Throwing Down the Visual Intelligence Gauntlet

Cheston Tan; Joel Z. Leibo; Tomaso Poggio

doi:10.1007/978-3-642-28661-2_1

Throwing Down the Visual Intelligence Gauntlet

Cheston Tan, Joel Z. Leibo, Tomaso Poggio

Research output: Chapter in Book/Report/Conference proceeding › Chapter

5 Scopus citations

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.

Original language	English (US)
Title of host publication	Studies in Computational Intelligence
Publisher	Springer Nature
Pages	1-15
Number of pages	15
ISBN (Print)	9783642286605
DOIs	https://doi.org/10.1007/978-3-642-28661-2_1
State	Published - 2013
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This report describes research done at the Center for Biological & Computational Learning, which is in the McGovern Institute for Brain Research at MIT, as well as in the Dept. of Brain & Cognitive Sciences, and which is affiliated with the Computer Sciences & Artificial Intelligence Laboratory (CSAIL). This research was sponsored by grants from DARPA (IPTO and DSO), National Science Foundation (NSF-0640097, NSF-0827427), AFSOR-THRL (FA8650-05-C-7262). Additional support was provided by: Adobe, Honda Research Institute USA, King Abdullah University of Science and Technology grant to B. DeVore, NEC, Sony and especially by the Eugene McDermott Foundation.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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10.1007/978-3-642-28661-2_1

Cite this

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title = "Throwing Down the Visual Intelligence Gauntlet",

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.",

author = "Cheston Tan and Leibo, {Joel Z.} and Tomaso Poggio",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This report describes research done at the Center for Biological & Computational Learning, which is in the McGovern Institute for Brain Research at MIT, as well as in the Dept. of Brain & Cognitive Sciences, and which is affiliated with the Computer Sciences & Artificial Intelligence Laboratory (CSAIL). This research was sponsored by grants from DARPA (IPTO and DSO), National Science Foundation (NSF-0640097, NSF-0827427), AFSOR-THRL (FA8650-05-C-7262). Additional support was provided by: Adobe, Honda Research Institute USA, King Abdullah University of Science and Technology grant to B. DeVore, NEC, Sony and especially by the Eugene McDermott Foundation. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2013",

doi = "10.1007/978-3-642-28661-2_1",

language = "English (US)",

isbn = "9783642286605",

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AU - Tan, Cheston

AU - Leibo, Joel Z.

AU - Poggio, Tomaso

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This report describes research done at the Center for Biological & Computational Learning, which is in the McGovern Institute for Brain Research at MIT, as well as in the Dept. of Brain & Cognitive Sciences, and which is affiliated with the Computer Sciences & Artificial Intelligence Laboratory (CSAIL). This research was sponsored by grants from DARPA (IPTO and DSO), National Science Foundation (NSF-0640097, NSF-0827427), AFSOR-THRL (FA8650-05-C-7262). Additional support was provided by: Adobe, Honda Research Institute USA, King Abdullah University of Science and Technology grant to B. DeVore, NEC, Sony and especially by the Eugene McDermott Foundation. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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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BT - Studies in Computational Intelligence

PB - Springer Nature

ER -

Throwing Down the Visual Intelligence Gauntlet

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Bibliographical note

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