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: Aspects of the invention include identifying each solution component of a plurality of solution components described in a text of a solution template of a plurality of solution templates, wherein the solution template includes a first combination of solution components. Identifying each solution component of a plurality of solution component described by an object in the solution template of a plurality of solution templates. Detecting a respective number of instances of each solution component in the solution template and a respective number of instances of each solution component in each other solution template of the plurality of solution templates. Generating analytics for a source company based on the respective number of instances of each solution component in the solution template and the respective number of instances of each solution component in each other solution template of the plurality of solution templates.

Abstract: One embodiment of the invention provides a method for automated code annotation in machine learning (ML) and data science. The method comprises receiving, as input, a section of executable code. The method further comprises classifying, via a ML model, the section of executable code with a stage classification label indicative of a stage within a workflow for automated ML that the executable code applies to. The method further comprises categorizing, based on the stage classification label, the section of executable code with a category of annotation that is most appropriate for the section of executable code. The method further comprises generating a suggested annotation for the section of executable code based on the category of annotation. The method further comprises providing, as output, the suggested annotation to a display of an electronic device for user review. The suggested annotation is user interactable via the electronic device.

Abstract: A system and method for automatically generating deep neural network architectures for time series prediction. The system includes a processor for: receiving a prediction context associated with a current use case; based on the associated prediction context, selecting a prediction model network configured for a current use case time series prediction task; replicating the selected prediction model network to create a plurality of candidate prediction model networks; inputting a time series data to each of the plurality of the candidate prediction model network; train, in parallel, each respective candidate prediction model network of the plurality with the input time series data; modifying each of the plurality of the candidate prediction model network by applying a respective different set of one or more model parameters while being trained in parallel; and determine a fittest modified prediction model network for solving the current use case time series prediction task.

Abstract: Technology for computer systems for helping candidates (that is, candidate entities, like people or enterprises) for performing a task to know what portions of the entity's profile could use revision or improvement in order to increase the probability that the candidate entity will, in due course, be selected to perform the task. In some embodiments: (i) the task is the provision of cloud computing services; and (ii) the candidates are various companies in the business of providing cloud computing services.

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: A method for generating a summary text composition can include obtaining historical reading data of a user. The method can include generating, based on the historical reading data, a reading proficiency level of the user. The method can include selecting, based on the reading proficiency level, a summarization model from a set of summarization models. The method can include obtaining a target composition. The target composition can be selected from the group consisting of a literary work, a video recording, and an audio recording. The method can include generating, by the summarization model, the summary text composition. The summary text composition can correspond to the target composition and have a first reading level classification that matches the reading proficiency level. The method can include transmitting the summary text composition to a computing device.

Abstract: Aspects of the present invention disclose a method for generating speech recommendations for a user based on feedback data corresponding to a plurality of viewers of the user. The method includes one or more processors identifying speech of a user in audio data of the user. The method further includes identifying feedback of one or more audience members of the user associated with the speech of the user. The method further includes generating an assessment of the speech of the user, wherein the assessment is based at least in part on the feedback of the one or more audience members. The method further includes generating a speech recommendation for the speech of the user based at least in part on the assessment of the speech.

Abstract: A method for generating an operational rule associated with a building management system includes identifying, with a processing device, a first pattern associated with a series of operational observations corresponding to a property of the building management system, correlating a first contextual attribute with the first pattern, and deriving the operational rule at least in part based on the first pattern and the first contextual attribute.

Abstract: The invention relates to heterocyclic compounds of the formula (I) in which all of the variables are as defined in the specification; capable of modulating the activity of CFTR. The invention further provides a method for manufacturing compounds of the invention, and its therapeutic uses. The invention further provides methods to their preparation, to their medical use, in particular to their use in the treatment and management of diseases or disorders including Cystic fibrosis and related disorders.

Abstract: One embodiment of the invention provides a method for automated code annotation in machine learning (ML) and data science. The method comprises receiving, as input, a section of executable code. The method further comprises classifying, via a ML model, the section of executable code with a stage classification label indicative of a stage within a workflow for automated ML that the executable code applies to. The method further comprises categorizing, based on the stage classification label, the section of executable code with a category of annotation that is most appropriate for the section of executable code. The method further comprises generating a suggested annotation for the section of executable code based on the category of annotation. The method further comprises providing, as output, the suggested annotation to a display of an electronic device for user review. The suggested annotation is user interactable via the electronic device.

