While Google is only one of many data centre operators in the world, many are not powered by renewable energy as we are. This will also help other companies who run on Google’s cloud to improve their own energy efficiency. The implications are significant for Google’s data centres, given its potential to greatly improve energy efficiency and reduce emissions overall. Given how sophisticated Google’s data centres are already, it’s a phenomenal step forward. In any large scale energy-consuming environment, this would be a huge improvement. Major breakthroughs, however, are few and far between - which is why we are excited to share that by applying DeepMind’s machine learning to our own Google data centres, we’ve managed to reduce the amount of energy we use for cooling by up to 40 percent. Compared to five years ago, we now get around 3.5 times the computing power out of the same amount of energy, and we continue to make many improvements each year. Reducing energy usage has been a major focus for us over the past 10 years: we have built our own super-efficient servers at Google, invented more efficient ways to cool our data centres and invested heavily in green energy sources, with the goal of being powered 100 percent by renewable energy. Large-scale commercial and industrial systems like data centres consume a lot of energy, and while much has been done to stem the growth of energy use, there remains a lot more to do given the world’s increasing need for computing power.

But it can also help us to tackle some of the world’s most challenging physical problems - such as energy consumption. From smartphone assistants to image recognition and translation, machine learning already helps us in our everyday lives.

Heating and cooling calculations