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Machine Learning at All Levels: A Pathway to “Autonomous” AI

Event Type
Seminar/Symposium
Sponsor
C3.ai Digital Transformation Institute
Date
Oct 6, 2022   3:00 - 4:00 pm  
Speaker
Eric Xing, Professor of Computer Science, Carnegie Mellon University
Registration
required.
Contact
C3.ai Digital Transformation Institute
Views
71
Originating Calendar
NCSA External Events Feed

An integrative AI system is not a monolithic blackbox, but a modular, standardizable, and certifiable assembly of building blocks at all levels: data, model, algorithm, computing, and infrastructure. In this talk, we summarize our work on developing principled and “white-box” approaches, including formal representations, optimization formalisms, intra- and inter-level mapping strategies, theoretical analysis, and production platforms, for optimal and potentially automatic creation and configuration of AI solutions at all levels, namely, data harmonization, model composition, learning to learn, scalable computing, and infrastructure orchestration. We argue that traditional benchmark/leaderboard-driven bespoke approaches or the massive end-to-end “AGI” models in the Machine Learning community are not suited to meet the highly demanding industrial standards beyond algorithmic performance, such as cost-effectiveness, safety, scalability, and automatability, typically expected in production systems; and there is a need to work on ML-at-All-Levels as a necessity step toward industrializing AI that can be considered transparent, trustworthy, cost effective, and potentially autonomous.

Eric Xing is a professor of Computer Science at Carnegie Mellon University, President of the Mohamed bin Zayed University of Artificial Intelligence, and Founder and Chairman of Petuum Inc., a 2018 World Economic Forum Technology Pioneer company that builds standardized AI development platforms and operating systems for industrial AI applications. He completed his PhD in Computer Science at UC Berkeley. His 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. Xing currently serves or has served as associate editor of the Journal of the American Statistical AssociationAnnals of Applied Statistics, and IEEE’s Journal of Pattern Analysis and Machine Intelligence; and as action editor of the Machine Learning Journal and Journal of Machine Learning Research. He is a board member of the International Machine Learning Society.

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