Abstract: Embodiments for implementing intelligent opportunity recommendation and management by a processor. A channel selection model mat be applied to a selected opportunity in view of a plurality of opportunity attributes to identify one or more team candidates of an entity or one or more entity partners ranked by alignment with the selected opportunity and determine a recommended opportunity owner from the one or more team candidates of the entity, the one or more entity partners, or a combination thereof.

Abstract: Use a computerized trained graph neural network model to classify an input instance with a predicted label. With a computerized graph neural network interpretation module, compute a gradient-based saliency matrix based on the input instance and the predicted label, by taking a partial derivative of class prediction with respect to an adjacency matrix of the model. With a computerized user interface, obtain user input responsive to the gradient-based saliency matrix. Optionally, modify the trained graph neural network model based on the user input; and re-classify the input instance with a new predicted label based on the modified trained graph neural network model.

Abstract: In an approach for discourse-level text optimization, a processor receives an initial text in a first language. A processor applies one or more operators to modify the initial text. A processor evaluates the modified text using a scoring function. A processor determines whether a score generated from the scoring function on the modified text is above a predefined threshold. In response to determining the score is above the predefined threshold, a processor outputs the modified text.

Abstract: An automated analytic tool (AAT) apparatus analyzes a machine learning system (MLS). The tool comprises a processor configured to receive experiment parameters associated with an experiment performed on the MLS, and captures information from a plurality of stages of the experiment. The information comprises information regarding MLS results and choices made during the experiment. The tool automatically revise the captured information into revised information utilizing a knowledge base comprising information from prior experiments. The tool then presents the revised information to a user.

Abstract: A method for generating a summary text composition can include obtaining historical reading data of a user. The method can include generating, based on the historical reading data, a reading proficiency level of the user. The method can include selecting, based on the reading proficiency level, a summarization model from a set of summarization models. The method can include obtaining a target composition. The target composition can be selected from the group consisting of a literary work, a video recording, and an audio recording. The method can include generating, by the summarization model, the summary text composition. The summary text composition can correspond to the target composition and have a first reading level classification that matches the reading proficiency level. The method can include transmitting the summary text composition to a computing device.

Abstract: Techniques and systems for facilitating artificial intelligence for interactive preparation of electronic documents are provided. In one example, a system includes a mapping component and a document editing component. The mapping component maps data provided by a recording device into an editing action for an electronic document. The document editing component applies the editing action associated with the recording device to the electronic document to generate a modified version of the electronic document.

Abstract: Embodiments for self-managed adaptable models for prediction systems by one or more processors. One or more adaptive models may be applied to data streams from a plurality of data sources according to one or more data recipes such that the one or more adaptive models predict a plurality of target variables.

Abstract: In accordance with implementations of the present disclosure, a solution for converting a natural language query is provided. In this solution, a first natural language query and a second natural language query for one or more data tables are received, wherein semantics of the second natural language query is dependent on the first natural language query. A third natural language query for one or more data tables is generated based on the first natural language query and the second natural language query, wherein semantics of the third natural language query is identical to the semantics of the second natural language query and independent of the first natural language query. In this way, this solution can convert a context-dependent natural language query into a context-independent natural language query, thereby enabling interfacing with any semantic parsers which can convert a natural language query into a computer-executable query.

Abstract: A computer automatically selects a machine learning model pipeline using a meta-learning machine learning model. The computer receives ground truth data and pipeline preference metadata. The computer determines a group of pipelines appropriate for the ground truth data, and each of the pipelines includes an algorithm. The pipelines may include data preprocessing routines. The computer generates hyperparameter sets for the pipelines. The computer applies preprocessing routines to ground truth data to generate a group of preprocessed sets of said ground truth data and ranks hyperparameter set performance for each pipeline to establish a preferred set of hyperparameters for each of pipeline. The computer selects favored data features and applies each of the pipelines, with associated sets of preferred hyperparameters, to score the favored data features of the preprocessed ground truth data. The computer ranks pipeline performance and selects a candidate pipeline according to the ranking.