Self-Learning Control Systems to Optimize Critical Industrial Outputs


Phaidra deploys control systems powered by deep reinforcement learning to act as 'co-pilots' for plant operators in industrial facilities - such as data centers, district energy sites, or chemical reactors - to optimize critical industrial outputs. That could be meeting sustainability targets by optimizing for energy consumption, or managing process stability, yield or safety by optimizing for variable process reagents. Several of Phaidra's team originate from DeepMind, including those that set up and led the team that achieved a 40% reduction in energy used for cooling and 15% reduction in overall energy overhead at Google's data centers, increased Google's windfarm revenues by 20%, and who worked on projects such as AlphaFold and AlphaGo.

Key People

Jim Gao
Vedavyas Panneershelvam
Katie Hoffman


Brain & Artificial Intelligence
Planet & Efficient Energy

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