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SUMMARY:Annual AI & Analytics Online Symposium - Mind Meets Machines: Insight and Artificial Intelligence Converge
DESCRIPTION:At the heart of the Center for Technology Innovation’s AI and Analytics Symposium is a fundamental shift: as these technologies become integral to decision-making across industries\, the question is no longer if we’ll collaborate with machines—but how. This event explores that evolving collaboration: how data\, algorithms\, and human judgment come together to drive innovation\, shape policy\, and influence business strategy. Through keynote talks\, cutting-edge research\, and a thought-provoking industry panel\, we’ll examine the practical realities\, challenges\, and opportunities of working at the intersection of human and machine intelligence. \nINDUSTRY KEYNOTE\nAgentic AI and IT’s Use Cases\n9:00am – 9:55am\, followed by a 5 minute break \nBalamurugan Balakreshnan\nChief Architect WMW Microsoft\n \nMicrosoft Agentic AI represents a transformative approach to artificial intelligence\, integrating autonomous agents with human ambition to drive business innovation and productivity. These AI-powered agents\, such as Microsoft 365 Copilot\, Azure AI Foundry Agents\, are designed to automate routine tasks\, enhance decision-making\, and streamline workflows across various industries. \n\nACADEMIC KEYNOTE\nIncentivizing The Content Creator When Tasks Can Be Delegated to Artificial Intelligence\n10:00am – 10:50am\, followed by a 10 minute break \nDJ Wu\nErnest Scheller Jr. Chair in Innovation\, Entrepreneurship and Commercialization\, Georgia Tech\n \nWe develop a game-theoretic model to examine how platforms should adjust revenue-sharing to incentivize human creators in the presence of AI tools. We show that while AI can aid content creation\, excessively strong AI may cause creators to shirk effort\, ultimately harming the platform and reducing social welfare. \n\n \nRESEARCH INSIGHTS\nTake Caution in Using LLMs as Human Surrogates: Scylla Ex Machina\n11:00am – 11:50am\, followed by a 10 minute break \nDokyun Lee\nKelli Questrom Associate Professor in Information Systems\, Boston University \nRecent studies suggest large language models (LLMs) can exhibit human-like reasoning\, aligning with human behavior in economic experiments\, surveys\, and political discourse. This has led many to propose that LLMs can be used as surrogates or simulations for humans in social science research. However\, LLMs differ fundamentally from humans\, relying on probabilistic patterns\, absent the embodied experiences or survival objectives that shape human cognition. We assess the reasoning depth of LLMs using the 11-20 money request game. Nearly all advanced approaches fail to replicate human behavior distributions across many models. Causes of failure are diverse and unpredictable\, relating to input language\, roles\, and safeguarding. These results advise caution when using LLMs to study human behavior or as surrogates or simulations. \n\nREALITY CHECK: WHERE AI MEETS BUSINESS\n12:00pm – 12:45pm \nSandeep Gangarapu\, Senior Applied Scientist\, Apple\nTimothy Johnson\, Demand Planning Manager\, Charter Steel\nWaqar Hasan\, CEO\, InComm Benefits \nModerator: Scott Schanke\, Director\, Center for Technology Innovation & Assistant Professor\, University of Wisconsin-Milwaukee \n  \nRegistration Required
URL:/business/event/annual-ai-analytics-online-symposium-mind-meets-machines-insight-and-artificial-intelligence-converge/
LOCATION:Webinar
CATEGORIES:Alumni & Community,Center for Technology Innovation,Faculty and Staff,Lectures Conferences and Symposiums,Public,Students,51ÁÔÆæ Campus Events
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