Theoretical Foundations of Artificial General Intelligence
Volume 4 of the series Atlantis Thinking Machines pp 89-102
Deep Reinforcement Learning as Foundation for Artificial General Intelligence
- Itamar ArelAffiliated withMachine Intelligence Lab, Department of Electrical Engineering and Computer Science, University of Tennessee Email author
Abstract
Deep machine learning and reinforcement learning are two complementing fields within the study of intelligent systems. When combined, it is argued that they offer a promising path for achieving artificial general intelligence (AGI). This chapter outlines the concepts facilitating such merger of technologies and motivates a framework for building scalable intelligent machines. The prospect of utilizing custom neuromorphic devices to realize large-scale deep learning architectures is discussed, paving the way for achieving human level AGI.
- Title
- Deep Reinforcement Learning as Foundation for Artificial General Intelligence
- Book Title
- Theoretical Foundations of Artificial General Intelligence
- Pages
- pp 89-102
- Copyright
- 2012
- DOI
- 10.2991/978-94-91216-62-6_6
- Print ISBN
- 978-94-91216-61-9
- Online ISBN
- 978-94-91216-62-6
- Series Title
- Atlantis Thinking Machines
- Series Volume
- 4
- Series ISSN
- 1877-3273
- Publisher
- Atlantis Press
- Copyright Holder
- ATLANTIS PRESS
- Additional Links
- Topics
- Industry Sectors
- eBook Packages
- Editors
-
- Pei Wang (ID1)
- Ben Goertzel (ID2)
- Editor Affiliations
-
- ID1. , Department of Computer, Temple University
- ID2. Biomind LLC, Novamente LLC/
- Authors
-
-
Itamar Arel
(1)
-
Itamar Arel
- Author Affiliations
-
- 1. Machine Intelligence Lab, Department of Electrical Engineering and Computer Science, University of Tennessee, Chattanooga, TN, USA
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