Skip to main content
SpringerLink
Log in
Book cover

International Conference on Neural Information Processing

ICONIP 2016: Neural Information Processing pp 275–281Cite as

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

  • Conference paper
  • First Online:

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

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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    http://www.nengo.ca/.

  2. 2.

    http://wba-initiative.org/.

  3. 3.

    http://wbawakate.jp/.

References

  1. Yann, L., Bengio, Y., Hinton, J.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  2. Goertzel, B.: Artificial general intelligence: concept, state of the art, and future prospects. J. JSAI 29(3), 228–233 (2014)

    Google Scholar 

  3. Goertzel B., et. al.: A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures. Neurocomputing 74, 30–49 (2010)

    Google Scholar 

  4. Oh, S.W., et al.: A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014)

    Article  Google Scholar 

  5. Eliasmith, C.: How to Build a Brain: A Neural Architecture for Biological Cognition. Oxford Series on Cognitive Models and Architectures (2013)

    Google Scholar 

  6. Petersen, S.E., Sporns, O.: Brain networks and cognitive architectures. Neuron 88(1), 207–219 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

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

Author information

Authors and Affiliations

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

    Hiroshi Yamakawa, Masahiko Osawa & Yutaka Matsuo

Authors
  1. Hiroshi Yamakawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Masahiko Osawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yutaka Matsuo
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46686-6

  • Online ISBN: 978-3-319-46687-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation