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PRODID:IEEE vTools.Events//EN
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TZID:Asia/Kolkata
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DTSTART:19451014T230000
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DTSTAMP:20260717T065004Z
UID:B53536AF-CE95-41D5-8943-57B6848B7474
DTSTART;TZID=Asia/Kolkata:20260807T190000
DTEND;TZID=Asia/Kolkata:20260807T200000
DESCRIPTION:Artificial Intelligence (AI) has undergone a remarkable transfo
 rmation over the past decades\, beginning with the development of neural n
 etworks that enabled machines to learn patterns from data. The rise of dee
 p learning in the 2010s\, fueled by advances in computational power and la
 rge datasets\, unlocked breakthroughs in image recognition\, speech proces
 sing\, and natural language understanding. Building on these foundations\,
  the introduction of transformer architectures paved the way for foundatio
 n models—large\, pre‑trained systems such as GPT and BERT—that can b
 e adapted to a wide range of tasks with minimal fine‑tuning.\n\nThis evo
 lution reflects a trajectory from specialized models to general‑purpose 
 AI systems\, highlighting both opportunities and challenges. Foundation mo
 dels demonstrate unprecedented scalability and versatility\, but they also
  raise concerns about bias\, explainability\, energy consumption\, and gov
 ernance. As AI continues to advance\, the integration of interpretability\
 , ethical safeguards\, and human oversight will be crucial to ensure that 
 these powerful models serve society responsibly.\n\nSpeaker(s): Dr. Yajnas
 eni Dash\, \n\nVirtual: https://events.vtools.ieee.org/m/568339
LOCATION:Virtual: https://events.vtools.ieee.org/m/568339
ORGANIZER:amiya87@gmail.com
SEQUENCE:43
SUMMARY:The Evolution of AI: From Neural Networks to Foundation Models
URL;VALUE=URI:https://events.vtools.ieee.org/m/568339
X-ALT-DESC:Description: <br /><p>Artificial Intelligence (AI) has undergone
  a remarkable transformation over the past decades\, beginning with the de
 velopment of <strong>neural networks</strong> that enabled machines to lea
 rn patterns from data. The rise of <strong>deep learning</strong> in the 2
 010s\, fueled by advances in computational power and large datasets\, unlo
 cked breakthroughs in image recognition\, speech processing\, and natural 
 language understanding. Building on these foundations\, the introduction o
 f <strong>transformer architectures</strong> paved the way for <strong>fou
 ndation models</strong>&mdash\;large\, pre‑trained systems such as GPT a
 nd BERT&mdash\;that can be adapted to a wide range of tasks with minimal f
 ine‑tuning.</p>\n<p>This evolution reflects a trajectory from <strong>sp
 ecialized models</strong> to <strong>general‑purpose AI systems</strong>
 \, highlighting both opportunities and challenges. Foundation models demon
 strate unprecedented scalability and versatility\, but they also raise con
 cerns about <strong>bias\, explainability\, energy consumption\, and gover
 nance</strong>. As AI continues to advance\, the integration of interpreta
 bility\, ethical safeguards\, and human oversight will be crucial to ensur
 e that these powerful models serve society responsibly.</p>
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