Stacked neural networks must emulate evolution's hierarchical complexity

World Futures 64 (5-7):444 – 451 (2008)
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.
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DOI 10.1080/02604020802301568
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