Abstract: Techniques and a framework are described herein for gathering information about developing events from multiple live data streams and pushing new pieces of information to interested individuals as those pieces of information are learned. In various implementations, a plurality of live data streams may be monitored. Based on the monitoring, a data structure that models diffusion of information through a population may be generated and applied as input across a machine learning model to generate output. The output may be indicative of a likelihood of occurrence of a developing event and/or a predicted measure of relevancy of the developing event to a particular user. Based on a determination that the likelihood and/or measure of relevancy satisfies a criterion, one or more computing devices may render, as output, information about the developing event.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for extending application access across devices. In some implementations, an electronic device receives a request to provide access to the electronic device to a particular user that is not registered as a user of the electronic device. The electronic device receives authentication credentials for the particular user. The electronic device provides the authentication credentials to a server system and receives data from the server system that (i) indicates that the providing access to the electronic device in a guest mode is authorized, and (ii) indicates a state of an instance of an application installed on a second device. The electronic device provides access to the electronic device in the guest mode that provides an interface that at least partially recreates the state of the instance of the application installed on the second device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing actionable suggestions are disclosed. In one aspect, a method includes receiving (i) an indication that an event detection module has determined that a shared event of a particular type is presently occurring or has occurred, and (ii) data referencing an attribute associated with the shared event. The method includes selecting, from among multiple output templates that are each associated with a different type of shared event, a particular output template associated with the particular type of shared event detected by the module. The method generates a notification for output using at least (i) the selected particular output template, and (ii) the data referencing the attribute associated with the shared event. The method then provides, for output to a user device, the notification that is generated.

Abstract: Techniques are described herein for determining an information gain score for one or more documents of interest to the user and present information from the documents based on the information gain score. An information gain score for a given document is indicative of additional information that is included in the document beyond information contained in documents that were previously viewed by the user. In some implementations, the information gain score may be determined for one or more documents by applying data from the documents across a machine learning model to generate an information gain score.

Abstract: Implementing and applying an adaptive and self-training CAPTCHA (“Completely Automated Public Turing test to tell Computers and Humans Apart”) assistant that distinguishes between a computer-generated communication (e.g., speech and/or typed) and communication that originates from a human. The CAPTCHA assistant utilizes a generative adversarial network that is self-training and includes a generator to generate synthetic answers and a discriminator to distinguish between human answers and synthetic answers. The trained discriminator is applied to potentially malicious remote entities, which are provided challenge phrases. Answers from the remote entities are provided to the discriminator to predict whether the answer originated from a human or was computer-generated.

Abstract: The disclosure provides technology for adaptively creating and adjusting reminder notifications for a first user based on activity of a second user. An example method includes identifying an electronic message transmitted between a first user device and a second user device; accessing data associated with a user of the second device, wherein the data comprises activity state data for the user; determining a notification time for the first device based on the data associated with the user of the second device; and updating a reminder related to the electronic message based on the notification time.

Abstract: Techniques are described herein for enabling an automated assistant to adjust its behavior depending on a detected vocabulary level or other vocal characteristics of an input utterance provided to an automated assistant. The estimated vocabulary level or other vocal characteristics may be used to influence various aspects of a data processing pipeline employed by the automated assistant. In some implementations, one or more tolerance thresholds associated with, for example, grammatical tolerances or vocabulary tolerances, may be adjusted based on the estimated vocabulary level or vocal characteristics of the input utterance.

Abstract: Implementations described herein determine, for a given document generated by a given source, one or more portions of content (e.g., phrase(s), image(s), paragraph(s), etc.) of the given document that may be influenced by a source perspective of the given source. Further, implementations determine one or more additional resources that are related to the given source and that are related to the portion(s) of content of the given document. Yet further, implementations utilize the additional resource(s) to determine additional content that provides context for the portion(s) that may be influenced by a source perspective. A relationship, between the additional resource(s) and the portions of the given document, can be defined. Based on the relationship being defined, the additional content can be caused to be rendered at a client device in response to the client device accessing the given document.

Abstract: Methods, apparatus, and computer readable media related to receiving textual input of a user during a dialog between the user and an automated assistant (and optionally one or more additional users), and generating responsive reply content based on the textual input and based on user state information. The reply content is provided for inclusion in the dialog. In some implementations, the reply content is provided as a reply, by the automated assistant, to the user's textual input and may optionally be automatically incorporated in the dialog between the user and the automated assistant. In some implementations, the reply content is suggested by the automated assistant for inclusion in the dialog and is only included in the dialog in response to further user interface input.

Abstract: Techniques are described herein for enabling an automated assistant to adjust its behavior depending on a detected vocabulary level or other vocal characteristics of an input utterance provided to an automated assistant. The estimated vocabulary level or other vocal characteristics may be used to influence various aspects of a data processing pipeline employed by the automated assistant. In some implementations, one or more tolerance thresholds associated with, for example, grammatical tolerances or vocabulary tolerances, may be adjusted based on the estimated vocabulary level or vocal characteristics of the input utterance.

