“John McCarthy, one of the founders of (and the one who supposedly coined the term) artificial intelligence (AI), stated on several occasions that if we insist on building AI systems based on empirical methods (e.g., neural networks or evolutionary models), we might be successful in building “some kind of an AI,” but even the designers of such systems will not understand how such systems work (see, for an example, [1]). In hindsight, this was an amazing prediction, since the deep neural networks (DNNs) that currently dominate AI are utterly unexplainable, and their unexplainability is paradigmatic: there are no concepts and human-understandable features in distributed connectionist architectures but microfeatures that are conceptually and cognitively hollow, not to mention that these microfeatures are semantically meaningless.”