Google Inc.

Goolge Inc.

Ed Huai-Hsin Chi

Lichan Hong

Jilin Chen

Huazhong Ning

Aakanksha Chowdhery

Tianjiao Zhang

Peng Dai

Quoc V Le

Shumin Zhai

Roy Want

William Noah Schilit

Bay-Wei W Chang

Yang Li

Dengyong Zhou

Elin R Pedersen

David J Bieber

Augustus Quadrozzi Odena

Maarten Paul Bosma

Jacob Austin

David Martin Dohan

Dale Eric Schuurmans

Jason Weng Wei

Sagar Jain

Henryk Michalewski

Aitor Lewkowycz

Nathanael Schärli

Sharan Narang

Nathan Scales

Hussein Hazimeh

Maheswaran Sathiamoorthy

Yihua Chen

Maxwell Nye

Olivier Jean Andrè Bousquet

Le Hou

Charles Aloysius Sutton

Guy Gur-Ari Krakover

Jiageng Luan

Amy Xian Zhang

Anders Johan Andreassen

Xuezhi Wang

Zhe Zhao

A system including a main neural network for performing one or more machine learning tasks on a network input to generate one or more network outputs. The main neural network includes a Mixture of Experts (MoE) subnetwork that includes a plurality of expert neural networks and a gating subsystem. The gating subsystem is configured to: apply a softmax function to a set of gating parameters having learned values to generate a respective softmax score for each of one or more of the plurality of expert neural networks; determine a respective weight for each of the one or more of the plurality of expert neural networks; select a proper subset of the plurality of expert neural networks; and combine the respective expert outputs generated by the one or more expert neural networks in the proper subset to generate one or more MoE outputs.

Sparse and differentiable mixture of experts neural networks

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

Implementations provide an improved system for presenting search results based on entity associations of the search items. An example method includes generating first-level clusters of items responsive to a query, each cluster representing an entity in a knowledge base and including items mapped to the entity, merging the first-level clusters based on entity ontology relationships, applying hierarchical clustering to the merged clusters, producing final clusters, and initiating display of the items according to the final clusters. Another example method includes generating first-level clusters from items responsive to a query, each cluster representing an entity in a knowledge base and including items mapped to the entity, producing final clusters by merging the first-level clusters based on an entity ontology and an embedding space that is generated from an embedding model that uses the mapping, and initiating display of the items responsive to the query according to the final clusters.

Clustering search results

Implementations provide an improved system for presenting search results based on entity associations of the search items. An example method includes, for each of a plurality of crowdsource workers, initiating display of a first randomly selected cluster set from a plurality of cluster sets to the crowdsource worker. Each cluster set represents a different clustering algorithm applied to a set of search items responsive to a query. The method also includes receiving cluster ratings for the first cluster set from the crowdsource worker and calculating a cluster set score for the first cluster set based on the cluster ratings. This is repeated for remaining cluster sets in the plurality of cluster sets. The method also includes storing a cluster set definition for a highest scoring cluster set, associating the cluster set definition with the query, and using the definition to display search items responsive to the query.

Crowdsourced evaluation and refinement of search clusters

A system for creating an activity-based social includes receiving information from a computing device of a participant in an activity, and determining that the information qualifies the participant for membership in a social network associated with the activity. The system also includes associating the participant with the social network, and enabling access, by the participant, to an electronic portal that provides access to the social network.

Creating a social network based on an activity

Growing community of inventors

Palo Alto, CA, United States of America

Ed Huai-Hsin Chi