Skip to main content
Log in

In defense of the Turing test

  • Original Article
  • Published:
AI & SOCIETY Aims and scope Submit manuscript

Abstract

In 2014, widespread reports in the popular media that a chatbot named Eugene Goostman had passed the Turing test became further grist for those who argue that the diversionary tactics of chatbots like Goostman and others, such as those who participate in the Loebner competition, are enabled by the open-ended dialog of the Turing test. Some claim a new kind of test of machine intelligence is needed, and one community has advanced the Winograd schema competition to address this gap. We argue to the contrary that implicit in the Turing test is the cooperative challenge of using language to build a practical working understanding, necessitating a human interrogator to monitor and direct the conversation. We give examples which show that, because ambiguity in language is ubiquitous, open-ended conversation is not a flaw but rather the core challenge of the Turing test. We outline a statistical notion of practical working understanding that permits a reasonable amount of ambiguity, but nevertheless requires that ambiguity be resolved sufficiently for the agents to make progress.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. As is evident, we accept the standard, gender-neutral interpretation of the Turing test, whereby the interrogator must decide which conversation partner is human and which is a machine. Our acceptance of the non-gendered version of the test is based on evidence internal to Turing’s mind paper (1950) as well as some later remarks (Turing et al. 1952). This issue is thoroughly discussed by Copeland and Proudfoot (2008), Moor (2001), and Piccinini (2000).

  2. The reader can see selected transcripts, with commentary, in Warwick and Shah (2016).

References

  • Aaronson S (2014) My conversation with “Eugene Goostman,” the chatbot that’s all over the news for allegedly passing the Turing test. https://www.scottaaronson.com/blog/?p=1858. Accessed 15 Jan 2019

  • Alchourrón CE, Gärdenfors P, Makinson D (1985) On the logic of theory change: partial meet contraction and revision functions. J Symb Log 50:510–530

    Article  MathSciNet  MATH  Google Scholar 

  • Bacchus F (1990) Representing and reasoning with probabilistic knowledge: a logical approach to probabilities. MIT Press, Cambridge

    Google Scholar 

  • Commonsense reasoning (2019) Winograd schema challenge. https://www.commonsensereasoning.org/winograd.html. Accessed 9 Mar 2019

  • Copeland J, Proudfoot D (2008) Turing’s test: a philosophical and historical guide. In: Epstein R, Roberts G, Beber G (eds) Parsing the Turing test: philosophical and methodological issues in the quest for the thinking computer. Springer, Netherlands, pp 119–138

    Google Scholar 

  • Davis E, Morgenstern L, Ortiz C (2019) The Winograd schema challenge. https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html. Accessed 9 Mar 2019

  • de Kleer J (1986) An assumption-based TMS. Artif Intell 28(2):127–162

    Article  Google Scholar 

  • Dennett DC (2012) Turing’s gradualist vision: making minds from proto-minds Turing in context II, Brussels Invited talk

  • Gärdenfors P (1992) Belief revision: Cambridge tracts in theoretical computer science. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Harkaway N (2018) Will computers be able to think? Five books to help us understand AI. The Guardian. https://www.theguardian.com. Accessed 12 Mar 2019

  • Huang X, McCalla GI, Greer JE, Neufeld E (1991) Revising deductive knowledge and stereotypical knowledge in a student model. User Model User-Adap Inter 1(1):87–115

    Article  Google Scholar 

  • Kyburg HE Jr (1974) The logical foundations of statistical inference, vol 65. Springer Science and Business Media, New York

    Book  MATH  Google Scholar 

  • Levesque HJ (2011) The Winograd schema challenge. In: Logical formalizations of commonsense reasoning: papers from the 2011 AAAI Spring Symposium. Technical Report SS-11-06. AAAI Press, Palo Alto

