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The Case for Quantifying Artificial General Intelligence with Entropy Semifields

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Artificial Intelligence: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 973))

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Abstract

In this chapter we hypotesize that Information Semifields are the underlying calculi that brains operate on and we postulate that strong artificial intelligences should try to imitate them. Information semifields have recently been proposed to describe the calculations that pertain to the manipulation of Renyi entropies. These are semifields that emerge from the pseudo-calculus by the rational choice of generator functions that respect the classical postulates of information theory, and align with the Renyi entropy and divergence. To support our hypothesis we gather evidence in this respect from Neuroscience, Machine Learning, Cognitive Computation, Information Theory and Complex Systems theory. This evidence would also constrain other attempts at describing the workings of an intelligence embodied in a brain.

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Notes

  1. 1.

    Neuromorphic in a wide sense not necessarily hardware systems.

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Authors and Affiliations

  1. Department of Signal Theory and Communications, Universidad Carlos III de Madrid Leganés, 28911, Leganés, Spain

    Francisco J. Valverde-Albacete & Carmen Peláez-Moreno

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  1. Francisco J. Valverde-Albacete
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  2. Carmen Peláez-Moreno
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Correspondence to Francisco J. Valverde-Albacete .

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  1. Singidunum University, Belgrade, Serbia

    Endre Pap

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Valverde-Albacete, F.J., Peláez-Moreno, C. (2021). The Case for Quantifying Artificial General Intelligence with Entropy Semifields. In: Pap, E. (eds) Artificial Intelligence: Theory and Applications. Studies in Computational Intelligence, vol 973. Springer, Cham. https://doi.org/10.1007/978-3-030-72711-6_5

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