【Time】2025/11/25  21:00~22:00

【Topic】Who will think and feel in the AI future?

【Format】Online session, conducted entirely in English

【Registration Link】https://reurl.cc/gYL2ML

【Capacity】Unlimited

【Speaker Profile】

Ming-Hui Huang is a Distinguished University Chair Professor in the Department of Information Management at National Taiwan University. She received her PhD from the University of Wisconsin-Madison, USA. Globally recognized for her contributions, Professor Huang has been honored as a ScholarGPS 2024 Top 0.5% Artificial Intelligence Scholar, a Scopus 2022-2024 World’s Top 2% Scientist, and a Clarivate Highly Cited Researcher (Top 1% worldwide) in 2023 and 2025. In 2023, she was also named among the 86 most impactful researchers worldwide across all fields of business and economics. Professor Huang has been named a Fellow of the Association for Information Systems (AIS) and the European Marketing Academy (EMAC), and is a Distinguished Research Fellow at the Center for Excellence in Service, University of Maryland, USA. She has also been an International Research Fellow at the Centre for Corporate Reputation, University of Oxford, UK.

Specializing in artificial intelligence (AI), service, and marketing, Professor Huang’s AI research has been published in leading academic and managerial journals, including the Journal of Marketing, Journal of Consumer Research, Journal of Service Research (JSR), Journal of the Academy of Marketing Science (JAMS), Marketing Science, Journal of Retailing, Harvard Business Review, California Management Review, and MIT Sloan Management Review. She is currently the Editor-in-Chief of the Journal of Service Research, the leading journal in the field of service studies.

【Lecture Overview】

This talk reflects my research program on AI, beginning with the fundamental multiple AI intelligences view and how it gives rise to the Feeling Economy. It then elaborates on how AI can be made creative and how feeling advantages can be offered by AI. The talk also addresses what can go wrong when AI takes on too much, such as the risks of the average trap and model collapse. The “who” in the title is intentionally left vague to refer to both AI and humans, inviting reflection on how the boundaries between the two may evolve in the AI future.