The environmental impact of artificial intelligence (AI) has been a hot topic as of late—and I believe it will be a defining issue for AI this decade. The conversation began with a recent study from the Allen Institute for AI that argued for the prioritization of “Green AI” efforts that focus on the energy efficiency of AI systems.
This study was motivated by the observation that many high-profile advances in AI have staggering carbon footprints. A 2018 blog post from OpenAI revealed that the amount of compute required for the largest AI training runs has increased by 300,000 times since 2012. And while that post didn’t calculate the carbon emissions of such training runs, others have done so. According to a paper by Emma Strubel and colleagues, an average American is responsible for about 36,000 tons of CO2 emissions per year; training and developing one machine translation model that uses a technique called neural architecture search was responsible for an estimated 626,000 tons of CO2.
Unfortunately, these so-called “Red AI” projects may be even worse from an environmental perspective than what’s being reported, as a project’s total cost in time, energy, and money is typically an order of magnitude more than the cost of generating the final reported results.
Read article at Institute of Electrical and Electronics Engineers