Learning from Nature — Artificial Neural Networks

8 LEARNING FROM NATURE — ARTIFICIAL NEURAL NETWORKS 

The development efforts on expert systems in the 1970s and 1980s pointed out a particular weakness the inability to mimic certain important capabilities of the human brain, like association and learning aptitude. To achieve these capabilities in a computer program, it seemed to be necessary to build systems comprising architecture similar to human brain. 

Artificial neural networks have been applied successfully across an extraordinary range of classification, prediction, association, and mapping problem domains. The success of this technique can be attributed to a few key factors  

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Concomitantly with the increase in hardware capabilities, better software techniques will have to be developed. It will pay us to continue to learn how nature tackles problems. Artificial neural networks are a far cry away from the capabilities of the human brain. There is a lot of room left from the information processing of the human brain in order to develop more powerful artificial neural networks. Nature has developed over millions of years efficient optimization methods for adapting to changes in the environment. The development of evolutionary and genetic algorithms will continue. 

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