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BEGIN:VEVENT
UID:0af874ed228f97acc76b0500793e7abf34632384@swoogo.com
DTSTAMP:20260817T131054Z
DESCRIPTION:Join me as I share my journey from utilizing Convolutional Neur
 al Networks (CNNs) and multi-modal systems before the era of Large Languag
 e Models (LLMs)\, to my recent experiences at Hugging Face. With fresh eye
 s\, I've explored and benefited from remarkable tools that weren't availab
 le just a few years ago\, and I'm excited to share these insights with you
 . We'll delve into multi-modal video LLMs—examining the state of the art\,
  understanding their limitations\, and exploring the tools and datasets th
 at are shaping the future of the field. 
DTSTART:20241022T191500Z
DTEND:20241022T194500Z
LAST-MODIFIED:20260817T131055Z
LOCATION:Session Room 2
SEQUENCE:0
STATUS:CONFIRMED
SUMMARY:From CNNs to Cutting-Edge Multi-Modal LLMs: My Journey with Hugging
  Face and the Future of AI
TRANSP:OPAQUE
X-ALT-DESC;FMTTYPE=text/html:<p><span style='font-size:12pt\;'><span style=
 'font-family:Aptos\, sans-serif\;'>Join me as I share my journey from util
 izing Convolutional Neural Networks (CNNs) and multi-modal systems before 
 the era of Large Language Models (LLMs)\, to my recent experiences at Hugg
 ing Face. With fresh eyes\, I've explored and benefited from remarkable to
 ols that weren't available just a few years ago\, and I'm excited to share
  these insights with you. We'll delve into multi-modal video LLMs—examinin
 g the state of the art\, understanding their limitations\, and exploring t
 he tools and datasets that are shaping the future of the field. </span></s
 pan></p>
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ACTION:DISPLAY
DESCRIPTION:Join me as I share my journey from utilizing Convolutional Neur
 al Networks (CNNs) and multi-modal systems before the era of Large Languag
 e Models (LLMs)\, to my recent experiences at Hugging Face. With fresh eye
 s\, I've explored and benefited from remarkable tools that weren't availab
 le just a few years ago\, and I'm excited to share these insights with you
 . We'll delve into multi-modal video LLMs—examining the state of the art\,
  understanding their limitations\, and exploring the tools and datasets th
 at are shaping the future of the field. 
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