Safety-first AI for autonomous data centre cooling and industrial control — Google DeepMind Skip to main content Explore our next generation AI systems Explore models Gemini Gemini Build intelligent agents Gemini Omni Create anything from anything Nano Banana Create and edit detailed images Gemini Audio Talk, create and control audio Specialized models Veo Generate cinematic video with audio Imagen Generate high-quality images from text Lyria Generate high fidelity music and audio World models & embodied AI Genie 3 Generate and explore interactive worlds Gemini Robotics Perceive, reason, use tools and interact Open models Gemma Build responsible AI applications at scale Our latest AI breakthroughs and updates from the lab Explore research Breakthroughs SIMA 2 An agent that plays, reasons, and learns with you Genie 3 Generate and explore interactive worlds AlphaGo Mastering the game of Go Gemini Robotics Perceive, reason, use tools and interact Learn more Evals Publications Responsibility Unlocking a new era of discovery with AI Explore science Breakthroughs AlphaFold Predict protein structures with high accuracy WeatherNext Fast and accurate AI weather forecasting AlphaEarth Map our planet in unprecedented detail AlphaEvolve Design advanced algorithms for math and applications in computing Learn more Gemini for Science Experimental Tools Science Skills Our mission is to build AI responsibly to benefit humanity About Google DeepMind Responsibility Ensuring AI safety through proactive security, even against evolving threats News Discover our latest AI breakthroughs, projects, and updates Careers We’re looking for people who want to make a real, positive impact on the world Learn more Education Our National Partnerships for AI Accelerator programs The Podcast Models Explore our next generation AI systems Explore models Gemini Gemini Build intelligent agents Gemini Omni Create anything from anything Nano Banana Create and edit detailed images Gemini Audio Talk, create and control audio Specialized models Veo Generate cinematic video with audio Imagen Generate high-quality images from text Lyria Generate high fidelity music and audio World models & embodied AI Genie 3 Generate and explore interactive worlds Gemini Robotics Perceive, reason, use tools and interact Open models Gemma Build responsible AI applications at scale Research Our latest AI breakthroughs and updates from the lab Explore research Breakthroughs SIMA 2 An agent that plays, reasons, and learns with you Genie 3 Generate and explore interactive worlds AlphaGo Mastering the game of Go Gemini Robotics Perceive, reason, use tools and interact Learn more Evals Publications Responsibility Science Unlocking a new era of discovery with AI Explore science Breakthroughs AlphaFold Predict protein structures with high accuracy WeatherNext Fast and accurate AI weather forecasting AlphaEarth Map our planet in unprecedented detail AlphaEvolve Design advanced algorithms for math and applications in computing Learn more Gemini for Science Experimental Tools Science Skills About Our mission is to build AI responsibly to benefit humanity About Google DeepMind Learn more Education Our National Partnerships for AI Accelerator programs The Podcast Responsibility Ensuring AI safety through proactive security, even against evolving threats News Discover our latest AI breakthroughs, projects, and updates Careers We’re looking for people who want to make a real, positive impact on the world Build with Gemini Try Gemini Google DeepMind Google AI Learn about all our AI Google DeepMind Explore the frontier of AI Google Labs Try our AI experiments Google Research Explore our research Products and apps Gemini app Chat with Gemini Google AI Studio Build with our next-gen AI models Google Antigravity Our agentic development platform Models Research Science About Build with Gemini Try Gemini August 17, 2018 ResearchSafety-first AI for autonomous data centre cooling and industrial control Chris Gamble, Jim Gao Share Copied Many of society’s most pressing problems have grown increasingly complex, so the search for solutions can feel overwhelming. At DeepMind and Google, we believe that if we can use AI as a tool to discover new knowledge, solutions will be easier to reach. In 2016, we jointly developed an AI-powered recommendation system to improve the energy efficiency of Google’s already highly-optimised data centres. Our thinking was simple: even minor improvements would provide significant energy savings and reduce CO2 emissions to help combat climate change. Now we’re taking this system to the next level: instead of human-implemented recommendations, our AI system is directly controlling data centre cooling, while remaining under the expert supervision of our data centre operators. This first-of-its-kind cloud-based control system is now safely delivering energy savings in multiple Google data centres.How it works Every five minutes, our cloud-based AI pulls a snapshot of the data centre cooling system from thousands of sensors and feeds it