Neural networks for socio-labor regulation: a neuromorphic approach to human-centric AI in urban economies

Building on the high-throughput processing capabilities of the human brain, researchers have developed an innovative method for AI systems to more effectively process olfactory data and other sensory signals. The olfactory bulb, which acts as a natural firewall, filters information between the external environment and the brain, sorting it out without causing excessive energy consumption. This approach, known as neuromorphic, could inspire the d…

Neural networks for socio-labor regulation: a neuromorphic approach to human-centric AI in urban economies

This study investigates the implementation and future potential of neural networks for socio-labor regulation within the urban economies of BRICS megacities, emphasizing a human-centric AI approach. Analysis reveals significant disparities in AI development across these urban centers, with Bei...