Abstract

Are there aspects of human intelligence that artificial intelligence cannot emulate? Harry Collins uses a distinction between tacit aspects of knowing, which cannot be digitized, and explicit aspects, which can be, to formulate an answer to this question. He postulates three purported areas of the tacit and argues that only “collective tacit knowing” cannot be adequately digitized. I argue, first, that Collins’s approach rests upon problematic Cartesian assumptions—particularly his claim that animal knowing is strictly deterministic and, thus, radically different from human knowing. I offer evidence that human linguistic intelligence depends upon embodied forms of animal intelligence. Second, I suggest the development of deep-learning algorithms means that Collins’s mimesis assumption is no longer appropriate; equivalent accomplishment of goals is what counts. However, persons must realize and integrate many goals and also deal with failures; a general-purpose AI capable of integrating all the needs and goals of human existence resists development. Third, I explain how Michael Polanyi’s understanding of tacit knowing, quite different than Collins’s concept of the tacit, exemplifies features that are missing in contemporary AI. I make use of evolutionary theory, studies of animal intelligence, biological insights, individual construction of meaning, and notions of human responsibility to argue for the existence of three categories that distinguish human from artificial intelligence.