Abstract: Systems and methods for a multi-autonomous model architecture. For example, an autonomous model operates independently, within the architecture to perform tasks on behalf of itself or another system. These models possess the ability to make decisions and act without intervention, based on their programming and the information they perceive from their environment, thereby increasing autonomy, adaptability, and/or perception. More specifically, the system uses a multi-autonomous model architecture that comprises an understanding model (e.g., tasked with generating a contextual representation of inputted information), a planning model (e.g., tasked with generating an action graph for generating a complex solution), and an evaluation model (e.g., tasked with independently validating the action graph prior to recommending to a user).

Abstract: Systems and methods for a multi-autonomous model architecture. For example, an autonomous model operates independently, within the architecture to perform tasks on behalf of itself or another system. These models possess the ability to make decisions and act without intervention, based on their programming and the information they perceive from their environment, thereby increasing autonomy, adaptability, and/or perception. More specifically, the system uses a multi-autonomous model architecture that comprises an understanding model (e.g., tasked with generating a contextual representation of inputted information), a planning model (e.g., tasked with generating an action graph for generating a complex solution), and an evaluation model (e.g., tasked with independently validating the action graph prior to recommending to a user).

Abstract: Systems and methods for a multi-autonomous model architecture. For example, an autonomous model operates independently, within the architecture to perform tasks on behalf of itself or another system. These models possess the ability to make decisions and act without intervention, based on their programming and the information they perceive from their environment, thereby increasing autonomy, adaptability, and/or perception. More specifically, the system uses a multi-autonomous model architecture that comprises an understanding model (e.g., tasked with generating a contextual representation of inputted information), a planning model (e.g., tasked with generating an action graph for generating a complex solution), and an evaluation model (e.g., tasked with independently validating the action graph prior to recommending to a user).

Abstract: A system and techniques for configuring a trained model to dedicate a portion of its output data to include signature data that may be used to identify information about the model. The model may be configured so that some portion of its least-significant output bits may represent the signature data. The signature data may be a unique code that corresponds to the particular model. The signature data may also include encoded data that may represent information such as a model version, model author, or the like. A recipient of the model output data may thus use the signature data to determine information about the particular model, even if the model itself is inaccessible.

Abstract: In accordance with one disclosed method, first data representing first text may be processed using a first machine learning model configured to generate second data representing an explanation as to why the first text is humorous. The second data may be processed, together with the first data, by a second machine learning model configured to generate a value indicating that the first data and the second data correspond to a pun. A device may be caused to take at least a first action based at least in part on the value.

Abstract: Techniques for generating a natural language prompt to further a goal of a dialog, are described. During a dialog, the system receives one or more user inputs including a user question, a user response to the question, and a request to generate a further question following the response. The system determines ASR output data corresponding to the user inputs, and determines dialog history data of the dialog. Using the ASR output data and the dialog history data, the system determines a category and an explanation of relevance corresponding to the category. Using the ASR output data, the dialog history, the category, and the explanation, the system determines the further question to be output to the user.

Abstract: In accordance with one disclosed method, first data representing one or more first keywords may be processed by at least one first component to determine a first pair of words suitable for generating a pun. The first pair of words may then be processed using a first machine learning model configured to generate an output pun based at least in part on one or more input keywords and an input word pair such that the output pun is contextually related to the one or more input keywords. A first pun that is contextually related to the one or more first keywords may be received from the first machine learning model, and a first device may be caused to output a representation of the first pun.

Abstract: A system configured to perform style-aware listener animation. By representing different listening styles (e.g., facial expressions) using an embedding space, a single model can be trained to generate unique facial animations for a number of distinct listeners. Thus, individual listening styles can be associated with a listener identifier, enabling the system to (i) animate a plurality of different listeners with unique nonverbal behavior and/or (ii) select a particular listener identifier or desired type of listener style with which to animate. This enables the model to be generalized to new listeners to generate additional listener facial responses without needing training data for each new listener. The model may process a listener representation style or listener identifier, along with input data corresponding to a speaker talking, to generate unique facial animation responsive to the speech.

Abstract: Devices and techniques are generally described for cohort determination in natural language processing. In various examples, a first natural language input to a natural language processing system may be determined. The first natural language input may be associated with a first account identifier. A first machine learning model may determine first data representing one or more words of the first natural language input. A second machine learning model may determine second data representing one or more acoustic characteristics of the first natural language input. Third data may be determined, the third data including a predicted performance for processing the first natural language input by the natural language processing system. The third data may be determined based on the first data representation and the second data representation.

Abstract: Devices and techniques are generally described for learning personalized concepts for natural language processing. In various examples, a first natural language input may be received. In some examples, a determination may be made that the first natural language input comprises non-actionable slot data. A dialog session may be initiated with the user. In some examples, first slot data that is indicated by the user during the dialog session may be determined. In various examples, data representing the first slot data may be stored in a database in association with the first natural language input.

