公开(公告)号：US20230393817A1

公开(公告)日：2023-12-07

申请号：US17832199

申请日：2022-06-03

Applicant: Google LLC

Inventor： Daniel Dun-ning Woo Johnson ,  Daniel Stefan Tarlow ,  Maxim Tabachnyk ,  Marc Hatcher Rasi ,  Jacob Austin ,  Hassan Abolhassani ,  Jacob Hanson Hegna

IPC: G06F8/33

CPC classification number: G06F8/33

Abstract: 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.

公开(公告)号：US20250094838A1

公开(公告)日：2025-03-20

申请号：US18967327

申请日：2024-12-03

Applicant: Google LLC

Inventor： Jason Weng Wei ,  Dengyong Zhou ,  Xuezhi Wang ,  Dale Eric Schuurmans ,  Quoc V. Le ,  Maarten Paul Bosma ,  Ed Huai-Hsin Chi ,  Olivier Jean Andrè Bousquet ,  Le Hou ,  Charles Aloysius Sutton ,  Nathanael Martin Schärli ,  Nathan Kemp Sekiguchi Scales ,  Augustus Quadrozzi Odena ,  Sharan Ajit Narang ,  Guy Gur-Ari Krakover ,  Aakanksha Chowdhery ,  David Martin Dohan ,  Aitor Lewkowycz ,  Jacob Austin ,  Henryk Michalewski ,  David Luan ,  David J. Bieber ,  Anders Johan Andreassen ,  Maxwell Isaac Nye

IPC: G06N5/022

Abstract: 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.

公开(公告)号：US11972234B2

公开(公告)日：2024-04-30

申请号：US17832199

申请日：2022-06-03

Applicant: Google LLC

Inventor： Daniel Dun-Ning Woo Johnson ,  Daniel Stefan Tarlow ,  Maxim Tabachnyk ,  Marc Hatcher Rasi ,  Jacob Austin ,  Hassan Abolhassani ,  Jacob Hanson Hegna

IPC: G06F8/33

CPC classification number: G06F8/33

Abstract: 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.

公开(公告)号：US20240231765A1

公开(公告)日：2024-07-11

申请号：US18618371

申请日：2024-03-27

Applicant: Google LLC

Inventor： Daniel Dun-ning Woo Johnson ,  Daniel Stefan Tarlow ,  Maxim Tabachnyk ,  Marc Hatcher Rasi ,  Jacob Austin ,  Hassan Abolhassani ,  Jacob Hanson Hegna

IPC: G06F8/33

CPC classification number: G06F8/33

Abstract: 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.

公开(公告)号：US20230394328A1

公开(公告)日：2023-12-07

申请号：US17881746

申请日：2022-08-05

Applicant: Google LLC

Inventor： Jason Weng Wei ,  Dengyong Zhou ,  Dale Eric Schuurmans ,  Quoc V. Le ,  Maarten Paul Bosma ,  Ed Huai-Hsin Chi ,  Olivier Jean Andrè Bousquet ,  Le Hou ,  Nathan Kemp Sekiguchi Scales ,  David J. Bieber ,  Charles Aloysius Sutton ,  Nathanael Martin Schärli ,  Augustus Quadrozzi Odena ,  Sharan Ajit Narang ,  Guy Gur-Ari Krakover ,  Aakanksha Chowdhery ,  Aitor Lewkowycz ,  Jiageng Luan ,  David Martin Dohan ,  Henryk Michalewski ,  Jacob Austin ,  Anders Johan Andreassen ,  Maxwell Isaac Nye ,  Xuezhi Wang

IPC: G06N5/02

CPC classification number: G06N5/022

Abstract: Example embodiments of aspects of the present disclosure provide an example computer-implemented method for improved prompting of a machine-learned model. The example method can include 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 method can include inputting, to a machine-learned model, the instructive sequence and an operative query, 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 response.

公开(公告)号：US20230244938A1

公开(公告)日：2023-08-03

申请号：US18160776

申请日：2023-01-27

Applicant: Google LLC

Inventor： Jason Weng Wei ,  Dengyong Zhou ,  Xuezhi Wang ,  Dale Eric Schuurmans ,  Quoc V. Le ,  Maarten Paul Bosma ,  Ed Huai-Hsin Chi ,  Olivier Jean Andrè Bousquet ,  Le Hou ,  Charles Aloysius Sutton ,  Nathanael Martin Schärli ,  Nathan Kemp Sekiguchi Scales ,  Augustus Quadrozzi Odena ,  Sharan Ajit Narang ,  Guy Gur-Ari Krakover ,  Aakanksha Chowdhery ,  David Martin Dohan ,  Aitor Lewkowycz ,  Henryk Michalewski ,  Jiageng Luan ,  David J. Bieber ,  Jacob Austin ,  Anders Johan Andreassen ,  Maxwell Isaac Nye ,  Yi Tay ,  Mostafa Dehghani

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: An example method for pretraining a machine-learned model is provided. The example method includes obtaining a plurality of different combinations of configuration parameters of a pretraining objective framework. The example method includes generating, using the pretraining objective framework, a plurality of corrupted training examples from one or more training examples, wherein the plurality of corrupted training examples are respectively generated according to the plurality of different combinations. The example method includes inputting the plurality of corrupted training examples into the machine-learned model, wherein the machine-learned model is configured to generate uncorrupted subportions corresponding to corrupted subportions of the corrupted training examples. The example method includes obtaining, from the machine-learned model, a plurality of outputs respectively generated by the machine-learned model based on the plurality of corrupted training examples. The example method includes updating one or more parameters of the machine-learned model based on an evaluation of the plurality of outputs.