A computer scientist's view of life, the universe, and everything (original) (raw)

Abstract

Is the universe computable? If so, it may be much cheaper in terms of information requirements to compute all computable universes instead of just ours. I apply basic concepts of Kolmogorov complexity theory to the set of possible universes, and chat about perceived and true randomness, life, generalization, and learning in a given universe.

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References

1. G.J. Chaitin. Algorithmic Information Theory. Cambridge University Press, Cambridge, 1987.
   Google Scholar
2. A.N. Kolmogorov. Three approaches to the quantitative definition of information. Problems of Information Transmission, 1:1–11, 1965.
   MATH Google Scholar
3. L. A. Levin. Laws of information (nongrowth) and aspects of the foundation of probability theory. Problems of Information Transmission, 10(3):206–210, 1974.
   Google Scholar
4. L. A. Levin. Randomness conservation inequalities: Information and independence in mathematical theories. Information and Control, 61:15–37, 1984.
   Article MATH MathSciNet Google Scholar
5. J. Schmidhuber. Discovering neural nets with low Kolmogorov complexity and high generalization capability. Neural Networks, 1997. In press.
   Google Scholar
6. J. Schmidhuber, J. Zhao, and N. Schraudolph. Reinforcement learning with selfmodifying policies. In S. Thrun and L. Pratt, editors, Learning to learn. Kluwer, 1997. To appear.
   Google Scholar
7. J. Schmidhuber, J. Zhao, and M. Wiering. Shifting inductive bias with successstory algorithm, adaptive Levin search, and incremental self-improvement. Machine Learning, 26, 1997. In press.
   Google Scholar
8. C. E. Shannon. A mathematical theory of communication (parts I and II). Bell System Technical Journal, XXVII:379–423, 1948.
   MathSciNet Google Scholar
9. R.J. Solomonoff. A formal theory of inductive inference. Part I. Information and Control, 7:1–22, 1964.
   Article MATH MathSciNet Google Scholar
10. D. H. Wolpert. The lack of a priori distinctions between learning algorithms. Neural Computation, 8(7):1341–1390, 1996.
    Google Scholar

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Authors and Affiliations

1. IDSIA, Corso Elvezia 36, CH-6900, Lugano, Switzerland
   Jürgen Schmidhuber

Authors

1. Jürgen Schmidhuber

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Christian Freksa Matthias Jantzen Rüdiger Valk

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© 1997 Springer-Verlag Berlin Heidelberg

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Schmidhuber, J. (1997). A computer scientist's view of life, the universe, and everything. In: Freksa, C., Jantzen, M., Valk, R. (eds) Foundations of Computer Science. Lecture Notes in Computer Science, vol 1337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0052088

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• DOI: https://doi.org/10.1007/BFb0052088
• Published: 16 May 2006
• Publisher Name: Springer, Berlin, Heidelberg
• Print ISBN: 978-3-540-63746-2
• Online ISBN: 978-3-540-69640-7
• eBook Packages: Springer Book Archive

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