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Minds and Machines

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25.05.2018

What is a Computer? A Survey

verfasst von: William J. Rapaport

Erschienen in: Minds and Machines | Ausgabe 3/2018

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Abstract

A critical survey of some attempts to define ‘computer’, beginning with some informal ones (from reference books, and definitions due to H. Simon, A.L. Samuel, and M. Davis), then critically evaluating those of three philosophers (J.R. Searle, P.J. Hayes, and G. Piccinini), and concluding with an examination of whether the brain and the universe are computers.

Vorheriger Artikel A Computational Conundrum: “What is a Computer?” A Historical Overview

Nächster Artikel The Role of Observers in Computations

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http://familycircus.com/comics/march-6-2012/.

http://www.gocomics.com/agnes/2013/3/7.

See brief overviews of both in Rapaport (2017a, §1.2), and Rapaport (2017c, §1).

I discuss it further in Rapaport (2018a, Chs. 7, 10, and 11), which examine the nature of algorithms and the Church–Turing Computability Thesis.

At least, not engineered by humans. Dennett (2017) would say that they were engineered—by Mother Nature, via the natural-selection algorithm.

Thanks to my colleague Stuart C. Shapiro for many of these points.

Unless they don’t realize that a laptop or a tablet is a computer! See the iPad advertisement at https://www.youtube.com/watch?v=sQB2NjhJHvY.

http://www.oed.com/view/Entry/37975 (my bracketed interpolations).

The high-school characterization of a mathematical function as a “function machine” with a crank can be traced to Gödel (see Rapaport 2018a, §7.3.1.3 for discussion):

[Turing] has shown that the computable functions defined in this way [i.e., in terms of TMs] are exactly those for which you can construct a machine with a finite number of parts which will do the following thing. If you write down any number n\(_1\), …, n\(_r\) on a slip of paper and put the slip into the machine and turn the crank, then after a finite number of turns the machine will stop and the value of the function for the argument n\(_1\), …, n\(_r\) will be printed on the paper. (Gödel 1938, p. 168; my bracketed interpolation)

The use of the male gender here is balanced by Samuel’s earlier statement that computers have “advantages in terms of the reductions in clerical manpower and woman power” (Samuel 1953, p. 1223; my italics).

Cf. Chalmers (2011), quoted at the end of Sect. 5.1, below.

For further discussion, see Rapaport (2018a, Ch. 11).

A curious phrase: In contemporary American English, it refers to hints, suggestions, informal rules (often with many exceptions), as in Polya (1957). But Turing (1947, p. 383) used the phrase to refer to algorithms!

See Rapaport (2018a, Ch. 11) for further discussion.

Contrary to what one referee suggested, not being programmable doesn’t rule out brains by definition as computers; surely(?), brains are programmable! (See Sect. 2.3, above, and Sect. 5.1, below. On the “surely” operator, see Dennett 2013, Ch. 10.)

On analog computers, see Rubinoff (1953), Samuel (1953, p. 1224, § “The Analogue Machine”), Jackson (1960), Pour-El (1974), Copeland (1997, “Nonclassical Analog Computing Machines”, pp. 699–704), Hedger (1998), Shagrir (1999), Holst (2000), Stoll (2006), Care (2007), Piccinini (2008), Fortnow (2010), Piccinini (2011), McMillan (2013), Corry (2017); and http://hrl.harvard.edu/analog/. For alternative ways to compute with real numbers other than with analog computers, see Blum et al. (1989), Buzen (2011).

For other attempts at defining ‘computer’, see Shagrir (1999), Harnish (2002), Anderson (2006), Kanat-Alexander (2008), Chalmers (2011, “What about computers?”, pp. 335–336, 2012), Egan (2012), Rescorla (2012), Scheutz (2012), Shagrir (2012) and Chirimuuta et al. (2014).

Rapaport (1999, 2005a) argue that implementation is semantic interpretation.

Register machines—although better models of real computers than TMs are—do not, as one referee implied, have fixed-size registers (as real computers do); they are mathematical idealizations.

For discussion of this, see von Neumann (1945, §2.3, p. 2), Carpenter and Doran (1977, p. 270), Randell (1994), Copeland (2013), Daylight (2013), Haigh (2013), Vardi (2013), and Rapaport (2018a, §9.4.2).

For more detailed critiques and other relevant commentary, see Piccinini (2006, 2007b, 2010), and Rapaport (2007).

