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Seminar Series

October 21

CSAIL Forum with Shafi Goldwasser

Shafi Goldwasser
CSAIL

Part Of

12:00P
- 1:00P

Location

TBD
Add to Calendar 2025-10-21 12:00:00 2025-10-21 13:00:00 America/New_York CSAIL Forum with Shafi Goldwasser https://mit.zoom.us/meeting/register/lMyzkgHoRCektZTWtK3R3w Please join us for the next CSAIL Forum, featuring Prof. Shafi GoldwasserCSAIL Forum hosted by Daniela RusSpeaker: Shafi Goldwasser, Leighton Family ProfessorDate/time: Tuesday 12:00-1:00 EDT, October 21, 2025 Venue: Live stream via Zoom: Registration requiredBio: https://www.csail.mit.edu/person/shafi-goldwasser Title: What Cryptography Can Tell Us about AI Abstract: For decades now cryptographic tools and models have at their essence transformed technology platforms controlled by worst case adversaries to trustworthy platforms. In this talk I will describe how to use cryptographic tools and cryptographic modeling to build trust in various phases of the machine learning pipelines. We will touch on privacy in the training and inference stage, verification protocols for the quality of machine learning models, and robustness in presence of adversaries.  If time permits, I will show how cryptographic assumptions can help characterize  the limits and possibilities of AI safety.  TBD

September 16

CSAIL Forum with Josh Tenenbaum

Joshua Tenenbaum
Brain & Cognitive Sciences & CSAIL, MIT

Part Of

12:00P
- 1:00P

Location

TBD
Add to Calendar 2025-09-16 12:00:00 2025-09-16 13:00:00 America/New_York CSAIL Forum with Josh Tenenbaum Please join us for the next CSAIL Forum, featuring Prof. Josh TenenbaumCSAIL Forum hosted by Daniela RusSpeaker: Joshua Tenenbaum, Professor of Computational Cognitive ScienceDate/time: Tuesday 12:00-1:00 EDT, September 16, 2025 Venue: Live stream via Zoom: Registration requiredBio: https://bcs.mit.edu/directory/joshua-b-tenenbaum Title: Scaling Intelligence the Human Way: Rebuilding the Bridge between AI and Cog Sci Abstract: Today's leading AI systems have achieved one of the field's oldest dreams and promises: They can take in any language as input and produce reasonable responses – often very much like the responses that a reasonable (and knowledgeable and helpful) person would produce.  Yet the processes at work inside these systems, and how they are built, do not (at least obviously) have much in common with the mechanisms or origins of the human mind.  What would it take to build a model with something like the input-output behavior of ChatGPT but whose inner workings actually instantiated a theory of human cognition – and even our best current scientific theory?  I will discuss several possible routes to this goal, and the challenges and opportunities they present. I will argue that now more than ever is the time for a bidirectional exchange between the fields of AI and Cog Sci – fields that grew up together starting in the 1950s, but have followed very different trajectories recently.  AI tools and techniques have much to offer cognitive theories, but cognitive science has just as much if not more to offer back to AI.  Understanding and using AI tools in a framework guided by foundational thinking in cognitive science represents the best hope to deliver on the theoretical goals, dreams, and promises of both fields.  TBD

May 13

CSAIL Forum with Armando Solar-Lezama: Programming the way to better AI

Armando Solar-Lezama
CSAIL

Part Of

12:00P
- 1:00P

Location

TBD
virtual event via Zoom (Registration required)
Add to Calendar 2025-05-13 12:00:00 2025-05-13 13:00:00 America/New_York CSAIL Forum with Armando Solar-Lezama: Programming the way to better AI Registration required: https://mit.zoom.us/meeting/register/TG0DF7tFQp-hS2BXku5ahAAbstract: For decades, programming was the way through which we told machines what to do, but modern AI techniques promise new ways of creating software directly from data and natural language. But programming has a number of advantages that have enabled us to build reliable large scale computing infrastructure. In this presentation, I explain some new approaches to learn from data while preserving some of the benefits of programming, and some of their applications in domains ranging from robotics to computational biology. TBD

May 06

CSAIL Forum with Manish Raghavan: The role of information diversity in AI systems

Part Of

12:00P
- 1:00P

Location

TBD
Add to Calendar 2025-05-06 12:00:00 2025-05-06 13:00:00 America/New_York CSAIL Forum with Manish Raghavan: The role of information diversity in AI systems Registration required: https://mit.zoom.us/meeting/register/GP_RXB5BSTy_Ubf3wNJwxQAbstract:AI systems often consist of multiple actors or agents with different goals, incentives and critically, information. In this talk, we explore the role that heterogeneous information plays. Across decision-making, pricing, and production tasks, we show that social outcomes improve as information diversity increases. We discuss implications for the development, deployment, and use of AI.Bio: Manish Raghavan is the Drew Houston (2005) Career Development Professor at the MIT Sloan School of Management and Department of Electrical Engineering and Computer Science. Before that, he was a postdoctoral fellow at the Harvard Center for Research on Computation and Society (CRCS). His research centers on the societal impacts of algorithms and AI. TBD

April 22

CSAIL Forum with Prof Yoon Kim: Efficient and Expressive Architectures for Language Modeling

Part Of

12:00P
- 1:00P

Location

TBD
Add to Calendar 2025-04-22 12:00:00 2025-04-22 13:00:00 America/New_York CSAIL Forum with Prof Yoon Kim: Efficient and Expressive Architectures for Language Modeling Efficient and Expressive Architectures for Language ModelingSpeaker: Yoon Kim, Assistant Professor, CSAIL Tuesday 12:00-1:00 EDT, April 22, 2025 live stream via Zoom: Registration requiredAbstract:Transformers are the dominant architecture for language modeling (and generative AI more broadly). The attention mechanism in Transformers is considered core to the architecture and enables accurate sequence modeling at scale. However, the complexity of attention is quadratic in input length, which makes it difficult to apply Transformers to model long sequences. Moreover, Transformers have theoretical limitations when it comes to the class of problems it can solve, which prevents their being able to model certain kinds of phenomena such as state tracking. This talk will describe some recent work on efficient alternatives to Transformers which can overcome these limitations.Bio: Yoon Kim is an assistant professor at MIT EECS and a principal investigator at CSAIL, where he works on natural language processing and machine learning. He obtained his Ph.D. in computer science from Harvard University. TBD

April 15

CSAIL Forum with Prof Phillip Isola: The Platonic Representation Hypothesis

Part Of

12:00P
- 1:00P

Location

32-G882
Hewlett Seminar Room
Add to Calendar 2025-04-15 12:00:00 2025-04-15 13:00:00 America/New_York CSAIL Forum with Prof Phillip Isola: The Platonic Representation Hypothesis The Platonic Representation HypothesisSpeaker: Phillip Isola, Associate Professor, CSAIL Tuesday 12:00-1:00 EDT, April 15, 2025 In person: Hewlett 32-G882 in the Stata Center, 32 Vassar Street and live stream via Zoom: Registration requiredAbstract: I will argue that representations in different deep nets are converging. First, I will survey examples of convergence in the literature: over time and across multiple domains, the ways by which different neural networks represent data are becoming more aligned. Next, I will demonstrate convergence across data modalities: as vision models and language models get larger, they measure distance between datapoints in a more and more alike way. I will hypothesize that this convergence is driving toward a shared statistical model of reality, akin to Plato's concept of an ideal reality. We term such a representation the platonic representation and discuss several possible selective pressures toward it. Finally, I'll discuss the implications of these trends, their limitations, and counterexamples to our analysis.Bio: https://web.mit.edu/phillipi/www/bio.html TBD