BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Pacific BEGIN:DAYLIGHT DTSTART:20250309T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250914T232321Z UID:DA31A1A3-2757-4B7B-890A-5DFC16399452 DTSTART;TZID=US/Pacific:20250829T153000 DTEND;TZID=US/Pacific:20250829T200000 DESCRIPTION:IEEE SCV WIE AI Summit 2025\n\n[]\n\nIn an era where AI technol ogies are rapidly transforming industries and redefining possibilities\, i t is crucial to explore both the innovations driving this change and the r esponsibilities that come with it. Today\, we will delve into a diverse ar ray of topics that highlight the multifaceted nature of AI and its profoun d impact on our lives.\n\nOur sessions will cover the latest developments in Large Language Models and Foundation Models\, exploring efficient fine- tuning\, multilingual adaptation\, and the role of LLMs as knowledge bases . We will also examine the evolution of AI agents\, focusing on autonomous task completion\, multi-agent collaboration\, and the integration of exte rnal knowledge for robust decision-making.\n\nIn the realm of Vision and M ultimodality\, we will explore the integration of text\, image\, and video understanding\, as well as advanced techniques like zero-shot learning an d self-supervised learning. Our discussions on MLOps for LLMs will provide insights into best practices for training\, deploying\, and evaluating la rge models.\n\nWe will also address the critical areas of Knowledge-Ground ed Reasoning\, On-Device Learning\, and the ethical dimensions of AI\, inc luding bias mitigation\, privacy preservation\, and the detection of misin formation.\n\nTalk tracks are broadly classified but not limited to\,\n1. Large Language Models (LLMs) & Foundation Models\n2. AI Agents\n3. Vision & Multimodality\n4. MLOps for LLMs\n5. Knowledge-Grounded & Reasoning\n6. On-Device Learning for LLMs and Multi-Modal AI\n7. Ethics\, Bias & Fairnes s\n\nSpeaker(s): Jagruti Mahante & Shreya Anand\, Jesmin Jahan Tithi\, Har ika Rama Tulasi Karatapu\, Pallishree Panigrahi\, Rong Wang and Sushma Ven katesh Reddy\, Disha Ahuja\, Shubhi Asthana\n\nAgenda: \n3:15-4:00 Registr ation\n4:00-4:10 IEEE SCV WIE & Santa Clara WIN Welcome Message\n4:10-4:20 Welcome Keynote Message by Vivian Lien\, Vice President\, CCG & General M anager\, Client Graphics Product Management/ Intel\n4:20-4:50 Multi-Agenti c Synthetic Data Generation for Realistic\, Multi-Turn Conversations - By Disha Ahuja\, Cisco\n4.55-5.25 The LLM Imperative: Secure Your AI Frontier Before It Fractures Your Enterprise - By Jagruti Mahante & Shreya Anand\, Workday\n5:25-5:55 Designing Data Centers for Next-Gen Language Models - By Jesmin Jahan Tithi\, Intel\n5:55-6:30 Networking and Refreshments\n6:30 -6:50 ReAct Prompt Design: Enhancing LLM Reasoning & Tool Use - By Shubhi Asthana\, IBM\n6:55-7:15 AI Powered SaaS on GCP - By Harika Rama Tulasi Ka ratapu\, Google\n7:20-7:40 Designing for Trust: Responsible AI from Data t o Deployment - By Pallishree Panigrahi\, Amazon\n7:45-8:05 The First Enabl ement of GPU On-Device LLMs on Intel Chromebook Platforms - By Rong Wang a nd Sushma Venkatesh Reddy\,\n8:05-8:10 Final Wrap-up\n\nBldg: SC12\, Intel \, 3600 Juliette Ln\, Santa Clara\, CA 95054\, US\, Santa Clara\, Californ ia\, United States\, 95054 LOCATION:Bldg: SC12\, Intel\, 3600 Juliette Ln\, Santa Clara\, CA 95054\, U S\, Santa Clara\, California\, United States\, 95054 ORGANIZER:sbehere@ieee.org SEQUENCE:252 SUMMARY:IEEE SCV WIE AI Summit 2025 URL;VALUE=URI:https://events.vtools.ieee.org/m/479108 X-ALT-DESC:Description: <br /><div