Abstract
This paper outlines the non-behavioral Algorithmic Similarity criterion for machine intelligence, and assesses the likelihood that it will eventually be satisfied by computers programmed using Machine Learning (ML). Making this assessment requires overcoming the Black Box Problem, which makes it difficult to characterize the algorithms that are actually acquired via ML. This paper therefore considers Explainable AI’s prospects for solving the Black Box Problem, and for thereby providing a posteriori answers to questions about the possibility of machine intelligence. In addition, it suggests that the real-world nurture and situatedness of ML-programmed computers constitute a priori reasons for thinking that they will not only learn to behave like humans, but that they will also eventually acquire algorithms similar to the ones that are implemented in human brains.
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Notes
- 1.
This appeal to brain-implemented algorithms is in line with cognitive science orthodoxy. Analogous non-behavioral criteria may invoke other theoretical posits.
- 2.
Those impressed by Searle’s (1980) Chinese Room would of course disagree. For them, the algorithms being executed have no bearing on the intelligence that may or may not be possessed.
- 3.
This is an admittedly strong empiricist thesis. Lest it be considered too strong, it is worth considering recent attempts to increase the psychological plausibility of ML methods. For example, Lake et al. (2017) review several ways in which such methods can be modified to incorporate known principles of human learning and development. This research may obviate the need for (overly) strong empiricism.
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Zednik, C. (2018). Will Machine Learning Yield Machine Intelligence?. In: Müller, V. (eds) Philosophy and Theory of Artificial Intelligence 2017. PT-AI 2017. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 44. Springer, Cham. https://doi.org/10.1007/978-3-319-96448-5_23
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DOI: https://doi.org/10.1007/978-3-319-96448-5_23
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