https://paperswithcode.com/paper/making-ai-less-thirsty-uncovering-and
"The growing carbon footprint of artificial intelligence (AI) models,
especially large ones such as GPT-3 and GPT-4, has been undergoing public
scrutiny. Unfortunately, however, the equally important and enormous water
footprint of AI models has remained under the radar. For example, training
GPT-3 in Microsoft's state-of-the-art U.S. data centers can directly consume
700,000 liters of clean freshwater (enough for producing 370 BMW cars or 320
Tesla electric vehicles) and the water consumption would have been tripled if
training were done in Microsoft's Asian data centers, but such information has
been kept as a secret. This is extremely concerning, as freshwater scarcity has
become one of the most pressing challenges shared by all of us in the wake of
the rapidly growing population, depleting water resources, and aging water
infrastructures. To respond to the global water challenges, AI models can, and
also should, take social responsibility and lead by example by addressing their
own water footprint. In this paper, we provide a principled methodology to
estimate fine-grained water footprint of AI models, and also discuss the unique
spatial-temporal diversities of AI models' runtime water efficiency. Finally,
we highlight the necessity of holistically addressing water footprint along
with carbon footprint to enable truly sustainable AI."
Via Wayne Radinsky.
Cheers,
*** Xanni ***
--
mailto:xanni@xanadu.net Andrew Pam
http://xanadu.com.au/ Chief Scientist, Xanadu
https://glasswings.com.au/ Partner, Glass Wings
https://sericyb.com.au/ Manager, Serious Cybernetics
