Brian Cantwell Smith, Reid Hoffman Professor of Artificial Intelligence and the Human, University of Toronto Classical models of inference, such as those based on logic, take inference to be conceptual – i.e., to involve representations formed of terms, predicates, relation symbols, and the like. Conceptual representation of this sort is assumed to reflect the structure of the world: objects of various types, exemplifying properties, standing in relations, grouped together in sets, etc. These paired roughly algebraic assumptions (one epistemic, the other ontological) form the basis of classical logic and traditional AI (GOFAI). In this talk, Professor Smith will argue that the world itself is not conceptual, in the sense of not consisting (at least au fond) of objects, properties, relations, etc. That is, he will argue against the ontological assumption. Rather, he believes that taking the world to consist of the familiar ontological furniture of objects, properties, etc. results from
