Surveys energy footprints of image ML and proposes modest technical solutions including tiny models, low-precision hardware, and true-cost accounting driven by critiques of shareholder efficiency metrics.
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Modest, artistic, and radical solutions to the environmental impact of image-generating machine learning
Surveys energy footprints of image ML and proposes modest technical solutions including tiny models, low-precision hardware, and true-cost accounting driven by critiques of shareholder efficiency metrics.