Abstract: A method, apparatus, and system for creating a script for rendering audio and/or video streams include identifying at least one prosodic speech feature in a received audio stream and/or a received language model, creating a respective prosodic speech symbol for each of the at least one identified prosodic speech features, converting the received audio stream and/or the received language model into a text stream, temporally inserting the created at least one prosodic speech symbol into the text stream, identifying in a received video stream at least one prosodic gesture of at least a portion of a body of a speaker of the received audio stream, creating at least one respective gesture symbol for each of the at least one identified prosodic gestures, and temporally inserting the created at least one gesture symbol into the text stream along with the at least one prosodic speech symbol to create a prosodic script.

Abstract: A method, apparatus and system for lifelong reinforcement learning include receiving features of a task, communicating the task features to a learning system, where the learning system learns or performs a task related to the features based on learning or performing similar previous tasks, determining from the features if the task has changed and if so, communicating the features of the changed task to the learning system, where the learning system learns or performs the changed task based on learning or performing similar previous tasks, automatically annotating feature characteristics of received features including differences between the features of the original task and the features of the changed task to enable the learning system to more efficiently learn or perform at least the changed task, and if the task has not changed, processing the task features of a current task by the learning system to learn or perform the current task.

Abstract: Method and apparatus for processing data using a reconfigurable, hyperdimensional neural network architecture comprising a feature extractor and a classifier. The feature extractor comprises a neural network for encoding input information into hyperdimensional (HD) vectors and extracting at least one particular HD vector representing at least one feature within the input information, wherein the neural network comprises no more than one multiply and accumulate operator. The classifier is coupled to the feature extractor for classifying the at least one particular HD vector to produce an indicium of classification for the at least one particular HD vector and wherein the classifier does not comprise any multiply and accumulate operators.