Learning from Collapse: Self-Consuming Loops in AI and the Humans Who Feed Them

Prof. Richard Baraniuk, Rice University

Monday, October 26th, 13:30 - 15:10

Training generative models on synthetic data creates feedback loops that amplify artifacts and bias and degrade quality and diversity, a phenomenon known as model collapse or MADness. This talk makes two turns. First, we show how to learn from collapse: the way a model degrades under self-training can tell us how to improve it. Second, we ask whether collapse might reach beyond models: fresh real data can stabilize self-consuming loops, but generative AI is beginning to reshape human language, ideas, and attention. Humans may no longer be outside the loop.

Speaker Biography:
Richard G. Baraniuk is the C. Sidney Burrus Professor of Electrical and Computer Engineering at Rice University and the Founding Director of OpenStax and SafeInsights. His research interests lie in new theory, algorithms, and hardware for machine learning, signal processing, and sensing. He is a Member of the National Academy of Engineering and American Academy of Arts and Sciences and a Fellow of the National Academy of Inventors, AAAS, and IEEE. He has received the DOD Vannevar Bush Faculty Fellow Award, the IEEE Jack S. Kilby Signal Processing Medal, the IEEE Signal Processing Society Technical Achievement and Society Awards, the Harold W. McGraw, Jr. Prize in Education, and the IEEE James H. Mulligan, Jr. Education Medal, among others.