Abstract: A method, computer system, and a computer program product for executing workloads in a distributed system is provided. The present invention may include: determining a current resource utilization status in a distributed system; inputting, to a second AI model, the current resource utilization status and an in-use split configuration of a first AI model, the second AI model configured for predicting a split configuration for the first AI model; receiving an output from the second AI, the output indicating a current split configuration for the first AI model; splitting the first AI model using the current split configuration; deploying the split first AI model such that the input and output blocks may be executed on first computer systems of the set of first computer systems and such that the intermediate block may be executed on a second computer system of the at least one second computer system; executing the workload.

Abstract: The present disclosure relates to a method comprising receiving a request to execute a workload using an artificial intelligence model. A current resource utilization status in the distributed system may be determined. The current resource utilization status may be used to define a deployment configuration of the artificial intelligence model, wherein the deployment configuration is defined by: a number and structure of input blocks, a number and structure of output blocks and the intermediate block of the artificial intelligence model, a second computer system to execute the intermediate block, and one or more first computer systems to execute the input and output blocks. The artificial intelligence model may be deployed in accordance with the defined deployment configuration and the workload may be executed.

Abstract: The present disclosure relates to a method comprising receiving a request to execute a workload using an artificial intelligence model. A current resource utilization status in the distributed system may be determined. The current resource utilization status may be used to define a deployment configuration of the artificial intelligence model, wherein the deployment configuration is defined by: a number and structure of input blocks, a number and structure of output blocks and the intermediate block of the artificial intelligence model, a second computer system to execute the intermediate block, and one or more first computer systems to execute the input and output blocks. The artificial intelligence model may be deployed in accordance with the defined deployment configuration and the workload may be executed.

Abstract: Systems and methods for a cognitive system to interact with a user are provide. Aspects include receiving a cognitive system profile and observational data associated with the user. Environmental data associated with the user is received and features are extracted from the observations data and the environmental data. The features are stored in the user profile and analyzed to determine a situational context for each of the features based on the cognitive system profile and the user profile. Trigger events are identified based on the situational context for each of the features. One or more proposed actions are determined based at least in part on the one or more trigger events. At least one action is initiated from the one or more proposed actions and are stored in the user profile along with the one or more trigger events and the one or more features.

Abstract: Embodiments relate to an intelligent computer platform to selectively amend one or more document elements. A first document is subjected to natural language processing (NLP) and two or more document characteristics are subjected to an assessment to produce a characteristic value. The document characteristics and corresponding characteristic values are analyzed to produce a characteristic profile for each identified document characteristic. Upon receipt of a new document, document characteristic data and corresponding characteristic value(s) are identified. The corresponding characteristic value(s) of the new document is applied against the produced characteristic profile. New document characteristic data is selectively amended responsive to the comparison, and a new document version is created from the selective amendment.

Abstract: Embodiments relate to an intelligent computer platform to selectively amend one or more tokens in a document. A first document set is subjected to natural language processing (NLP) and a vector score is identified for two or more documents of the first document set. Upon receipt of a new document, the new document is subjected to NLP and a new document vector score is identified. The new document is analyzed against the first document set, and the identified vector score of the first document set is compared to the vector score of the new document. One or more tokens of the new document are amended responsive to the comparison, and a new document version is created from the selective amendment.

Abstract: Embodiments for identifying entities relevant to queries are provided. At least one query is received from a user. The at least one query is associated with at least one entity. Results of the at least one query are analyzed to identify related entities. The related entities are analyzed based on a relevancy score and an information enhancement score for each of the related entities to generate a ranking of the related entities. At least one of the related entities is provided to the user based on the ranking of the related entities.

Abstract: Embodiments relate to an intelligent computer platform to selectively amend one or more document elements. A first document is subjected to natural language processing (NLP) and two or more document characteristics are subjected to an assessment to produce a characteristic value. The document characteristics and corresponding characteristic values are analyzed to produce a characteristic profile for each identified document characteristic. Upon receipt of a new document, document characteristic data and corresponding characteristic value(s) are identified. The corresponding characteristic value(s) of the new document is applied against the produced characteristic profile. New document characteristic data is selectively amended responsive to the comparison, and a new document version is created from the selective amendment.

Abstract: Embodiments relate to an intelligent computer platform to selectively amend one or more tokens in a document. A first document set is subjected to natural language processing (NLP) and a vector score is identified for two or more documents of the first document set. Upon receipt of a new document, the new document is subjected to NLP and a new document vector score is identified. The new document is analyzed against the first document set, and the identified vector score of the first document set is compared to the vector score of the new document. One or more tokens of the new document are amended responsive to the comparison, and a new document version is created from the selective amendment.

Abstract: Embodiments for identifying entities relevant to queries are provided. At least one query is received from a user. The at least one query is associated with at least one entity. Results of the at least one query are analyzed to identify related entities. The related entities are analyzed based on a relevancy score and an information enhancement score for each of the related entities to generate a ranking of the related entities. At least one of the related entities is provided to the user based on the ranking of the related entities.

Abstract: Systems and methods for a cognitive system to interact with a user are provide. Aspects include receiving a cognitive system profile and observational data associated with the user. Environmental data associated with the user is received and features are extracted from the observations data and the environmental data. The features are stored in the user profile and analyzed to determine a situational context for each of the features based on the cognitive system profile and the user profile. Trigger events are identified based on the situational context for each of the features. One or more proposed actions are determined based at least in part on the one or more trigger events. At least one action is initiated from the one or more proposed actions and are stored in the user profile along with the one or more trigger events and the one or more features.