Abstract: A method of information retrieval is provided. The method comprises receiving a query from a user and parsing the query in real-time as the user enters the query. The parsed query is interpreted dynamically based on a defined schema of a knowledge base, and a number of query interpretations is displayed in real-time as the user enters the query. When a selection of one of the query interpretations is received from the user information is retrieved from the knowledge base according to the selected query interpretation.

Abstract: A method of information retrieval is provided. The method comprises receiving a query from a user and parsing the query in real-time as the user enters the query. The parsed query is interpreted dynamically based on a defined schema of a knowledge base, and a number of query interpretations is displayed in real-time as the user enters the query. When a selection of one of the query interpretations is received from the user information is retrieved from the knowledge base according to the selected query interpretation.

Abstract: Systems and methods for improvements in AI model learning and updating are provided. The model updating may reuse existing business conversations as the training data set. Features within the dataset may be defined and extracted. Models may be selected and parameters for the models defined. Within a distributed computing setting the parameters may be optimized, and the models deployed. The training data may be augmented over time to improve the models. Deep learning models may be employed to improve system accuracy, as can active learning techniques. The models developed and updated may be employed by a response system generally, or may function to enable specific types of AI systems. One such a system may be an AI assistant that is designed to take use cases and objectives, and execute tasks until the objectives are met. Another system capable of leveraging the models includes an automated question answering system utilizing approved answers.