Conceptualization as a Basis for Cognition

What do we mean by 'understanding'?  Can we check that off if it means we can perform some specified tasks?

Conceptualization as a Basis for Cognition — Human and Machine

A missing link to Machine understanding and Cognitive AI

By Gadi Singer  in TowardsDatascience

 While most contemporary discussions and classifications of AI capabilities center around what a system can do, I believe the path to higher intelligence and machine cognition relies on what a system can know and understand. Using rich AI knowledge representation frameworks and comprehensive models of the world can increase an AI system’s ability to transform information into deep knowledge, understanding, and functionality. To pursue this path to better AI, it is essential to understand what “understanding” really means for the human brain. Doing so allows for implementing frameworks that enable machine learning to parallel human understanding by integrating modeling and conceptualization with data and task generalization.

Conceptualization: The Basis for Human Thought

“Concepts” are the most basic building block in human thinking. Concepts serve as ontological roots for objects that we think about. Concepts represent a persistent set of essential attributes of an object class, which can change and expand with experience. Existing concepts can be abstracted or linked through analogy to additional domains and object classes. Examples of concepts include |dog|, |democracy|, |white|, and |uncle|. Physical or mental objects can be stored as a concept and accrue more data and attributes over time (e.g., |my dog Lucky| and |snow white| versus |off-white|). Even if the referent is invisible or abstract, like |love|, it can still be stored as a concept. Our understanding of the world relies on concepts, attributes of concepts, and relationships between concepts. We use concepts and facts composed of concepts and the relations between them to construct our world model. ... '