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Comparative Analysis of Advanced AI Architectures: Fourier Analysis Networks, Google Titan Transformer 2.0, and MoE-JEPA World Models

The field of artificial intelligence has experienced remarkable evolution with several novel architectures emerging to address the limitations of conventional deep learning approaches. This research provides a comprehensive comparative analysis of three cutting-edge AI architectures: Fourier Analysis Networks (FANs), Google Titan Transformer 2.0, and Mixture of Experts Joint Embedding Predictive Architecture (MoE-JEPA) World Models. Each model employs distinct approaches to overcome current AI limitations, particularly in handling periodic structures, long-term dependencies, and context understanding. Through detailed examination of their architectures, operational mechanisms, advantages, limitations, and empirical performance, this study offers insights into their potential impact on the future trajectory of artificial intelligence research and applications.