Stability-First AI: Exploring Continuous Learning and Memory Retention Mechanisms in Neural Networks

This project focuses on the catastrophic forgetting problem in neural networks, aiming to achieve knowledge stability and modular learning capabilities in continuous learning scenarios. It is a key exploration direction towards general artificial intelligence.

Catastrophic forgetting refers to the phenomenon where neural networks overwrite old knowledge when learning new tasks, which exists in supervised learning, reinforcement learning, and real-world applications (such as recommendation systems and autonomous driving). Humans avoid forgetting through neural plasticity regulation, memory consolidation, modular organization, and replay mechanisms, providing inspiration for AI solutions.

The project unfolds from four directions: 1. Weight stability protection (EWC, SI, MAS); 2. Architectural modular design (Progressive NN, MoE, Modular NN); 3. Memory replay and generation (experience replay, generative replay, feature replay); 4. Meta-learning and fast adaptation (MAML, GEM).

Benchmark datasets such as Split MNIST/CIFAR and Permuted MNIST are used. Evaluation indicators include average accuracy, forgetting rate, learning ability, and computational efficiency to ensure the effectiveness and practicality of the methods.

Catastrophic forgetting has not yet been fully resolved, but the technical paths explored by the project provide hope for continuous learning. In the future, it is necessary to integrate multiple technologies, deeply draw on biological mechanisms, explore dynamic architectures and knowledge representation learning, and promote the development of general AI.

It can be applied in fields such as personalized AI assistants, lifelong learning robots, continuously evolving recommendation systems, and continuous updates of medical AI, addressing the needs of knowledge accumulation and adaptation in practical scenarios.