Abstract: Implementations include providing, by the PKG platform, an initial knowledge graph based on user-specific data associated with a user, and a domain-specific knowledge graph, receiving, by the PKG platform, data representative of at least one answer provided from the user to a respective question, providing, by the PKG platform, an expanded knowledge graph based on the initial knowledge graph, the expanded knowledge graph including one or more nodes and respective edges based on the data, generating, by the PKG platform, a weighted knowledge graph based a groundtruth knowledge graph, and a targeted knowledge graph, the groundtruth knowledge graph including one or more true answers, and the targeted knowledge graph including the at least one answer provided from the user, and generating, by the PKG platform, the hyper-personalized knowledge graph (hpKG) based on the weighted knowledge graph, the hpKG being unique to the user within a domain.

Abstract: Implementations for selectively enabling override of an inference result provided by an artificial intelligence (AI) system can include receiving an input case, outputting a first inference result by processing the input case through a machine learning (ML) model, and determining that a confidence score associated with the first inference result fails to meet a threshold, and in response: providing an adapted ML model based on a set of additional cases, outputting a second inference result by processing a current case through the adapted ML model, the current case including the input case, and selectively transmitting instructions to display an override element with the first inference result in a user interface.

Abstract: Implementations include providing, by the PKG platform, an initial knowledge graph based on user-specific data associated with a user, and a domain-specific knowledge graph, receiving, by the PKG platform, data representative of at least one answer provided from the user to a respective question, providing, by the PKG platform, an expanded knowledge graph based on the initial knowledge graph, the expanded knowledge graph including one or more nodes and respective edges based on the data, generating, by the PKG platform, a weighted knowledge graph based a groundtruth knowledge graph, and a targeted knowledge graph, the groundtruth knowledge graph including one or more true answers, and the targeted knowledge graph including the at least one answer provided from the user, and generating, by the PKG platform, the hyper-personalized knowledge graph (hpKG) based on the weighted knowledge graph, the hpKG being unique to the user within a domain.

Abstract: Implementations include providing, by the PKG platform, an initial knowledge graph based on user-specific data associated with a user, and a domain-specific knowledge graph, receiving, by the PKG platform, data representative of at least one answer provided from the user to a respective question, providing, by the PKG platform, an expanded knowledge graph based on the initial knowledge graph, the expanded knowledge graph including one or more nodes and respective edges based on the data, generating, by the PKG platform, a weighted knowledge graph based a groundtruth knowledge graph, and a targeted knowledge graph, the groundtruth knowledge graph including one or more true answers, and the targeted knowledge graph including the at least one answer provided from the user, and generating, by the PKG platform, the hyper-personalized knowledge graph (hpKG) based on the weighted knowledge graph, the hpKG being unique to the user within a domain.

Abstract: A system for providing data security and protection using distributed ledgers to store validated data in a global knowledge graph for fraud detection is disclosed. The system may comprise a data access interface, a processor, and an output interface. The data access interface may receive data associated with an individual from a data source. The processor may convert the data into knowledge graph data by: extracting entities and relations from the data; and translating the data into knowledge graph triples to generate the knowledge graph data. The processor may validate the knowledge graph data using a cryptographic validation, to provide secured contents to update a global knowledge graph to determine a fraudulent activity level associated with the individual based on the updated global knowledge graph. The output interface may transmit a report associated with the fraudulent activity level to a report requestor at a computing device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a conversation monitoring system.

Abstract: Implementations include providing a baseline multi-dimensional model of a cultivar, determining an encoding based on the baseline multi-dimensional model, and a target multi-dimensional model, the encoding defining a string of symbols, and being based on an alphabet and a set of rules, providing an expected multi-dimensional model based on the encoding, and a modified set of rules, the modified set of rules being based on the set of rules, the expected multi-dimensional model representing the cultivar after a period of time, selecting a set of actions by determining multiple predicted multi-dimensional models for each set of actions in a plurality of sets of actions, and, for each predicted multi-dimensional model, providing a predicted yield that can be used to determine impact with respect an expected yield, the set of actions being selected based on a respective impact, and providing the set of actions as output for executing on the cultivar.

Abstract: Implementations include providing, by the PKG platform, an initial knowledge graph based on user-specific data associated with a user, and a domain-specific knowledge graph, receiving, by the PKG platform, data representative of at least one answer provided from the user to a respective question, providing, by the PKG platform, an expanded knowledge graph based on the initial knowledge graph, the expanded knowledge graph including one or more nodes and respective edges based on the data, generating, by the PKG platform, a weighted knowledge graph based a groundtruth knowledge graph, and a targeted knowledge graph, the groundtruth knowledge graph including one or more true answers, and the targeted knowledge graph including the at least one answer provided from the user, and generating, by the PKG platform, the hyper-personalized knowledge graph (hpKG) based on the weighted knowledge graph, the hpKG being unique to the user within a domain.

Abstract: Implementations include providing a baseline multi-dimensional model of a cultivar, determining an encoding based on the baseline multi-dimensional model, and a target multi-dimensional model, the encoding defining a string of symbols, and being based on an alphabet and a set of rules, providing an expected multi-dimensional model based on the encoding, and a modified set of rules, the modified set of rules being based on the set of rules, the expected multi-dimensional model representing the cultivar after a period of time, selecting a set of actions by determining multiple predicted multi-dimensional models for each set of actions in a plurality of sets of actions, and, for each predicted multi-dimensional model, providing a predicted yield that can be used to determine impact with respect an expected yield, the set of actions being selected based on a respective impact, and providing the set of actions as output for executing on the cultivar.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for to enabling an action to accomplish a goal for a complex system. In one aspect, a method includes applying a slicing policy to an area within the complex system; receiving sensor data from a plurality of sensor devices positioned to collect readings from the complex system; extracting a knowledge graph based on the respective sensor data; extracting a macro knowledge graph based on a relationship between grid sections; merging the macro knowledge graph with a domain knowledge graph and an actions knowledge graph to form a recommendation knowledge graph; identifying an implementation plan for accomplishing a set goal for the complex system based on an analysis of the recommendation knowledge graph, the implementation plan including actions to accomplish a set goal; and enabling an implementation of at least one of the actions in the implementation plan.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a conversation monitoring system.

Abstract: A system for providing data security and protection using distributed ledgers to store validated data in a global knowledge graph for fraud detection is disclosed. The system may comprise a data access interface, a processor, and an output interface. The data access interface may receive data associated with an individual from a data source. The processor may convert the data into knowledge graph data by: extracting entities from the data; extracting relations from the data; and translating the data into knowledge graph triples to generate the knowledge graph data. The processor may validate the knowledge graph data using a cryptographic validation, such as blockchain or distributed ledgers, on the knowledge graph data to provide secured contents to update a global knowledge graph. The processor may then determine a fraudulent activity level associated with the individual based on the updated global knowledge graph.

Abstract: A device may receive individual information associated with individual activities of an individual, and may aggregate the individual information, based on a time period, to generate aggregated individual information. The device may identify patterns in the aggregated individual information, and may determine states for the patterns based on state information associated with activities capable of being performed by individuals. The device may generate a sequential knowledge graph based on modifying a knowledge graph with the states and adding a sequence of activities to the knowledge graph, and may determine embeddings for the individual activities based on the sequential knowledge graph.