Abstract: A method includes receiving an indication that a recording event has been initiated on an initial computing device responsive to an input from a user, the initial computing device including an initial sensor recording first sensor data during the recording event. The method also includes, responsive to the indication of the initiation of the recording event, identifying, based on a respective proximity to the initial computing device of each computing device from a plurality of other computing devices associated with the user, one or more additional computing devices from the plurality of other computing devices, each computing device including at least one respective sensor device, and transmitting, to the one or more additional computing devices, an activation command that causes the one or more additional computing devices to activate the at least one respective sensor device which initiates capturing of second sensor data during the recording event.

Abstract: Methods, apparatus, and computer readable media related to receiving textual input of a user during a dialog between the user and an automated assistant (and optionally one or more additional users), and generating responsive reply content based on the textual input and based on user state information. The reply content is provided for inclusion in the dialog. In some implementations, the reply content is provided as a reply, by the automated assistant, to the user's textual input and may optionally be automatically incorporated in the dialog between the user and the automated assistant. In some implementations, the reply content is suggested by the automated assistant for inclusion in the dialog and is only included in the dialog in response to further user interface input.

Abstract: Techniques are described herein for enabling an automated assistant to adjust its behavior depending on a detected age range and/or “vocabulary level” of a user who is engaging with the automated assistant. In various implementations, data indicative of a user's utterance may be used to estimate one or more of the user's age range and/or vocabulary level. The estimated age range/vocabulary level may be used to influence various aspects of a data processing pipeline employed by an automated assistant. In various implementations, aspects of the data processing pipeline that may be influenced by the user's age range/vocabulary level may include one or more of automated assistant invocation, speech-to-text (“STT”) processing, intent matching, intent resolution (or fulfillment), natural language generation, and/or text-to-speech (“TTS”) processing. In some implementations, one or more tolerance thresholds associated with one or more of these aspects, such as grammatical tolerances, vocabularic tolerances, etc.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collaboration between multiple voice controlled devices are disclosed. In one aspect, a method includes the actions of identifying, by a first computing device, a second computing device that is configured to respond to a particular, predefined hotword; receiving audio data that corresponds to an utterance; receiving a transcription of additional audio data outputted by the second computing device in response to the utterance; based on the transcription of the additional audio data and based on the utterance, generating a transcription that corresponds to a response to the additional audio data; and providing, for output, the transcription that corresponds to the response.

Abstract: The present disclosure provides systems and methods that leverage machine-learned models (e.g., neural networks) to provide enhanced communication assistance. In particular, the systems and methods of the present disclosure can include or otherwise leverage a machine-learned communication assistance model to detect problematic statements included in a communication and/or provide suggested replacement statements to respectively replace the problematic statements. In one particular example, the communication assistance model can include a long short-term memory recurrent neural network that detects an inappropriate tone or unintended meaning within a user-composed communication and provides one or more suggested replacement statements to replace the problematic statements.

Abstract: Techniques are described herein for enabling an automated assistant to adjust its behavior depending on a detected age range and/or “vocabulary level” of a user who is engaging with the automated assistant. In various implementations, data indicative of a user's utterance may be used to estimate one or more of the user's age range and/or vocabulary level. The estimated age range/vocabulary level may be used to influence various aspects of a data processing pipeline employed by an automated assistant. In various implementations, aspects of the data processing pipeline that may be influenced by the user's age range/vocabulary level may include one or more of automated assistant invocation, speech-to-text (“STT”) processing, intent matching, intent resolution (or fulfillment), natural language generation, and/or text-to-speech (“TTS”) processing. In some implementations, one or more tolerance thresholds associated with one or more of these aspects, such as grammatical tolerances, vocabularic tolerances, etc.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collaboration between multiple voice controlled devices are disclosed. In one aspect, a method includes the actions of identifying, by a first computing device, a second computing device that is configured to respond to a particular, predefined hotword; receiving audio data that corresponds to an utterance; receiving a transcription of additional audio data outputted by the second computing device in response to the utterance; based on the transcription of the additional audio data and based on the utterance, generating a transcription that corresponds to a response to the additional audio data; and providing, for output, the transcription that corresponds to the response.

Abstract: Aspects of the subject technology relate to dynamically adjusting a UI based on the current modality. Layout features of a UI may be determined based on an input modality of a computing device. The arrangement of the UI elements may be determined based on the layout features and respective importance scores of the UI elements. The UI elements arranged based on the arrangement may be provided for display of the computing device.

Abstract: Implementations described herein determine, for a given document generated by a given source, one or more portions of content (e.g., phrase(s), image(s), paragraph(s), etc.) of the given document that may be influenced by bias of the given source. Further, implementations determine one or more additional resources that are related to the given source and that are related to the portion(s) of content of the given document. Yet further, implementations utilize the additional resource(s) to determine additional content that provides context for the portion(s) that may be influenced by bias. A relationship, between the additional resource(s) and the portions of the given document, can be defined. Based on the relationship being defined, the additional content can be caused to be rendered at a client device in response to the client device accessing the given document.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collaboration between multiple voice controlled devices are disclosed. In one aspect, a method includes the actions of identifying, by a first computing device, a second computing device that is configured to respond to a particular, predefined hotword; receiving audio data that corresponds to an utterance; receiving a transcription of additional audio data outputted by the second computing device in response to the utterance; based on the transcription of the additional audio data and based on the utterance, generating a transcription that corresponds to a response to the additional audio data; and providing, for output, the transcription that corresponds to the response.