Paper Title
Universal AI For Multi-Domain Integration

Abstract
Artificial Intelligence (AI) systems have made remarkable progress in specialized tasks, but their ability to generalize across multiple domains remains a significant challenge. Traditional AI models are typically trained for a single domain, limiting their adaptability to new environments. This paper proposes a Universal AI framework that integrates Meta-Learning (Model-Agnostic Meta-Learning, MAML) and Multi-Modal Transformers (CLIP, Flamingo) to create a robust AI system capable of cross-domain generalization. The proposed system enables knowledge transfer between domains while efficiently handling heterogeneous data types. We evaluate the model across diverse benchmark datasets and demonstrate its superiority in adaptability, cross-modal learning, and real-world application potential.