into our deep neural networks, which predict how different combinations of potential actions will affect future energy consumption. The AI system then identifies which actions will minimise the energy consumption while satisfying a robust set of safety constraints. Those actions are sent back to the data centre, where the actions are verified by the local control system and then implemented. The idea evolved out of feedback from our data centre operators who had been using our AI recommendation system. They told us that although the system had taught them some new best practices—such as spreading the cooling load across more, rather than less, equipment—implementing the recommendations required too much operator effort and supervision. Naturally, they wanted to know whether we could achieve similar energy savings without manual implementation. We’re pleased to say the answer was yes!Designed for safety and reliability Google's data centres contain thousands of servers that power popular services including Google Search, Gmail and YouTube. Ensuring that they run reliably and efficiently is mission-critical. We've designed our AI agents and the underlying control infrastructure from the ground up with safety and reliability in mind, and use eight different mechanisms to ensure the system will behave as intended at all times. One simple method we’ve implemented is to estimate uncertainty. For every potential action—and there are billions—our AI agent calculates its confidence that this is a good action. Actions with low confidence are eliminated from consideration. Another method is two-layer verification. Optimal actions computed by the AI are vetted against an internal list of safety constraints defined by our data centre operators. Once the instructions are sent from the cloud to the physical data centre, the local control system verifies the instructions against its own set of constraints. This redundant check ensures that the system remains within local constraints and operators retain full control of the operating boundaries. Most importantly, our data centre operators are always in control and can choose to exit AI control mode at any time. In these scenarios, the control system will transfer seamlessly from AI control to the on-site rules and heuristics that define the automation industry today. Find out about the other safety mechanisms we’ve developed, below: Increasing energy savings over time Whereas our original recommendation system had operators vetting and implementing actions, our new AI control system directly implements the actions. We’ve purposefully constrained the system’s optimisation boundaries to a narrower operating regime to prioritise safety and reliability, meaning there is a risk/reward trade off in terms of energy reductions. Despite being in place for only a matter of months, the system is already delivering consistent energy savings of around 30 percent on average, with further expected improvements. That’s because these systems get better over time with more data, as the graph below demonstrates. Our optimisation boundaries will also be expanded as the technology matures, for even greater reductions. This graph plots AI performance over time relative to the historical baseline before AI control. Performance is measured by a common industry metric for cooling energy efficiency, kW/ton (or energy input per ton of cooling achieved). Over nine months, our AI control system performance increases from a 12 percent improvement (the initial launch of autonomous control) to around a 30 percent improvement. Our direct AI control system is finding yet more novel ways to manage cooling that have surprised even the data centre operators. Dan Fuenffinger, one of Google’s data centre operators who has worked extensively alongside the system, remarked: "It was amazing to see the AI learn to take advantage of winter conditions and produce colder than normal water, which reduces the energy required for cooling within the data centre. Rules don’t get better over time, but AI does." We’re excited that our direct AI control system is operating safely and dependably, while consistently delivering energy savings. However, data centres are just the beginning. In the long term, we think there's potential to apply this technology in other industrial settings, and help tackle climate change on an even grander scale. Follow us Sign up for updates on our latest innovations I accept Google's Terms and Conditions and acknowledge that my information will be used in accordance with Google's Privacy Policy. Sign up Build AI responsibly to benefit humanity Models Gemini Gemini Omni Nano Banana Gemini Audio Gemma Genie Lyria Veo Research Gemini Robotics Breakthroughs Evals Publications Responsibility Science AlphaFold AlphaGenome WeatherNext AlphaEarth AlphaEvolve Products Gemini app Google AI Studio Google Antigravity Learn more About News Careers National Partnerships for AI Accelerator programs The Podcast About Google Google products Privacy Terms Cookies management controls