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AI Explained — A Lecture for Curious Minds
A plain-language talk for library and community audiences.
Technology
Artificial Intelligence
I am a hands-on, skeptical AI optimist. I use these tools on real problems, from public documents and conservation review to websites, technical systems, research, and writing. They expand what I can attempt and the rigor I can bring, but I keep responsibility for the result.
A short AI manifesto
My working model is a sharp junior analyst: fast, tireless, inventive, and always in need of direction and review. I use AI to explore, organize, explain, draft, and test my own thinking. I also expect it to fill gaps, miss context, and state weak answers with confidence. I ask for sources, separate fact from inference, look for the strongest contrary case, and verify what matters.
I teach by showing rather than preaching. My lectures and workshops use live demos, real prompts, personal stories, household analogies, and honest failures. I invite people to bring their own problems. I want beginners, especially older adults, to leave curious enough to experiment and confident enough to question the result.
The field changes too quickly for anyone to be finished learning. That is part of its appeal. I keep testing new capabilities, admit when I do not know, and translate what I learn into practical use. Education turns a private advantage into shared capacity and helps keep a new digital divide from growing.
Lecture and workshop material
Notes
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