Implementation of AGI on Brain-Like Neuro-Network Structure

The proposed approach to creating AGI using brain-like neural networks combines principles that teach systems to effectively generalize, remember, plan, and reason across a variety of tasks, drawing on ideas from neuromorphic architectures, dynamic hierarchical information processing, and hybrid neural-symbolic methods. Brain-like architectures dynamically process event-driven information over time, provide online learning and low power consumption, and implement research projects on hardware-based implementation of online plasticity. Within the framework of brain-like neural networks, this involves hybrid approaches to world reconstruction and action planning. The goal is to enable the network to reason, manipulate symbols, and use rules, similar to natural intelligence. This is achieved using differentiable logic, modal induction, neural memory processors, neural program interpreters, and neural-modular networks for task composition. This improves long-term memory, planning, and reasoning accuracy with a limited training set. It also enables skill transfer from one task to another, simulating alternative actions without multiple interactions with the real world. Brain-like neural networks have a modular, scalable architecture with routing and an ensemble of specialized modules. Neural modules for vision, planning, dialogue, and motor control can be dynamically assembled without complete retraining. Memory, planning, and perception are separated into separate modules with mechanisms for collaboration and joint goal learning. AGI based on brain-like neural networks enables knowledge transfer across domains. Neuromorphic modules motivate themselves to explore and investigate the environment, which supports long-term adaptability, similar to biological mechanisms. This is essential for AGI to function in the real world.