[Theory] 4/25 TTIC Distinguished Lecture Series: Antonio Torralba, MIT

[Brandie Jones via Theory]

*When:    * Friday, April 25th at *10:30 AM CT*



*Where:    *Talk will be given *live, in-person* at

                     TTIC, 6045 S. Kenwood Avenue

                     5th Floor, Room 530


*Virtually:  *via Panopto (Livestream
<https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=eaed2d1c-3093-4fe3-aa73-b248011ffd78>
)



*Who:         *Antonio Torralba, MIT

*Title:        *Understanding large vision models

*Abstract:  *In the last few years, large pretrained models have shown
impressive performance in a diverse set of tasks. These models must be
trained with large datasets and are, in most cases, opaque on how they
process information internally. In this talk I will focus on tools to
understand the inner workings of existing pretrained models. Our current
line of research aims to build tools that help users understand models,
while combining the flexibility of human experimentation with the
scalability of automated techniques. We introduce the Multimodal Automated
Interpretability Agent (MAIA), which designs experiments to answer user
queries about components of AI systems. MAIA iteratively generates
hypotheses, runs experiments that test these hypotheses, observes
experimental outcomes, and updates hypotheses until it can answer the user
query. MAIA equips a pre-trained vision-language model with a set of tools
that support iterative experimentation on subcomponents of other models to
explain their behavior. These include tools commonly used by human
interpretability researchers: for synthesizing and editing inputs,
computing maximally activating exemplars from real-world datasets, and
summarizing and describing experimental results. Interpretability
experiments proposed by MAIA compose these tools to describe and explain
system behavior. To conclude, I will talk about the role of data to train
large vision models and ask if we can do away with real image datasets
entirely when building a computer vision system, instead learning from
noise processes.


*Bio:*  Antonio Torralba is the Delta electronics Professor and head of the
AI+D faculty at the Department of EECS at MIT. He received the 2010 J. K.
Aggarwal Prize, the 2020 PAMI Mark Everingham Prize, the Inaugural Thomas
Huang Memorial Prize by the PAMITC in 2021. In 2022, he was invested
Honoris Causa doctor by the Universitat Politècnica de Catalunya -
BarcelonaTech (UPC). He is a AAAI fellow.

Hos*t:  <avrim at ttic.edu>**Greg Shakhnarovich <greg at ttic.edu>*


-- 
*Brandie Jones *
*Executive **Administrative Assistant*
Toyota Technological Institute
6045 S. Kenwood Avenue
Chicago, IL  60637
www.ttic.edu
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