Building Thinking Machines by Solving Animal Cognition Tasks

Minds and Machines 30 (4):589-615 (2020)
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Abstract

In ‘Computing Machinery and Intelligence’, Turing, sceptical of the question ‘Can machines think?’, quickly replaces it with an experimentally verifiable test: the imitation game. I suggest that for such a move to be successful the test needs to be relevant, expansive, solvable by exemplars, unpredictable, and lead to actionable research. The Imitation Game is only partially successful in this regard and its reliance on language, whilst insightful for partially solving the problem, has put AI progress on the wrong foot, prescribing a top-down approach for building thinking machines. I argue that to fix shortcomings with modern AI systems a nonverbal operationalisation is required. This is provided by the recent Animal-AI Testbed, which translates animal cognition tests for AI and provides a bottom-up research pathway for building thinking machines that create predictive models of their environment from sensory input.

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Matthew Crosby
Imperial College London

Citations of this work

Black Boxes or Unflattering Mirrors? Comparative Bias in the Science of Machine Behaviour.Cameron Buckner - 2023 - British Journal for the Philosophy of Science 74 (3):681-712.
The Turing test.Graham Oppy & D. Dowe - 2003 - Stanford Encyclopedia of Philosophy.
Transcendence: Measuring Intelligence.Marten Kaas - 2023 - Journal of Science Fiction and Philosophy 6.

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References found in this work

Minds, brains, and programs.John Searle - 1980 - Behavioral and Brain Sciences 3 (3):417-57.
The origin of concepts.Susan Carey - 2009 - New York: Oxford University Press.
Computing machinery and intelligence.Alan M. Turing - 1950 - Mind 59 (October):433-60.
Embodied Cognition.Lawrence A. Shapiro - 2010 - New York: Routledge.

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