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APA Newsletter on Philosophy and Computers, Vol. 15, No. 1 (Fall 2015)

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APA Newsletter on Philosophy and Computers, Vol. 15, No. 1 (Fall 2015) NEWSLETTER | The American Philosophical Association Philosophy and Computers FALL 2015 VOLUME 15 | NUMBER 1 Ricardo R. Gudwin FROM THE EDITOR Computational Semiotics: The Peter Boltuc Background Infrastructure to New Kinds of Intelligent Systems FROM THE CHAIR Thomas M. Powers USING THE TECHNOLOGY FOR PHILOSOPHY CALL FOR PAPERS Shai Ophir FEATURED ARTICLE Trend Analysis of Philosophy Revolutions Troy D. Kelley and Vladislav D. Veksler Using Google Books Archive Sleep, Boredom, and Distraction—What Are the Computational Benefits for Christopher Menzel Cognition? The Logic Daemon: Colin Allen’s Computer-Based Contributions to Logic PAPERS ON SEARLE, SYNTAX, Pedagogy AND SEMANTICS BOOK HEADS UP Selmer Bringsjord A Refutation of Searle on Bostrom (re: Robert Arp, Barry Smith, and Andrew Malicious Machines) and Floridi (re: Spear Information) Book Heads Up: Building Ontologies with Basic Formal Ontology Marcin J. Schroeder Towards Autonomous Computation: LAST-MINUTE NEWS Geometric Methods of Computing The 2015 Barwise Prize Winner Is William Rapaport VOLUME 15 | NUMBER 1 FALL 2015 © 2015 BY THE AMERICAN PHILOSOPHICAL ASSOCIATION ISSN 2155-9708 APA NEWSLETTER ON Philosophy and Computers PETER BOLTUC, EDITOR VOLUME 15 | NUMBER 1 | FALL 2015 questions Searle’s objection, raised against Floridi, that FROM THE EDITOR information is necessarily observer relative. Bringsjord points out that the main problem visible in Searle’s paper Peter Boltuc is his “failure to understand how logic and mathematics, UNIVERSITY OF ILLINOIS, SPRINGFIELD as distinguished from informal analytic philosophy, work.” While Bingsjord accepts Searle’s well-known point that Human cognitive architecture used to be viewed as inferior computers and robots function just at the semantic level, to artificial intelligence (AI). Some authors thought it could Marcin Schroeder argues that this point is contingent to easily be reprogrammed using standard AI so as to be Turing’s architecture. He points out that “in the description more efficient; as Aaron Sloman once put it, our brains of Turing machines there is nothing that could serve are a strange mixture of amphibian and early mammalian as interpreter of the global configuration” so that “this remnants. In this issue, we feature an article that seems interpretation is always made by a human mind.” Yet, to show otherwise.1 Research by Troy Kelley and Vlad Schroeder argues, “we can consider a machine built based Veksler demonstrates that “many of the seemingly on the design of the Turing machine, but with an additional suboptimal aspects of human cognitive processes are component, which assumes the role currently given to a actually beneficial and finely tuned to both the regularities human agency.” Schroeder’s paper is an attempt to sketch and uncertainties of the physical world” and even to the out the conditions of such a semantic machine. most optimal information processing. Sleep, distraction, even boredom turn out to be optimal cognitive solutions; Schroeder argues that “integration of information is . in earlier work, Kelley and Veksler showed how learning the most fundamental characteristic of consciousness.” details in early childhood and then much more cursory According to the author, in order to lay the “foundations acquaintance with new situations and objects is also an not only for the syntactic of information, i.e., its structural optimal learning strategy.2 For instance, sleep allows for characteristics, but also for its semantics (. .) we can employ “offline memory processing,” which produces an order of the mathematical theory of functions preserving information magnitude performance advantage over other competing structures—homomorphisms of closure spaces.” This is an storage/retrieval strategies.” Boredom is also “an essential attempt to “cross the border between two very different part of a self-sustaining cognitive system.” This is because realms, that of language, i.e., symbols, and that of entities in “our higher level novelty/boredom algorithm and the the physical world.” Historically, “since symbols seemed to lower level habituation algorithm” turn out “to be a useful require an involvement of the conscious subject associating and constructive response to a variety of situations.” each symbol with its denotation, the border was identified In particular, “boredom/novelty algorithm can be used with the one between mind and body.” This is the classical for . landmark identification in navigation,” while “the Brentano’s approach developed by Searle in his early work habituation algorithm” allows for much needed shifts of in semantics. “Intention of a symbol (. .) directs the mind to attention. Even