Abstract
This paper argues for a revival of a mentalist approach to modeling human intelligence. The fields of artificial intelligence and natural language processing have over the past two decades been dominated by empirical approaches based on analogical reasoning, distributional semantics, machine learning and what is today called Big Data. This has led to a variety of gradual technological advances. True advances, however, are predicated on developing and testing explanatory theories of human behavior. This latter activity must include accounts of “directly unobservable” phenomena, such as human beliefs, emotions, intentions, plans and biases. This task is addressed by the field of cognitive systems. It is extraordinarily complex but unavoidable if the goal is success in modeling complex—and not entirely rational—human behavior.
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Notes
- 1.
Unfortunately, this complexity was not sufficiently understood even by many of the early AI researchers who on numerous occasions overplayed their hands by claiming imminent success of advanced applications—from machine translation to expert systems—that never materialized. Note, however, that this does not mean that the scientific paradigm in which they worked is invalid.
- 2.
Minsky believes that “a clear definition can make things worse, until we are sure that our ideas are right” (op.cit., p. 95).
- 3.
This opinion may be a manifestation of the exposure effect cognitive bias described by Kahneman (2011): “A reliable way to make people believe in falsehoods is frequent repetition, because familiarity is not easily distinguished from truth” (ibid: 62).
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Acknowledgements
Many thanks to Marge McShane for productive criticism, apt suggestions and editorial help and to Pat Langley for critiquing an earlier draft of this paper.
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Nirenburg, S. (2015). Cognitive Systems as Explanatory Artificial Intelligence. In: Gala, N., Rapp, R., Bel-Enguix, G. (eds) Language Production, Cognition, and the Lexicon. Text, Speech and Language Technology, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-319-08043-7_4
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