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Real-World Limits to Algorithmic Intelligence

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Artificial General Intelligence (AGI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6830))

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Abstract

Recent theories of universal algorithmic intelligence, combined with the view that the world can be completely specified in mathematical terms, have led to claims about intelligence in any agent, including human beings. We discuss the validity of assumptions and claims made by theories of universally optimal intelligence in relation to their application in actual robots and intelligence tests. Our argument is based on an exposition of the requirements for knowledge of the world through observations. In particular, we will argue that the world can only be known through the application of rules to observations, and that beyond these rules no knowledge can be obtained about the origin of our observations. Furthermore, we expose a contradiction in the assumption that it is possible to fully formalize the world, as for example is done in digital physics, which can therefore not serve as the basis for any argument or proof about algorithmic intelligence that interacts with the world.

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Author information

Authors and Affiliations

  1. IDSIA, University of Lugano, 6928, Manno-Lugano, Switzerland

    Leo Pape

  2. Tilburg University, PO Box 90153, 5000 LE, Tilburg, The Netherlands

    Arthur Kok

Authors
  1. Leo Pape
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  2. Arthur Kok
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Editor information

Editors and Affiliations

  1. Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Lugano, Switzerland

    Jürgen Schmidhuber

  2. Reykjavik University, CADIA, Menntavegi 1, 101, Reykjavik, Iceland

    Kristinn R. Thórisson

  3. Google Research, 1600 Amphitheatre Parkway, Mountain View, 94043, CA, USA

    Moshe Looks

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Pape, L., Kok, A. (2011). Real-World Limits to Algorithmic Intelligence. In: Schmidhuber, J., Thórisson, K.R., Looks, M. (eds) Artificial General Intelligence. AGI 2011. Lecture Notes in Computer Science(), vol 6830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22887-2_49

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  • DOI: https://doi.org/10.1007/978-3-642-22887-2_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22886-5

  • Online ISBN: 978-3-642-22887-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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