Cf. Hilbert’s Gesammelte Abhandlungen, vol. 3, p. 403, as cited in Coffa (1991, p. 135); cf. Stewart Shapiro (2009, p. 176).

Not to be confused with the TM’s “machine table”, i.e., its hardwired program.

However, for arguments that syntax does “suffice” for semantics—that semantics is a kind of syntax—see Rapaport (1988, 2017b).

An alternative view of this was given in Goodman (1987, p. 484):

Suppose that a student is successfully doing an exercise in a recursive function theory course which consists in implementing a certain Turing machine program. There is then no reductionism involved in saying that he is carrying out a Turing machine program. He intends to be carrying out a Turing machine program. … Now suppose that, unbeknownst to the student, the Turing machine program he is carrying out is an implementation of the Euclidean algorithm. His instructor, looking at the pages of more or less meaningless computations handed in by the student, can tell from them that the greatest common divisor of 24 and 56 is 8. The student, not knowing the purpose of the machine instructions he is carrying out, cannot draw the same conclusion from his own work. I suggest that the instructor, but not the student, should be described as carrying out the Euclidean algorithm. (This is a version … of Searle’s Chinese room argument ….)

For more discussion on what ‘intrinsic’ means, see Lewis (1983), Langton and Lewis (1998), Skow (2007), Bader (2013), Marshall (2016), Weatherson and Marshall (2018).

A dishwasher might, however, be described by a (non-computable?) function that takes dirty dishes as input and that returns clean ones as output. The best and most detailed study of what it means for a machine to compute is Piccinini (2015). See also Bacon (2010).

See Rapaport (2018a, §8.8.2.8.1) for discussion.

For a nice description of what a switch is in this context, see Samuel (1953, p. 1225). For more on computers as switch-setting devices, see the discussions in Stewart (1994) and Brian Hayes (2007) of how train switches can implement computations. Both of these are also examples of TMs implemented in very different media than silicon (namely, trains)!

A third version will be discussed in Sect. 6, below.

In the other two papers in his trilogy, Piccinini gives slightly different characterizations of what a digit is, but these need not concern us here; see Piccinini (2007c, p. 510, 2008 p. 34).

A slight modification of this might be necessary to avoid the possibility that a physical device might be considered to be a computer even if it doesn’t compute: We probably want to rule out “real magic”, for instance Rapaport (2017c, §13.6).

Before computers came along, there were many other physical metaphors for the brain: The brain was considered to be like a telephone system or like a plumbing system. See, e.g., Lewis (1953), Squires (1970), Sternberg (1990), Gigerenzer and Goldstein (1996), Angier (2010), Guernsey (2009), Pasanek (2015), and US National Library of Medicine (2015).

For more such sentiments, see Rapaport (2012, §2). On computationalism more generally, see Rescorla (2015).

And I do not assume that all cognition is computable; instead, one should ask, “How much of cognition is computable?” (Rapaport 1998, p. 405).

For some other arguments that the brain is not a computer, see Naur (2007, p. 85), Schulman (2009), Linker (2015).

It is one thing to argue that brains are (or are not) computers of some kind. It is quite another to argue that they are TMs in particular. The earliest suggestion to that effect is McCulloch and Pitts (1943). For a critical and historical review of that classic paper, see Piccinini (2004). More recently, the cognitive neuroscientist Stanislas Dehaene and his colleagues have made similar arguments; see Sackur and Dehaene (2009), Zylberberg et al. (2011).

For a survey, see https://en.wikipedia.org/wiki/Biological_computing.

For humorous illustrations of this, see the fake Google search page at http://abstrusegoose.com/115 and the cartoon at http://abstrusegoose.com/219.

The easiest way to think of a cellular automaton is as a two-dimensional TM tape for which the symbol in any cell is a function of the symbols in neighboring cells (https://en.wikipedia.org/wiki/Cellular_automaton). On cellular automata, see Burks (1970).

See, e.g., https://en.wikipedia.org/wiki/Turing_completeness, https://en.wikipedia.org/wiki/Rule_110, and https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life.

For more on Wolfram, see http://www.stephenwolfram.com/ and Wolfram (2002a). For a critical review, see Weinberg (2002). Aaronson (2011) claims that quantum computing has “overthrown” views like those of Wolfram (2002b) that “the universe itself is basically a giant computer … by showing that if [it is, then] it’s a vastly more powerful kind of computer than any yet constructed by humankind.”

