William J. Rapaport

For a formal language, one usually only considers semantic interpretations which are complete: for each singular referring expression in the language, there corresponds an element of the universe of discourse. However, natural languages only have a partial interpretation function when given such a set- or model-theoretic semantics whose universe of discourse (or "model") is taken to be the real, physical world. To put semantics on a par with syntax for parity of treatment of natural and formal languages, two alternatives suggest themselves. (1) The syntax of a natural language can be changed so that the interpretation function becomes total. What I urge, on the other hand, is (2) that we change the semantics of natural language, as outlined earlier, by enlarging the range of the interpretation function to make it total. I suggest making the world fit our language by adding Meinongian objects to it.

_A unique resource exploring the nature of computers and computing, and their relationships to the world._ _Philosophy of Computer Science_ is a university-level textbook designed to guide readers through an array of topics at the intersection of philosophy and computer science. Accessible to students from either discipline, or complete beginners to both, the text brings readers up to speed on a conversation about these issues, so that they can read the literature for themselves, form their own reasoned opinions, and become part of the conversation by contributing their own views. Written by a highly qualified author in the field, the book looks at some of the central questions in the philosophy of computer science, including: What is philosophy? (for readers who might be unfamiliar with it) What is computer science and its relationship to science and to engineering? What are computers, computing, algorithms, and programs?(Includes a line-by-line reading of portions of Turing’s classic 1936 paper that introduced Turing Machines, as well as discussion of the Church-Turing Computability Thesis and hypercomputation challenges to it) How do computers and computation relate to the physical world? What is artificial intelligence, and should we build AIs? Should we trust decisions made by computers? A companion website contains annotated suggestions for further reading and an instructor’s manual. _Philosophy of Computer Science_ is a must-have for philosophy students, computer scientists, and general readers who want to think philosophically about computer science.

Deliberate contextual vocabulary acquisition (CVA) is a reader’s ability to figure out a meaning for an unknown word from its “context.” without external sources of help. The appropriate context for such CVA is the “belief-revised integration” of the reader’s prior knowledge with the reader’s “internalization” of the text. We present and defend a computational theory of CVA that we have adapted to a new classroom curriculum designed to help students use CVA to improve their reading comprehension.

I advocate a theory of “syntactic semantics” as a way of understanding how computers can think (and how the Chinese-Room-Argument objection to the Turing Test can be overcome): (1) Semantics, considered as the study of relations between symbols and meanings, can be turned into syntax – a study of relations among symbols (including meanings) – and hence syntax (i.e., symbol manipulation) can suffice for the semantical enterprise (contra Searle). (2) Semantics, considered as the process of understanding one domain (by modeling it) in terms of another, can be viewed recursively: The base case of semantic understanding –understanding a domain in terms of itself – is “syntactic understanding.” (3) An internal (or “narrow”), first-person point of view makes an external (or “wide”), third-person point of view otiose for purposes of understanding cognition.

I survey a common theme that pervades the philosophy of computer science (and philosophy more generally): the relation of computing to the world. Are algorithms merely certain procedures entirely characterizable in an “indigenous,” “internal,’ “intrinsic,” “local,” “narrow,” “syntactic” (more generally: “intra-system”), purely-Turing-machine language? Or must algorithms interact with the real world, having a purpose that is expressible only in a language with an “external,” “extrinsic,” “global,” “wide,” “inherited” (more generally: “extra” or “inter-“system) semantics?

In their Why Machines Will Never Rule the World, Landgrebe and Smith (2023) argue that it is impossible for artificial general intelligence (AGI) to succeed, on the grounds that it is impossible to perfectly model or emulate the “complex” “human neurocognitive system”. However, they do not show that it is logically impossible; they only show that it is practically impossible using current mathematical techniques. Nor do they prove that there could not be any other kinds of theories than those in current use. Even if perfect theories were impossible or unlikely, perfection may not be needed and may even be unhelpful.

I advocate a theory of syntactic semantics as a way of understanding how computers can think (and how the Chinese-Room-Argument objection to the Turing Test can be overcome): (1) Semantics, considered as the study of relations between symbols and meanings, can be turned into syntax – a study of relations among symbols (including meanings) – and hence syntax (i.e., symbol manipulation) can suffice for the semantical enterprise (contra Searle). (2) Semantics, considered as the process of understanding one domain (by modeling it) in terms of another, can be viewed recursively: The base case of semantic understanding –understanding a domain in terms of itself – is syntactic understanding. (3) An internal (or narrow ), first-person point of view makes an external (or wide ), third-person point of view otiose for purposes of understanding cognition

“Contextual” vocabulary acquisition is the active, deliberate acquisition of a meaning for a word in a text by reasoning from textual clues and prior knowledge, including language knowledge and hypotheses developed from prior encounters with the word, but without external sources of help such as dictionaries or people. But what is “context”? Is it just the surrounding text? Does it include the reader’s background knowledge? I argue that the appropriate context for contextual vocabulary acquisition is the reader’s “internalization” of the text “integrated” into the reader’s “prior” knowledge via belief revision.

Terence Parsons's informal theory of intentional objects, their properties, and modes of predication does not adequately reflect ordinary ways of speaking and thinking. Meinongian theories recognizing two modes of predication are defended against Parsons's theory of two kinds of properties. Against Parsons's theory of fictional objects, I argue that no existing entities appear in works of fiction. A formal version of Parsons's theory is presented, and a curious consequence about modes of predication is indicated.