In the February 2024 Communications article titled, "Energy and Emissions of Machine Learning on Smartphones vs. the Cloud," the authors found and corrected two arithmetic errors after it was printed. The corrections affected numerous derived values in the tables and the discussion, but the magnitude of the changes was not sufficient to affect the paper's conclusions. Therefore, ACM published a Corrected Version of Record (CVoR) on January 25, 2024. For reference purposes, the VoR may still be accessed via the Supplemental Material section in the ACM Digital Library. The following is a detailed explanation from the authors on how the errors occurred and the ramifications on their findings. Our article "Energy and Emissions of Machine Learning on Smartphones vs. the Cloud" on page 86 had two arithmetic errors that we caught shortly before the online publication but too late for the paper magazine.

Global climate change is a huge challenge facing society today. The rapid growth of computing overall and of machine learning (ML) in particular rightfully raises concerns about their carbon footprints. As an early and enthusiastic adopter of ML, a manufacturer of millions of smartphones annually, and a significant cloud provider, Google is in a nearly unique position to compare the impact and efficiency of ML on the two ends of the information technology (IT) computing spectrum. Keep in mind this article is not a comparison of all computation done on phones and the cloud, but solely on the impact of ML on energy use and operational CO2e. We provide the data to support these insights. While primarily focused on operational CO2e generated from computer use, we also address the relative impact of embodied CO2e. Computers in datacenters draw electricity from the grid continuously. Because smartphones operate from a battery, they only draw electricity from the grid when connected to a charger. To account for smartphone ML energy accurately, we must include the energy overhead of their chargers. Wireless charging is increasingly popular due to its convenience and the reduction in smartphone wear and tear by avoiding the repeated insertion of a cable. For wired charging, energy is lost from the AC/DC power adapter in the charger and in the power management integrated circuit (PMIC) battery charger in the phone. Wireless charging loses additional energy through the inductive coils.

Businesses generate GHG emissions both directly through their business activities, and indirectly by the things they use, buy, sell and invest in. The GHG Protocol defined these into three Scopes deftly portrayed in their image below. Scope 1 incorporates emissions from the organisations assets, buildings and fleets. Scope 2 is still reasonably easy to get your head around, encompassing purchased energy. This includes electricity, gas but also other energy like heat or steam.

As part of the CleanTechnica series on the use of machine learning in advancing our low-carbon future, it would be remiss to not point out the carbon debt. However, it's not as bad as was reported earlier this year, in my estimation. Let's talk about the study itself, and the assumptions it made. The paper that made some headlines was Energy and Policy Considerations for Deep Learning in NLP by Strubell, Ganesh, and McCallum of the University of Massachusetts Amherst, and it was published in June of 2019. Strubell and McCallum are part of the team that built a state-of-the-art natural language processing model, LISA.