A predictive model pipeline data store may contain electronic records defining a predictive model pipeline composed of operation nodes. Based on the information in the data store, an execution framework platform may calculate a hash value for each operation node by including all recursive dependencies using ancestor node hash values and current node parameters. The platform may then compare each computed hash value with a previously computed hash value associated with a prior execution of a prior version of the pipeline. Operation nodes that have an unchanged hash value may be tagged “idle.” Operation nodes that have a changed hash value may be tagged “train and apply” or “apply” based on current node parameters (and an “apply” tag may propagate backwards through the pipeline to ancestor nodes). The platform may then ignore the operation nodes tagged “idle” when creating a physical execution plan to be provided to a target platform.

Systems and methods are provided for self-learning natural language predictive searching including receiving a first input, the first input being related to the desired outcome; retrieving a first information related to the first input; determining a first output based on at least the first input and the first information; outputting the first output; receiving a second input based on the outputted first output in response to the first output being different from the desired outcome, the second input being related to the desired outcome; retrieving, by the processor, a second information related to the second input; determining a second output based on at least the second input, the second information, the first input and the first information; and outputting the second output.

An inference apparatus 10 includes: a hypothesis candidate generation unit 11 configured to perform inference by applying inference knowledge that includes information indicating a temporal sequence to observation in which facts that have been observed are expressed using logical expressions, and thereby generate a hypothesis candidate from which the observation can be derived; and a contradiction examination unit 12 configured to determine, on the basis of the information indicating a temporal sequence, whether or not the generated hypothesis candidate includes a temporal contradiction.

Disclosed herein is a system for facilitating accomplishing tasks based on a natural language conversation. Accordingly, the system may include a direct graph unit. Further, the direct graph unit may include a directed graph. Further, the directed graph models a non-linearity of the natural language conversation. Further, the directed graph may include a set of nodes connected by at least one edge. Further, the system may include a context-encoded language understanding unit may include a learning unit and an inferring unit. Further, the learning unit may be configured for receiving a plurality of inputs. Further, the learning unit may be configured for generating a model based on the plurality of inputs. Further, the inferring unit may be configured for receiving a plurality of inputs. Further, the inferring unit may be configured for generating an output based on the plurality of inputs and the model.

Methods, computer program products, and/or systems are provided that perform the following operations: obtaining a textual knowledge base; filtering the textual knowledge base to obtain a subset of the textual knowledge base, wherein the filtering is based on textual query data; generating reasoning data based on the subset of the textual knowledge base and the textual query data; generating classification data based on the subset of the textual knowledge base, the textual query data, and the reasoning data; and providing label data as output for the textual query data based on the classification data.

Examples of artificial intelligence-based reasoning explanation are described. In an example implementation, a knowledge model having a plurality of ontologies and a plurality of inferencing rules is generated. Once the knowledge model is generated, based on a real-world problem, a knowledge model from amongst various knowledge models is selected to be used for resolving a real-world problem. The data procured from the real-world problem is clustered and classified into an ontology of the determined knowledge model. Inferencing rules to be used for deconstructing the real-world problem are identified, and a machine reasoning is generated to provide a hypothesis for the problem and an explanation to accompany the hypothesis.

Techniques are described herein for reducing the computing cost of decision-making when simulating a real-world system. A machine learning model is trained using data generated by a simulator of the real-world system. Knowledge about how the simulator is implemented is used to improve the efficiency of the machine learning model and to improve the relevance of data selected to train the machine learning model. For example, structural knowledge—the flow of input variables through components of the simulator—is used to determine a causal relationship between input variables. Having identified the causal relationship, the number of simulator iterations used to generate training data may be reduced. Furthermore, large complex machine learning models may be replaced with smaller, more efficient models. Additionally, or alternatively, causal relationships between input variables are identified during training, enabling further refinement of input selection and model design.

The disclosed technology relates to improved content deployment and orchestration to more effectively convert customers or users to paperless communication channels and facilitate increased digital engagement. An exemplary system may obtain user context data associated with a user following a trigger event. The system may then apply a trained machine learning model to the user context data to generate a likelihood score. The likelihood score may be indicative of a likelihood the user will enroll in a particular delivery option (e.g., paperless delivery) following the trigger event. Responsive to determining the likelihood score exceeds a threshold, the system may output content to the second user that may be identified based on a type of the trigger event and may be targeted to encourage enrollment in the delivery option. In addition to outputting the content, the system may be configured to establish orchestration for subsequent automated content delivery for the user.

An anthropomorphic, artificial intelligence-based system and method to quote, compare, and purchase personal lines and commercial lines property and casualty insurance or benefits products and services and quoting, comparing, purchasing, or transferring residential services using a cognitive virtual assistant. The system and method collects information from an online advertising platform during the process and returns the collected information to the online advertising platform for optimization of the online advertising platform.

Systems, technologies and techniques for generating prospective legal strategies are disclosed. The system and technologies employ data mining, natural language processing and machine learning approaches to generate prospective legal strategies. The system and technologies analyze given case facts (i.e., background facts, event type such as accident, injury, malpractice, discrimination) and provide a rich set of insights that assist in formulating effective legal arguments and strategies.