Google Inc.

Deepmind Technologies Limited

Christopher Gamble

Norman Casagrande

Christopher Sigurd Fougner

Benjamin Hal Murdoch

Thomas Degris

Peeyush Agarwal

Ian Goodfellow

Sven Adrian Gowal

Jim Gao

Vedavyas Panneershelvam

David Barker

Dustin Reishus

Brian Kim

Rudy Bunel

Abigail Ward

Amanda Gasparik

Daniel Fuenffinger

Timothy Jason Kieper

Jamie Hayes

Sree Meghana Thotakuri

Stephen Webster

Sylvestre-Alvise Guglielmo Rebuffi

Florian Nils Stimberg

Miklós Zsigmond Horváth

Jerry Luo

Olivia Anne Wiles

Mark Schwabacher

David Stutz

Zakerey Bennett

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for verifying the provenance of a digital object generated by a neural network, such as an image or audio object. Also methods, systems, and apparatus, including computer programs, for training a watermarking neural network and a watermark decoding neural network. The described techniques make efficient use of computing resources and are robust to attack.

Verifying the provenance of a digital object using watermarking and embeddings

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

A computer-implemented method for adaptive display brightness adjustment, the method comprising: obtaining current state data characterizing a current state of a device having a display with an adjustable brightness; providing the current state data as input to a brightness prediction machine learning model, wherein the model is configured to process the current state data in accordance with current values of a set of model parameters to generate as output a proposed display brightness for the display of the device; setting the brightness of the display to a brightness that is lower than the proposed display brightness in accordance with an exploration policy; determining whether a user of the device manually adjusts the display brightness; and in response to determining that the user did not manually adjust the display brightness, using the lower brightness as a target output for adjusting the current values of the set of model parameters.

Adaptive display brightness adjustment

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London, United Kingdom

Christopher Gamble