The 5th Global Grand
Challenges Summit


September 18-20, 2023 Beijing, China
Featured Guests

Alfred Spector

Time: September 8, 2023

Alfred Spector is a Visiting Scholar at MIT and a Senior Advisor at Blackstone. His career has led him from innovation in large scale, networked computing systems to broad engineering and research leadership. Recently, he co-authored a Cambridge University Press textbook, Data Science in Context: Foundations, Challenges, Opportunities. Previously, Dr. Spector was CTO and Head of Engineering at Two Sigma Investments. Before that, he spent eight years as VP of Research and Special Initiatives at Google, and he held various senior-level positions at IBM, including as global VP of Services and Software Research and global CTO of IBM’s Software Business. Earlier in his career, he founded Transarc Corporation, a pioneer in distributed transaction processing and wide-area file systems, and he was a tenured professor at Carnegie Mellon University. Dr. Spector was a Hertz Fellow at Stanford, a Fellow of both the ACM and the IEEE, and a member of the American Academy of Arts and Sciences. He won the 2001 IEEE Kanai Award for Distributed Computing and the 2016 ACM Software Systems Award. In 2018-19, Dr. Spector lectured widely as a Phi Beta Kappa Scholar on the growing importance of computer science across all disciplines based on the evocative phrase, “CS+X”. He has been a member of the ACM Turing Award Committee and has done national service through chairing the NSF’s CISE Advisory Board. Dr. Spector obtained a Ph.D. in computer science from Stanford and a B.A. in applied math from Harvard. He was elected to membership in the National Academy of Engineering in 2004 for the design, implementation, and commercialization of reliable, scalable architectures for distributed file systems, transaction systems, and other applications. 

Title: Beyond Models – Applying Data Science/AI Effectively

Abstract: Applying data science and artificial intelligence effectively requires a considerably broader focus than just data, statistics, and machine learning. Based on the speaker and his co-authors' recent book1, this presentation distills these additional challenges into a simple rubric and illustrates its application with a number of examples. The rubric is practically useful to engineers and product managers, and it illustrates the limitations inherent in the current state of the art.

PreNews:Chen Xiaohong

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