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A Roadmap Towards Machine Intelligence

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Computational Linguistics and Intelligent Text Processing (CICLing 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9623))

Abstract

The development of intelligent machines is one of the biggest unsolved challenges in computer science. In this paper, we propose some fundamental properties these machines should have, focusing in particular on communication and learning. We discuss a simple environment that could be used to incrementally teach a machine the basics of natural-language-based communication, as a prerequisite to more complex interaction with human users. We also present some conjectures on the sort of algorithms the machine should support in order to profitably learn from the environment.

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Acknowledgments

We thank Léon Bottou, Yann LeCun, Gabriel Synnaeve, Arthur Szlam, Nicolas Usunier, Laurens van der Maaten, Wojciech Zaremba and others from the Facebook AI Research team, as well as Gemma Boleda, Katrin Erk, Germán Kruszewski, Angeliki Lazaridou, Louise McNally, Hinrich Schütze and Roberto Zamparelli for many stimulating discussions. An early version of this proposal has been discussed in several research groups since 2013 under the name Incremental learning of algorithms (Mikolov 2013).

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Authors and Affiliations

  1. Facebook AI Research, Menlo Park, USA

    Tomas Mikolov, Armand Joulin & Marco Baroni

  2. University of Trento, Trento, Italy

    Marco Baroni

Authors
  1. Tomas Mikolov
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  2. Armand Joulin
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  3. Marco Baroni
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Correspondence to Tomas Mikolov .

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Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Mikolov, T., Joulin, A., Baroni, M. (2018). A Roadmap Towards Machine Intelligence. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9623. Springer, Cham. https://doi.org/10.1007/978-3-319-75477-2_2

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  • DOI: https://doi.org/10.1007/978-3-319-75477-2_2

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