Distinguished Lecture Series: Thoughts and Efforts on AI Meeting Production

Thoughts and Efforts on AI Meeting Production

Abstract:

Machine Learning systems for complex tasks – such as controlling industrial manufacturing processes in real-time; or writing medical imaging case reports – are becoming increasingly sophisticated and consist of a large number of data, model, algorithm, and system elements and modules. Traditional benchmark/leaderboard-driven bespoke approaches in the ML community are not suited to meet highly demanding industrial standards beyond algorithmic performance, such as safety, energy efficiency, and scalability typically expected in production systems in the industry. In this talk, I discuss some technical issues toward production- and industrial-AI from the following aspects: theoretical foundation for trustworthy and panoramic learning with all experiences; compositional strategies for building Pan-ML programs from Lego-like blocks; optimization methods for auto-tunning and federated learning; and systems framework for scaling up and scaling out ML productions. I will provide a few examples of our effects to address each of these challenges in the form of first principle formula, new algorithms, and software toolkits.

Bio:

Eric P. Xing is the President of the Mohamed bin Zayed University of Artificial Intelligence, a Professor of Computer Science at Carnegie Mellon University, and the Founder and Chairman of Petuum Inc., a 2018 World Economic Forum Technology Pioneer company that builds standardized artificial intelligence development platform and operating system for broad and general industrial AI applications. He completed his PhD in Computer Science at UC Berkeley. His main research interests are the development of machine learning and statistical methodology; and composable, automatic, and scalable computational systems, for solving problems involving automated learning, reasoning, and decision-making in artificial, biological, and social systems. Prof Xing is a board member of the International Machine Learning Society; he has served as the Program Chair (2014) and General Chair (2019) of the International Conference of Machine Learning (ICML).