This is the introductory essay to a collection commemorating the 100th anniversary of the publication in Mind of Bertrand Russell’s paper ‘On Denoting’.

Despite the renown of ‘On Denoting’, much criticism has ignored or misconstrued Russell's treatment of scope, particularly in intensional, but also in extensional contexts. This has been rectified by more recent commentators, yet it remains largely unnoticed that the examples Russell gives of scope distinctions are questionable or inconsistent with his own philosophy. Nevertheless, Russell is right: scope does matter in intensional contexts. In Principia Mathematica, Russell proves a metatheorem to the effect that the scope of a single occurrence of (...) a description in an extensional context does not matter, provided existence and uniqueness conditions are satisfied. But attempts to eliminate descriptions in more complicated cases may produce an analysis with more occurrences of descriptions than featured in the analysand. Taking alternation and negation to be primitive (as in the first edition of Principia), this can be resolved, although the proof is non-trivial. Taking the Sheffer stroke to be primitive (as proposed by Russell in the second edition), with bad choices of scope the analysis fails to terminate. (shrink)

Ernie Lepore: Quine, Analyticity, and Transcendence: In “Two Dogmas of Empiricism” Quine characterizes and rejects three approaches to making sense of analyticity. One approach attempts to reduce putative analytic statements to logical truths by synonym substitution. A second approach is to identify analytic statements with “semantic rules,” or “meaning postulates.” A third approach relies on the verificationist theory of meaning. According to that theory, “every meaningful statement is held to be translatable into a statement (true or false) about immediate experience, (...) or, less radically, “each statement, taken in isolation from its fellows, can admit of confirmation or information.” Analytic statements are, then, those that are confirmed come what experiences may. If either version of the verificationist approach were correct, then there would be objective facts about the extensions of terms from intuitive semantics, for example, “‘…’ is synonymous with ‘___’” and “‘…’ is analytic,” across all languages. In this essay, I focus my discussion primarily on the third, verificationist, approach – though what I shall have to say will bear on the second approach as well. In particular, I explain the models of language contained in verificationism and the weaker confirmationism, and why most positivists abandoned the former for the latter. I explain the connection between confirmationism and intuitive semantics and why considerations of holism alone are incapable of severing that connection. (shrink)

Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...) solution for the so-called NP-complete problems will also be a solution for any other such problems. Its artificial-intelligence analogue is the class of AI-complete problems, for which a complete mathematical formalization still does not exist. In this chapter we will focus on analysing computational classes to better understand possible formalizations of AI-complete problems, and to see whether a universal algorithm, such as a Turing test, could exist for all AI-complete problems. In order to better observe how modern computer science tries to deal with computational complexity issues, we present several different deep-learning strategies involving optimization methods to see that the inability to exactly solve a problem from a higher order computational class does not mean there is not a satisfactory solution using state-of-the-art machine-learning techniques. Such methods are compared to philosophical issues and psychological research regarding human abilities of solving analogous NP-complete problems, to fortify the claim that we do not need to have an exact and correct way of solving AI-complete problems to nevertheless possibly achieve the notion of strong AI. (shrink)

This paper presents an anti‐realist account of fictional objects. Arguing for the involvement of non‐veridical prepositional attitude ascriptions in the understanding of fiction, I maintain that there is no need to invoke Meinongian objects, possibilia or abstract objects for this purpose. In addition I argue against object dependent views . I make a case for empty names playing a more significant role than that accorded on direct reference accounts of names. I close by noting points of similarity and of difference (...) with Amie Thomasson and with Kendall Walton. (shrink)