More compute results in greater electricity consumption, and consequent carbon emissions. A 2019 study by researchers on the University of Massachusetts Amherst estimated that the electricity consumed in the course of the training of a transformer, a variety of deep learning algorithm, can emit greater than 626,000 kilos (~284 metric tons) of carbon dioxide—equal to greater than 41 round-trip flights between Latest York City and Sydney, Australia. And that’s just training the model.

We’re also facing an explosion of knowledge storage. IDC projects that 180 zettabytes of knowledge—or, 180 billion terabytes—might be created in 2025. The collective energy required for data storage at this scale is big and might be difficult to deal with sustainably. Depending on the conditions of knowledge storage (e.g., hardware used, energy mixture of the power), a single terabyte of stored data can produce 2 tons of CO2 emissions annually. Now multiply that by 180 billion.

This current trajectory for intensifying AI with an ever-growing environmental footprint is just not sustainable. We’d like to rethink the established order and alter our strategies and behavior.

Driving sustainable improvements with AI

While there are undoubtedly serious carbon emissions implications with the increased prominence of AI, there are also enormous opportunities. Real-time data collection combined with AI can actually businesses quickly discover areas for operational improvement to assist reduce carbon emissions at a scale.

For instance, AI models can discover immediate improvement opportunities for aspects influencing constructing efficiency, including heating, ventilation, and air con (HVAC). As a fancy, data-rich, multi-variable system, HVAC is well-suited to automated optimization, and enhancements can result in energy savings inside just a couple of months. While this chance exists in almost any constructing, it’s especially useful in data centers. Several years ago, Google shared how implementing AI to enhance data center cooling reduced its energy consumption by as much as 40%.

AI can be proving effective for implementing carbon-aware computing. Mechanically shifting computing tasks, based on the provision of renewable energy sources, can lower the carbon footprint of the activity.

Likewise, AI may also help diminish the ballooning data storage problem previously mentioned. To deal with the sustainability concerns of large-scale data storage, Gerry McGovern, in his book , recognized that as much as 90% of knowledge is unused—merely stored. AI may also help determine what data is beneficial, needed, and of high enough quality to warrant storage. Superfluous data can simply be discarded, saving each cost and energy.

Tips on how to design AI projects more sustainably

To responsibly implement AI initiatives, all of us have to rethink a couple of things and take a more proactive approach to designing AI projects.