Method and system for cognitive information processing using representation learning and decision learning on data

Abstract

Self-supervised machine learning is performed based on metadata that is acquired in real time and/or offline, via a single data source or multiple data sources. A cognitive analytics system (CAS) performs learning, based on metadata associated with structured and/or un-structured data, to generate data representations for use in decision learning. A cognitive engine compares the data representations to learned patterns stored in memory, for example as weights. Data can be transformed into representations, and condition(s) may be generated based on new data received from a behavioral network. Codelets matching the condition(s) can then be executed, as part of cognitive analytics, to perform pattern association with stored weights and/or inferences.