For more on Lloyd, see Lloyd (2000, 2002, 2006), Powell (2006), Schmidhuber (2006). Computer pioneer Konrad Zuse also argued that the universe is a computer (Schmidhuber 2002). For related views, see Bostrom (2003), Chaitin (2006), Bacon (2010), Hidalgo (2015), O’Neill (2015).

Aaronson, S. (2011, December 9). Quantum computing promises new insights, not just supermachines. New York Times, D5. http://www.nytimes.com/2011/12/06/science/scott-aaronson-quantum-computing-promises-new-insights.html.

Adleman, L. M. (1998). Computing with DNA. Scientific American, 54–61. http://www.usc.edu/dept/molecular-science/papers/fp-sciam98.pdf.

Aho, A. V. (2011, January). What is computation? Computation and computational thinking. Ubiquity, Article 1. http://ubiquity.acm.org/article.cfm?id=1922682.

Aizawa, K. (2010). Computation in cognitive science: It is not all about Turing-equivalent computation. Studies in History and Philosophy of Science, 41(3), 227–236. Reply to Piccinini (2004).

Anderson, D. L. (2006). The nature of computers. The Mind Project. http://www.mind.ilstu.edu/curriculum/modOverview.php?modGUI=196.

Angier, N. (2010, February 2). Abtract thoughts? The body takes them literally. New York Times/Science Times, D2. http://www.nytimes.com/2010/02/02/science/02angier.html.

Bacon, D. (2010, December). What is computation? Computation and fundamental physics. Ubiquity 2010(December). Article 4, http://ubiquity.acm.org/article.cfm?id=1920826.

Bader, R. M. (2013). Towards a hyperintensional theory of intrinsicality. Journal of Philosophy, 110(10), 525–563.

Ballard, D. H. (1997). An introduction to natural computation. Cambridge, MA: MIT Press.MATH

Benacerraf, P. (1965). What numbers could not be. Philosophical Review, 74(1), 47–73.MathSciNet

Biermann, A. (1990). Great ideas in computer science: A gentle introduction. Cambridge, MA: MIT Press.MATH

Blum, L., Shub, M., & Smale, S. (1989). On a theory of computation and complexity over the real numbers: NP-completeness, recursive functions, and universal machines. Bulletin of the American Mathematical Society, 21(1), 1–46.MathSciNetMATH

Borbely, R. (2005). Letter to the editor. Scientific American, 12, 15.

Bostrom, N. (2003). Are you living in a computer simulation? Philosophical Quarterly, 53(211), 243–255.

Burks, A. W. (Ed.). (1970). Essays on cellular automata. Urbana, IL: University of Illinois Press.MATH

Buzen, J. P. (2011, January). Computation, uncertainty and risk. Ubiquity, Article 5. http://ubiquity.acm.org/article.cfm?id=1936886.

Care, C. (2007). Not only digital: A review of ACM’s early involvement with analog computing technology. Communications of the ACM, 50(5), 42–45.

Carpenter, B., & Doran, R. (1977). The other Turing machine. The Computer Journal, 20(3), 269–279.MathSciNetMATH

Chaitin, G. (2006). How real are real numbers? International Journal of Bifurcation and Chaos. http://www.cs.auckland.ac.nz/~chaitin/olympia.pdf (2006 version); http://www.umcs.maine.edu/~chaitin/wlu.html (2009 version).

Chalmers, D. J. (1996). Does a rock implement every finite-state automaton? Synthese, 108, 309–333. http://consc.net/papers/rock.html; Chalmers corrects “an error in my arguments” in Chalmers (2012, pp. 236–238).

Chalmers, D. J. (2011). A computational foundation for the study of cognition. Journal of Cognitive Science (South Korea), 12(4), 323–357.

Chalmers, D. J. (2012). The varieties of computation: A reply. Journal of Cognitive Science (South Korea), 13(3), 211–248.

Chirimuuta, M., Boone, T., & DeMedonsa, M. (2014, September 19). Is your brain a computer? (video). Instant HPS. https://www.youtube.com/watch?v=-8q_UXpHsaY.

Chow, S. J. (2015). Many meanings of ‘heuristic’. British Journal for the Philosophy of Science, 66, 977–1016.

Churchland, P. S., & Sejnowski, T. J. (1992). The computational brain. Cambridge, MA: MIT Press.MATH

Coffa, J. (1991). The semantic tradition from Kant to Carnap: To the Vienna Station. Cambridge: Cambridge University Press.

Copeland, B. J. (1996). What is computation? Synthese, 108, 335–359.MathSciNetMATH

Copeland, B. J. (1997). The broad conception of computation. American Behavioral Scientist, 40(6), 690–716.

