Abstract
The rise of probability and statistics is striking in contemporary science, ranging from quantum physics to artificial intelligence. Here we discuss two issues: one is the computational theory of mind as the fundamental underpinning of AI, and the quantum nature of computation therein; the other is the shift from symbolic to statistical AI, and the nature of truth in data science as a new kind of science. In particular we argue as follows: if the singularity thesis is true the computational theory of mind must ultimately be quantum in light of recent findings in quantum biology and cognition; data science is concerned with a new form of scientific truth, which may be called “post-truth”; whereas conventional science is about establishing idealised, universal truths on the basis of pure data carefully collected in a controlled situation, data science is about indicating useful, existential truths on the basis of real-world data gathered in contingent real-life and contaminated in different ways.
Supported by JST PRESTO Grant (JPMJPR17G9) and JSPS Kakenhi Grant (JP17K14231).
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Maruyama, Y. (2018). Quantum Pancomputationalism and Statistical Data Science: From Symbolic to Statistical AI, and to Quantum AI. 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_20
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DOI: https://doi.org/10.1007/978-3-319-96448-5_20
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