Systems and methods are described to enhance interactive engagement during simultaneous delivery of serial or digital content (e.g., audio, video) to a plurality of users. A machine-based awareness of the context of the content and/or one or more user reactions to the presentation of the content may be used as a basis to interrupt content delivery in order to intersperse a snippet that includes a virtual agent with an awareness of the context(s) of the content and/or the one or more user reactions. This “contextual virtual agent” (CVA) enacts actions and/or dialog based on the one or more machine-classified contexts coupled with identified interests and/or aspirations of individuals within the group of users. The CVA may also base its activities on a machine-based awareness of “future” content that has not yet been delivered to the group, but classified by natural language and/or computer vision processing. Interrupting the delivery of content substantially simultaneously to a group of users and initiating dialog regarding content by a CVA enhances opportunities for users to engage with each other about their shared interactive experience.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent interacting with an environment. In one aspect, a method comprises, at each of one or more time steps: generating a respective action score for each action in a set of possible actions, wherein the set of possible actions comprises: (i) a plurality of atomistic actions, and (ii) one or more optimization actions, wherein each optimization action is associated with a respective objective function that measures performance of the agent on a corresponding auxiliary task; selecting an action from the set of possible actions in accordance with the action scores, wherein the selected action is an optimization action; in response to selecting the optimization action, performing a numerical optimization to identify a sequence of one or more atomistic actions that are predicted to optimize the objective function.

The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data acquired continuously by a plurality of types of sensors deployed on the vehicle are first received, where the plurality of types of sensor data provide information about surrounding of the vehicle. Based on at least one model, one or more items are tracked from a first of the plurality of types of sensor data acquired by one or more of a first type of the plurality of types of sensors, wherein the one or more items appear in the surrounding of the vehicle. At least some of the one or more items are then automatically labeled on-the-fly via either cross modality validation or cross temporal validation of the one or more items and are used to locally adapt, on-the-fly, the at least one model in the vehicle.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

Techniques for monitoring a computing environment for anomalous activity are presented. An example method includes receiving a request to invoke an action within the computing environment. An anomaly score is generated for the received request by applying a probabilistic model to properties of the request. The anomaly score generally indicates a likelihood that the properties of the request correspond to historical activity within the computing environment for a user associated with the request. The probabilistic model generally comprises a model having been trained using historical activity within the computing environment for a plurality of users, the historical activity including information identifying an action performed in the computing environment and contextual information about a historical request. Based on the generated anomaly score, one or more actions are taken to process the request such that execution of requests having anomaly scores indicative of unexpected activity may be blocked pending confirmation.

In one implementation, a method for improved motion planning. The method includes: obtaining a macro task for a virtual agent within a virtual environment; generating a search-tree based on at least one of the macro task, a state of the virtual environment, and a state of the virtual agent, wherein the search-tree includes a plurality of task nodes corresponding to potential tasks for performance by the virtual agent in furtherance of the macro task; and determining physical motion plans (PMPs) for at least some of the plurality of task nodes within the search-tree in order to generate a lookahead planning gradient for the first time, wherein a granularity of a PMP for a respective task node in the first search-tree is a function of the temporal distance of the respective task node from the first time.

A method and system are described that provide responses to natural language queries regarding the performance of a business. The method and system processes data from multiple data sources including information generated by the business and analyzes the data to provide actionable suggestions as to how to determine how to improve the performance of the business. The use of natural language queries allows for a merchant without a business intelligence background obtain these insights easily.

This invention relates to non-mechanical robotic software for conducting, dictating and monitoring day-to-day regular activities of business operations of an organisation being automated by adaptation of principle of servomechanism closed loop feedback control system of engineering. A “Business Operation” is defined as a sequence of particular order in which related structured activities that serve a particular business goal follow each other, and structured activities are processes and events. The control system operates in real time and automatic generation of feedback for identifying next activity to be conducted is enabled by an innovative data driven automatic generation of feedback technique. This closed loop feedback control system continues conducting activities of all occurrences of business operations automatically until a feedback identifies a terminator activity of a particular occurrence marking completion of conducting that occurrence.

Embodiments described herein are directed to providing contextually relevant cues to users and to providing cues based on predicted conditions. In one scenario, a computer system identifies a task that is to be performed by a user. The computer system accesses data structures to identify current conditions related to the identified task. The computer system then generates, based on the identified current conditions related to the task, contextually relevant cues for the task. The contextually relevant cues provide suggestive information associated with the task. The computer system further provides the generated cue to the user. In other scenarios, the computer system identifies anticipated conditions related to the task using accessed historical information, and generates contextually relevant cues based on the identified anticipated conditions.

In an example embodiment, a screening question-based online screening mechanism is provided to assess job applicants automatically. More specifically, job-specific questions are automatically generated and asked to applicants to assess the applicants using the answers they provide. Answers to these questions are more recent than facts contained in a user profile and thus are more reliable measures of an appropriateness of an applicant's skills for a particular job.