Abstract: 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.

Abstract: 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.

Abstract: Example embodiments of the present disclosure provide for an example method that includes obtaining via a conversational campaign assistant interface, by a custom language model, natural language input. The method includes generating, by the custom language model, an output comprising a predicted user intent. The method includes determining actions to perform and determining a natural language response. The method includes transmitting, to an action component, the action data structure comprising executable instructions that cause the action component to automatically perform operations associated with completing the action. The method includes transmitting to the conversation campaign assistant interface, the response data structure comprising the natural language response to be provided for display to a user via the conversational campaign assistant interface.

Abstract: 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.

Abstract: 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.

Abstract: 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.