William Rapaport | SUNY: University at Buffalo (original) (raw)

Papers by William Rapaport

This document is a draft of an article for the Encyclopedia of Language and Linguistics, 2nd Edit... more This document is a draft of an article for the Encyclopedia of Language and Linguistics, 2nd Edition (Elsevier, forthcoming). This article describes the Turing Test for determining whether a computer can think. It begins with a description of an "imitation game" for discriminating between a man and a woman, discusses variations of the Test, standards for passing the Test, and experiments with real Turing-like tests (including Eliza and the Loebner competition). It then considers what a 1 computer must be able to do in order to pass a Turing Test, including whether written linguistic behavior is a reasonable replacement for "cognition", what counts as understanding natural language, the role of world knowledge in understanding natural language, and the philosophical implications of passing a Turing Test, including whether passing is a sufficient demonstration of cognition, briefly discussing two counterexamples: a table-lookup program and the Chinese Room Argument.

Cognitive Science, Dec 1, 1986

Minds and Machines, May 25, 2018

Your article is protected by copyright and all rights are held exclusively by Springer Science+Bu... more Your article is protected by copyright and all rights are held exclusively by Springer Science+Business Media B.V., part of Springer Nature. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com".

Philosophical studies series, 2017

I survey a common theme that pervades the philosophy of computer science (and philosophy more gen... more 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 they interact with the real world, with a purpose that is expressible only in a language with an "external", "extrinsic", "global", "wide", "inherited" (more generally: "extra-" or "inter-"sytem) semantics? If you begin with Computer Science, you will end with Philosophy. 1 I was simultaneously surprised and deeply honored to receive the 2015 Covey Award from the International Association for Computing and Philosophy. 2 The honor is due in part to linking me to the illustrious predecessors who have received this award, but also to its having been named for Preston Covey, 3 whom I knew and who inspired me as I began my twin journeys in philosophy and computing. 1.1 From Philosophy to Computer Science, and Back Again Contrary to the motto above, I began with philosophy, found my way to computer science, and have returned to a mixture of the two. Inspired by Douglas Hofstadter's review [Hofstatder, 1980] of Aaron Sloman's The Computer Revolution in Philosophy [Sloman, 1978], which quoted Sloman to the effect that a philosopher of mind who knew no AI was like a philosopher of physics who knew no quantum mechanics, 4 my philosophical interests in philosophy of mind led me to study AI at SUNY Buffalo with Stuart C. Shapiro. 5 This eventually led to a faculty appointment in computer science at Buffalo. (Along the way, my philosophy colleagues and I at SUNY Fredonia published one of the first introductory logic textbooks to use a computational approach [Schagrin et al., 1985].) At Buffalo, I was amazed to discover that my relatively arcane philosophy dissertation on Alexius Meinong was directly relevant to Shapiro's work in AI, providing an intensional semantics for his SNePS semantic-network processing system (see, e.g., [Shapiro and Rapaport, 1987], [Shapiro and Rapaport, 1991]). 6 And then I realized that the discovery of quasi-indexicals ('he himself', 'she herself', etc.; [Castañeda, 1966]) by my dissertation advisor, Hector-Neri Castañeda 1 "Clicking on the first link in the main text of a Wikipedia article, and then repeating the process for subsequent articles, usually eventually gets you to the Philosophy article. As of May 26, 2011, 94.52% of all articles in Wikipedia lead eventually to the article Philosophy" (http://en.wikipedia.org/wiki/Wikipedia:Getting to Philosophy). If you begin with "Computer Science", you will end with "Philosophy" (in 12 links). 2 http://www.iacap.org/awards/ 3 http://en.wikipedia.org/wiki/Covey Award 4 "I am prepared to go so far as to say that within a few years, if there remain any philosophers who are not familiar with some of the main developments in artificial intelligence, it will be fair to accuse them of professional incompetence, and that to teach courses in philosophy of mind, epistemology, aesthetics, philosophy of science, philosophy of language, ethics, metaphysics, and other main areas of philosophy, without discussing the relevant aspects of artificial intelligence will be as irresponsible as giving a degree course in physics which includes no quantum theory" [Sloman, 1978, p. 5].

