Date: October 8, 2026
Time: 11:45 a.m.-12:45 p.m.
Location: G01 Gates Hall
Title: Are we (Still!) Not Giving Data Enough Credit?
Speaker: Alexei (Alyosha) Efros, UC Berkeley
Host: Abe Davis
Abstract: For most of its existence, computer science in general, and visual computing in particular, has been primarily focused on algorithms, with data treated largely as an afterthought. Only recently, with the advances in AI, did our field start to truly appreciate the singularly crucial role played by data. But even now we might still be underestimating it (e.g. the word "data" is missing even in Sutton's famous "Bitter Lesson" essay). In this talk, I will begin with some examples illustrating the importance of large visual data for visual analysis and synthesis. I will then share some of our recent work demonstrating the power of very simple algorithms when used with the right data, including scene analysis, model interpolation, and triggering reasoning in LLM base models. I will finish with a discussion of "surface data" vs. "deep data".
Bio: Alexei Efros is professor of electrical engineering and computer science at UC Berkeley. His research is on data-driven computer vision and its applications to computer graphics and computational photography using vast amounts of unlabelled visual data to understand, model, and recreate the visual world. Other interests include human vision, visual data mining, robotics, and the applications of computer vision to the visual arts and the humanities. Visual data can also enhance the interaction capabilities of AI systems, potentially bridging the gap between visual perception and language understanding in robotics.