class="ahS2Le">\n<h1 class="F9yp7e ikZYw f LgNcQe" dir="auto" role="heading" aria-level="1">IEEE SCV WIE AI Summit 2025</h1>\n<p><img src="https://events.vtools.ieee.org/vtools_ui/media/dis play/8d913857-1543-4b01-a824-47293f961925" alt="" width="784" height="784" ></p>\n</div>\n<p><em>In an era where AI technologies are rapidly transfor ming industries and redefining possibilities\, it is crucial to explore bo th the innovations driving this change and the responsibilities that come with it. Today\, we will delve into a diverse array of topics that highlig ht the multifaceted nature of AI and its profound impact on our lives.</em ></p>\n<p><em>Our sessions will cover the latest developments in Large Lan guage Models and Foundation Models\, exploring efficient fine-tuning\, mul tilingual adaptation\, and the role of LLMs as knowledge bases. We will al so examine the evolution of AI agents\, focusing on autonomous task comple tion\, multi-agent collaboration\, and the integration of external knowled ge for robust decision-making.</em></p>\n<p><em>In the realm of Vision and Multimodality\, we will explore the integration of text\, image\, and vid eo understanding\, as well as advanced techniques like zero-shot learning and self-supervised learning. Our discussions on MLOps for LLMs will provi de insights into best practices for training\, deploying\, and evaluating large models.</em></p>\n<p><em>We will also address the critical areas of Knowledge-Grounded Reasoning\, On-Device Learning\, and the ethical dimens ions of AI\, including bias mitigation\, privacy preservation\, and the de tection of misinformation.</em></p>\n<p>Talk tracks are broadly classified but not limited to\,</p>\n<div><span dir="ltr">1.&nbsp\;<strong>Large Lan guage Models (LLMs) &amp\; Foundation Models</strong></span></div>\n<div>< span dir="ltr"><strong>2. AI Agents</strong></span></div>\n<div><span dir= "ltr"><strong>3. Vision &amp\; Multimodality</strong></span></div>\n<div>< span dir="ltr"><strong>4. MLOps for LLMs</strong></span></div>\n<div><span dir="ltr"><strong>5. Knowledge-Grounded &amp\; Reasoning</strong></span>< /div>\n<div><strong><span dir="ltr">6. </span>On-Device Learning for LLMs and Multi-Modal AI</strong></div>\n<div><span dir="ltr"><strong>7. Ethics\ , Bias &amp\; Fairness</strong></span></div>\n<div><span dir="ltr"><strong >&nbsp\;</strong></span></div><br /><br />Agenda: <br /><div dir="ltr">\n< div>3:15-4:00 Registration&nbsp\;<br>4:00-4:10 IEEE SCV WIE &amp\; Santa C lara WIN Welcome Message</div>\n<div>4:10-4:20 Welcome Keynote Message by Vivian Lien\, Vice President\, CCG &amp\; General Manager\, Client Graphic s Product Management/ Intel</div>\n<div>4:20-4:50 Multi-Agentic Synthetic Data Generation for Realistic\, Multi-Turn Conversations - By Disha Ahuja\ , Cisco<br>4.55-5.25&nbsp\; The LLM Imperative: Secure Your AI Frontier Be fore It Fractures Your Enterprise - By Jagruti Mahante &amp\; Shreya Anand \, Workday&nbsp\;<br>5:25-5:55 Designing Data Centers for Next-Gen Languag e Models - By Jesmin Jahan Tithi\, Intel<br>5:55-6:30 Networking and Refre shments<br>6:30-6:50 ReAct Prompt Design: Enhancing LLM Reasoning &amp\; T ool Use - By Shubhi Asthana\, IBM<br>6:55-7:15 AI Powered SaaS on GCP - By Harika Rama Tulasi Karatapu\, Google<br>7:20-7:40 Designing for Trust: Re sponsible AI from Data to Deployment - By Pallishree Panigrahi\, Amazon<br >7:45-8:05 The First Enablement of GPU On-Device LLMs on Intel Chromebook Platforms - By Rong Wang and Sushma Venkatesh Reddy\,&nbsp\;<br>8:05-8:10 Final Wrap-up</div>\n</div> END:VEVENT END:VCALENDAR