At first glance I thought this was going to be another 'tech X uses energy Y and so tech X is bad' but reading the article shows it focuses mainly on lowering the energy cost of computations using a number of different approaches that look interesting. Sparse matrices where every element doesn't need to be re-computed as most stay zero for example. I'm not sure what actual improvement this would bring, but they do follow a good basic notion: if you're going to discuss a problem, try to discuss possible solutions to that problem, and improving the energy-efficiency of computation is always a good idea.

The one issue is that translating electricity usage into fossil fuel equivalents for this specific application, without contextual information about similar energy demands, such as streaming video, data collection/storage/processing (be it at Google or the NSA), total router energy consumption in the global Internet, etc. might result in a distorted view of the relative importance of energy demand for their particular issue (training complex models).

Furthermore, it's not necessary to generate electric power with fossil fuels, is it? My view is that solar/wind/storage is the optimal global-scale solution, but placing energy-hungry steady-load data centers near baseload nuclear power plants is arguably an efficient solution (ask the insurers first, however). Hydropower is region-specific and as the drought shows, subject to going offline when needed most to run AC etc. It's not inevitable that power demands equate to fossil carbon emissions, in other words.