Copeland, B. J. (2002). Hypercomputation. Minds and Machines, 12(4), 461–502.MATH

Copeland, B. J. (Ed.). (2004). The essential Turing. Oxford: Oxford University Press.MATH

Copeland, B. J. (2013, August 12). What Apple and Microsoft owe to Turing. Huff[ington] Post Tech/The Blog. http://www.huffingtonpost.com/jack-copeland/what-apple-and-microsoft-_b_3742114.html.

Corry, L. (2017). Turing’s pre-war analog computers: The fatherhood of the modern computer revisited. Communications of the ACM, 60(8), 50–58.

Davis, M. D. (2000). Overheard in the park. American Scientist, 88, 366–367.

Davis, M. D. (2004). The myth of hypercomputation. In C. Teuscher (Ed.), Alan Turing: The life and legacy of a great thinker (pp. 195–212). Berlin: Springer.

Davis, M. D. (2006a). The Church–Turing thesis: Consensus and opposition. In A. Beckmann, U. Berger, B. Löwe, & J. Tucker (Eds.), Logical approaches to computational barriers: Second conference on computability in Europe, CiE 2006, Lecture notes in computer science (Vol. 3988, pp. 125–132), Swansea, UK, June 30–July 5, 2006. Berlin: Springer.

Davis, M. D. (2006b). Why there is no such discipline as hypercomputation. Applied Mathematics and Computation, 178, 4–7.MathSciNetMATH

Daylight, E. G. (2013). Towards a historical notion of “Turing—The father of computer science”. http://www.dijkstrascry.com/sites/default/files/papers/Daylightpaper91.pdf.

Dennett, D. C. (2013). Intuition pumps and other tools for thinking. New York: W.W. Norton.

Dennett, D. C. (2017). From bacteria to bach and back: The evolution of mind. New York: W.W. Norton.

Edelman, S. (2008a). Computing the mind. New York: Oxford University Press.

Edelman, S. (2008b). On the nature of minds; or: Truth and consequences. Journal of Experimental & Theoretical Artificial Intelligence, 20(3), 181–196.

Egan, F. (2012). Metaphysics and computational cognitive science: Let’s not let the tail wag the dog. Journal of Cognitive Science (South Korea), 13(1), 39–49.

Fortnow, L. (2010, December). What is computation? Ubiquity, Article 5. http://ubiquity.acm.org/article.cfm?id=1921573.

Gelenbe, E. (2011, February). Natural computation. Ubiquity, Article 1. http://ubiquity.acm.org/article.cfm?id=1940722.

Gigerenzer, G., & Goldstein, D. G. (1996). Mind as computer: Birth of a metaphor. Creativity Research Journal, 9(2–3), 131–144.

Gödel, K. (perhaps about 1938). Undecidable diophantine propositions. In S. Feferman, et al. (Eds.), Collected works (Vol. III, pp. 164–175). Oxford: Oxford University Press.

Goldin, D., Smolka, S. A., & Wegner, P. (Eds.). (2006). Interactive computation: The new paradigm. Berlin: Springer.MATH

Goodman, N. D. (1987). Intensions, Church’s thesis, and the formalization of mathematics. Notre Dame Journal of Formal Logic, 28(4), 473–489.MathSciNetMATH

Gopnik, A. (2009). The philosophical baby: What children’s minds tell us about truth, love, and the meaning of life. New York: Farrar, Straus and Giroux.

Guernsey, L. (2009). Computers track the elusive metaphor. Chronicle of Higher Education, 55, A11.

Guzdial, M., & Kay, A. (2010, May 24). The core of computer science: Alan Kay’s ‘triple whammy’. Computing Education Blog. http://computinged.wordpress.com/2010/05/24/the-core-of-computer-science-alan-kays-triple-whammy/.

Haigh, T. (2013). ‘Stored program concept’ considered harmful: History and historiography. In P. Bonizzoni, V. Brattka, & B. Löwe (Eds.), CiE 2013, Lecture notes in computer science (Vol. 7921, pp. 241–251). Berlin: Springer.

Harnish, R. M. (2002). Coda: Computation for cognitive science, or what IS a computer, anyway? In Minds, brains, computers: An historical introduction to the foundations of cognitive science (pp. 394–412). Malden, MA: Blackwell. https://www.amazon.com/Minds-Brains-Computers-Introduction-Foundations/dp/0631212604/.

