This is a (the) critical point. All these estimates have to be based on some CO2/kWh figure, which could vary from almost 0 for wind & solar, to a lot with a coal-based power source. I don’t know what value they are using, probably an average. My point would be that compute can be clean, so the conclusions only apply if efforts are not made to clean up data center energy resources.
Another point I will make is that much of commercial AI is moving toward transfer learning-based AutoML NAS’s (Neural Architecture Search) approaches, which is much more efficient overall in that Data scientists don’t have to start from scratch, users don’t have to be a Data Scientist, and they don’t have to personally try various models to find the best one.
So overall, the only point that I feel is valid from this summary of a study I have n’t read is that inefficient modelling on a dirty power grid is indeed bad. IMHO there’s not much critical academic-thinking or journalism (more FUD hype) in highlighting (“shocking!”) roundly estimated and poorly qualified huge CO2 emissions.
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