International Business Machines Corporation

Massachusetts Institute of Technology

Abel Nicolas Valente

Dakuo Wang

Chuang Gan

Daniel Karl I Weidele

Arunima Chaudhary

Bei Chen

Elizabeth Daly

Adi Ionel Botea

Ji Hui Yang

Oznur Alkan

Sijia Liu

Mo Yu

Pablo Pedemonte

Qian Pan

Jing James Xu

Jun Song Wang

Si Er Han

Lisa Amini

Horst Cornelius Samulowitz

Long H Vu

Dhavalkumar C Patel

Syed Yousaf Shah

Massimiliano Mattetti

Parikshit Ram

Udayan Khurana

Inge Lise Vejsbjerg

Voranouth Supadulya

John Dillon Eversman

Carolina Maria Spina

Dongyu Liu

Yi Sun

A dataset including features and values associated with the features can be received. Each of the features in the dataset can be mapped to a corresponding node in a knowledge graph based on the concept represented by the corresponding node. The knowledge graph can be traversed to find a candidate node connected to at least one mapped node, the candidate node not being mapped to a feature in the dataset. A concept associated with the candidate node can be identified as a new feature. A machine learning model pipeline can use the features in the dataset and the new feature to select a subset of features for training a machine learning model.

Interactive feature engineering in automatic machine learning with domain knowledge

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.

Interpretable visualization system for graph neural network

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.

Automatic formulation of data science problem statements

Systems, computer-implemented methods, and computer program products to facilitate visualization of a model selection process are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interaction backend handler component that obtains one or more assessment metrics of a model pipeline candidate. The computer executable components can further comprise a visualization render component that renders a progress visualization of the model pipeline candidate based on the one or more assessment metrics.

Visualization of a model selection process in an automated model selection system

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.

Automated data table discovery for automated machine learning

Systems, computer-implemented methods, and computer program products to facilitate conditional parallel coordinates in automated artificial intelligence with constraints are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a visualization component that renders a pipeline constraint as a constraint axis having constraint scores of machine learning pipelines in a conditional parallel coordinates visualization. The computer executable components can further comprise a model generation component that generates a machine learning model based on the constraint scores of the machine learning pipelines.

Conditional parallel coordinates in automated artificial intelligence with constraints

Embodiments for intelligent career planning assessment in a computing environment by a processor. A career planning pathway of a career planning model for a user may be monitored for achieving a career goal. One or more deviations from the career planning pathway may be identified according a user profile, one or more behaviors of the user, one or more environmental factors, or a combination thereof.

Intelligent career monitoring and correction in a computing environment

Various embodiments are provided for providing intelligent dialog re-elicitation in a dialog system in a computing environment by a processor. Information, provided during a dialog using the dialog system, may be detected that has been subsequently revised. One or more variables impacted by the revised information provided during the dialog may be dynamically re-elicited.

Intelligent dialog re-elicitation of information

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Capital Federal, Argentina

Abel Nicolas Valente