This paper introduces a preliminary concept aimed at achieving Artificial General Intelligence (AGI) by leveraging a novel approach rooted in two key aspects. Firstly, we present the General Intelligent Network(GIN) paradigm, which integrates information entropy principles with a generative network, reminiscent of Generative Adversarial Networks(GANs). Within the GIN network, original multimodal information is encoded as low information entropy hidden state representations (HPPs). These HPPs serve as efficient carriers of contextual information, enabling reverse parsing by contextually relevant generative networks to reconstruct observable information.Secondly, we propose a Generalized Machine Learning Operating System (GML System) to facilitate the seamless integration of the GINparadigm into the AGI framework. The GML system comprises three fundamental components: an Observable Processor (AOP) responsiblefor real-time processing of observable information, an HPP Storage Systemfor the efficient retention of low entropy hidden state representations, and a Multimodal Implicit Sensing/Execution Network designed to handle diverse sensory inputs and execute corresponding actions.