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WILLIAM: A Monolithic Approach to AGI

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Artificial General Intelligence (AGI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11654))

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Abstract

We present WILLIAM – an inductive programming system based on the theory of incremental compression. It builds representations by incrementally stacking autoencoders made up of trees of general Python functions, thereby stepwise compressing data. It is able to solve a diverse set of tasks including the compression and prediction of simple sequences, recognition of geometric shapes, write code based on test cases, self-improve by solving some of its own problems and play tic-tac-toe when attached to AIXI and without being specifically programmed for it.

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Notes

  1. 1.

    Christoph von der Malsburg, personal communication.

  2. 2.

    What we have called “parameter” in [4] is now called “residual description”.

  3. 3.

    The proof would be beyond the scope of the present paper and will be published soon.

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Authors and Affiliations

  1. Odessa Competence Center for Artificial intelligence and Machine learning (OCCAM), Odesa, Ukraine

    Arthur Franz, Victoria Gogulya & Michael Löffler

Authors
  1. Arthur Franz
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  2. Victoria Gogulya
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  3. Michael Löffler
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Corresponding author

Correspondence to Arthur Franz .

Editor information

Editors and Affiliations

  1. Temple University, Philadelphia, PA, USA

    Patrick Hammer

  2. University of Memphis, Memphis, TN, USA

    Pulin Agrawal

  3. SingularityNET, Tai Po, Hong Kong, China

    Ben Goertzel

  4. SingularityNET, Alamosa, CO, USA

    Matthew Iklé

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Franz, A., Gogulya, V., Löffler, M. (2019). WILLIAM: A Monolithic Approach to AGI. In: Hammer, P., Agrawal, P., Goertzel, B., Iklé, M. (eds) Artificial General Intelligence. AGI 2019. Lecture Notes in Computer Science(), vol 11654. Springer, Cham. https://doi.org/10.1007/978-3-030-27005-6_5

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  • DOI: https://doi.org/10.1007/978-3-030-27005-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27004-9

  • Online ISBN: 978-3-030-27005-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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