NDLI: Evolving Intelligence in Humans and Machines: Integrative Evolving Connectionist Systems Approach

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Front cover - IEEE Computational Intelligence Magazine - Aug. 2008

2009 IEEE Computational Intelligence Society Awards

Table of contents - Vol 3 No 3

Fuzzy Intelligence [Editor's Remarks]

Let the Games Begin [President's Message]

Interview with Teuvo Kohonen, Helsinki University of Technology, Finland [Career Profile]

Ottawa Chapter Report [Family Corner]

Fuzzy Modeling and Fuzzy Control [Book Review]

Toward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift

Evolving Intelligence in Humans and Machines: Integrative Evolving Connectionist Systems Approach

Biomimicry and Fuzzy Modeling: A Match Made in Heaven

Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory

Conference Calendar

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Evolving Intelligence in Humans and Machines: Integrative Evolving Connectionist Systems Approach

Content Provider	IEEE Xplore Digital Library
Author	Kasabov, N.
Copyright Year	2005
Abstract	One of the main properties of human intelligence is that it is evolving (developing, revealing, unfolding) based on: (1) Genetically "wired" rules; (2) Experience and learning during life time. The paper argues that we need to understand how the brain operates at its different levels of information processing and then use some of these principles "when building intelligent machines. Without "drowning" into the sea of details, some main principles of information processing in the brain at cognitive-, neuronal-, genetic-, and particle field information levels are reviewed. The paper takes the approach towards understanding and building integrative connectionist systems, that integrate principles and rules from different hierarchical levels of information processing in their dynamic interaction, as an approach to develop intelligent machines. Examples given include: simple evolving connectionist systems; evolving spiking neural networks; integrative neurogenetic models; genetically defined robots; quantum evolutionary algorithms for exponentially faster optimization; integrative quantum neural networks. Some of the new integrative models are significantly faster in feature selection and learning and can be used to solve efficiently NP complete biological and engineering problems for adaptive, incremental learning in a large dimensional space-an important feature of the human intelligence. They can also help to better understand complex information processes in the brain, especially how information processes at different information levels interact to achieve a higher level intelligent human behavior. Open questions, challenges and directions for further research are presented.
File Size	2246843
File Format	PDF
ISSN	1556603X
Volume Number	3
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine intelligence Humans Learning systems Information processing Intelligent structures Biological neural networks Biological system modeling Intelligent robots Orbital robotics Evolutionary computation
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Theoretical Computer Science

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