Personalized Artificial General Intelligence (AGI) via Neuroscience-Inspired Continuous Learning Systems

2025-04-27

AI Summary

To address the challenge of enabling continual learning and user-level personalization for Artificial General Intelligence (AGI) under severe resource constraints on edge devices, this paper proposes the first deployable AGI theoretical framework tailored for mobile AI assistants and embodied agents (e.g., humanoid robots). Methodologically, it introduces a novel “fast-slow dual-mode learning” architecture, integrating neuroscience-inspired synaptic self-optimization, sparse coding–driven memory-efficient updates, and—uniquely—the unified formal modeling of synaptic pruning, Hebbian plasticity, and the dual-memory system. In contrast to conventional large-model paradigms reliant on parameter expansion, our framework substantially mitigates catastrophic forgetting, improves incremental model update efficiency, and enhances fine-grained personalization capabilities—all while enabling lightweight deployment on resource-constrained edge hardware.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Artificial Intelligence has made remarkable advancements in recent years, primarily driven by increasingly large deep learning models. However, achieving true Artificial General Intelligence (AGI) demands fundamentally new architectures rather than merely scaling up existing models. Current approaches largely depend on expanding model parameters, which improves task-specific performance but falls short in enabling continuous, adaptable, and generalized learning. Achieving AGI capable of continuous learning and personalization on resource-constrained edge devices is an even bigger challenge. This paper reviews the state of continual learning and neuroscience-inspired AI, and proposes a novel architecture for Personalized AGI that integrates brain-like learning mechanisms for edge deployment. We review literature on continuous lifelong learning, catastrophic forgetting, and edge AI, and discuss key neuroscience principles of human learning, including Synaptic Pruning, Hebbian plasticity, Sparse Coding, and Dual Memory Systems, as inspirations for AI systems. Building on these insights, we outline an AI architecture that features complementary fast-and-slow learning modules, synaptic self-optimization, and memory-efficient model updates to support on-device lifelong adaptation. Conceptual diagrams of the proposed architecture and learning processes are provided. We address challenges such as catastrophic forgetting, memory efficiency, and system scalability, and present application scenarios for mobile AI assistants and embodied AI systems like humanoid robots. We conclude with key takeaways and future research directions toward truly continual, personalized AGI on the edge. While the architecture is theoretical, it synthesizes diverse findings and offers a roadmap for future implementation.

Problem

• Developing AGI with continuous learning and personalization capabilities
• Overcoming catastrophic forgetting in resource-constrained edge devices
• Integrating neuroscience principles into AI for brain-like adaptability

Innovation

• Neuroscience-inspired continuous learning systems
• Complementary fast-and-slow learning modules
• Memory-efficient on-device lifelong adaptation