Databases for All of the World's Bytes or How I Learned to Start Querying and Love AI

Samuel Madden, MIT 

Abstract Over the past five decades, The relational database model has proven to be a scaleable and adaptable model for querying a variety of structured data, with use cases in analytics, transactions, graphs, streaming and more. However, most of the world’s data is unstructured. Thus, despite their success, the reality is that the vast majority of the world’s data has remained beyond the reach of relational systems.  The rise of deep learning and generative AI offers an opportunity to change this. These models provide a stunning capability to extract semantic understanding from almost any type of document, including text, images, and video which can extend the reach of databases to all the world's data. In this talk I explore how these new technologies will transform the way we build database management software, creating new that systems that can ingest, store, process, and query all data. Building such systems presents many opportunities and challenges. In this I talk focus on three: scalability, correctness, and reliability, and argue that the declarative programming paradigm that has served relational systems so well offers a path forward in the new world of AI data systems as well. To illustrate this, I describe several examples of such declarative AI systems we have built in document and video processing, and provide a set of research challenges and opportunities to guide research in this exciting area going forward.

Bio: Samuel Madden is a the College of Computing Distinguished Professor of Computing at MIT. His research interests include databases, distributed computing, and networking. Research projects include learned database systems, the C-Store column-oriented database system, and the CarTel mobile sensor network system. Madden heads the Data Systems Group at MIT and the Data Science and AI Lab (DSAIL), an industry supported collaboration focused on developing systems that use AI and machine learning. Madden received his Ph.D. from the University of California at Berkeley in 2003 where he worked on the TinyDB system for data collection from sensor networks. Madden was named one of Technology Review's Top 35 Under 35 in 2005 and an ACM Fellow in 2020, and is the recipient of several awards, including an NSF CAREER award, a Sloan Foundation Fellowship, the ACM SIGMOD Edgar F. Codd Innovations Award, and "test of time" awards from VLDB, SIGMOD, SIGMOBILE, and SenSys. He is the co-founder and Chief Scientist at Cambridge Mobile Telematics, which develops technology to make roads safer and drivers better.

Refreshments will be available from Koffee.