Abstract: Machine learning model change management in an online Software as a Medical Device (“SaMD”) is provided. One or more machine learning models implemented in a medical domain may be monitored where the one or more machine learning models are continuously updated. One or more changes to the one or more machine learning models. The one or more machine learning models, having the one or more changes, are certified as being in compliance with performance characteristics and compliance criteria.

Abstract: Learning and correcting errors in text input fields to an artificial intelligence (AI) system includes receiving, by the AI system from a target system, an input text. The AI system executes a text processing operation on the input text by applying at least one transformer from a library of transformers to the input text to generate transformed text. At least one proposed correction to the input text is output by the AI system based on an analysis of the transformed text. Feedback data, associated with the at least one proposed correction, is then received from a user of the target system to iteratively learn, by the AI system, which of the transformers need be applied on future input text to accurately generate future proposed corrections on the future input text.

Abstract: A method, system, and computer program product for behavior-based Internet of Things (IoT) device security are provided. The method detects an action from a set of IoT devices. A context is identified for the action and at least one IoT device of the set of IoT devices. The action and the context are validated for the at least one IoT device. The action is identified as an anomaly based on the validating of the action and the context. A potential state change is identified for the at least one IoT device based on the anomaly. The method determines a responsive action based on the potential state change and the anomaly.

Abstract: Embodiments are disclosed for a method. The method includes receiving feedback decision rules for multiple predictions by a trained machine learning model. generating a feedback rule set based on the feedback decision rules. The method further includes generating an updated training dataset based on an original training dataset and an updated feedback rule set. The updated feedback rule set resolves one or more conflicts of the feedback rule set, and the updated training dataset is configured to train the machine learning model to move a decision boundary. Generating the updated training dataset includes generating multiple updated training instances by applying one of the feedback decision rules to a training instance of the original training dataset.

Abstract: An online machine learning model such as an autonomous agent predicts an action. A processor associated with, or running, the online machine learning model observes an environment for an interval of time for a real reward associated with the action. Responsive to determining that the real reward is not received within the interval of time, the processor determines based on a criterion whether to allocate an immediate reward received within the interval of time to the online machine learning model, where the immediate reward is an approximation of the real reward. Responsive to determining that the immediate reward is to be allocated, the processor allocates the immediate reward to the online machine learning model. The online machine learning model further learns or retrains itself based on the immediate reward.

Abstract: Embodiments of the invention are directed to a computer-implemented method. A non-limiting example of the computer-implemented method includes accessing, using an explanation generator module of a processor system, information of a recommendation associated with an application, information of the application, and information of a user of the application. The explanation generator module of the processor system is used to determine an explanation format of an explanation of the recommendation based at least in part on the information of the recommendation associated with the application, the information of the application, and the information of the user of the application.

Abstract: Techniques regarding determining sufficiency of one or more machine learning models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in memory. The computer executable components can comprise a measurement component that measures maximum deviation of a supervised learning model from a reference model over a certification set and an analysis component that determines sufficiency of the supervised learning model based at least in part on the maximum deviation.

Abstract: Techniques regarding discovering configuration information for one or more computer applications are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a configuration component that can discover configuration information associated with a containerized computer application. The configuration information can be characterized by a set of environment attributes extracted by querying a source code of the containerized computer application.

Abstract: Learning and correcting errors in text input fields to an artificial intelligence (AI) system includes receiving, by the AI system from a target system, an input text. The AI system executes a text processing operation on the input text by applying at least one transformer from a library of transformers to the input text to generate transformed text. At least one proposed correction to the input text is output by the AI system based on an analysis of the transformed text. Feedback data, associated with the at least one proposed correction, is then received from a user of the target system to iteratively learn, by the AI system, which of the transformers need be applied on future input text to accurately generate future proposed corrections on the future input text.

Abstract: A method, system, and computer program product for behavior-based Internet of Things (IoT) device security are provided. The method detects an action from a set of IoT devices. A context is identified for the action and at least one IoT device of the set of IoT devices. The action and the context are validated for the at least one IoT device. The action is identified as an anomaly based on the validating of the action and the context. A potential state change is identified for the at least one IoT device based on the anomaly. The method determines a responsive action based on the potential state change and the anomaly.

