I am working on an experimental research project focused on catastrophic forgetting and the efficiency of incremental learning. Current AI paradigms often rely on scaling massive models with billions of parameters to integrate new knowledge. While effective, this approach can be inefficient and computationally expensive for incremental or domain-specific learning.
In this project, the model is structured as a system of interconnected modules, where each module specializes in a specific domain and maintains its knowledge independently, coordinated by a Global Bayesian Node.
Challenges addressed:
• Catastrophic forgetting
• Scalability of specialized knowledge modules
• Efficient incremental learning with small datasets
I’m interested in discussing ideas, design choices, or related research. If anyone has worked on similar challenges, I’d love to hear your insights.
(I can share more architectural details if helpful.)