Abstract: Techniques are provided for machine learning-based user sentiment prediction using audio and video sentiment analysis. One method comprises obtaining audio sensor data and video sensor from at least one sensor associated with a user; applying the audio sensor data to a first machine learning model that analyzes an audio sentiment of the user to provide an audio sentiment score; applying the video sensor data to a second machine learning model that analyzes a video sentiment of the user to provide a video sentiment score; applying the audio sentiment score and the video sentiment score to an ensemble model that determines an aggregate sentiment score based on the audio sentiment score and the video sentiment score; and initiating an automated remedial action based on the aggregate sentiment score. An output of the ensemble model can be applied to a feedback agent that updates the first and/or second machine learning models.

Abstract: A first computing node of a system can receive sensor data about a physical environment. The first computing node can analyze the sensor data with a restricted Boltzmann machine (RBM) neural network to determine whether there is a fault condition in the physical environment, an identification of the fault condition being omitted from data used to train the RBM neural network. The first computing node can update the RBM neural network based on the sensor data to produce a first updated RBM neural network. The first computing node can send a first patch indicative of the first updated RBM neural network to a central server. The first computing node can receive, from the central server, information indicative of a second updated RBM neural network, the second updated RBM neural network being based on an aggregation of the first patch and of a second patch generated by a second computing node.

Abstract: A system can initiate a training session for a neural network that comprises inputting first data to the neural network to facilitate training of the neural network, wherein use of the neural network increases an accuracy of performing a task associated with the neural network according to a defined accuracy criterion. The system can render a first visual representation of the neural network during the training session via a user interface associated with a virtual reality environment, and render a second visual representation of a possible unintended behavior of the neural network as a result of being trained based on the first data. The system can modify the neural network with respect to the second visual representation in the neural network of the possible unintended behavior in response to receiving second data indicative of a user input via the user interface, the modify resulting in a modified neural network.