CONTRARY TO GOODMAN’S VIEW, A CLEAR DISTINCTION CAN BE MADE BETWEEN QUALITATIVE AND POSITIONAL PREDICATES. HENCE WE CAN EXPLAIN THAT WE OUGHT TO PROJECT ’GREEN’ RATHER THAN ’GRUE’ BECAUSE THE LATTER IS A POSITIONAL PREDICATE, RATHER THAN BECAUSE THE LATTER IS LESS WELL ENTRENCHED. A PREDICATE IS POSITIONAL IF, TO FIND OUT AS CERTAINLY AS WE CAN WHETHER IT APPLIES TO AN OBJECT, WE HAVE TO FIND OUT THE LATTER’S SPATIO-TEMPORAL LOCATION.

In his new foreword to this edition, Hilary Putnam forcefully rejects these nativist claims.

In this book Saul Kripke brings his powerful philosophical intelligence to bear on Wittgenstein's analysis of the notion of following a rule.

Meaning, Understanding, and Practice is a selection of the most notable essays of leading contemporary philosopher Barry Stroud on a set of topics central to analytic philosophy. In this collection, Stroud offers penetrating studies of meaning, understanding, necessity, and the intentionality of thought. Throughout he asks how much can be expected from a philosophical account of one's understanding of the meaning of something, and questions whether such an account can succeed without implying that the person understands many other things as (...) well. Most of the essays work with ideas derived from Wittgenstein, and five of the essays focus specifically on Wittgenstein's philosophy. Stroud's helpful introduction draws out the recurring themes he pursues and explains how his ideas and aims have developed over the years. (shrink)

A computational theory of induction must be able to identify the projectible predicates, that is to distinguish between which predicates can be used in inductive inferences and which cannot. The problems of projectibility are introduced by reviewing some of the stumbling blocks for the theory of induction that was developed by the logical empiricists. My diagnosis of these problems is that the traditional theory of induction, which started from a given (observational) language in relation to which all inductive rules are (...) formulated, does not go deep enough in representing the kind of information used in inductive inferences. As an interlude, I argue that the problem of induction, like so many other problems within AI, is a problem of knowledge representation. To the extent that AI-systems are based on linguistic representations of knowledge, these systems will face basically the same problems as did the logical empiricists over induction. In a more constructive mode, I then outline a non-linguistic knowledge representation based on conceptual spaces. The fundamental units of these spaces are "quality dimensions". In relation to such a representation it is possible to define "natural" properties which can be used for inductive projections. I argue that this approach evades most of the traditional problems. (shrink)