IAC24 Main Conference Talk
Description
AI is here, and so is a flood of urgent rhetoric from tech-ideologues, business thought leaders, and concerned humanists about its abilities, its risks, and how to not get left behind as AI takes over the information professions. It can be hard to sort through the BS to figure out what “Artificial Intelligence” actually is, and how AI systems are different than previous information technologies, and what that might mean for your information work.
When pressure to “use AI” comes down through your organizational hierarchy, or a sales team touts their software’s integrated AI tools, are you prepared to cut through the buzz and figure out why it does or doesn’t make sense to use AI for particular tasks?
This talk will use information theory to talk practically about, what AI systems are doing with information, how AI ‘reasoning’ is different from human reasoning, and what sort of tasks AI is useful for. You’ll learn about the relationship between probability and information, about human meaning-making, and leave with a foundation for thinking critically about specific AI implementations in terms of their informational coherence.
About the speaker(s)
Kat King is an information architect interested in language, and meaning, and what it takes to do Good work. She has a master’s degree in information science from the University of Michigan School of Information.
She works as a business intelligence analyst for University of Michigan Library Operations division, where she works on projects that help the library strategically plan for changes to their spaces and work processes, understand data about collections and building use, and shift towards a more user centered service design process. Her previous work has included consulting for non-profits, and teaching IA to undergraduates.
When pressure to “use AI” comes down through your organizational hierarchy, or a sales team touts their software’s integrated AI tools, are you prepared to cut through the buzz and figure out why it does or doesn’t make sense to use AI for particular tasks?
This talk will use information theory to talk practically about, what AI systems are doing with information, how AI ‘reasoning’ is different from human reasoning, and what sort of tasks AI is useful for. You’ll learn about the relationship between probability and information, about human meaning-making, and leave with a foundation for thinking critically about specific AI implementations in terms of their informational coherence.
She works as a business intelligence analyst for University of Michigan Library Operations division, where she works on projects that help the library strategically plan for changes to their spaces and work processes, understand data about collections and building use, and shift towards a more user centered service design process. Her previous work has included consulting for non-profits, and teaching IA to undergraduates.