Hartree, D. (1949). Calculating instruments and machines. Urbana, IL: University of Illinois Press.MATH

Hayes, B. (2007). Trains of thought. American Scientist, 95(2), 108–113.

Hayes, P. J. (1997). What is a computer? Monist, 80(3), 389–404.

Hedger, L. (1998). Analog computation: Everything old is new again (Vol. 21). Indiana University Research & Creative Activity. http://www.indiana.edu/~rcapub/v21n2/p24.html.

Hidalgo, C. (2015). Planet hard drive. Scientific American, 313(2), 72–75.

Hintikka, J., & Mutanen, A. (1997). An alternative concept of computability. In J. Hintikka (Ed.), Language, truth, and logic in mathematics (pp. 174–188). Dordrecht: Springer.

Hogarth, M. (1992). Does general relativity allow an observer to view an eternity in a finite time? Foundations of Physics Letters, 5(2), 173–181.MathSciNet

Holst, P. A. (2000). Analog computer. In A. Ralston, E. D. Reilly, & D. Hemmendinger (Eds.), Encyclopedia of computer science (4th ed., pp. 53–59). New York: Grove’s Dictionaries.

Jackson, A. S. (1960). Analog computation. New York: McGraw-Hill.MATH

Kanat-Alexander, M. (2008, October 10). What is a computer? Code Simplicity. http://www.codesimplicity.com/post/what-is-a-computer/.

Kleene, S. C. (1995). Turing’s analysis of computability, and major applications of it. In R. Herken (Ed.), The universal turing machine: A half-century survey (2nd ed., pp. 15–49). Vienna: Springer.

Kugel, P. (2002). Computing machines can’t be intelligent (… and Turing said so). Minds and Machines, 12(4), 563–579.MATH

Lamport, L. (2011). Euclid writes an algorithm: A fairytale. International Journal of Software and Informatics 5(1–2, Part 1), 7–20.

Langton, R., & D. Lewis (1998). Defining ‘intrinsic’. Philosophy and Phenomenological Research, 58(2), 333–345.

Lewis, D. (1983). Extrinsic properties. Philosophical Studies, 44(2), 197–200.

Lewis, W. (1953). Electronic computers and telephone switching. Proceedings of the Institute of Radio Engineers, 41(10), 1242–1244.

Linker, D. (2015, July 1). No, your brain isn’t a computer. The Week. http://theweek.com/articles/563975/no-brain-isnt-computer.

Lloyd, S. (2000). Ultimate physical limits to computation. Nature, 406, 1047–1054.

Lloyd, S. (2002). Computational capacity of the universe. Physical Review Letters, 88(23), 237901-1–237901-4.MathSciNet

Lloyd, S. (2006). Programming the universe: A quantum computer scientist takes on the cosmos. New York: Alfred A. Knopf.

Lloyd, S., & Ng, Y. J. (2004). Black hole computers. Scientific American, 291(5), 52–61.

Marcus, G. (2015, June 28). Face it, your brain is a computer. New York Times Sunday Review, SR12. http://www.nytimes.com/2015/06/28/opinion/sunday/face-it-your-brain-is-a-computer.html.

Marshall, D. (2016). The varieties of intrinsicality. Philosophy and Phenomenological Research, 92(2), 237–263.

McCulloch, W. S., & Pitts, W. H. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 7, 114–133. Reprinted in Warren S. McCulloch, Embodiments of mind (Cambridge, MA: MIT Press, 1965): 19–39.

McMillan, R. (2013, July 7). The end of digital tyranny: Why the future of computing is analog. Wired. http://www.wired.com/wiredenterprise/2013/07/analogfuture/.

Mitchell, M. (2011, February). What is computation? Biological computation. Ubiquity, Article 3. http://ubiquity.acm.org/article.cfm?id=1944826.

Moor, J. H. (1978). Three myths of computer science. British Journal for the Philosophy of Science, 29(3), 213–222.

Morris, G. J., & Reilly, E. D. (2000). Digital computer. In A. Ralston, E. D. Reilly, & D. Hemmendinger (Eds.), Encyclopedia of computer science (4th ed., pp. 359–545). New York: Grove’s Dictionaries.

Naur, P. (2007). Computing versus human thinking. Communications of the ACM, 50(1), 85–94.

O’Neill, S. (2015). The human race is a computer. New Scientist, 227(3029), 26–27. Interview with César Hidalgo.

Oommen, B. J., & Rueda, L. G. (2005). A formal analysis of why heuristic functions work. Artificial Intelligence, 164(1–2), 1–22.MathSciNetMATH

Pasanek, B. (2015). The mind is a metaphor. http://metaphors.iath.virginia.edu/.