  • Levesque HJ (2014) On our best behaviour. Artif Intell 212:27–35

    Article  MathSciNet  MATH  Google Scholar 

  • Levesque HJ (2017) Common sense, the Turing test, and the quest for real AI: reflections on natural and artificial intelligence. MIT Press, Cambridge

    Book  MATH  Google Scholar 

  • Leibniz G (1996) New essays on human understanding. In: Remnant P, Bennett J (eds & trans) Cambridge texts in the history of philosophy, 2nd edn. Cambridge University Press (Original work published 1765)

  • Leviathan Y, Matias Y (2018) Google duplex: an AI system for accomplishing real-world tasks over the phone. Google AI Blog. https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html. Accessed 11 Mar 2019

  • Lopatto E (2014) The AI that wasn’t: why ‘Eugene Goostman’ didn’t pass the turing test. Daily beast. https://www.thedailybeast.com. Accessed 12 Mar 2019

  • Luger GF, Chakrabarti C (2017) From Alan Turing to modern AI: practical solutions and an implicit epistemic stance. AI Soc 32(3):321–338

    Article  Google Scholar 

  • Marcus G (2014) Why Can’t my computer understand me? The New Yorker. https://newyorker.com. Accessed 12 Mar 2019

  • Martins JP, Shapiro SC (1988) A model for belief revision. Artif Intell 35(1):25–79

    Article  MathSciNet  MATH  Google Scholar 

  • Moor JH (2001) The status and future of the Turing test. Mind Mach 11(1):77–93

    Article  MATH  Google Scholar 

  • Piccinini G (2000) Turing’s rules for the imitation game. Mind Mach 10(4):573–582

    Article  Google Scholar 

  • Poole D, Goebel R, Aleliunas R (1987) Theorist: A logical reasoning system for defaults and diagnosis. In: Cercone N, McCalla G (eds) The knowledge frontier. Springer, New York, pp 331–352

    Chapter  Google Scholar 

  • Shieber SM (2004) The Turing test’s evidentiary value. In: Shieber SM (ed) The Turing test: verbal behavior as the hallmark of intelligence. MIT Press, Cambridge, pp 293–295

    Chapter  MATH  Google Scholar 

  • Shotter J (2019) Why being dialogical must come before being logical: the need for a hermeneutical–dialogical approach to robotic activities. AI Soc 34(1):29–35

    Article  Google Scholar 

  • The Society for the Study of Artificial Intelligence and Simulation of Behaviour (2019) Loebner Prize (n.d.) https://www.aisb.org.uk/events/loebner-prize. Accessed 12 Mar 2019

  • Trausan-Matu S (2019) Is it possible to grow an I-Thou relation with an artificial agent? A dialogistic perspective. AI Soc 34(1):9–17

    Article  Google Scholar 

  • Turing AM (1950) Computing machinery and intelligence. Mind Lix 236:433–460

    Article  MathSciNet  Google Scholar 

  • Turing AM, Braithwaite R, Jefferson G, Newman M (2004) Can automatic calculating machines be said to think? In: Copeland BJ (ed) The essential Turing. Clarendon, Oxford, pp 487–506

    Google Scholar 

  • University of Reading (2014) Turing test success marks milestone in computing history. https://www.reading.ac.uk/news-and-events/releases/PR583836.aspx. Accessed 12 Mar 2019

  • Warwick K, Shah H (2016) The importance of a human viewpoint on computer natural language capabilities: a Turing test perspective. AI Soc 31(2):207–221

    Article  Google Scholar 

Download references

Acknowledgements

Thanks to David Mould for reminding us of the role of the interrogator and to Wlodek Zadrozny for suggesting the idea of language as collaborative planning. Thanks to the University of Saskatchewan for funding this research, and thanks to the numerous reviewers and commentators on earlier presentations of this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eric Neufeld.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Neufeld, E., Finnestad, S. In defense of the Turing test. AI & Soc 35, 819–827 (2020). https://doi.org/10.1007/s00146-020-00946-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00146-020-00946-8

Keywords

Navigation