BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20240420T001545Z UID:B4E640B2-8888-4315-99A7-0F80344CFD02 DTSTART;TZID=America/New_York:20240418T200000 DTEND;TZID=America/New_York:20240418T213000 DESCRIPTION:[]\n\nLarge Language Models (LLMs) are being used widely in cur rent Generative AI systems. Unfortunately\, LLMs demonstrate significant c apabilities but face challenges such as hallucination\, outdated knowledge \, and non-transparent\, untraceable reasoning processes.\n\nRetrieval Aug mented Generation (RAG) has emerged as a promising solution by incorporati ng knowledge from external databases. This enhances the accuracy and credi bility of the models\, particularly for knowledge-intensive tasks\, and al lows for continuous knowledge updates and integration of domain-specific i nformation. RAG synergistically merges LLMs' intrinsic knowledge with the vast\, dynamic repositories of external databases. This talk gives an over view of the structure of RAG systems and includes a demo of their capabili ties.\n\nMadhu Chinnambeti is an SVP and Senior Data Scientist at SupportV ectors. In his current role\, Madhu advises the companies\, aspiring engin eers\, and entrepreneurs on ML\, AI\, and Gen AI technology stack. Madhu h as over 28 years of experience in Computer Science and Engineering and he is currently working on his PhD dissertation in the area of Graph Neural N etworks (GNNs) and deep learning at Boise State University. Madhu is curre ntly working on research and publications that advance GNNs under Cybersec urity and fraud detection.\n\nHis passion also includes tech education in the evolving fields like Generative AI. Madhu is a volunteer advisory boar d member of Disability:In New Jersey affiliate to help individuals with di sabilities to get jobs.\n\nSpeaker(s): Madhu Chinnambeti\, \n\nRoom: Room 105\, Bldg: Computer Science Building\, 35 Olden St.\, Princeton Universit y\, Princeton\, New Jersey\, United States\, 08544\, Virtual: https://even ts.vtools.ieee.org/m/414598 LOCATION:Room: Room 105\, Bldg: Computer Science Building\, 35 Olden St.\, Princeton University\, Princeton\, New Jersey\, United States\, 08544\, Vi rtual: https://events.vtools.ieee.org/m/414598 ORGANIZER:dmancl@acm.org SEQUENCE:17 SUMMARY:Madhu Chinnambeti Presents: Retrieval Augmented Generation (RAG) URL;VALUE=URI:https://events.vtools.ieee.org/m/414598 X-ALT-DESC:Description: <br /><p><img src="https://events.vtools.ieee.org/v tools_ui/media/display/fc23bfd2-a868-4ef4-9655-c78be7c1b7a2" alt="" width= "640" height="360"></p>\n<p>Large Language Models (LLMs) are being used wi dely in current Generative AI systems. Unfortunately\, LLMs demonstrate si gnificant capabilities but face challenges such as hallucination\, outdate d knowledge\, and non-transparent\, untraceable reasoning processes.</p>\n <p>Retrieval Augmented Generation (RAG) has emerged as a promising solutio n by incorporating knowledge from external databases. This enhances the ac curacy and credibility of the models\, particularly for knowledge-intensiv e tasks\, and allows for continuous knowledge updates and integration of d omain-specific information. RAG synergistically merges LLMs' intrinsic kno wledge with the vast\, dynamic repositories of external databases. This ta lk gives an overview of the structure of RAG systems and includes a demo o f their capabilities.</p>\n<p>&nbsp\;</p>\n<p>Madhu Chinnambeti is an SVP and Senior Data Scientist at SupportVectors. In his current role\, Madhu a dvises the companies\, aspiring engineers\, and entrepreneurs on ML\, AI\, and Gen AI technology stack. Madhu has over 28 years of experience in Com puter Science and Engineering and he is currently working on his PhD disse rtation in the area of Graph Neural Networks (GNNs) and deep learning at B oise State University. Madhu is currently working on research and publicat ions that advance GNNs under Cybersecurity and fraud detection. &nbsp\;</p >\n<p>His passion also includes tech education in the evolving fields like Generative AI. Madhu is a volunteer advisory board member of Disability:I n New Jersey affiliate to help individuals with disabilities to get jobs.< /p> END:VEVENT END:VCALENDAR