Disclosed is an optimization decision-making method of an industrial process fusing domain knowledge and multi-source data. The method comprises the steps of: acquiring the domain knowledge of the industrial process by using probability soft logic, and building an domain rule knowledge base of the industrial process; fusing multi-source data semantics and multi-source data features to form a new semantic knowledge representation of the industrial process, and constructing a semantic knowledge base of the industrial process; under a posteriori regularization framework, utilizing the domain rule knowledge base of the industrial process and the semantic knowledge base of the industrial process to obtain an optimization decision-making model embedded with the domain rule knowledge and obtain a posteriori distribution model; and migrating knowledge in the optimization decision-making model embedded with the domain rule knowledge into the posteriori distribution model through the knowledge distillation technology.

A computing system identifies broadcast video for a plurality of games in a first league. The broadcast video includes a plurality of video frames. The computing system generates tracking data for each game from the broadcast video of a corresponding game. The computing system enriches the tracking data. The enriching includes merging play-by-play data for the game with the tracking data of the corresponding game. The computing system generates padded tracking data based on the tracking data. The computing system projects player performance in a second league for each player based on the tracking data and the padded tracking data.

A natural language understanding (NLU) system utilizes a knowledge network having interconnected actor perceiver predictor (APP) nodes associated with context, action, and result. For a received utterance, an utterance type is determined, and an input message is generated based on the utterance type. The input message includes word-concept groupings, which include words of the received utterance and concept tags associated with the words. An action type is determined based on the utterance type. The knowledge network is searched to locate a subset of the APP nodes that exceed a threshold connection weight and thereby discriminate the word-concept groupings from other word-concept groupings represented by nodes of the network. Metadata for the word-concept groupings is retrieved from the subset of the APP nodes, and a response to the received utterance is generated by incorporating the metadata into a response template.

A computer vision learning system and corresponding computer-implemented method extract meaning from image content. The computer vision learning system comprises at least one image sensor that transforms light sensed from an environment of the computer vision learning system into image data representing a scene of the environment. The computer vision learning system further comprises a digital computational learning system that includes a network of actor perceiver predictor (APP) nodes and a library of visual methods available to the APP nodes for applying to the image data. The digital computational learning system employs the network in combination with the library to determine a response to a query and outputs the response determined. The query is associated with the scene. The computer vision learning system is capable of answering queries not just about what is happening in the scene, but what would happen based on the scene in view of hypothetical conditions and/or actions.

A digital computational learning system and corresponding method plan a series of actions to accomplish tasks. The system learns, automatically, a plurality of actor perceiver predictors (APP) nodes. Each APP node is associated with a context, action, and result. The result is expected to be achieved in response to the action being taken as a function of the context having been satisfied. Each APP node is associated with an action-controller that includes an instance of a planner that includes allied planners. The action-controller is associated with a goal state and employs the allied planners to determine a sequence of actions for reaching the goal state. The allied planners enable the system to plan a series of actions to accomplish complex tasks in a manner that is more robust and resilient relative to current state of the art artificial intelligence based learning systems and methods.

An abductive inference apparatus 10 includes: a data receiving unit 11 that receives observed event data indicating an observed event; a data specifying unit 12 that specifies observed event data that will not be needed from the received pieces of observed event data based on other pieces of observed event data other than the received pieces of observed event data and knowledge data; and a hypothesis generation unit 13 that generates a hypothesis with which the observed event data that has not been specified by the data specifying unit 12 can be derived using the pieces of observed event data that have not been specified by the data specifying unit 12 and the knowledge data.

An observed event determination apparatus 10, includes: a data receiving unit 11 configured to receive observed event data indicating an observed event; and a data determining unit 12 configured to determine whether or not the received observed event data is not needed based on pieces of observed event data other than the received observed event data and knowledge data.

Disclosed is a multi-device based inference method and apparatus, where the multi-device based inference method includes receiving information related to operation devices performing an operation included in a neural network and a graph corresponding to the neural network, obtaining a size of an output of the operation in a forward direction of the graph based on the information and the graph, dividing an input of the operation in a backward direction of the graph based on the information, the graph, and the size of the output, and performing an inference based on the divided input.

Technology for using a computing device to interpret entity and relationship occurrences a natural language understanding system that includes the following operations (not necessarily in the following order): (i) receiving a corpus that includes unstructured data and/or structured data; (ii) parsing the corpus to obtain parsed corpus information; (iii) applying artificial intelligence to the parsed corpus information to determine a plurality of logical relationships manifested by the corpus; and (iv) expressing, by machine logic, the plurality of logical relationships as a respectively corresponding plurality of logical rule expressions, with each logical rule expression of the plurality of logical rule expressions expressing the respectively corresponding logical relationship as fact(s) with regard to the corpus.

Consciousness is widely considered to be a mysterious and uniquely human trait, which cannot be achieved artificially. On the contrary, a system and method are disclosed for a computational machine that can recognize itself and other agents in a dynamic environment, in a way that seems quite similar to biological consciousness in humans and animals. The machine comprises an artificial neural network configured to identify correlated temporal patterns and attribute causality and agency. The machine is further configured to construct a virtual reality environment of agents and objects based on sensor inputs, to create a coherent narrative, and to select future actions to pursue goals. Such a machine may have application to enhanced decision-making in autonomous vehicles, robotic agents, and intelligent digital assistants.