Abstract: In an approach to improve detecting and correcting errors in one or more machine learning pipelines. Embodiments comprise generating a plurality of test machine learning pipeline instances based upon a target machine learning pipeline and evaluating the plurality of test machine learning pipeline instances for failure in a task. Further, embodiments identify one or more root causes of error based upon the evaluated plurality of test machine learning pipeline instances and failure in the task, and create a remediated target machine learning pipeline based upon the identified one or more root causes of error. Additionally, embodiments output the remediated machine learning pipelines.

Abstract: Embodiments of the present invention provide a computer system a computer program product, and a method that comprises analyzing identified data for a determined conversion of the identified data, wherein the identified data is input data stored on an external database; automatically converting the analyzed data to a uniform domain by mapping a data route within the analyzed data, predicting a plurality of outcomes based on an application of a plurality of scenarios associated with the mapped data route, ranking the predicted outcomes based on a positive match percentage for the analyzed data, and converting the analyzed data associated with at least one ranked outcome using a relational algebra algorithm; and dynamically transmitting the converted, analyzed data into at least one section of a machine learning data pipeline.

Abstract: Embodiments are disclosed for a method. The method includes identifying a prefix updated by a searcher of a machine learning model. The machine learning model is configured to generate source code in a programming language. The method also includes determining whether the prefix violates a semantic correctness property of the programming language. Additionally, the method includes instructing the searcher, in response to the determination, to prune the prefix from a set of prefixes under consideration by the searcher.

Abstract: A computer-implemented method according to one embodiment includes identifying a machine learning pipeline and a plurality of training data batches; creating a plurality of tasks, based on the machine learning pipeline; and determining an order in which the plurality of tasks is executed, utilizing a resource usage-aware approach.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate inter-operator backpropagation in AutoML frameworks are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a selection component that selects a subset of deep learning and non-deep learning operators. The computer executable components further comprise a training component which trains the subset of deep learning and non-deep learning operators, wherein deep learning operators in the subset of deep learning and non-deep learning operators are trained using backpropagation across at least two deep learning operators of the subset of deep learning and non-deep learning operators.

Abstract: A machine-learning model task deduction method, system, and computer program product include extracting data schema of a machine-learning model and analyzing the data schema to determine an intended task of the machine-learning model.

Abstract: In an approach to improve detecting and correcting errors in one or more machine learning pipelines. Embodiments comprise generating a plurality of test machine learning pipeline instances based upon a target machine learning pipeline and evaluating the plurality of test machine learning pipeline instances for failure in a task. Further, embodiments identify one or more root causes of error based upon the evaluated plurality of test machine learning pipeline instances and failure in the task, and create a remediated target machine learning pipeline based upon the identified one or more root causes of error. Additionally, embodiments output the remediated machine learning pipelines.

Abstract: Embodiments are disclosed for a method. The method includes identifying a prefix updated by a searcher of a machine learning model. The machine learning model is configured to generate source code in a programming language. The method also includes determining whether the prefix violates a semantic correctness property of the programming language. Additionally, the method includes instructing the searcher, in response to the determination, to prune the prefix from a set of prefixes under consideration by the searcher.

Abstract: Techniques facilitating guiding machine learning models and related components are provided. In one example, a computer-implemented method comprises identifying, by a device operatively coupled to a processor, a set of models, wherein the set of models includes respective model components; determining, by the device, one or more model relations among the respective model components, wherein the one or more model relations respectively comprise a vector of component relations between respective pairwise ones of the model components; and suggesting, by the device, a subset of the set of models based on a mapping of the component relations.

Abstract: Embodiments are disclosed for a method. The method includes identifying a prefix updated by a searcher of a machine learning model. The machine learning model is configured to generate source code in a programming language. The method also includes determining whether the prefix violates a semantic correctness property of the programming language. Additionally, the method includes instructing the searcher, in response to the determination, to prune the prefix from a set of prefixes under consideration by the searcher.