Perruchet, P., & Vinter, A. (2002). The self-organizing consciousness. Behavioral and Brain Sciences, 25(3), 297–388.

Piccinini, G. (2004). The first computational theory of mind and brain: A close look at McCulloch and Pitts’s ‘Logical calculus of ideas immanent in nervous activity’. Synthese, 141, 175–215. For a reply, see Aizawa (2010).

Piccinini, G. (2005). Symbols, strings, and spikes: The empirical refutation of computationalism. Abstract at https://philpapers.org/rec/PICSSA; unpublished paper at https://web.archive.org/web/20080216023546/http://www.umsl.edu/~piccininig/Symbols%20Strings%20and%20Spikes%2019.htm. superseded by Piccinini and Bahar (2013).

Piccinini, G. (2006). Computational explanation in neuroscience. Synthese, 153(3), 343–353.MathSciNet

Piccinini, G. (2007a). Computational explanation and mechanistic explanation of mind. In M. Marraffa, M. D. Caro, & F. Ferretti (Eds.), Cartographies of the mind: Philosophy and psychology in intersection (pp. 23–36). Dordrecht: Springer.

Piccinini, G. (2007b). Computational modelling vs. computational explanation: Is everything a Turing machine, and does it matter to the philosophy of mind? Australasian Journal of Philosophy, 85(1), 93–115.MathSciNet

Piccinini, G. (2007c). Computing mechanisms. Philosophy of Science, 74(4), 501–526.MathSciNet

Piccinini, G. (2008). Computers. Pacific Philosophical Quarterly, 89, 32–73.

Piccinini, G. (2010). The mind as neural software? Understanding functionalism, computationalism, and computational functionalism. Philosophy and Phenomenological Research, 81(2), 269–311.

Piccinini, G. (2011). The physical Church–Turing thesis: Modest or bold? British Journal for the Philosophy of Science, 62, 733–769.MathSciNetMATH

Piccinini, G. (2015). Physical computation: A mechanistic account. Oxford: Oxford University Press.MATH

Piccinini, G. (2018). Computation and representation in cognitive neuroscience. Minds and Machines, 28(1), 1–6.

Piccinini, G., & Bahar, S. (2013). Neural computation and the computational theory of cognition. Cognitive Science, 34, 453–488.

Polya, G. (1957). How to solve it: A new aspect of mathematical method (2nd ed.). Garden City, NY: Doubleday Anchor.MATH

Pour-El, M. B. (1974). Abstract computability and its relation to the general purpose analog computer (some connections between logic, differential equations and analog computers). Transactions of the American Mathematical Society, 199, 1–28.MathSciNetMATH

Powell, C. S. (2006, April 2). Welcome to the machine. New York Times Book Review, 19. http://www.nytimes.com/2006/04/02/books/review/02powell.html.

Putnam, H. (1965). Trial and error predicates and the solution to a problem of Mostowski. Journal of Symbolic Logic, 30(1), 49–57.MathSciNetMATH

Putnam, H. (1988). Representation and Reality. Cambridge, MA: MIT Press.

Randell, B. (1994). The origins of computer programming. IEEE Annals of the History of Computing, 16(4), 6–14.MathSciNetMATH

Rapaport, W. J. (1988). Syntactic semantics: Foundations of computational natural-language understanding. In J. H. Fetzer (Ed.), Aspects of artificial intelligence (pp. 81–131). Dordrecht: Kluwer. http://www.cse.buffalo.edu/~rapaport/Papers/synsem.pdf; reprinted with numerous errors in Eric Dietrich (Ed.). (1994). Thinking machines and virtual persons: Essays on the intentionality of machines (San Diego: Academic Press): 225–273.

Rapaport, W. J. (1998). How minds can be computational systems. Journal of Experimental and Theoretical Artificial Intelligence, 10, 403–419.MATH

Rapaport, W. J. (1999). Implementation is semantic interpretation. The Monist, 82, 109–130.

Rapaport, W. J. (2005a). Implemention is semantic interpretation: Further thoughts. Journal of Experimental and Theoretical Artificial Intelligence, 17(4), 385–417.MathSciNet

Rapaport, W. J. (2005b). Philosophy of computer science: An introductory course. Teaching Philosophy, 28(4), 319–341.MathSciNet

Rapaport, W. J. (2007). Searle on brains as computers. American Philosophical Association Newsletter on Philosophy and Computers, 6(2), 4–9.

