Artificial Intelligence: The Development, Principles and Practice of Artificial Intelligence from the Perspective of Economics

Course 10: Artificial Intelligence: The Development, Principles and Practice of Artificial Intelligence from the Perspective of Economics

I. Course Description

With the rapid development and wide application of artificial intelligence (AI) technology, AI has had a profound impact on economic activities and social life in various fields. From automated production to smart finance, from medical diagnostics to market forecasting, AI is reshaping our world. However, this change is not just technical, it also involves profound economic principles and implications.

This interdisciplinary course aims to bridge the students economic and computational thinking. Course refined a variety of basic economic principles, and through the case study how to promote the influential design of the artificial intelligence technology, such as AlphaGo (a beat in the go champion artificial intelligence algorithm), ChatGPT (a human level of chatbot large model, with more than 100 billion carefully adjusted parameters) and digital advertising (a radical change the way advertising trillion-dollar industry). The course is arranged in the form of case studies, namely, for each economic principle, we will study how it has inspired the design of important AI / ML technologies such as AlphaGo and ChatGPT.

II. Professors Introduction

Haifeng Xu – Professor of the University of Chicago

Haifeng Xu Is an assistant professor in the Department of Computer Science at the University of Chicago, leading the Machine Agency Strategic Intelligence (SIGMA) Laboratory. He studies the economics of data and machine learning, including designing learning algorithms for multi-agent decisions, and designing markets for data and ML algorithms. Haifeng regularly presents papers at leading machine learning and computational economics conferences and serves as the field chairman or member of senior procedural committees for top conferences of ICML, EC, AAAI, IJCA, etc.

His research has won multiple awards, Including AI2050 Early Career fellow, IJCAI Early Career Spotlight, Google Faculty Research Award, ACM SIGecom Dissertation Award (honorary award), IFAAMAS Distinguished Dissertation Award (runner-up) and a number of best paper awards; His work has received generous support by multiple agencies, Including NSF, ARO, ONR, Schmidt Science, and Google Research.

III. Syllabus

1. Application of exploration / development trade-offs to learning best recommendations
2. Recommendation system: collaborative filtering and content-based methods
3. Efficient dichotomous matching
4. Figure theory basis
5. PageRank algorithn
6. Social network analysis indicators
7. Community detection in social networks
8. Algorithm fairness
9. Optimal pricing market algorithm for online products
10. Case study of market algorithms