CSIS Seminar

Data Science at the Singularity

Abstract

A purported “AI Singularity” has been much in the public eye recently, especially since the release of ChatGPT last November, spawning social media “AI Breakthrough” threads promoting Large Language Model (LLM) achievements. Alongside this, mass media and national political attention focused on “AI Doom” hawked by social media influencers, with twitter personalities invited to tell congresspersons about the coming "End Times." In my opinion, “AI Singularity” is the wrong narrative; it drains time and energy with pointless speculation. We do not yet have general intelligence, we have not yet crossed the AI singularity, and the remarkable public reactions signal something else entirely. Something fundamental to science really has changed in the last ten years. In certain fields which practice Data Science according to three principles I will describe, progress is simply dramatically more rapid than in those fields that don’t yet make use of it. Researchers in the adhering fields are living through a period of very profound transformation, as they make a transition to frictionless reproducibility. This transition markedly changes the rate of spread of ideas and practices, and marks a kind of singularity, because it affects mindsets and paradigms and erases memories of much that came before. Many phenomena driven by this transition are misidentified as signs of an AI singularity. Data Scientists should understand what's really happening and their driving role in these developments.

Speaker Bio

David Donoho is one of the most accomplished statisticians in our lifetime. He is a Professor of Statistics, and serves as the Anne T. and Robert M. Bass Professor in the Humanities and Sciences at Stanford University. He is a COPSS Presidents' Awardee, an elected Fellow of the American Academy of Arts and Sciences, and an elected Member of the United States National Academy of Sciences. In 2019, he was also elected a Member of the American Philosophical Society. Among his many accomplishments, he also received the John von Neumann Prize (2001, Society for Industrial and Applied Mathematics), the Norbert Wiener Prize in Applied Mathematics (2010, from SIAM and the AMS), the Shaw Prize for Mathematics (2013), the Gauss Prize from IMU (2018), and, most recently, the IEEE Jack S. Kilby Signal Processing Medal in 2022. His research interests include large-scale covariance matrix estimation, large-scale matrix denoising, detection of rare signals, compressed sensing, and empirical deep learning.