Google Inc.

Mehmet Levent Koc

Wei Luo

Sylvanus Garnet Bent, Iii

Xiaolan Zhou

Hrishikesh Balkrishna Aradhye

Gregory Sean Corrado

Tushar Deepak Chandra

Wei Chai

Tal Shaked

Jeremiah Harmsen

Glen Anderson

Mustafa Ispir

Rohan Anil

Heng-Tze Cheng

Xiaobing Liu

Example embodiments of the present disclosure provide for an example method. The example method includes generating an initial user interface including a content assistant component. The example method include obtaining user input data. The example method includes processing, by a machine learned model interfacing with the content assistant component, the data indicative of the input received from the user. The method includes obtaining output data, from the machine learned model interfacing with the content assistant component, indicative of one or more content item components. The method includes transmitting data which causes the content item components to be provided for display via an updated user interface. The method includes obtaining data indicative of user selection of approval of the content item components. The method includes generating, in response to obtaining the data indicative of the user selection of the approval of the content item components, content items.

Conversational interface for content creation and editing using large language models

A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.

Wide and deep machine learning models

Growing community of inventors

Redwood City, CA, United States of America

Mehmet Levent Koc