Abstract: Learning iterations, individual ones of which include a respective bucket group selection phase and a class boundary refinement phase, are performed using a source data set whose records are divided into buckets. In the bucket group selection phase of an iteration, a bucket is selected for annotation based on output obtained from a classification model trained in the class boundary refinement phase of an earlier iteration. In the class boundary refinement phase, records of buckets annotated as positive-match buckets for a target class in the bucket group selection phase are selected for inclusion in a training set for a new version of the model using a model enhancement criterion. The trained version of the model is stored.

Abstract: Learning iterations, individual ones of which include a respective bucket group selection phase and a class boundary refinement phase, are performed using a source data set whose records are divided into buckets. In the bucket group selection phase of an iteration, a bucket is selected for annotation based on output obtained from a classification model trained in the class boundary refinement phase of an earlier iteration. In the class boundary refinement phase, records of buckets annotated as positive-match buckets for a target class in the bucket group selection phase are selected for inclusion in a training set for a new version of the model using a model enhancement criterion. The trained version of the model is stored.

Abstract: At an artificial intelligence system, during a labeling feedback session, a visualization data set is presented via a programmatic interface. The visualization data set comprises a representation of data items for which labeling feedback is requested for generating a training set of a classifier. At least one of the data items is selected based on an estimated rank with respect to a metric associated with including the data item in a training set. During the session, respective labels for the data items and a filter criterion to be used to select additional data items are obtained. A classifier trained using the labels is stored.

Abstract: At an artificial intelligence system, one or more classifier training iterations are performed until a training completion criterion is met. A particular iteration comprises obtaining, via an interactive interface, asynchronously with respect to the start of the iteration, class labels for data items identified in a previous iteration as candidates for labeling feedback. The particular iteration also comprises identifying, based on an analysis of classification predictions generated using classifiers trained using class labels obtained via the interface, another set of data items as candidates for labeling feedback. After the training criterion is met, a classifier trained using labels obtained during the iterations is stored.

Abstract: At an artificial intelligence system, during a labeling feedback session, a visualization data set is presented via a programmatic interface. The visualization data set comprises a representation of data items for which labeling feedback is requested for generating a training set of a classifier. At least one of the data items is selected based on an estimated rank with respect to a metric associated with including the data item in a training set. During the session, respective labels for the data items and a filter criterion to be used to select additional data items are obtained. A classifier trained using the labels is stored.

Abstract: At an artificial intelligence system, respective status indicators for classifier training iterations are determined. A visualization data set comprising the status indicators is presented via an interactive programmatic interface. A training enhancement action, based at least partly on an objective associated with a status indicator, is initiated. The action includes selecting data items for which labeling feedback is to be obtained programmatically during one or more classifier training iterations. A classification model that is trained using the labeling feedback obtained as a result of the action is stored.

Abstract: An unstructured event is detected and an unstructured event record is generated for the detected event. Case identifier (ID) conflation is performed to estimate a case ID that corresponds to the detected event, and event type identification is performed to estimate a type of the unstructured event. A business process model is applied to the unstructured event record, to identify a process that the unstructured event is related to. A user experience is generated based upon the event type, the case ID, and the corresponding process identified for the detected event.

Abstract: An unstructured event is detected and an unstructured event record is generated for the detected event. Case identifier (ID) conflation is performed to estimate a case ID that corresponds to the detected event, and event type identification is performed to estimate a type of the unstructured event. A business process model is applied to the unstructured event record, to identify a process that the unstructured event is related to. A user experience is generated based upon the event type, the case ID, and the corresponding process identified for the detected event.

Abstract: In one example, user interactions with a computing system are detected, and information that represents an insight is identified and extracted from the detected user interaction. If the information is relatively sensitive, the user is asked to approve of storing the insight for potential access by others. Other users can then request insights (or requests can be automatically generated) about certain items, and the stored insights can be searched and returned in response to the request. If the insights to be returned contain sensitive material, then the owner of the insight can be asked to approve dissemination of the insight to the requestor or requesting system.

Abstract: Discrete sets of data are divided into collections in accordance with strata delineated along multiple dimensions of data. Each dimension of data represents criteria to be evaluated and the stratification of a dimension is based on a distribution of the discrete sets of data along such a dimension. Once divided into the multidimensional strata, one or more discrete sets of data are randomly selected from each stratum and are provided to human judges to generate corresponding classifications of such a discrete set of data. Such human-generated classifications are compared with computer-generated classifications associated with the same discrete sets of data for purposes of evaluating the computer-implemented functionality generating such classifications. Such human-generated classifications are also associated with the corresponding discrete sets of data for purposes of training, and thereby improving, computer-implemented functionality.