Do Computers "Have Syntax, But No Semantics"?


The heyday of discussions initiated by Searle's claim that computers have syntax, but no semantics has now past, yet philosophers and scientists still tend to frame their views on artificial intelligence in terms of syntax and semantics. In this paper I do not intend to take part in these discussions; my aim is more fundamental, viz. to ask what claims about syntax and semantics in this context can mean in the first place. And I argue that their sense is so unclear that that their ability to act as markers within any disputes on artificial intelligence is severely compromised; and hence that their employment brings us nothing more than an illusion of explanation.

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  1. 1.

    See also Searle (1982); Searle (1984, p. 33).

  2. 2.

    In a recent paper, for example, Bozşahin (2018) writes not even about computers "having" syntax/semantics, but about them "being" syntax/semantics. For further examples see Figdor (2009), Ford (2011), Rapaport (2019), Lyre (2020) etc.

  3. 3.

    Though, as it was pointed out to me by a reviewer of this paper, Oxford English dictionary records an employment of this term, in Northern American Review, already from 1874.

  4. 4.

    See Posner (1986) for a general historical overview.

  5. 5.

    Similar arguments were put forward by a number of authors, including Rey (1986) or Rapaport (2000).

  6. 6.

    Of course, these examples are slightly odd, for a normal computer need not be taught how to add – on the contrary, addition is one of the few things it is able to do "by itself".

  7. 7.

    This, in effect, is the plot of what Searle famously presented under the name of the "Chines room" (Preston & Bishop, 2002; Searle, 1980) as his challenge to functionalists such as Newell & Simon (1963) (and, indeed, Turing, 1950).

  8. 8.

    Which, of course, is a concept with a venerable philosophical history: see Jacob (2019).

  9. 9.

    See also Searle (1983).

  10. 10.

    This is not to say that this notion must be devious, nor that it is peculiar to Searle. There are certainly other philosophers who want to erect semantics on similar foundations (from Husserl and his followers to Schiffer, 1972; 1987, or Fodor, 1975; 2008). But many semanticists are adamant that semantics is a public business not to be sealed within minds. Thus Quine (1969) urges that language is "a social art we all acquire on the evidence solely of other people’s overt behavior under publicly recognizable circumstances" (p. 26) and therefore "the question whether two expressions are alike or unlike in meaning has no determinate answer, known or unknown, except insofar as the answer is settled by people’s speech dispositions, known or unknown" (p. 29). A similar approach is taken by Davidson (1984) and his followers (Lepore & Ludwig, 2007) and by various exponents of the use theories of meaning to be discussed later (Brandom, 1994; Dummett, 1993; Horwich, 1998) etc. Also partisans of denotational semantics, such as the exponents of post-Carnapian formal semantics (Cresswell, 1973; Montague 1974; Cann 1993; and also some followers of Davidson, like Larson & Segal, 1995) build theories which are not based on intentionality.

  11. 11.

    Let us leave aside the objection that knowing the Peano axioms is not enough, for our human understanding of the language of arithmetic involves, as shown by Gödel, also having the knowledge of the truth of some sentences that are not derivable from the axioms. If we were to accept the objection, then nobody, save a few mathematical logicians, could ever be said to understand "4" (unless we count also those who "know" the truth of the sentences only because they have been told they are true, which again can then validate the case for computers).

  12. 12.

    See Rapaport (2000) for a similar argument.

  13. 13.

    As Dennett (1998, p. 24) puts it: "But, of course, most people have something more in mind when they speak of self-consciousness. It is that special inner light, that private way that it is with you that nobody else can share, something that is forever outside the bounds of computer science. How could a computer ever be conscious in this sense?".

  14. 14.

    In case of programming languages, the internal semantics has been presented also in the denotational form known from formal semantics of the formal languages of logic and subsequently also of natural languages. See already Gordon (1979).

  15. 15.

    Rescorla (2012, p. 707) works with a related, though different opposition: "inherited" vs. "indigenous" semantics. While the latter is close to the "internal" semantics of Piccinini and "syntactic" semantics of Rappaport (a semantics that is generated alone by the system), it is opposed not to an "external" semantics, but to one that not only comes from without, but is conferred on the system by some other system.

  16. 16.

    As Dummett (1993, p. 37) puts it, a theory of meaning is "to present analysis of the complex skill which constitutes mastery of a language, to display, in terms of what he may be said to know, just what it is that someone who possesses that mastery is able to do; it is not concerned to describe any inner psychological mechanisms which may account for his having those abilities".

  17. 17.

    See Brandom (1994); see also  Peregrin (2014).

  18. 18.

    See, e.g., Kusch (2006).

  19. 19.

    This is what Wittgenstein illustrated by his famous "beetle-in-the-box thought experiment"—see, e.g., Stern (2013).

  20. 20.

    For why this difference matters see Peregrin (2017).

  21. 21.

    Carnap (1934) claims that language is based on formation rules (the rules of well-formedness) and transformation rules (those of deduction). What I call syntax in the narrow sense amounts to the former only, whereas that in the broad sense comprises also the latter. See also Peregrin (2020).

  22. 22.

