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X-WR-CALNAME:Challenges in Improving and Applying Generative Models
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UID:2024-04-10-karen-ullrich@cml.ics.uci.edu
DTSTAMP:20240410T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20240410T110000
DTEND;TZID=America/Los_Angeles:20240410T120000
SUMMARY:[CML Seminar] Karen Ullrich: Challenges in Improving and Applying G
enerative Models
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Karen Ullrich\, Research Scientist\, Fundamental AI Research (F
AIR) at Meta\, New York\n\nTitle: Challenges in Improving and Applying Gen
erative Models\n\nAbstract: The emergence of powerful\, ever more universa
l models such as ChatGPT\, and stable Diffusion\, made generative modeling
(GM) undoubtedly a focal point for modern AI research. In the talk\, we w
ill discuss applications of GM and how GM fits into a vision of autonomous
machine intelligence. We will critically examine the sustainability of sc
aling AI models\, a prevalent approach driving remarkable advancements in
GM. Despite significant successes\, I highlight the substantial physical\,
economic\, and environmental limitations of continuous scaling\, question
ing its long-term feasibility. Furthermore\, we will discuss inherent limi
tations in current high performance models that lead to a lack of tractabi
lity of statistical queries necessary to enable reasoning.\n\nhttps://cml.
ics.uci.edu/seminars/2024-04-10-karen-ullrich
X-ALT-DESC;FMTTYPE=text/html:Karen Ullrich\, Research Sc
ientist\, Fundamental AI Research (FAIR) at Meta\, New York
Titl
e: Challenges in Improving and Applying Generative Models
Ab
stract: The emergence of powerful\, ever more universal models such as
ChatGPT\, and stable Diffusion\, made generative modeling (GM) undoubtedl
y a focal point for modern AI research. In the talk\, we will discuss appl
ications of GM and how GM fits into a vision of autonomous machine intelli
gence. We will critically examine the sustainability of scaling AI models\
, a prevalent approach driving remarkable advancements in GM. Despite sign
ificant successes\, I highlight the substantial physical\, economic\, and
environmental limitations of continuous scaling\, questioning its long-ter
m feasibility. Furthermore\, we will discuss inherent limitations in curre
nt high performance models that lead to a lack of tractability of statisti
cal queries necessary to enable reasoning.
https://cml.ics.uci.edu/semina
rs/2024-04-10-karen-ullrich
URL:https://cml.ics.uci.edu/seminars/2024-04-10-karen-ullrich
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