Abstract: A computer-implemented method, a computer program product, and a computer system for designing a fair machine learning model through user interaction. A computer system receives from a user a request for reviewing one or more biased subgroups in a dataset used in training a machine learning model and presents to the user the one or more biased subgroups and respective bias scores thereof. A computer system preprocesses the dataset to mitigate bias, in response to receiving from the user a request for mitigating the bias associated with the one or more biased subgroups. A computer system retrains the machine learning model, using a new dataset obtained from preprocessing the dataset. A computer system presents to the user respective new bias scores of the one or more biased subgroups in the new dataset. The user reviews the respective new bias scores to determine whether the fair machine learning model is built.

Abstract: Embodiments for entity transaction interaction analysis and summarization by a processor. Transaction elements relating to one or more entity transaction interactions may be identifies and extracted from one or more communications. The transaction elements may be combined with one or more transaction opportunities and transaction historical data to provide a transaction summary.

Abstract: Embodiments are provided for enabling visual editing of machine learning models in a computing environment by a processor. A multidimensional dataset may be received. The multidimensional dataset may be processed. Visualization and exploration of an interactive representation of a plurality of datasets and decision boundaries of one or more machine learning models built upon multidimensional dataset are provided. Behavior of the one or more machine learning models may be edited via the interactive representation using one or more logical rules or moving the decision boundaries of one or more machine learning models.

Abstract: Systems, computer-implemented methods and/or computer program products that facilitate answering questions that involve mathematical computations are provided. In one embodiment, a computer-implemented method comprises: transforming, by a system operatively coupled to a processor, a natural language query into a first logical representation and extrinsic knowledge into a second logical representation relevant to the natural language query; merging, by the system, the first logical representation and the second logical representation into a third logical representation; and generating, by the system, answers for the natural language query based on processing of the third logical representation.

Abstract: Embodiments for recommending actions to improve machine learning predictions by a processor. One or more recommended actions may be linked to one or more features that influence a predicted outcome of a prediction model of a machine learning operation. One or more features having one or more negative factors that negatively impact the predicted outcome of the prediction model may be determined and selected. One or more of the linked recommended actions may be applied to one or more of the features to mitigate a negative impact upon the predicted outcome of the prediction model.

Abstract: Embodiments of the invention are directed to techniques that include predicting, by a computer system, a number of predicted opportunities and signatures of the predicted opportunities expected in a time window. Based on the signatures of the predicted opportunities, the computer system generates a listing of entities ranked according to signatures of the predicted opportunities. The computer system selects the entities to be assigned to the predicted opportunities based, at least in part, on computing capacity related to sales while accounting for any current opportunities having been assigned to the entities.

Abstract: Various embodiments are provided for providing enhanced data allocation for machine learning operations in a computing environment by one or more processors in a computing system. One or more data sampling strategies may be determined based on a dataset. One or more enhanced training data allocations may be suggested for machine learning operations in a cloud computing environment based on the one or more data sampling strategies.

Abstract: Techniques regarding discovering configuration information for one or more computer applications are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a configuration component that can discover configuration information associated with a containerized computer application. The configuration information can be characterized by a set of environment attributes extracted by querying a source code of the containerized computer application.

Abstract: Machine learning model change management in an online Software as a Medical Device (“SaMD”) is provided. One or more machine learning models implemented in a medical domain may be monitored where the one or more machine learning models are continuously updated. One or more changes to the one or more machine learning models. The one or more machine learning models, having the one or more changes, are certified as being in compliance with performance characteristics and compliance criteria.

Abstract: A method incrementally solves a current journey planning request from a user. The solving step performs a current search for at least one journey plan that satisfies the request by accessing a database storing journey planning information derived from results to a plurality of previous requests. The solving step stores, in the database, information discovered during the current search for responding to a subsequent request. The information discovered during the current search for responding to the request includes a reusable portion of a search graph, pairs of a state and a lower bound on a best arrival time and pairs of a state and an exact value for the arrival time. The lower bound is employed to increase an accuracy of a pre-computer heuristic function which guides the search based on state dominance in search spaces in which heuristic values are back propagated and stored in the database.