The CSAIL Forum event series is hosted by Professor Daniela Rus, CSAIL Director. This virtual series was created to inspire conversation, share insights, and shape the future of computer science and artificial intelligence. Registration is required. 
See past forum recordings and learn more here

Please join us for CSAIL Forum with Prof. Sam Madden

Speaker: Sam Madden, College of Computing Distinguished Professor
Date/time: Tuesday 12:00-1:00 EDT, November 18, 2025 
Venue: Live stream via Zoom: Registration required
Title: How I Learned to Start Querying and Love AI

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 systems that can ingest, store, process, and query all data. Building such systems presents many opportunities and challenges. In this talk I 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 the College of Computing Distinguished Professor of Computing at MIT. His research interests include databases, distributed computing, and AI systems. Past 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. Registration required

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 the 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.

The CSAIL Forum event series is hosted by Professor Daniela Rus, CSAIL Director. This virtual series was created to inspire conversation, share insights, and shape the future of computer science and artificial intelligence. Registration is required. 
See past forum recordings and learn more here

Please join us for the CSAIL Forum with Alison Gopnik

Speaker: Alison Gopnik Dept. of Psychology, UC Berkeley
Venue: Live stream via Zoom: Registration required
Abstract: Learning about the causal structure of the world is a fundamental problem for human cognition, and causal knowledge is central to both intuitive and scientific world models. However, causal models and especially causal learning have proved to be difficult for standard Large Models using standard techniques of deep learning. In contrast, cognitive scientists have applied advances in our formal understanding of causation in computer science, particularly within the Causal Bayes Net formalism, to understand human causal learning. These approaches also face challenges when it comes to learning however. In parallel, in the very different tradition of reinforcement learning, researchers have developed the idea of an intrinsic reward signal called “empowerment”. An agent is rewarded for maximizing the mutual information between its actions and their outcomes, regardless of the external reward value of those outcomes. In other words, the agent is rewarded if variation in an action systematically leads to parallel variation in an outcome so that variation in the action predicts variation in the outcome. Empowerment, then has two dimensions , it involves both controllability and variability. The result is an agent that has maximal control over the maximal part of its environment. “Empowerment” may be an important bridge between classical Bayesian causal learning and reinforcement learning and may help to characterize causal learning in humans and enable it in machines.  If an agent learns an accurate causal model of the world they will necessarily increase their empowerment, and, vice versa, increasing empowerment will lead to a more accurate (if implicit) causal model of the world. Empowerment may also explain distinctive empirical features of children’s causal learning, as well as providing a more tractable computational account of how that learning is possible.

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