Abstract: Devices and techniques are generally described for class-agnostic object detection. In some examples, a first frame of image data comprising a first plurality of pixels may be received. First class-agnostic feature data representing the first plurality of pixels may be generated. A first object detection component may be used to determine that the first plurality of pixels corresponds to an arbitrary object represented in the first frame of image data based at least in part on the first class-agnostic feature data. Class-agnostic data indicating that the first plurality of pixels in the first frame of image data corresponds to the arbitrary object may be generated.

Abstract: Techniques for using a federated learning framework to update machine learning models for spoken language understanding (SLU) system are described. The system determines which labeled data is needed to update the models based on the models generating an undesired response to an input. The system identifies users to solicit labeled data from, and sends a request to a user device to speak an input. The device generates labeled data using the spoken input, and updates the on-device models using the spoken input and the labeled data. The updated model data is provided to the system to enable the system to update the system-level (global) models.

Abstract: A system can operate a speech-controlled device in a mode where the speech-controlled device determines that an utterance is directed at the speech-controlled device using image data showing the user speaking the utterance. If the user is directing the user's gaze at the speech-controlled device while speaking, the system may determine the utterance is system directed and thus may perform further speech processing based on the utterance. If the user's gaze is directed elsewhere, the system may determine the utterance is not system directed (for example directed at another user) and thus the system may not perform further speech processing based on the utterance and may take other actions, for example discarding audio data of the utterance.

Abstract: In a method and system for generating vehicle image data, and to improve the generation of vehicle image data, 2D object information having at least 2D object data and object location data of one or more objects, perspective vehicle image data, and vehicle image location data for at least a portion of said vehicle image data are obtained. The object location data is compared with said vehicle image location data; and in case said object location data corresponds to said image location data, said 2D object data is correlated with said perspective vehicle image data using the object location data and the vehicle image location data to obtain a segmented vehicle image dataset.

Abstract: An in-vehicle system for identifying exit-only lanes and shared exit lanes on a roadway having a first camera for obtaining image data regarding lane markings on the roadway, a second camera for obtaining image data regarding exit signs, a lane marking detection module for detecting lane markings on the roadway, an exit sign detection module for detecting exit signs, and an exit sign analyzer for detecting arrows on the detected exit signs. The in-vehicle system categorizes detected lane markings as one of standard frequency dashed lane markings, high frequency dashed lane markings, and solid lane markings, and identifies an exit-only lane and a shared exit lane in response to the categorizations of the detected lane markings and characteristics of the detected arrows.

Abstract: An in-vehicle system for identifying exit-only lanes and shared exit lanes on a roadway having a first camera for obtaining image data regarding lane markings on the roadway, a second camera for obtaining image data regarding exit signs, a lane marking detection module for detecting lane markings on the roadway, an exit sign detection module for detecting exit signs, and an exit sign analyzer for detecting arrows on the detected exit signs. The in-vehicle system categorizes detected lane markings as one of standard frequency dashed lane markings, high frequency dashed lane markings, and solid lane markings, and identifies an exit-only lane and a shared exit lane in response to the categorizations of the detected lane markings and characteristics of the detected arrows.

Abstract: In a method and system for generating vehicle image data, and to improve the generation of vehicle image data, 2D object information having at least 2D object data and object location data of one or more objects, perspective vehicle image data, and vehicle image location data for at least a portion of said vehicle image data are obtained. The object location data is compared with said vehicle image location data; and in case said object location data corresponds to said image location data, said 2D object data is correlated with said perspective vehicle image data using the object location data and the vehicle image location data to obtain a segmented vehicle image dataset.

Abstract: An in-vehicle system for identifying exit-only lanes and shared exit lanes on a roadway having a first camera for obtaining image data regarding lane markings on the roadway, a second camera for obtaining image data regarding exit signs, a lane marking detection module for detecting lane markings on the roadway, an exit sign detection module for detecting exit signs, and an exit sign analyzer for detecting arrows on the detected exit signs. The in-vehicle system categorizes detected lane markings as one of standard frequency dashed lane markings, high frequency dashed lane markings, and solid lane markings, and identifies an exit-only lane and a shared exit lane in response to the categorizations of the detected lane markings and characteristics of the detected arrows.

Abstract: A system and method for early detection of turning intent and automatic actuation of a turn signal in a vehicle is provided. The system has an image recognition system and turn determination unit for identifying turn predictors and processing them to determine if a turn is about to happen. In response, a corresponding turn signal is automatically initiated for a predetermined period of time, and further vehicle systems can be updated.

Abstract: A system and method for early detection of turning intent and automatic actuation of a turn signal in a vehicle is provided. The system has an image recognition system and turn determination unit for identifying turn predictors and processing them to determine if a turn is about to happen. In response, a corresponding turn signal is automatically initiated for a predetermined period of time, and further vehicle systems can be updated.