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DESCRIPTION:Click for Latest Location Information: http://dgiq2024east.data
 versity.net/sessionPop.cfm?confid=162&proposalid=15816\nNOTE: This Seminar 
 continues from Thursday.\n\nThe hype of Generative AI is rapidly dissipatin
 g as organizations rapidly normalize the adoption of large language models 
 (LLMs) and other AI and machine learning techniques. Many organizations are
  piloting and assessing the business value of LLMs and generative AI techni
 ques. Yet because of the need for greater technological depth, those tasked
  with evaluating these technologies may misunderstand how these emergent AI
  environments and models are trained, fine-tuned, and produce results, resu
 lting in raised expectations about both the trustworthiness of the outputs 
 and the implications for misinterpreting produced results.\n\nIn these nasc
 ent AI implementations, risks abound – whether the AI systems produce
  hallucinations, are subject to integrated bias, inadvertently expose sensi
 tive data or open other information vulnerabilities. Allowing these systems
  to run in an ungoverned manner only magnifies these issues. There are ways
  that existing knowledge management techniques can mitigate these risks, an
 d in this tutorial, we explore ways that taxonomies, ontologies, and knowle
 dge graphs can lend credibility and increased trustworthiness to an AI impl
 ementation.\n\nAttendees will learn about:\n\n
 Information risks associated with emergent AI technologies\n
 Establishing the case for trusted AI\n
 Fundamentals of Semantic Data Modeling\n
 Introduction to taxonomies, ontologies, and knowledge graphs\n
 Practical integration of semantic technologies with AI implementations\n\n
DTSTART:20241213T083000
SUMMARY:S2: Applying Semantic Technologies to AI Governance and Information
  Risk Mitigation
DTEND:20241213T115959
LOCATION: See Description
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