The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018. This trend has led to unprecedent 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, challenges in adoption of the technology, 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 discuss these two ideas, presenting recent efforts and open questions.
This is joint work with Dallas Card, Jesse Dodge, Oren Etzioni, Suchin Gururangan and Noah A. Smith.