International Business Machines Corporation

Massachusetts Institute of Technology

Prabhat Maddikunta Reddy

Vatche Isahagian

Vinod Muthusamy

Siyu Huo

Liana Liyow Fong

Seetharami R Seelam

Yara Rizk

Sebastian Carbajales

Subhro Das

Kushal Mukherjee

Allen Vi Cuong Chan

Prerna Agarwal

Punleuk Oum

Debashish Saha

Dario Andres Silva Moran

Ganesh Venkataraman

Archit Verma

Wyatt Clarke

Sebastian Steffen

Erik Brynjolfsson

Martin Fleming

Obtain, as input, in electronic form, structured information including tasks for a plurality of occupations in a plurality of industries over a length of time; compute, from the structured information, a time series of normalized occupation task shares over the length of time; train a computerized machine learning model, on the time series, to predict future task shares for the plurality of occupations in the plurality of industries; and, with the trained computerized machine learning model, predict the future task shares.

Skills and tasks demand forecasting

A method, computer system, and a computer program product for generating a conversational bot for an application programming interface (API) is provided. The present invention may include parsing an API schema. The present invention may include generating sentences for the conversational bot from the parsed API schema. The present invention may include constructing the conversational bot by training a deep learning model. The present invention may include receiving a natural language expression from a user. The present invention may include determining whether the natural language expression is enough to activate the bot.

Generating and updating conversational artifacts from APIS

A method, computer system, and a computer program product for anomaly detection is provided. The present invention may include converting business process logs into a graphical data structure. The present invention may include generating an optimized graph encoding for anomaly detection using an unsupervised machine learning model. The present invention may include computing an anomaly score for each activity of the business process log using a process aware metric based on feature representation. The present invention may include labeling each of the one or more data points with a high anomaly score.

Graph encoders for business process anomaly detection

Embodiments relate to a system, program product, and method for supporting elastic execution of a machine learning (ML) workload using application based profiling. A joint profile comprised of both ML application execution and resource usage data is generated. One or more feature(s) and signature(s) from the joint profile are identified, and a ML execution model for ML application execution and resource usage is built. The ML execution model leverages the feature(s) and signature(s) and is applied to provide one or more directives to subsequent application execution. The application of the ML execution model supports and enables the ML execution to elastically allocate and request one or more resources from a resource management component, with the elastic allocation supporting application execution.

Elastic execution of machine learning workloads using application based profiling

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Danbury, CT, United States of America

Prabhat Maddikunta Reddy