Deepmind Technologies Limited

Google Inc.

Jim Gao

Michael C Ryan

Richard Andrew Evans

Todd Andrew Hester

Jonathan Karl Scholz

Gabriel Dulac-Arnold

Christopher Gamble

David Barker

Vedavyas Panneershelvam

Dustin Reishus

Brian Kim

Daniel Fuenffinger

Timothy Jason Kieper

Amanda Gasparik

Stephen Webster

Jerry Luo

Mark Schwabacher

Zakerey Bennett

Abigail Ward

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for improving operational efficiency within a data center by modeling data center performance and predicting power usage efficiency. An example method receives a state input characterizing a current state of a data center. For each data center setting slate, the state input and the data center setting slate are processed through an ensemble of machine learning models. Each machine learning model is configured to receive and process the state input and the data center setting slate to generate an efficiency score that characterizes a predicted resource efficiency of the data center if the data center settings defined by the data center setting slate are adopted t. The method selects, based on the efficiency scores for the data center setting slates, new values for the data center settings.

Optimizing data center controls using neural networks

Methods, systems, apparatus and computer program products for implementing machine learning within control systems are disclosed. An industrial facility setting slate can be received from a machine learning system and a determination can be made as to whether to adopt the settings in the industrial facility setting slate. The machine learning model can be a neural network, e.g., a deep neural network, that has been trained, e.g., using reinforcement learning to predict a data setting slate that is predicted to optimize an efficiency of a data center.

Integrating machine learning into control systems for industrial facilities

Growing community of inventors

Menlo Park, CA, United States of America

Jim Gao