In one embodiment, a method for adaptive training of a machine learning system configured to predict answers to questions associated with textual data includes receiving predicted answers to questions associated with textual data. The predicted answers are generated based at least in part on one or more first models of a machine learning system. The one or more first models are associated with a first accuracy score. The method further includes determining based at least in part on a quality control parameter whether an evaluation of the questions by one or more external entities is required. In response to determining based at least in part on the quality control parameter that an evaluation of the questions by one or more external entities is required, the questions associated with the textual data and the textual data are sent to the one or more external entities for evaluation.

A predictor interactive learning system of the present invention includes machine learning unit configured to perform machine learning of a predictor that outputs a predicted value indicating a likelihood of being a predetermined intrinsic expression, by using teacher data and teacher labels, an interest score calculation unit configured tip obtain an interest score according to statistical data of a corresponding word in a corpus including the predicted value of the predictor for each of words of the corpus, an interactive learning frame unit configured to extract the word serving as the teacher data used in next learning of the predictor according to the interest score, and a question-response unit configured to output a question of whether the extracted teacher data is an intrinsic expression of which the likelihood is predicted by the predictor, and to acquire a teacher label corresponding to the teacher data, as a response to the question, in which the machine learning unit performs machine learning of the predictor using teacher data extracted by a teacher word extraction unit and a teacher label acquired by an interaction unit.

A method trains a neural network to identify an event based on discrepancies in answers to factoid questions at different times. One or more processors identify answers to a series of factoid questions. The processor(s) compare the answers from the series of factoid questions in order to determine discrepancies in the answers at different times, and then train a neural network to identify an event based on the discrepancies in the answers at the different times.

Artificial intelligence (AI) technology can be used in combination with composable communication goal statements to facilitate a user's ability to quickly structure story outlines using “explanation” communication goals in a manner usable by an NLG narrative generation system without any need for the user to directly author computer code. This AI technology permits NLG systems to determine the appropriate content for inclusion in a narrative story about a data set in a manner that will satisfy a desired explanation communication goal such that the narratives will express various ideas that are deemed relevant to a given explanation communication goal.

A dialogue generation method, a network training method and apparatus, a storage medium, and a device are provided. The method includes: predicting, based on a plurality of a plurality of pieces of candidate knowledge text in a first candidate knowledge set, a preliminary dialogue response of a first dialogue preceding text; processing the first dialogue preceding text based on the preliminary dialogue response to obtain a first dialogue preceding text vector; obtaining a piece of target knowledge text based on a probability value of the piece of target knowledge text of being selected to be used in generating a final dialogue response, the probability value being obtained based on the first dialogue preceding text vector; and generating the final dialogue response based on the first dialogue preceding text and the piece of target knowledge text.

Mechanisms are provided for performing artificial intelligence-based video question answering. A video parser parses an input video data sequence to generate situation data structure(s), each situation data structure comprising data elements corresponding to entities, and first relationships between entities, identified by the video parser as present in images of the input video data sequence. First machine learning computer model(s) operate on the situation data structure(s) to predict second relationship(s) between the situation data structure(s). Second machine learning computer model(s) execute on a received input question to predict an executable program to execute to answer the received question. The program is executed on the situation data structure(s) and predicted second relationship(s). An answer to the question is output based on results of executing the program.

Systems and methods are disclosed for a specialized warranty bot that can interact with users to evaluate warranty coverage. A warranty interpreter interprets warranty aspects of a given system and converts the warranty terms it into human understandable text. A situation analyzer enhances warranty interpretation by scanning logs across various components that are covered by the warranty and identifying whether the current problems are covered by the warranty. Assessment guidance processing anticipates questions from the customer and provides information regarding why a problem may not be covered by the warranty. Using a solution advisor, based on the problem details and historical analysis, the warranty bot provides insights regarding potential minimum time for resolution of a problem in order to assist the user to decide whether the system needs to be isolated or if the system can remain in production in a non-critical path.

A method may include determining, by a computing device and based on at least one user coherency factor, a user coherency level. The coherency level may include a predicted ability of a user to comprehend information. The method may also include determining, by the computing device and based on the user coherency level, information having a complexity that satisfies the predicted ability of the user to comprehend information. The method may further include outputting, by the computing device, at least a portion of the information.

A system for document extraction and targeted dissection, the system comprising: a memory device; a communication device; and a processing device configured to: receive a first document via communication channel over the network; extract user information from a first data field of the first document, wherein the first data field has a first data type and a first data format; store the user information and the first document in a document database; identify a second document comprising a second data field, wherein the second data field has the first data type; populate, automatically, the second data field of the second document with the extracted user information; display the second document in an electronic presentation via a user interface of a user device; and augment the electronic presentation of the second document in the user interface with supplemental data associated with the second document.

A method, system and computer readable medium for performing a cognitive search operation comprising: receiving training data, the training data comprising information based upon user interaction with cognitive attributes; performing a machine learning operation on the training data; generating a cognitive profile based upon the information generated by performing the machine learning operation; and, performing a cognitive search operation on a corpus of content based upon the cognitive profile, the cognitive search operation returning cognitive results specific to the cognitive profile of the user.