Artificial intelligence (AI) is a major player in the climate change movement. Technology will help us combat the effects of climate change and commit to more sustainable practices in industries of all types. But there is a downside—AI, and the costs associated with developing and using these systems, isn’t always sustainable in and of itself.
The question becomes, how could AI be made “greener?”
Promote holistic and sustainable model evaluation.
The bulk of research and innovation in AI has been focused on improving accuracy of models or creating new algorithms. These goals tend to consume large amounts of data in order to build more complex models. Deep learning is a good example—computational resource use went up 300,000 times in the years between 2012 and 2018. So, recognizing and promoting the trade-off between the accuracy of models and the model’s carbon footprint is a great way to start making AI greener.
Assess the number of operations needed to train a model.
AI projects should assess the number of floating-point operations needed in a model. That means the number of mathematical operations (addition and subtraction, multiplication, variable assignment, etc.) needed to train a model. That way, there is a good idea of the energy consumption that will be needed to train a model, plus other resources like hardware and training. Taking all of these factors into account ahead of time can give project managers a better idea of the energy consumption and resources that will be necessary to train a model, and how that can be adjusted to be more efficient and sustainable.
Know where the energy is sourced from.
As you know, energy sourced from renewable resources as compared to non-renewable ones, like gas or coal, will have a reduced carbon footprint. Knowing where the energy is sourced from for an AI project is another big part of making this kind of technology greener. Again, users of AI must ask themselves about the trade-off between the amount of energy and resources needed to compute and the benefit of that computation in the long run.
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