Abstract: Disclosed herein is a method of training an artificial intelligence model with adjustable parameters that is trained to provide an analysis result in response to receiving an input data set comprising one or more chosen variables. The method comprises: receiving a training data set comprising multiple groups of training input data paired with a training analysis result, receiving a trial analysis result from the artificial intelligence model in response to inputting the multiple groups of training input data into the artificial intelligence model, calculating an accuracy metric descriptive of a comparison between the trial analysis result and the training analysis result, calculating a fairness score metric by comparing the one or more chosen variables to the trial analysis result, calculating a combined metric from the fairness score metric and the accuracy metric, and modifying the adjustable parameters using a training algorithm that receives at least the combined metric.

Abstract: Embodiments are disclosed for a method. The method includes identifying a prefix updated by a searcher of a machine learning model. The machine learning model is configured to generate source code in a programming language. The method also includes determining whether the prefix violates a semantic correctness property of the programming language. Additionally, the method includes instructing the searcher, in response to the determination, to prune the prefix from a set of prefixes under consideration by the searcher.

Abstract: Embodiments of the present invention provide a computer system a computer program product, and a method that comprises analyzing identified data for a determined conversion of the identified data, wherein the identified data is input data stored on an external database; automatically converting the analyzed data to a uniform domain by mapping a data route within the analyzed data, predicting a plurality of outcomes based on an application of a plurality of scenarios associated with the mapped data route, ranking the predicted outcomes based on a positive match percentage for the analyzed data, and converting the analyzed data associated with at least one ranked outcome using a relational algebra algorithm; and dynamically transmitting the converted, analyzed data into at least one section of a machine learning data pipeline.

Abstract: Embodiments are disclosed for a method. The method includes identifying one or more source code signals in a source code. The method also include generating an amplified code based on the identified signals and the source code. The amplified code is functionally equivalent to the source code. Further, the amplified code includes one or more amplified signals. The method additionally includes providing the amplified code for a machine learning model that is trained to perform a source code relevant task.

Abstract: A computer-implemented conversational agent engages in a natural language conversation with a user, interpreting the natural language conversation by parsing and tokenizing utterances in the natural language conversation. Based on interpreting, a set of utterances in the natural language conversation to be recorded as a macro is determined. The macro is stored in a database with an associated macro identifier. Replaying of the macro executes a function specified in the set of utterances.

Abstract: A machine-learning model task deduction method, system, and computer program product include extracting data schema of a machine-learning model and analyzing the data schema to determine an intended task of the machine-learning model.

Abstract: Methods and systems are provided for configurable and non-invasive protection of private information in a user input to a software application that handles real-time information. A method includes detecting, by a filter in real-time, private information in the user input. The method further includes forming, by the filter, a filtered user input from the user input, by maintaining non-private information from the user input in the filtered user input, extracting and encrypting the private information in the user input and attaching the encrypted private information to the filtered user input, and replacing the private information in the user input with unique identifiers in the filtered user input. The unique identifiers are configured to be exploitable by the software application to achieve an intended function of the software application for the user. The method also includes transmitting, by a communications redirector, the filtered user input over a communication channel.

Abstract: Systems and methods to generate a cognitive model are described. A particular example of a system includes a memory including program code having an application programming interface and a user interface, and a processor configured to access the memory and to execute the program code to generate a cognitive model, to run analysis on the cognitive model to determine a factor that is impacting a performance of the cognitive model, to determine an action based on the factor, to report at least one of the factor and the action to a user, and to use the action to generate a second cognitive model.

Abstract: A system, apparatus and a method for constructing pipelines, including enumerate a subspace of valid integrated pipelines, collecting a set of metrics for each of the plurality of pipelines, reducing the set of metrics to a single metric, selecting a final integrated pipeline from among the valid integrate pipelines based on reduced metric.

Abstract: Automatic generation of a chat bot from an API specification to carry out a dialogue with a user and invoke an API call described in the API specification. Based on input API specification, a conversational bot specification representing a dialog flow is constructed. A natural language expression is received and transformed into instructions based on the conversational bot specification. Based on the instructions, a natural language prompt to the user and executable computer code for invoking the API call may be generated.