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

Socpus ID

84898875912 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84898875912

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