The computations required for deep learning research have been doubling every few months, resulting in an estimated 5,000x increase from 2018 to 2022. This trend has led to unprecedented success in a range of AI tasks. In this talk I will discuss a few troubling side-effects of this trend, touching on issues of lack of inclusiveness within the research community, and an increasingly large environmental footprint. I will then present Green AI – an alternative approach to help mitigate these concerns. Green AI is composed of two main ideas: increased reporting of computational budgets, and making efficiency an evaluation criterion for research alongside accuracy and related measures. I will focus on the latter topic, discussing some of our recent efforts for reducing the costs of AI. This is joint work with Michael Hassid, Yossi Adi, Matanel Oren, Tal Remez, Jesse Dodge, Noah A. Smith, Oren Etzioni and Jonas Gehring.
Bio: Roy Schwartz is a senior lecturer (assistant professor) at the School of Computer Science and Engineering at The Hebrew University of Jerusalem (HUJI). Roy studies natural language processing and artificial intelligence. Prior to joining HUJI, Roy was a postdoc (2016-2019) and then a research scientist (2019-2020) at the Allen institute for AI and at The University of Washington, where he worked with Noah A. Smith. Roy completed his Ph.D. in 2016 at HUJI, where he worked with Ari Rappoport. Roy’s work has appeared on the cover of the CACM magazine, and has been featured, among others, in the New York Times, Haaretz, and Ynet.