ISACA Vancouver Chapter reposted this
🚀 Excited to announce the upcoming event "AI Mastery - Part 1: What is AI? History and Fundamentals" on June 25, 2024, at Northeastern University - Vancouver! 🌟 Join us for an enlightening session led by Anthony Green of OpenRep, where we'll delve into the fascinating world of AI. Discover the journey of AI from its historical roots to the advanced applications we see today, including Large Language Models (LLMs). This session will cover the basics of machine learning, key research milestones, and crucial ethical considerations in AI. Whether you're a seasoned professional or just starting your AI journey, this event promises to deepen your understanding and spark insightful discussions. Don't miss this chance to expand your knowledge and network with fellow AI enthusiasts. Register now and be part of the AI revolution: https://lu.ma/AIMastery1 Mary Carmichael CPA CMA, CISM, CISA, CRISC Nick Maltchev Brenda Dhillon Rossilyne Tan Anthony Green Darren Yung Janice Scott Jack Wong Smayan Daruka Simon Chu, CA, CPA, CISA Augustine Wong Marleen Mavrow, CISM, PMP, CRISC Ashief Ahmed, CISSP, ISSMP, CCSP, SSCP, CEH, CC, ITIL, CISA, CISM, CGEIT, CDPSE, CCSK Daina McFarlane Danielle Cheng - Ting OpenRep ISACA Vancouver Chapter #AI #MachineLearning #TechEvents #NortheasternUniversity #OpenRep #ArtificialIntelligence #EthicsInAI #LLMs #AIHistory #ProfessionalDevelopment
Elena Kisel
2w
Why don't you mention the development of computer architectures and large computer farms. Without this, the creation and development of LLMs is hard to imagine.
Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
2wReflecting on the evolution of AI showcased in the upcoming event, it's evident that understanding its historical roots is crucial for grasping its current complexities. You talked about delving into AI's journey, including the pivotal role of Large Language Models (LLMs), in shaping its trajectory. Considering the ethical considerations highlighted, how do you envision addressing the challenges of bias and fairness in LLMs, especially in scenarios where these models are deployed for critical decision-making processes? If we imagine a scenario where LLMs are utilized in healthcare diagnostics, how would you technically ensure unbiased and equitable outcomes, particularly in underrepresented patient populations?