distraction is beneficial since: “An inability the denotation.” In response to this conception, Schreader to get distracted by external cues can be disastrous for an argues that “in reality when we associate a symbol with its agent residing in an unpredictable environment, and an denotation, we do not make an association with the physical inability to get distracted by tangential thoughts would object itself, but with the information integrated into what limit one’s potential for new and creative solutions.” is considered to be an object.” Hence, “the association Hence, Kelley and Veksler show how sleep, boredom, and between a symbol and its denotation is a relationship distraction are important components of a robot’s behavior. between two informational entities consisting of integrated information.” However, it is integrated in two different John Searle’s old argument that computers are syntactic information systems. The author argues that “the mental engines unable to do semantics is the background theme aspect of symbolic representation is not in its intention, or in of the following three papers. We begin with Selmer the act of directing towards denotation, but in the integration Bringsjord’s discussion piece. First, Bringsjord reacts to of information into objects.” Symbolic information is, in fact, Searle’s critique of N. Bostrom’s argument about potentially intentional, it is “about,” but this aboutness takes place malicious robots. According to Searle, “computing through correspondence between information systems. machines merely manipulate symbols” and so cannot Such “aboutness” does not require any correspondence be conscious, and to be malicious one would have to be between entities of different ontological status, which was conscious. Bringsjord questions Searle’s assumption that necessary in all approaches to intentionality from Scholastics maliciousness presumes consciousness and gives what to Franz Brentano and beyond.” Later in the article, Schroeder seems like a good case. In the second part, the author discusses mechanical manifestations of information, so as APA NEWSLETTER | PHILOSOPHY AND COMPUTERS to focus on a relatively formal presentation of what he calls the spring issue in mid-December. To give our potential geometric methods of computing. This is important in the authors a heads up, we give special attention to the controversy with Searle since “geometric computation of winners of the Barwise Prize. For the upcoming issue, we higher level can serve as a process of meaning generation are particularly interested in papers related to the work of for the lower level.” Helen Nissenbaum, the 2014 Barwise Prize winner. I hope to receive many more submissions, and I want to invite the Ricardo Gudwin also focuses on the problem highlighted by readers to contribute. Searle: “How to attribute meaning to symbols?”—the issue lies at the intersection of computer science, philosophy, and Last-minute news! William Rapaport is the laureate of the semiotics. Gudwin presents what he calls “computational 2015 Barwise Prize. See the note at the end of this issue. semiotics” viewed as an attempt to find an “alternative approach for addressing the problem of synthesizing NOTES artificial minds.” He argues that Peirce’s theory provides a 1. For instance, at the AI and Consciousness: Theoretical better model for computer engineering than main-stream Foundations and Current Approaches conference organized by semantic theories. In the process, Gudwin provides a helpful A. Chella and R. Manzotti (2007). history of intelligent systems (largely following Franklin’s 2. T. D. Kelley, “Robotic Dreams: A Computational Justification for classical account). He focuses on resolving the problem of the Post-Hoc Processing of Episodic Memories,” International Journal of Machine Consciousness 6, no. 2 (2014): 109–23. whether knowledge representation by a computer program is “symbolic” or “numerical.” He builds on Barsalou’s theory of perceptual symbols. Gudwin argues that Peirce’s semiotics is compatible with Barsalou’s proposal for a FROM THE CHAIR grounded cognition and, in fact, provides the best account of meaning very much applicable in artificial intelligence. Thomas M. Powers UNIVERSITY OF DELAWARE In the final part of the newsletter, we have three contributions: Shai Ophir uses big data analysis to show how As the summer conference season winds down, I thought concepts central to some of the most famous philosophers it would be a good time to reflect upon the organizational of the past were gaining popularity for over a generation structures for the scholarly field of philosophy and before those philosophers were even born. This is one more computing. These structures are not to be taken for granted; argument in favor of the thesis that philosophical thinking much intellectual inspiration and professional
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