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PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
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TZID:Asia/Kolkata
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DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
TZNAME:IST
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BEGIN:VEVENT
DTSTAMP:20260204T182335Z
UID:AE663357-ADCD-4ED6-8771-3D836681F372
DTSTART;TZID=Asia/Kolkata:20240912T140000
DTEND;TZID=Asia/Kolkata:20240912T150000
DESCRIPTION:Generative AI refers to a category of artificial intelligence m
 odels that are designed to generate new content\, such as text\, images\, 
 audio\, or other types of data. Probably the best known example of generat
 ive AI is ChatGPT\, the fastest consumer application to hit 100 million mo
 nthly active users. Generative AI models use machine learning algorithms t
 o learn patterns and structures from existing data and then produce new da
 ta that is similar in style or content to what they have been trained on.I
 n this presentation\, I will start with a tutorial on various approaches t
 o generative AI. Next I will talk about projects in our group at the TU De
 lft on deep generative models. I will briefly present novel kinds of deep 
 generative models that we are developing in our team. Next I will explain 
 how we are designing such models for rainfall nowcasting\, where we integr
 ate physical laws into the deep generative models. At last\, I will talk v
 arious AI related initiatives that our team is involved in.\n\nSpeaker(s):
  Dr. Justin Dauwels\, \n\nVirtual: https://events.vtools.ieee.org/m/433966
LOCATION:Virtual: https://events.vtools.ieee.org/m/433966
ORGANIZER:ieee.sps.sb.iitkgp@gmail.com
SEQUENCE:15
SUMMARY:IEEE SPS SBC Webinar: Generative AI (By Dr. Justin Dauwels)
URL;VALUE=URI:https://events.vtools.ieee.org/m/433966
X-ALT-DESC:Description: <br /><p dir="ltr">Generative AI refers to a catego
 ry of artificial intelligence models that are designed to generate new con
 tent\, such as text\, images\, audio\, or other types of data. Probably th
 e best known example of generative AI is ChatGPT\, the fastest consumer ap
 plication to hit 100 million monthly active users. Generative AI models us
 e machine learning algorithms to learn patterns and structures from existi
 ng data and then produce new data that is similar in style or content to w
 hat they have been trained on.In this presentation\, I will start with a t
 utorial on various approaches to generative AI. Next I will talk about pro
 jects in our group at the TU Delft on deep generative models. I will brief
 ly present novel kinds of deep generative models that we are developing in
  our team. Next I will explain how we are designing such models for rainfa
 ll nowcasting\, where we integrate physical laws into the deep generative 
 models.&nbsp\; At last\, I will talk various AI related initiatives that o
 ur team is involved in.</p>\n<p dir="ltr">&nbsp\;</p>\n<p>&nbsp\;</p>
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