Rapaport, W. J. (2012). Semiotic systems, computers, and the mind: How cognition could be computing. International Journal of Signs and Semiotic Systems 2(1), 32–71. Revised version published as Rapaport (2018b).

Rapaport, W. J. (2017a). On the relation of computing to the world. In T. M. Powers (Ed.), Philosophy and computing: Essays in epistemology, philosophy of mind, logic, and ethics (pp. 29–64). Cham: Springer. Paper based on 2015 IACAP Covey Award talk; preprint at http://www.cse.buffalo.edu/~rapaport/Papers/rapaport4IACAP.pdf.

Rapaport, W. J. (2017b). Semantics as syntax. American Philosophical Association Newsletter on Philosophy and Computers, 17(1), 2–11.

Rapaport, W. J. (2017c). What is computer science? American Philosophical Association Newsletter on Philosophy and Computers, 16(2), 2–22. Paper based on 2017 APA Barwise Award talk. http://c.ymcdn.com/sites/www.apaonline.org/resource/collection/EADE8D52-8D02-4136-9A2A-729368501E43/ComputersV16n2.pdf.

Rapaport, W. J. (2018a). Philosophy of computer science. Current draft in progress at http://www.cse.buffalo.edu/~rapaport/Papers/phics.pdf.

Rapaport, W. J. (2018b). Syntactic semantics and the proper treatment of computationalism. In M. Danesi (Ed.), Empirical research on semiotics and visual rhetoric (pp. 128–176). Hershey, PA: IGI Global.

Rescorla, M. (2012). How to integrate representation into computational modeling, and why we should. Journal of Cognitive Science (South Korea), 13(1), 1–38.

Rescorla, M. (2015). The computational theory of mind. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Winter 2015 ed.). http://plato.stanford.edu/archives/win2015/entries/computational-mind/.

Richards, W. (1988). Natural computation. Cambridge, MA: MIT Press.MATH

Robinson, J. A. (1994). Logic, computers, Turing, and von Neumann. In K. Furukawa, D. Michie, & S. Muggleton (Eds.), Machine Intelligence 13: Machine intelligence and inductive learning (pp. 1–35). Oxford: Clarendon Press.

Romanycia, M. H., & Pelletier, F. J. (1985). What is a heuristic? Computational Intelligence, 1(2), 47–58.

Rubinoff, M. (1953). Analogue vs. digital computers—a comparison. Proceedings of the IRE, 41(10), 1254–1262.MathSciNet

Sackur, J., & Dehaene, S. (2009). The cognitive architecture for chaining of two mental operations. Cognition, 111, 187–211.

Samuel, A. L. (1953). Computing bit by bit, or digital computers made easy. Proceedings of the IRE, 41(10), 1223–1230.

Scheutz, M. (2012). What it is not to implement a computation: A critical analysis of Chalmers’ notion of implementation. Journal of Cognitive Science (South Korea), 13(1), 75–106.

Schmidhuber, J. (2002). Zuse’s thesis: The universe is a computer. http://www.idsia.ch/~juergen/digitalphysics.html.

Schmidhuber, J. (2006). The computational universe. American Scientist, 94, 364ff.

Schulman, A. N. (2009). Why minds are not like computers. The New Atlantis, 23, 46–68.

Searle, J. R. (1990). Is the brain a digital computer? Proceedings and Addresses of the American Philosophical Association, 64(3), 21–37. Reprinted in slightly revised form as Searle (1992, Ch. 9).

Searle, J. R. (1992). The rediscovery of the mind. Cambridge, MA: MIT Press.

Shagrir, O. (1999). What is computer science about? The Monist, 82(1), 131–149.

Shagrir, O. (2012). Can a brain possess two minds? Journal of Cognitive Science (South Korea), 13(2), 145–165.

Shapiro, E., & Benenson, Y. (2006). Bringing DNA computers to life. Scientific American, 294(5), 44–51.

Shapiro, S. (2009). We hold these truths to be self-evident: But what do we mean by that? Review of Symbolic Logic, 2(1), 175–207.MathSciNetMATH

Shapiro, S. C. (2001). Computer science: The study of procedures. Technical report, Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY. http://www.cse.buffalo.edu/~shapiro/Papers/whatiscs.pdf; see also http://www.cse.buffalo.edu/~shapiro/Courses/CSE115/notes2.html.