World Wide Web, 2011

School is a full-time job. And managing your time is important. If you have a "real" job after sc... more School is a full-time job. And managing your time is important. If you have a "real" job after school that you do just for fun (or for some extra spending money), or if you participate in extra-curricular activities (whether school-related or not), keep your priorities in mind: Your education should come first! If you must work (in order to make ends meet), you should realize the limitations that this imposes on your study time.

Minds and Machines, Dec 17, 2018

Your article is protected by copyright and all rights are held exclusively by Springer Nature B.V... more Your article is protected by copyright and all rights are held exclusively by Springer Nature B.V.. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com".

Page 1. (' \ LOGIC: A COMPUTER APPROACH Morton L. Schagrin State University of New York at F... more Page 1. (' \ LOGIC: A COMPUTER APPROACH Morton L. Schagrin State University of New York at Fredonia William J. Rapaport State University of New York at Buffalo Randall R. Dipert State University of New York at Fredonia cl ' ,,~ A1-.a~-Jt~ fK T~~ ~~ ' rJ-t: ~ ~ \J~ ~ ~ ...

Journal of Artificial Intelligence and Consciousness

If qualia are mental, and if the mental is functional, then so are qualia. But, arguably, qualia ... more If qualia are mental, and if the mental is functional, then so are qualia. But, arguably, qualia are not functional. A resolution of this is offered based on a formal similarity between qualia and numbers. Just as certain sets “play the role of” the number 3 in Peano’s axioms, so a certain physical implementation of a color plays the role of, say, red in a (computational) cognitive agent’s “cognitive economy”.

Advances in Multimedia and Interactive Technologies

Computationalism should not be the view that (human) cognition is computation; it should be the v... more Computationalism should not be the view that (human) cognition is computation; it should be the view that cognition (simpliciter) is computable. It follows that computationalism can be true even if (human) cognition is not the result of computations in the brain. If semiotic systems are systems that interpret signs, then both humans and computers are semiotic systems. Finally, minds can be considered as virtual machines implemented in certain semiotic systems, primarily the brain, but also AI computers.

Journal of Artificial General Intelligence, 2020

Pei Wang's paper titled "On Defining Artificial Intelligence" was published in a special issue of... more Pei Wang's paper titled "On Defining Artificial Intelligence" was published in a special issue of the Journal of Artificial General Intelligence (JAGI) in December of last year (Wang, 2019). Wang has been at the forefront of AGI research for over two decades. His non-axiomatic approach to reasoning has stood as a singular example of what may lie beyond narrow AI, garnering interest from NASA and Cisco, among others. We consider his article one of the strongest attempts, since the beginning of the field, to address the long-standing lack of consensus for how to define the field and topic of artificial intelligence (AI). In the recent AGISI survey on defining intelligence (Monett and Lewis, 2018), Pei Wang's definition, The essence of intelligence is the principle of adapting to the environment while working with insufficient knowledge and resources. Accordingly, an intelligent system should rely on finite processing capacity, work in real time, open to unexpected tasks, and learn from experience. This working definition interprets "intelligence" as a form of "relative rationality" (Wang, 2008), 1. Most striking in these numbers is the glaring absence of female authors. A common reason among female academics for rejecting our invitation to contribute was overcommitment. As a community, we may want to think of new, different ways of engaging the full spectrum of AI practitioners if we value inclusion as an essential constituent of a healthy scientific growth. Self determination and willingness to participate are also essential. This is an open access article licensed under the Creative Commons BY-NC-ND License.

Minds Mach., 1995

Galbraith, M., Rapaport, W.J. Preface. Mind Mach 5, 513–515 (1995).

Grazer Philosophische studien, 1981

Language tempts us to employ locutions which rouse the fighting spirit of those who care about wh... more Language tempts us to employ locutions which rouse the fighting spirit of those who care about what exists and what doesn't. (Meyer and Lambert 1968: 15.) [Linguistics] is entirely obligated to deal with objects (Gegenstände) in word-and sentence-meanings.

