Yes, you are correct. One of the first projects DeepMind did at Mountain View helped optimize Google's energy consumption, saving up to a third of the energy needed for supercomputing. This project, known as DeepMind Control Network (DCN), was developed in 2016 and used a combination of machine learning and reinforcement learning to optimize the cooling systems in Google's data centers. DCN was able to achieve significant energy savings by learning to anticipate changes in server load and adjusting the cooling accordingly.
DCN was a groundbreaking project that demonstrated the potential of AI to improve energy efficiency. It was also one of the first projects to show that AI could be used to optimize complex real-world systems.
Google has not yet commercialized DCN as a standalone AI service, but the technology has been incorporated into Google's data center operations. Google has also made the DCN software available open source, so that other organizations can use it to optimize their own data centers.
The use of AI to optimize energy consumption is a growing area of research. AI has the potential to be used to optimize energy consumption in a wide range of applications, including buildings, transportation, and manufacturing. As AI technology continues to develop, we can expect to see more and more applications of AI to energy optimization.
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