Tech giant Google has taken a "phenomenal step forward" in its efforts to drive energy efficiency, after developing artificial intelligence (AI) that has reduced energy consumption at its data centres by 40%. Google has been able to achieve 3.5 times more computing power from the same amount of energy compared to five years ago Google's data centres, although powered by renewables, still consume vast amounts of energy during cooling processes. Over the past 10 years, Google has developed the AI system using the'DeepMind' research company to live test a system of neural networks - computer systems modelled on the human brain - that have led to a more efficient and adaptive framework for data centre management. DeepMind has managed to train these neural networks to predict the temperature and pressure outputs within the centres, 60 minutes in advance before establishing the appropriate requirements to lower output and energy consumption. The system not only delivered 40% cuts to energy consumption, but also reduced Power Usage Effectiveness (PUE) – the ratio of total building energy use to IT energy use – by as much as 15%.
Research at Google is at the forefront of innovation in Machine Intelligence, with active research exploring virtually all aspects of machine learning, including deep learning and more classical algorithms. Exploring theory as well as application, much of our work on language, speech, translation, visual processing, ranking and prediction relies on Machine Intelligence. In all of those tasks and many others, we gather large volumes of direct or indirect evidence of relationships of interest, applying learning algorithms to understand and generalize. Machine Intelligence at Google raises deep scientific and engineering challenges, allowing us to contribute to the broader academic research community through technical talks and publications in major conferences and journals. Contrary to much of current theory and practice, the statistics of the data we observe shifts rapidly, the features of interest change as well, and the volume of data often requires enormous computation capacity.
Google has finally revealed a commercial use for DeepMind -- a British artificial intelligence company it acquired for over 600 million in 2014. DeepMind made headlines for beating the best human in the world at the notoriously complex board game Go and it's recently started working with hospitals in the UK on a number of healthcare projects but the startup is yet to make any money for Google, until now. Google announced on Wednesday that it has been using a DeepMind-built AI system to control certain parts of its power-hungry data centres over the last few months as it looks to make its vast server farms more environmentally friendly. Last year, a Greenpeace report predicted that the electricity consumption of data centres is set to account for 12% of global electricity consumption by 2017 and companies like Google, Amazon, Facebook and Apple have some of the biggest data centres in the world. Google said it has been able to reduce the energy consumption of its data centre cooling units -- used to stop Google's self-built servers from overheating -- by as much as 40% with the help of a DeepMind AI system.
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.
Google has created artificial intelligence that's able to save the amount of electricity it uses to power its data centres. Using machine learning developed by the firm's AI research company, DeepMind, it was possible to reduce the energy used for cooling the centres by a staggering 40 per cent. By applying machine learning to its own centres, which power Google Search, Gmail, YouTube and all of Google's services, it was able to improve their efficiency. The algorithms and methods used could also be transferred to air conditioning systems in large manufacturing plants or, on an even larger scale, to reduce wastage in the energy grid. "What we've been trying to do is build a better predictive model that essentially uses less energy to power the cooling system by more accurately predicting when the incoming compute load is likely to land," Mustafa Suleyman, the co-founder of DeepMind told WIRED.