Towards Artificial Hydrocarbon Networks: The Chemical Nature of Data-Driven Approaches

Inspiration in nature has been widely explored, from macro to micro-scale. Natural phenomena mainly considers adaptability, optimization, robustness, organization, among other properties, to deal with complexity. When looking into chemical phenomena, stability and organization are two properties that emerge. Recently, artificial hydrocarbon networks (AHN), a supervised learning method inspired in the inner structures and mechanisms of chemical compounds, have been proposed as a data-driven approach in artificial intelligence. AHN have been successfully applied in data-driven approaches, such as: regression and classification models, control systems, signal processing, and robotics. To do so, molecules –the basic units of information in AHN–play an important role in the stability, organization and interpretability of this method. Until now, building the architecture of AHN has been treated as a whole entity; but distributed computing mechanisms, as well as the exploitation of hierarchical organization of molecules, can enhance the performance of AHN. Thus, this paper aims to discuss challenges and trends of artificial hydrocarbon networks as a data-driven method, with emphasis on packaging, distributed computing and hierarchical properties. Throughout this work, it presents a description of the main insights of AHN and the proposed distributed and hierarchical mechanisms in molecules. Potential applications and future trends on AHN are also discussed.