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Whole Brain Architecture Approach Is a Feasible Way Toward an Artificial General Intelligence

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9947))

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Abstract

In recent years, a breakthrough has been made in infant level AI due to the acquisition of representation, which was realized by deep learning. By this, the construction of AI that specializes in a specific task that does not require a high-level understanding of language is becoming a possibility. The primary remaining issue for the realization of human-level AI is the realization of general intelligence capable of solving flexible problems by combining highly reusable knowledge. Therefore, this research paper explores the possibility of approaching artificial general intelligence with such abilities based on mesoscopic connectome.

The Whole Brain Architecture Initiative, a specified non-profit organization.

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Notes

  1. 1.

    http://www.nengo.ca/.

  2. 2.

    http://wba-initiative.org/.

  3. 3.

    http://wbawakate.jp/.

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Acknowledgements

Thanks to all members, advisors and supporters of the WBAI and the various members of the WBA Future Leaders.

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Authors and Affiliations

  1. Dwango Artificial Intelligence Laboratory, DWANGO Co., Ltd., Kyoto, Japan

    Hiroshi Yamakawa, Masahiko Osawa & Yutaka Matsuo

Authors
  1. Hiroshi Yamakawa
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  2. Masahiko Osawa
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  3. Yutaka Matsuo
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Corresponding author

Correspondence to Hiroshi Yamakawa .

Editor information

Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

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Yamakawa, H., Osawa, M., Matsuo, Y. (2016). Whole Brain Architecture Approach Is a Feasible Way Toward an Artificial General Intelligence. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_30

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  • DOI: https://doi.org/10.1007/978-3-319-46687-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46686-6

  • Online ISBN: 978-3-319-46687-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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