    Another discussion within computer science engaging the concepts of syntax and semantics concerns the very nature of computation and the nature of concepts required for its characterization (Rapaport, 2018; Shagrir, 2020). This discussion, however, is somewhat orthogonal to the current one. In it syntax and semantics are used to distinguish features that are purely formal or structural from those that are a matter of content or have to do with the instantiation of the structure. But though I have doubts that syntax and semantics are the best conceptual tools to resolve this issue, here they are used in the clear sense, not obscuring the problem.


  1. Block, N. (2005). Conceptual role semantics. In E. Craig (Ed.), The shorter routledge encyclopedia of philosophy (p. 955). Routledge.

    Google Scholar 

  2. Boghossian, P. A. (1993). Does an inferential role semantics rest upon a mistake? In A. Villanueva (Ed.), Philosophical (Vol. 3, pp. 73–88). Atascadero.

    Google Scholar 

  3. Bozşahin, C. (2018). Computers aren’t syntax all the way down or content all the way up. Minds and Machines, 28, 543–567.

    Google Scholar 

  4. Brandom, R. (1994). Making it explicit: Reasoning, representing, and discursive commitment. Harvard University Press.

    Google Scholar 

  5. Bréal, M. (1897). Essai de sémantique, Paris: Hachette; English translation Semantics: Studies in the Science of Meaning. Heinemann, 1900.

  6. Brooks, R. (1991). Intelligence without representation. Artificial Intelligence, 47, 139–159.

    Google Scholar 

  7. Cann, R. (1993). Formal semantics. Cambridge University Press.

    Google Scholar 

  8. Carnap, R. (1934). Logische Syntax der Sprache, Vienna: Springer; quoted from the English translation The Logical Syntax of Language. Open Court, 2002.

  9. Carnap, R. (1942). Introduction to semantics. Harvard University Press.

    MATH  Google Scholar 

  10. Chemero, A. (2000). Anti-representationalism and the dynamical stance. Philosophy of Science, 67, 625–647.

    Google Scholar 

  11. Cresswell, M. J. (1973). Logic and Languages. Methuen.

    Google Scholar 

  12. Davidson, D. (1984). Inquiries into truth and interpretation. Clarendon Press.

    Google Scholar 

  13. Dennett, D. C. (1998). Brainchildren: Essays on designing minds. MIT Press.

    Google Scholar 

  14. Dummett, M. (1993). The seas of language. Clarendon Press.

    MATH  Google Scholar 

  15. Figdor, C. (2009). Semantic externalism and the mechanics of thought. Minds & Machines, 19, 1–24.

    Google Scholar 

  16. Fodor, J., & LePore, E. (1992). Holism. Blackwell.

    Google Scholar 

  17. Fodor, J. A. (1975). The language of thought. Harvard University Press.

    Google Scholar 

  18. Fodor, J. A. (2008). LOT 2: The language of thought revisited. Oxford University Press.

    Google Scholar 

  19. Ford, J. (2011). Helen Keller Was Never in a Chinese Room. Minds & Machines, 21, 57–72.

    Google Scholar 

  20. Frege, G. (1892). Über Sinn und Bedeutung. Zeitschrift Für Philosophie Und Philosophische Kritik, 100, 25–50.

    Google Scholar 

  21. Frege, G. (1918) Der Gedanke. Beiträge zur Philosophie des deutschen Idealismus 2, 58–77; English translation The Thought, Mind 65, 1956, 289–311.

  22. Gallagher, S. (2016). Do we (or our brains) actively represent or enactively engage with the world? In A. K. Engel, K. J. Friston, & D. Kragic (Eds.), The Pragmatic Turn (pp. 285–296). MIT Press.

  23. Gödel, K. (1930). Die Vollständigkeit der Axiome des logischen Funktionenkalküls. Monatshefte Für Mathematik Und Physik, 37, 349–360.

    MathSciNet  MATH  Google Scholar 

  24. Gödel, K. (1931). Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I. Monatshefte Für Mathematik Und Physik, 38, 173–198.

    MathSciNet  MATH  Google Scholar 

  25. Gordon, M. J. C. (1979). Denotational semantics of programming languages. Springer.

    Google Scholar 

  26. Harman, G. (1987). (Non-solipsistic) Conceptual role semantics. In E. LePore (Ed.) New directions in semantics (pp. 55–81). Academic Press.

  27. Horwich, P. (1998). Meaning. Oxford University Press.

    Google Scholar 

  28. Jacob, P. (2019). Intentionality. In E. N. Zalta (Ed.), The stanford encyclopedia of philosophy (Winter 2019 Edition).

  29. Kusch, M. (2006). A sceptical guide to meaning and rules. McGill-Queen’s University Press.

    Google Scholar 

  30. Larson, R. K., & Segal, G. (1995). Knowledge of meaning. MIT Press.

    Google Scholar 

  31. Lepore, E., & Ludwig, K. (2007). Donald Davidson’s truth-theoretic semantics. Oxford University Press.

    Google Scholar 

  32. Lyre, H. (2020). The state space of artificial intelligence. Minds & Machines, 30, 325–347.

    Google Scholar 

  33. Montague, R. (1974). Formal philosophy. Yale University Press.

    Google Scholar 

  34. Morris, C. W. (1938). Foundations of the theory of signs. University of Chicago Press.

    Google Scholar 

  35. Newell, A., & Simon, H. (1963). GPS, a program that simulates human thought. In A. Feigenbaum & V. Feldman (Eds.), Computers and thought (pp. 279–93). McGraw Hill.

