​Welcome to Binge Thinking. We invite professors and experts to give lectures in bars around Boston, NYC, and DC!

​In a world of AI slop, doomscrolling and brainrot, Binge Thinking brings real experts into real places for nights of learning, conversation, and community.

​Think Responsibly.

​Join us and grab a drink as we explore why AI sounds so sure when it’s wrong. How many of its mistakes have you already believed?

​TOPIC: Who Do You Trust: AI or Your Professor? When AI Makes Stuff Up, Would You Even Notice?

SPEAKER: Dr. Jennifer Hill, New York University

​Ask AI anything and it answers instantly, confidently, and with perfect grammar. But confident isn’t the same as correct. When a question falls outside what a model was trained on, it doesn’t say “I don’t know.” It improvises. Researchers call it hallucinating. The rest of us call it making stuff up.

​So the real question is: would you catch it? Dr. Jennifer Hill put AI to the test on a decade-old causal inference contest, then ran a randomized experiment to see whether newer researchers could tell when the machine was bluffing. Spoiler: it might be easier to fool us than we think.

​In this talk, Dr. Hill unpacks where AI falls short, why “it’ll just keep getting better” isn’t the full answer, and what it takes to stay sharp when your smartest-sounding source might be wrong.

​Topics include:

​How AI performed on a 10-year-old causal inference contest (hint: just okay)

​Why “AI will keep getting more accurate” doesn’t solve everything

​The tasks and information that will always fall outside an AI’s training data

​What happens in those gaps: hallucinations

​A randomized experiment testing whether inexperienced researchers can spot AI’s mistakes, and how easily they were misled

​Beyond the tech, this is a story about trust.

​Whether you’re a student checking your homework, a professional leaning on chatbots at work, or someone who just googles everything, knowing when to question an answer has never mattered more.

​This talk explores a question we’ll all be asking for years: who (or what) actually deserves your trust?

​About the Speaker

​Dr. Jennifer Hill is Professor of Applied Statistics at NYU Steinhardt, Co-Director of the PRIISM Center, and Co-Chair of the Department of Applied Statistics, Social Science, and Humanities. She earned her B.A. in Economics from Swarthmore College, her M.S. in Statistics from Rutgers University, and her Ph.D. in Statistics from Harvard University, followed by a postdoctoral fellowship at Columbia University.

​Her work focuses on methods for answering the cause-and-effect questions at the heart of policy research and science. She co-authored the widely used textbook Data Analysis Using Regression and Multilevel/Hierarchical Models with Andrew Gelman, created the thinkCausal software to make advanced methods more accessible, and co-founded NYU’s M.S. in Applied Statistics for Social Science Research (A3SR). She received NYU’s Distinguished Teaching Award in 2021.

​Agenda

​7:00 PM - Doors Open (Grab a seat and drinks!)
7:30 PM - Lecture Begins
8:10 PM - Audience Q&A
8:30 PM - Drinks & Conversation
9:00 PM - Event Ends