Methods, mediums, and systems for generating causal inference structure between concepts having predictive capabilities

Abstract

A machine learning (ML) system is provided for integrating common sense into the ML system. In contrast to existing machine learning algorithms that search for statistical correlations between concepts, the ML system is configured to learn the semantic relationships or causality between the concepts. This may be accomplished by training an algorithm or data structure to learn similar vector representations of words present in the same context (e.g., that are present together in the same sentence). The resulting AI/ML structure may be used to guide the generation of a causal graph having predictive capabilities. This causal graph may represent semantic relationships and/or causation between concepts, and hence may be employed to introduce a degree of common sense in the machine learning system.