    Google Scholar 

  36. Peacocke, C. (1992). A theory of concepts. MIT Press.

    MATH  Google Scholar 

  37. Peregrin, J. (2008). Inferentialist approach to semantics. Philosophy Compass, 3, 1208–1223.

  38. Peregrin, J. (2012). Semantics without meaning? In: R. Schantz (ed.), Prospects of meaning (pp. 479–502). Berlin: de Gruyter.

  39. Peregrin, J. (2014). Inferentialism: why rules matter. Basingstoke: Palgrave.

  40. Peregrin, J. (2017). Is inferentialism circular? Analysis, 78, 450–454.

  41. Peregrin, J. (2020). Carnap's inferentialism. In: R. Schuster (ed.), Vienna circle in Czechoslovakia (pp. 97–109). Cham: Springer.

  42. Peregrin, J. (2021). The complexities of syntax. In: R. Nefdt, C. Klippi & B. Karstens (Eds.), Philosophy and science of language (pp. 13–42). Basingstoke: Palgrave.

  43. Piccinini, G. (2004). Functionalism, computationalism, and mental contents. Canadian Journal of Philosophy, 34, 375–410.

    Google Scholar 

  44. Piccinini, G. (2006). Computation without representation. Philosophical Studies, 137, 204–241.

    MathSciNet  Google Scholar 

  45. Posner, R. (1986). Syntactics. Its relation to morphology and syntax, to semantics and pragmatics, and to syntagmatics and paradigmatics. In J. D. Evans & A. Helbo (Eds.), Semiotics and international scharship. Springer.

    Google Scholar 

  46. Preston, J., & Bishop, M. (Eds.). (2002). Views into the Chinese room. Clarendon Press.

    MATH  Google Scholar 

  47. Quine, W. V. O. (1969). Ontological srelativity and other essays. Columbia University Press.

    Google Scholar 

  48. Rapaport, W. J. (1988). Syntactic semantics: Foundations of computational natural-language understanding. In J. H. Fetzer (Ed.), Aspects of artificial intelligence (pp. 81–131). Kluwer.

    Google Scholar 

  49. Rapaport, W. J. (2000). How to pass a Turing test. Journal of Logic, Language, and Information, 9, 467–490.

    MATH  Google Scholar 

  50. Rapaport, W. J. (2019). Computers are syntax all the way down: Reply to Bozşahin. Minds and Machines, 29, 227–237.

    Google Scholar 

  51. Rapaport, W. J. (2018). “What is a Computer? A Survey.” Minds & Machines, 28, 385–426.

    Google Scholar 

  52. Rescorla, M. (2012). Are computational transitions sensitive to semantics? Australasian Journal of Philosophy, 90, 703–721.

    Google Scholar 

  53. Rey, G. (1986). What’s really going on in Searle’s “Chinese room.” Philosophical Studies, 50, 169–185.

    Google Scholar 

  54. Searle J. (1982): 'A reply to D. Dennett's The Myth of the Computer', The New York Review of Books, June 24.

  55. Searle, J. R. (1980). Minds, brains & programs. Behavioral and Brain Sciences, 3, 417–457.

    Google Scholar 

  56. Searle, J. R. (1983). Intentionality. Cambridge University Press.

    Google Scholar 

  57. Searle, J. R. (1984). Minds, brains, and science. Harvard University Press.

    Google Scholar 

  58. Shagrir, O. (2020). In defense of the semantic view of computation. Synthese, 197, 4083–4108.

    MathSciNet  Google Scholar 

  59. Schiffer, S. (1972). Meaning. Clarendon Press.

    MATH  Google Scholar 

  60. Schiffer, S. (1987). Remnants of meaning. MIT Press.

    Google Scholar 

  61. Stern, D. G. (2013). The uses of Wittgenstein’s beetle. In G. Kahane, E. Kanterian, & O. Kuusela (Eds.), Wittgenstein and his interpreters (pp. 248–268). Blackwell.

    Google Scholar 

  62. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433–460.

    MathSciNet  Google Scholar 

  63. Wittgenstein, L. (1953). Philosophische Untersuchungen. Blackwell; English translation Philosophical Investigations. Blackwell.

    Google Scholar 

  64. Wittgenstein, L. (1969). Über Gewissheit. Blackwell; English translation On Certainty. Blackwell.

    Google Scholar 

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Work on this paper was supported by the Czech Science Foundation, the EXPRO grant no. GX20 -05180X.

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Correspondence to Jaroslav Peregrin.

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Peregrin, J. Do Computers "Have Syntax, But No Semantics"?. Minds & Machines 31, 305–321 (2021).

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  • Syntax
  • Semantics
  • Searle
  • Artificial intelligence