From smartphone assistants to image recognition and translation, machine learning already helps us in our everyday lives. But it can also help us to tackle some of the world’s most challenging physical problems -- such as energy consumption. Large-scale commercial and industrial systems like data centers 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.
Google is taking many steps to reduce energy consumptions .
Compared to five years ago, Google now get around 3.5 times the computing power out of the same amount of energy.
By applying DeepMind’s machine learning to its own data centers
, Google managed to reduce the amount of energy it use for cooling by up to 40 percent. In any large scale energy-consuming environment, this would be a huge improvement. Given how sophisticated Google’s data centers are already, it’s a phenomenal step forward.
The implications are significant for Google’s data centers, given its potential to greatly improve energy efficiency and reduce emissions overall. This will also help other companies who run on Google’s cloud to improve their own energy efficiency.
Every improvement in data center efficiency reduces total emissions into our environment and with technology like DeepMind’s, we can use machine learning to consume less energy and help address one of the biggest challenges of all -- climate change.
One of the primary sources of energy use in the data center environment is cooling. Just as your laptop generates a lot of heat, Google's data centers -- which contain servers powering Google Search, Gmail, YouTube, etc. -- also generate a lot of heat that must be removed to keep the servers running. This cooling is typically accomplished via large industrial equipment such as pumps, chillers and cooling towers. However, dynamic environments like data centers make it difficult to operate optimally for several reasons.