A brain-inspired framework for evolutionary artificial general intelligence
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From the medical field to agriculture, from energy to transportation, every industry is going through a revolution by embracing artificial intelligence (AI); nevertheless, AI is still in its infancy when compared to the cognitive abilities of a biological brain. To bridge this gap, inspired by the evolution of the human brain, this research demonstrates a novel method and framework to synthesize an artificial brain with cognitive abilities by taking advantage of the same process responsible for the growth of the biological brain called “neuroembryogenesis." This framework shares some of the key behavioral aspects of the biological brain such as spiking neurons, neuroplasticity, neuronal pruning, and excitatory and inhibitory interactions between neurons, together making it capable of learning and memorizing. One of the highlights of the proposed design is its potential to incrementally improve itself over generations based on system performance, using genetic algorithms. Achieving artificial general intelligence (AGI) is not an easy task; yet, the methodology and approach discussed in this research could pave the way and make it achievable. A proof of concept at the end of this writing demonstrates how a simplified implementation of the human visual cortex using the proposed framework is capable of character recognition. The entire codebase has been open sourced and is accessible at www.FEAGI.org.
Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Electrical Engineering & Computer Science