[Steps-users] A New Neural Network Paper

[Shuling C. <shu...@gm...>]

To whom it may concern,



      This is Shuling Chen. I am a research assistant in Dare Institute,
Cambridge, MA. My supervisor Michael Lamport Commons, who is a professor
from department of Psychiatry at Harvard Medical School, published a paper
called "Stacked Neural Networks Must Emulate Evolution's Hierarchical
Complexity" (Commons, 2008). The paper mainly discussed current issues in
neural networks based on the Model of Hierarchical Complexity (Commons,
Trudeau, Stein, Richards, and Krause, 1998) and provided possible solutions
that can help resolve related problems. Feel free to contact me if you are
interested in the paper or related topics and I will email back to you as
soon as possible.





Stacked Neural Networks Must Emulate Evolution's Hierarchical Complexity

Abstract

The missing ingredients in efforts to develop neural networks and artificial
intelligence (AI) that can emulate human intelligence have been the
evolutionary processes of performing tasks at increased orders of
hierarchical complexity. Stacked neural networks based on the Model of
Hierarchical Complexity could emulate evolution’s actual learning processes
and behavioral reinforcement. Theoretically, this should result in
stability and reduce certain programming demands. The eventual success of
such methods begs questions of humans’ survival in the face of androids of
superior intelligence and physical composition. These raise future moral
questions worthy of speculation.



Reference:

Commons, M. L. (2008). Stacked Neural Networks Must Emulate Evolution's
Hierarchical Complexity. *World      Futures*, *64*(5-7), 444-451.



Commons, M. L., Trudeau, E. J., Stein, S. A., Richards, F. A., & Krause, S.
R. (1998). Hierarchical complexity of tasks shows the existence of
developmental stages. *Developmental Review*, *18*(3), 237-278.


-- 

Regards,





Shuling Chen
Research Assistant
Dare Institute
234 Huron Avenue
Cambridge, MA 02138

Phone: 631-406-3488Email Address: shu...@gm...