules and options of such markets from multiple dimensions, including assets in the markets (e.g., data versus models), platforms, and participants. Furthermore, we will summarize the latest advancements and examine the future directions of data and AI model markets as mechanisms for enabling and facilitating sharing, discovery, and integration.

Biography:

Dr. Jian Pei is the Arthur S. Pearse Distinguished Professor at Duke University. His research focuses on data science, big data, data mining, and database systems. His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications. He is recognized as a Fellow of the Royal Society of Canada (RSC), the Canadian Academy of Engineering, ACM, and IEEE. He is a productive and influential author. Since 2000, he has published one textbook, two monographs and over 300 research papers in refereed journals and conferences, which have been cited over 128,000 times. His research has generated remarkable impact substantially beyond academia. His algorithms have been adopted by industry in production and popular open source software suites. He is responsible for several commercial systems of unprecedentedly large scale. Jian Pei received many prestigious awards, including the 2017 ACM SIGKDD Innovation Award and the 2015 ACM SIGKDD Service Award.