More computing leads to more electricity consumption and subsequent carbon emissions. A 2019 study by researchers at the University of Massachusetts Amherst estimated that the electricity consumed while training a transformer, a type of deep learning algorithm, can emit more than 626,000 pounds (~284 metric tons) of carbon dioxide carbon, equal to more than 41 return flights between New York City and Sydney, Australia. And that’s just training the model.
We are also facing an explosion of data storage. IDC expects it 180 zettabytes of data, or 180 billion terabytes, will be created in 2025. The collective energy required to store data on this scale is enormous and will be difficult to manage sustainably. Depending on data storage conditions (eg, hardware used, facility energy mix), a single terabyte of data stored can produce 2 tons of CO2 emissions per year. Now multiply that by 180 billion.
This current trajectory of ramping up AI with an ever-increasing environmental footprint is simply not sustainable. We need to rethink the status quo and change our strategies and behaviors.
Drive sustainable improvements with AI
While there are undoubtedly serious carbon emissions implications with the increased prominence of artificial intelligence, there are also huge opportunities. Real-time data collection combined with AI actually can help companies quickly identify areas for operational improvement to help reduce carbon emissions at scale.
For example, AI models can identify immediate improvement opportunities for factors that affect building efficiency, including heating, ventilation and air conditioning (HVAC). As a complex, data-rich, multivariable system, HVAC is well suited for automated optimization, and improvements can lead to energy savings within months. While this opportunity exists in nearly every building, it’s especially useful in data centers. Several years ago, Google shared how implementing AI to improve data center cooling reduced energy consumption by up to 40%.
Artificial intelligence is also proving effective for implementing carbon-aware computing. Automatically shifting computing activities, based on the availability of renewable energy sources, can reduce the carbon footprint of the business.
Similarly, AI can help reduce the growing data storage problem mentioned earlier. To address the sustainability issues of large-scale data storage, Gerry McGovern, in his book Waste all over the world, recognized that up to 90% of data is unused, simply archived. AI can help determine what data is valuable, necessary, and of high enough quality to warrant archiving. Superfluous data can simply be deleted, saving costs and energy.
How to design AI projects more sustainably
To responsibly implement AI initiatives, we all need to rethink some things and take a more proactive approach to designing AI projects.