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Throwing Down the Visual Intelligence Gauntlet

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Machine Learning for Computer Vision

Part of the book series: Studies in Computational Intelligence ((SCI,volume 411))

Abstract

In recent years, scientific and technological advances have produced artificial systems that have matched or surpassed human capabilities in narrow domains such as face detection and optical character recognition. However, the problem of producing truly intelligent machines still remains far from being solved. In this chapter, we first describe some of these recent advances, and then review one approach to moving beyond these limited successes – the neuromorphic approach of studying and reverse-engineering the networks of neurons in the human brain (specifically, the visual system). Finally, we discuss several possible future directions in the quest for visual intelligence.

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Authors and Affiliations

  1. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, UK

    Cheston Tan, Joel Z. Leibo & Tomaso Poggio

Authors
  1. Cheston Tan
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  2. Joel Z. Leibo
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  3. Tomaso Poggio
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Corresponding author

Correspondence to Cheston Tan .

Editor information

Editors and Affiliations

  1. Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom

    Roberto Cipolla

  2. Dipartimento di Matematica e Informatica, Università di Catania, Viale Andrea Doria 6, Catania, 95125, Catania, Italy

    Sebastiano Battiato

  3. Dipartimento di Matematica e Informatica, Università di Catania, Viale A. Doria 6, Catania, 95125, Italy

    Giovanni Maria Farinella

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Tan, C., Leibo, J.Z., Poggio, T. (2013). Throwing Down the Visual Intelligence Gauntlet. In: Cipolla, R., Battiato, S., Farinella, G. (eds) Machine Learning for Computer Vision. Studies in Computational Intelligence, vol 411. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28661-2_1

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  • DOI: https://doi.org/10.1007/978-3-642-28661-2_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28660-5

  • Online ISBN: 978-3-642-28661-2

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