Abstract

Understanding is the foundational component of intelligence that makes adaptation to novel states possible. As artificial intelligence models advance towards artificial general and super intelligence, an objective, in contrast to human-centered, theory of understanding is required. One merit of an objective conceptual analysis of understanding is that it would unify human and artificial understanding. This study defines understanding as well-integrated use of reasoning across a network of conceptual, logical, and causal relationships, which are situated on an efficient conceptual web. The evolutionary function of understanding is the capacity to generalize knowledge into novel situations. This renders machine learning concept “generalization” a measure of understanding. The paper discusses two primary methods for assessing artificial understanding: behavioral methods measuring generalizability and mechanistic analyses revealing causal mechanisms of the neural network. We discuss the limitations of these methods. The most important problem, as this paper defends, is the difficulty of measuring objective understanding in contrast to human-understanding.