Title
Carving Out Evolutionary Paths Towards Greater Complexity
Abstract
We really know of only a single intelligence abstraction approach that truly does work, the one based on the interconnection of spatio-temporal signal integrators in a vast graph: Neural Network. We also know of only one method that was able to generate such abstracted intelligence: Evolution. The proof that this abstraction and this generative method works is us, you and I, the result of billions of years of trial and error. There is nothing mystical about the human brain, it is but a vast graph of signal integrators, carved out in flesh through billions of years of evolution. In this paper we discuss: intelligence abstraction based on neural networks, complex-valued artificial neurons and their computational potential to be equivalent to biological ones, the approaches that could result in the generation of such intelligent graphs of interconnected complex-valued neurons, an architecture of infomorphs whose brains are complex-valued neural substrates, and why an ALife approach on high enough granularity level is our best chance of evolving organisms that are truly intelligent. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.
Publication Date
1-1-2013
Publication Title
AAAI Fall Symposium - Technical Report
Volume
FS-13-02
Number of Pages
108-113
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
Copyright Status
Unknown
Socpus ID
84898875912 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84898875912
STARS Citation
Sher, Gene I., "Carving Out Evolutionary Paths Towards Greater Complexity" (2013). Scopus Export 2010-2014. 7633.
https://stars.library.ucf.edu/scopus2010/7633