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Eh, who cares? Maybe the energy usage from machine learning overall is negligible - I haven't looked into it. But still, what's wrong with showing that a result
by ShamelessC 5y ago
Eh, who cares? Maybe the energy usage from machine learning overall is negligible - I haven't looked into it. But still, what's wrong with showing that a result also has a reduced carbon footprint compared to modern methods?
- TrueDuality 5y agoThe easy answer is that reduced energy usage for an individual operation does not mean reduced energy overall with all other things being equal (See Jevon's Paradox). In practice algorithms don't work in isolation. You may have to do additional pre-processing on your data to get it into this kind of model, offsetting any energy usage that you reduce through the execution of your model. There are any number of small things that aren't taken into account when looking at this portion of an overall solution and you can't equally make statements about carbon usage based on energy usage alone, nor can you make statements about energy usage based on computation requirements alone. The actual energy usage is not an independently derived factor and unrelated to the work itself. Adding it as a benefit in your paper without the additional work of proving that is marketing fluff. It doesn't belong and actively hurts studies that are focused on energy usage reduction with the explicit intent of reducing carbon footprints.
- eterevsky 5y agoBecause this is supposed to be a scientific paper, it's supposed to be talking about quantifiable effects, not of some speculations. Especially in this case where the high energy and monetary cost are easy to estimate. OpenAI spent $3 million just on the compute for training. Even inference requires multiple TPUs each using hundreds of Watts.