Why Heideggerian AI Failed and How Fixing it Would Require Making it More Heideggerian

When I was teaching at MIT in the 1960s, students from the Artificial Intelligence Laboratory would come to my Heidegger course and say in effect: ‘‘You philosophers have been reflecting in your armchairs for over 2000 years and you still don’t understand intelligence. We in the AI Lab have taken over and are succeeding where you philosophers have failed.’’ But in 1963, when I was invited to evaluate the work of Alan Newell and Herbert Simon on physical symbol systems, I found to my surprise that, far from replacing philosophy, these pioneering researchers had learned a lot, directly and indirectly, from us philosophers: e.g., Hobbes’ claim that reasoning was calculating, Descartes’ mental representations, Leibniz’s idea of a ‘universal characteristic’ (a set of primitives in which all knowledge could be expressed), Kant’s claim that concepts were rules, Frege’s formalization of such rules, and Wittgenstein’s postulation of logical atoms in his Tractatus. In short, without realizing it, AI researchers were hard at work turning rationalist philosophy into a research program. But I began to suspect that the insights formulated in existentialist armchairs, especially Heidegger’s and Merleau-Ponty’s, were bad news for those working in AI laboratories—that, by combining representationalism, conceptualism, formalism,