Grazer Philosophische Studien, 1982

Ford's "Helen Keller Was Never in a Chinese Room" claims that my argument in "How Helen Keller Us... more Ford's "Helen Keller Was Never in a Chinese Room" claims that my argument in "How Helen Keller Used Syntactic Semantics to Escape from a Chinese Room" fails because Searle and I use the terms 'syntax' and 'semantics' differently, hence are at cross purposes. Ford has misunderstood me; this reply clarifies my theory. Jason Michael Ford's "Helen Keller Was Never in a Chinese Room" (2010) claims that my argument in "How Helen Keller Used Syntactic Semantics to Escape from a Chinese Room" (Rapaport 2006) fails because Searle and I use the terms 'syntax' and 'semantics' differently, hence are at cross purposes. I think Ford has misunderstood me, so I am grateful for this opportunity to clarify my theory. The theory of syntactic semantics (Rapaport 1988) underlies computationalism: the claim that cognition is computable, i.e., that there is an algorithm (or a family of algorithms) that compute cognitive functions (Rapaport 1998). The theory has three parts: First, cognitive agents have direct access only to internal representatives of external objects. As Ray Jackendoff (2002, §10.4) says, a cognitive agent understands the world by "pushing the world into the mind". Therefore, both words and their meanings (including external objects serving as their referents) are represented internally in a single language of thought (LOT). For humans, this LOT is a biological neural network; for computers, it might be some kind of knowledge-representation and reasoning system (such as SNePS; see Shapiro & Rapaport 1987). 1 Second, it follows that words, their meanings, and semantic relations between them are all syntactic, where syntax is the study of relations among members of a single set (of signs, or marks, or neurons, etc.), and semantics is the study of relations between two sets (of signs, marks, neurons, etc., on the one hand, and their meanings, on the other) (cf. Morris 1938). "Pushing" meanings into the same set as symbols for them allows semantics to be done syntactically: It turns semantic relations between two sets (a set of internal marks and a set of (external) meanings) into syntactic relations among the marks of a single (internal) LOT. For example, truth tables and formal semantics are both syntactic enterprises, as are the relations between neuron firings representing signs and neuron firings representing external meanings. Consequently, symbol-manipulating computers can do semantics by doing syntax. Finally, understanding is recursive: We understand a syntactic domain (call it 'SYN 1 ') indirectly by interpreting it in terms of a semantic domain (call it 'SEM 1 '). But SEM 1 must be antecedently understood by considering it as a syntactic domain (rename it 'SYN 2 ') interpreted in terms of yet another semantic domain, which also must be antecedently understood. And so on. But, in order not to make it go on ad infinitum, there must be a base case: a domain that is understood directly, i.e., in terms of itself (i.e., not "antecedently"). Such direct understanding is syntactic understanding (Rapaport 1986b). (And perhaps it is holistic understanding; cf. Rapaport 2002.) Thus, the theory of syntactic semantics asserts that syntax suffices for semantic cognition, that cognition is therefore computable, and that computers are hence capable of thinking.

Studies in Cognitive Systems, 1988

Thought, Language, and Ontology, 1998

In 1982, I made the transition from being a professional philosopher to being a professional comp... more In 1982, I made the transition from being a professional philosopher to being a professional computer scientist and "intelligence artificer" (to use Daniel Dennett's happy term)-"professional" in the sense that that is now how I earn my living, though not in the sense that that is how I live my professional life-for my philosophical and artificial-intelligence (AI) research have dovetailed so well that I am hard pressed to say where one leaves off and the other begins. I remember Castañeda telling me at the time that he, too, felt that philosophy and AI were intimately related-that the importance of AI lay in the fact that it filled in-indeed, had to fill in-all the gaps left in abstract philosophical theories; it was in AI that all the 'i's were dotted and 't's crossed, since AI programs had to be executable and could not leave anything to be specified at a later time. Thus, for Castañeda, AI would keep philosophers honest, while philosophy could provide AI with ideas and theories to be implemented.

International Journal of Signs and Semiotic Systems, 2012

In this reply to James H. Fetzer’s “Minds and Machines: Limits to Simulations of Thought and Acti... more In this reply to James H. Fetzer’s “Minds and Machines: Limits to Simulations of Thought and Action”, the author argues that computationalism should not be the view that (human) cognition is computation, but that it should be the view that cognition (simpliciter) is computable. It follows that computationalism can be true even if (human) cognition is not the result of computations in the brain. The author also argues that, if semiotic systems are systems that interpret signs, then both humans and computers are semiotic systems. Finally, the author suggests that minds can be considered as virtual machines implemented in certain semiotic systems, primarily the brain, but also AI computers. In doing so, the author takes issue with Fetzer’s arguments to the contrary.