Shepherdson, J., & Sturgis, H. (1963). Computability of recursive functions. Journal of the ACM, 10(2), 217–255.MathSciNetMATH

Simon, H. A. (1996). Computational theories of cognition. In W. O’Donohue & R. F. Kitchener (Eds.), The philosophy of psychology (pp. 160–172). London: Sage.

Skow, B. (2007). Are shapes intrinsic? Philosophical Studies, 133, 111–130.MathSciNet

Squires, R. (1970). On one’s mind. Philosophical Quarterly, 20(81), 347–356.

Sternberg, R. J. (1990). Metaphors of mind: Conceptions of the nature of intelligence. New York: Cambridge University Press.

Stewart, I. (1994). A subway named Turing. Scientific American, 104, 106–107.

Stoll, C. (2006). When slide rules ruled. Scientific American, 294(5), 80–87.MathSciNet

Suber, P. (1997). Formal systems and machines: An isomorphism. http://www.earlham.edu/~peters/courses/logsys/machines.htm.

Thomason, R. H. (2003). Dynamic contextual intensional logic: Logical foundations and an application. In P. Blackburn (Ed.), CONTEXT 2003: Lecture notes in artificial intelligence (Vol. 2680, pp. 328–341). Berlin: Springer.

Turing, A. M. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 42, 230–265.MathSciNetMATH

Turing, A. M. (1939). Systems of logic based on ordinals. Proceedings of the London Mathematical Society, S2-45(1), 161–228. Reprinted with commentary in Copeland (2004, Ch. 3).

Turing, A. M. (1947). Lecture to the London Mathematical Society on 20 February 1947. In B. J. Copeland (Ed.), The essential Turing (pp. 378–394). Oxford: Oxford University Press (2004). Editorial commentary on pp. 362–377. https://www.academia.edu/34875977/Alan_Turing_LECTURE_TO_THE_LONDON_MATHEMATICAL_SOCIETY_1947_.

Turing, A. M. (1951, 1996). Intelligent machinery, a heretical theory. Philosophia Mathematica, 4(3), 256–260. http://viola.informatik.uni-bremen.de/typo/fileadmin/media/lernen/Turing-_Intelligent_Machinery.pdf; typescripts and further bibliographic information at http://www.turing.org.uk/sources/biblio1.html.

US National Library of Medicine. (2015). Dream anatomy. https://www.nlm.nih.gov/dreamanatomy/da_g_IV-A-02.html.

Vardi, M. Y. (2013). Who begat computing? Communications of the ACM, 56(1), 5.

von Neumann, J. (1945 (1993)). First draft report on the EDVAC. IEEE Annals of the History of Computing, 15(4), 27–75

Wang, H. (1957). A variant to Turing’s theory of computing machines. Journal of the ACM, 4(1), 63–92.MathSciNet

Weatherson, B., & Marshall, D. (2018). Intrinsic vs. extrinsic properties. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Spring 2018 ed.). Metaphysics Research Lab, Stanford University. https://plato.stanford.edu/archives/spr2018/entries/intrinsic-extrinsic/.

Wegner, P. (1997). Why interaction is more powerful than algorithms. Communications of the ACM, 40(5), 80–91.

Weinberg, S. (2002). Is the universe a computer? New York Review of Books, 49(16), 43–47.

Weizenbaum, J. (1976). Computer power and human reason. New York: W.H. Freeman.

Wheeler, D. L. (1997, October 3). An ancient feud: Who invented the computer? Chronicle of Higher Education, B2.

Wolfram, S. (2002a). Introduction to A new kind of science. http://www.stephenwolfram.com/publications/introduction-to-a-new-kind-of-science/.

Wolfram, S. (2002b). A new kind of science. Wolfram Media. http://www.wolframscience.com/nks/.

Zobrist, A. L. (2000). Computer games: Traditional. In A. Ralston, E. D. Reilly, & D. Hemmendinger (Eds.), Encyclopedia of Computer Science (4th ed.). New York: Grove’s Dictionaries.

Zylberberg, A., Dehaene, S., Roelfsema, P. R., & Sigman, M. (2011). The human Turing machine: A neural framework for mental programs. Trends in Cognitive Science, 15(7), 293–300.

Titel: What is a Computer? A Survey
verfasst von: William J. Rapaport
Publikationsdatum: 25.05.2018
Verlag: Springer Netherlands
Erschienen in: Minds and Machines / Ausgabe 3/2018
Print ISSN: 0924-6495
Elektronische ISSN: 1572-8641
DOI: https://doi.org/10.1007/s11023-018-9465-6

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