On the Relationship of Emergence and Non-Linear Dynamics to Machine Learning and Synthetic Consciousness

Current research in artificial intelligence has been primarily focused on machine learning as applied to specific computer and engineering problems. For example, facial recognition or malware detection. This aspect of artificial intelligence has seen numerous advances and continues to advance. Research regarding synthetic consciousness has not progressed on par with research into other sub domains of artificial intelligence. While there has been speculation regarding the eventuality of some level of consciousness being developed via artificial intelligence, no specific research modalities have been adequately developed. This paper focuses on two synthetic consciousness issues. The first issue is to provide a basis for developing synthetic consciousness. The second issue is outlining specific research modalities that have a significant probability of achieving synthetic consciousness. The premise of this paper is to outline how to most effectively create algorithmic systems that have a high probability of leading to the emergent property of consciousness.