International Business Machines Corporation

Arunima Chaudhary

Dakuo Wang

Chuang Gan

Abel Nicolas Valente

Daniel Karl I Weidele

Qian Pan

Ji Hui Yang

Mo Yu

Jing James Xu

James Johnson

Justin David Weisz

Bei Chen

Maryam Ashoori

Jun Song Wang

Si Er Han

Ming Tan

Sijia Liu

Peter Daniel Kirchner

Horst Cornelius Samulowitz

Long H Vu

Dhavalkumar C Patel

Syed Yousaf Shah

Gregory Bramble

Yara Rizk

Parikshit Ram

Tathagata Chakraborti

Lin Ju

John Dillon Eversman

Voranouth Supadulya

Abhishek Bhandwaldar

Burak Aksar

Michelle Brachman

Benjamin Hoover

Gregory Filla

A method, computer program, and computer system are provided for explaining generation of a flow from natural language utterances. Data corresponding to a natural language utterance is received. One or more constraints corresponding to a flow to be generated are determined based on the received natural language utterance. A flow is constructed based on the determined constraints. An explanation associated with the constructed flow is provided, and the explanation identifies parameters corresponding to constructing the flow.

Explaining natural-language-to-flow pipelines

An embodiment of the invention may include a method, computer program product, and system for creating a data analysis tool. The method may include a computing device that generates an AI pipeline based on an input dataset, wherein the AI pipeline is generated using an Automated Machine Learning program. The method may include converting the AI pipeline to a non-native format of the Automated Machine Learning program. This may enable the AI pipeline to be used outside of the Automated Machine Learning program, thereby increasing the usefulness of the created program by not tying it to the Automated Machine Learning program. Additionally, this may increase the efficiency of running the AI pipeline by eliminating unnecessary computations performed by the Automated Machine Learning program.

Code generation for Auto-AI

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

An implicit bias monitoring system is provided. The system receives sensor data including video and audio that are captured during an interaction between a first individual and a second individual. The system determines a set of attributes associated with the interaction based on the received sensor data, the set of attributes comprising at least one of race and gender of the second individual. The system detects an implicit bias based on a combination of the determined set of attributes and a bias profile. The system generates an alert based on the detected implicit bias.

Detecting and notifying for potential biases in artificial intelligence applications

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

In accordance with an embodiment of the invention, a method is provided for personalizing machine learning models for users of an automated machine learning system, the machine learning models being generated by an automated machine learning system. The method includes obtaining a first set of datasets for training first, second, and third neural networks, inputting the training datasets to the neural networks, tuning hyperparameters for the first, second, and third neural networks for testing and training the neural networks, inputting a second set of datasets to the trained neural networks and the third neural network generating a third output data including a relevance score for each of the users for each of the machine learning models, and displaying a list of machine learning models associated with each of the users, with each of the machine learning models showing the relevance score.

Personalized automated machine learning

Growing community of inventors

Dehradun, India

Arunima Chaudhary