Various embodiments for systems and methods of evaluating perception systems for autonomous vehicles using a quality temporal logic are disclosed herein.

This application relates to systems, methods, devices, and other techniques for methods to generate and training models within a retail environment

An exemplary method for using a virtual assistant may include, at an electronic device configured to transmit and receive data, receiving a user request for a service from a virtual assistant; determining at least one task to perform in response to the user request; estimating at least one performance characteristic for completion of the at least one task with the electronic device, based on at least one heuristic; based on the estimating, determining whether to execute the at least one task at the electronic device; in accordance with a determination to execute the at least one task at the electronic device, causing the execution of the at least one task at the electronic device; in accordance with a determination to execute the at least one task outside the electronic device: generating executable code for carrying out the least one task; and transmitting the executable code from the electronic device.

Embodiments discussed herein may be generally directed to systems and techniques to generate a quality score based on an observation and an action caused by an actor agent during a testing phase. Embodiments also include determining a temporal difference between the quality score and a previous quality score based on a previous observation and a previous action, determining whether the temporal difference exceeds a threshold value, and generating an attack indication in response to determining the temporal difference exceeds the threshold value.

Techniques are provided for training, by a system operatively coupled to a processor, an attention weighted recurrent neural network encoder-decoder (AWRNNED) using an iterative process based on one or more paragraphs of agent sentences from respective transcripts of one or more conversations between one or more agents and one or more customers, and based on one or more customer response sentences from the respective transcripts, and generating, by the system, one or more groups respectively comprising one or more agent sentences and one or more customer response sentences selected based on attention weights of the AWRNNED.

Examples of a sentence phrasing system are provided. The system may obtain a user question from a user. The system may obtain question entailment data from a plurality of data sources. The system may implement an artificial intelligence component to identify a word index from the question entailment data and to identify a question premise from the user question. The system may implement a first cognitive learning operation to determine an answer premise corresponding to the question premise comprising a second-word data set. The system may determine a subject component corresponding to the question premise. The system may generate an object component and a predicate component from the second-word data set corresponding to the subject component. The system may generate an integrated answer relevant for resolving the user question and comprising the subject component, the object component, and the predicate component concatenated to form an answer sentence.

Methods and apparatuses are described for user authentication in a three-dimensional (3D) alternative reality software application. A computing device coupled to an alternative reality viewing device generates a 3D virtual environment for display in the alternative reality viewing device, the 3D virtual environment comprising a plurality of 3D objects. The computing device identifies a subset of the plurality of 3D objects selected by the user of the alternative reality viewing device. The computing device captures a first set of actions of the user with respect to the subset of 3D objects, including recording a sequence of the first set of actions. The computing device generates a multidimensional authentication credential for the user based upon the first set of actions and stores the multidimensional authentication credential in a database.

The present disclosure describes methods, devices, and non-transitory computer readable storage medium for managing lifecycle of a knowledge base (KB) in chatbot systems. The method includes receiving an update request and updating a current-version KB as a new current-version KB. The method also includes receiving a first test request from a knowledge point (KP) operator and determining whether the new current-version KB passes a first test; and in response to the determination that the new current-version KB passes the first test, submitting the new current-version KB as a new submit-version KB. The method further includes receiving a second test request from a KP manager and determining whether the new submit-version KB passes a second test; and in response to the determination that the new submit-version KB passes the second test, storing an original production-version KB as a pervious-version KB and submitting the new submit-version KB as a new production-version KB.

A system includes a neural network system implemented by one or more computers. The neural network system is configured to receive an observation characterizing a current state of a real-world environment being interacted with by a robotic agent to perform a robotic task and to process the observation to generate a policy output that defines an action to be performed by the robotic agent in response to the observation. The neural network system includes: (i) a sequence of deep neural networks (DNNs), in which the sequence of DNNs includes a simulation-trained DNN that has been trained on interactions of a simulated version of the robotic agent with a simulated version of the real-world environment to perform a simulated version of the robotic task, and (ii) a first robot-trained DNN that is configured to receive the observation and to process the observation to generate the policy output.

Embodiments of architecture, systems, and methods for modeling dynamics between behavior and emotional states in an artificial nervous system are described herein. A computer implemented emotion system of an artificial nervous system for animating a virtual object, digital entity, or robot, is provided, comprising: a plurality of states, each state of the plurality of states representing an emotional state (ES) of the artificial nervous system; a module for processing a plurality of inputs, the processed plurality of inputs applied to the plurality of states. Other embodiments may be described and claimed.