Google Inc.

Jacob Austin

Daniel S Tarlow

Maxim Tabachnyk

Jacob Hanson Hegna

Hassan Abolhassani

Marc Hatcher Rasi

Daniel Dun-ning Woo Johnson

Quoc V Le

Dengyong Zhou

David J Bieber

Ed Huai-Hsin Chi

Aakanksha Chowdhery

Augustus Quadrozzi Odena

David Martin Dohan

Maarten Paul Bosma

Jason Weng Wei

Dale Eric Schuurmans

Olivier Jean Andrè Bousquet

Le Hou

Charles Aloysius Sutton

Guy Gur-Ari Krakover

Jiageng Luan

Anders Johan Andreassen

Xuezhi Wang

Henryk Michalewski

Aitor Lewkowycz

Nathanael Schärli

Maxwell Nye

Sharan Narang

Nathan Scales

An example technique for image analysis is provided. An example image analysis method includes obtaining an instructive sequence descriptive of an instructive query, an instructive response, and an instructive trace of intermediate states from the instructive query to the instructive response. The example image analysis method includes inputting, to a machine-learned model, the instructive sequence and an operative image processing query that comprises image data, wherein the machine-learned model is configured to process the operative query with attention over the instructive sequence. The example method can include generating, using the machine-learned model and responsive to the operative query, an operative image processing response that comprises an analysis of the image data.

Image analysis by prompting of machine-learned models using chain of thought

Systems and methods of the present disclosure are directed to a method for machine-learned code segment prediction for optimizing software development. The method includes obtaining an incomplete segment of code. The method includes processing the incomplete segment of code with a machine-learned code prediction model to obtain a sampled set of segment completion predictions that include code that completes the incomplete segment of code. The method includes determining an aggregated segment completion prediction from the sampled set of segment completion predictions. The method includes replacing a portion of the aggregated segment completion prediction with an input field, wherein the portion of the aggregated segment completion prediction is associated with a degree of certainty less than a threshold degree of certainty.

Machine-learned models for generating code snippets with predicted placeholders for optimizing software development

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New York, NY, United States of America

Jacob Austin