Replacing the Data Processing Computer with
Brain-like Learning Machines
A new Communication and
Computing Paradigm based on the Autosophy Information Theory
Authors: Klaus Holtz, Eric Holtz, Diana Kalienky.
Published / Presented at: IPSI – 2005 USA, Cambridge, July 7 – 10, 2005. International Conference on Advances in the Internet, Processing, Systems, and Interdisciplinary Research. ISBN: 86-7466-117-3.
Level: Expert
Abstract: Data processing computers may soon be eclipsed by a next
generation of brain-like learning machines based on the "Autosophy"
information theory. This will have a profound impact on communication and
computing applications. Data processing computers are essentially adding or
calculating machines that cannot find "meaning" as our own brains
obviously can. No matter the speed of computation or the complexity of the
software, computers will not evolve into brain-like machines. All that can be
achieved are mere simulations. The basic problem can be traced back to an outdated
(Shannon) information theory that treats all data items (such as ASCII
characters or pixels) as "quantities" in meaningless bit streams. In
1974 Klaus Holtz developed a new Autosophy information theory, which treats all
data items as "addresses." The original Autosophy research explains
the functioning of self-assembling natural structures, such as chemical
crystals or living trees. The same natural laws and principles can also produce
self-assembling data structures, which grow like data crystals or data trees in
electronic memories, without computing or programming. Replacing the programmed
data processing computer with brain-like, self-learning, failure-proof
"autosopher" promises a true paradigm shift in technology, resulting
in system architectures with true "learning" and eventually true
Artificial Intelligence
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