Abstract: In an approach to improve prompt variations to enhance user-based experiences while interacting with artificial intelligent (AI) systems, embodiments generate variations of the prompts associated with the initial input by automatically leveraging machine learning techniques and collect the prompts from a user resulting from an interaction with an artificial intelligent (AI) system. Further, embodiments, identify a heuristic to organize suggestions and the variations of the prompts, and determine and utilize the heuristic to organize the variations of the prompts in systematic categories to produce an efficient display of the variations of the prompts, in a graphic user interface (GUI), for the user to view. Additionally, embodiments, update the variations of the prompts based on identified interaction data from the user, and dynamically output the updated variations of the prompts in response to the identified and collected user data.

Abstract: This disclosure related to a storage cluster includes two storage nodes, and the two storage nodes are respectively a primary storage node and a secondary storage node. The primary storage node is configured to obtain data from a container management cluster, store the data in a local database of the primary storage node, and send the data to the secondary storage node. Correspondingly, the secondary storage node is configured to receive the data from the primary storage node, and store the data in a local database of the secondary storage node.

Abstract: Provided are techniques for moderating Artificial Intelligence (AI) agent interlocution in group dialog environments. Under control of an interlocution module that has been trained with dialog content and dialog turns, an indication that interlocution is to be determined for a group dialog is received. Under control of the interlocution module, it is determined whether an AI agent is to participate in the group dialog based on a current dialog context and a dialog response. Under control of the interlocution module, in response to determining that the AI agent is to participate in the group dialog, the AI agent is triggered to post the dialog response to the group dialog.

Abstract: A present invention embodiment analyzes user input via natural language processing. A natural language utterance from a user is analyzed to determine one or more computing tasks. the natural language utterance is analyzed using a knowledge base to identify one or more modifications to the natural language utterance that are based on previous user modifications to a previous user utterance. An indication that the user accepted at least one modification of the one or more modifications is received, wherein the at least one modification modifies the one or more computing tasks. The modified one or more computing tasks are executed.

Abstract: A method, computer program, and computer system are provided for explaining generation of a flow from natural language utterances. Data corresponding to a natural language utterance is received. One or more constraints corresponding to a flow to be generated are determined based on the received natural language utterance. A flow is constructed based on the determined constraints. An explanation associated with the constructed flow is provided, and the explanation identifies parameters corresponding to constructing the flow.

Abstract: A method, computer system, and a computer program product for data labeling is provided. The present invention may include receiving a plurality of labeled data points. The present invention may include identifying one or more of the plurality of labeled data points with conflicting labels. The present invention may include determining that at least one of the one or more identified labeled data points exceeds one or more conflict thresholds. The present invention may include presenting the at least one or more identified labeled data points exceeding one or more conflict thresholds to a user. The present invention may include receiving a conflict resolution from the user for the one or more identified labeled data points exceeding the one or more conflict thresholds.

Abstract: A method, a computer program product, and a computer system generate an accurate mental model of an automated agent. The method includes receiving an input from a user device associated with a user during a communication session between the user and the automated agent. The method includes determining a response to the input. The method includes determining a confidence score of the response relative to a confidence threshold. The method includes determining an assertiveness feature associated with the response, the assertiveness feature comprising an expression of the automated agent based on the confidence score. The method includes transmitting the response and the assertiveness feature to the user device, the expression configured to update anthropomorphic characteristics of a graphical representation of the automated agent shown on a graphical user interface of the communication session displayed on a display device of the user device.

Abstract: Embodiments are provided that related to a computer system, a computer program product, and a computer-implemented method for dynamically managing knowledge graphs and their corresponding datasets. Embodiments include identifying a neologism from a virtual environment, and leveraging a virtual environment exploration to resolve a meaning of the identified neologism. The resolved meaning of the neologism is applied to a dynamic expansion of a dataset and a corresponding knowledge graph.

Abstract: A method comprises receiving a new data set; identifying at least one prior data set of a plurality of prior data sets that matches the new data set; generating a natural language data science problem statement for the new data set based on information associated with the at least prior one data set that matches the new data set; outputting the generated natural language data science problem statement for user verification; and in response to receiving user input verifying the natural language generated data science problem statement, generating one or more AutoAI configuration settings for the new data set based on one or more AutoAI configuration settings associated with the at least one prior data set that matches the new data set.

Abstract: A method, a computer program product, and a computer system generate an accurate mental model of an automated agent. The method includes receiving an input from a user device associated with a user during a communication session between the user and the automated agent. The method includes determining a response to the input. The method includes determining a confidence score of the response relative to a confidence threshold. The method includes determining an assertiveness feature associated with the response, the assertiveness feature comprising an expression of the automated agent based on the confidence score. The method includes transmitting the response and the assertiveness feature to the user device, the expression configured to update anthropomorphic characteristics of a graphical representation of the automated agent shown on a graphical user interface of the communication session displayed on a display device of the user device.

Abstract: A portable power supply device for an electric tool comprises: a power supply receiving portion, provided in or on a housing and used to receive a power supply supplying power to the electric tool, and a control unit, comprising a controller that is provided in the housing and is used to control the electric energy supplied to the electric tool. The power supply receiving portion comprises a power supply compartment, which is at least partially defined by a cover which can move relative to the housing between an opened position and a closed position. The power supply device further comprises a positioning accessory detachably and replaceably attached to the housing and/or the power supply receiving portion.

Abstract: Embodiments relate to a system, program product, and method for leveraging cognitive systems to facilitate the automated data table discovery for automated machine learning, and, more specifically, to leveraging a trained cognitive system to automatically search for additional data in an external data source that may be merged with an initial user-selected data table to generate a more robust machine learning model. Manual efforts to find and validate data appropriate for building and training a particular model for a particular task are significantly reduced. Specifically, a learning-based approach to leverage with machine learning models to automatically discover related datasets and join the datasets for a given initial dataset is disclosed herein. Operations that include dataset selection facilitate continued reinforcement learning of the systems.

Abstract: Embodiments relate to a system, program product, and method for leveraging cognitive systems to facilitate the automated data table discovery for automated machine learning, and, more specifically, to leveraging a trained cognitive system to automatically search for additional data in an external data source that may be merged with an initial user-selected data table to generate a more robust machine learning model. Manual efforts to find and validate data appropriate for building and training a particular model for a particular task are significantly reduced. Specifically, a learning-based approach to leverage with machine learning models to automatically discover related datasets and join the datasets for a given initial dataset is disclosed herein. Operations that include dataset selection facilitate continued reinforcement learning of the systems.

Abstract: A method comprises receiving a new data set; identifying at least one prior data set of a plurality of prior data sets that matches the new data set; generating a natural language data science problem statement for the new data set based on information associated with the at least prior one data set that matches the new data set; outputting the generated natural language data science problem statement for user verification; and in response to receiving user input verifying the natural language generated data science problem statement, generating one or more AutoAI configuration settings for the new data set based on one or more AutoAI configuration settings associated with the at least one prior data set that matches the new data set.