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X-WR-CALNAME:Understanding Generative Models Inside Out: From Representatio
n to Data
X-WR-TIMEZONE:America/Los_Angeles
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UID:2025-02-25-yanai-elazar@cml.ics.uci.edu
DTSTAMP:20250225T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20250225T110000
DTEND;TZID=America/Los_Angeles:20250225T120000
SUMMARY:[CML Seminar] Yanai Elazar: Understanding Generative Models Inside
Out: From Representation to Data
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Yanai Elazar\, Postdoctoral Researcher\, Allen Institute for AI
and University of Washington\n\nTitle: Understanding Generative Models In
side Out: From Representation to Data\n\nAbstract: Generative models\, suc
h as ChatGPT and DALL-E\, are used by millions of people daily for tasks r
anging from programming and content creation to resume filtering. These mo
dels often create the impression of being intelligent\, which can incentiv
ize careless use in critical applications. While generative models are emp
owering\, they appear to be black boxes\, and their misuse can result in h
armful or unlawful outcomes. In this talk\, I will present algorithms and
tools for dissecting and analyzing generative models using holistic\, caus
al\, and data-centric approaches. By applying these methods to state-of-th
e-art models\, we can foster trust in these technologies by uncovering hum
an-interpretable concepts that underpin their behavior\, scrutinizing thei
r extensive training data\, and evaluating their learning processes. Final
ly\, I will reflect on how generative models have transformed the field of
AI and discuss the challenges that remain in ensuring their responsible d
evelopment and use.\n\nhttps://cml.ics.uci.edu/seminars/2025-02-25-yanai-e
lazar
X-ALT-DESC;FMTTYPE=text/html:Yanai Elazar\, Postdoctoral
Researcher\, Allen Institute for AI and University of Washington
<
b>Title: Understanding Generative Models Inside Out: From Representati
on to Data
Abstract: Generative models\, such as ChatGPT and
DALL-E\, are used by millions of people daily for tasks ranging from prog
ramming and content creation to resume filtering. These models often creat
e the impression of being intelligent\, which can incentivize careless use
in critical applications. While generative models are empowering\, they a
ppear to be black boxes\, and their misuse can result in harmful or unlawf
ul outcomes. In this talk\, I will present algorithms and tools for dissec
ting and analyzing generative models using holistic\, causal\, and data-ce
ntric approaches. By applying these methods to state-of-the-art models\, w
e can foster trust in these technologies by uncovering human-interpretable
concepts that underpin their behavior\, scrutinizing their extensive trai
ning data\, and evaluating their learning processes. Finally\, I will refl
ect on how generative models have transformed the field of AI and discuss
the challenges that remain in ensuring their responsible development and u
se.
https://cml.ics.uci.edu/seminars/2025-02-25-yanai-elazar
URL:https://cml.ics.uci.edu/seminars/2025-02-25-yanai-elazar
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