A Survey of Mixture of Experts Models: Architectures and Applications in Business and Finance

This paper provides a comprehensive overview of MoE, covering its fundamental principles, architectural variations, advantages, limitations, and potential future directions. We delve into the core concepts of MoE, including the gating network, expert networks, and routing mechanisms, and discuss how these components work together to achieve specialization and efficiency. We also examine the application of MoE in models like GPT-4 and Mixtral, highlighting their impact on the field of AI. We cover theoretical foundations, hardware and software innovations, real-world deployments, and the evolving landscape of MoE research. This paper furthur provides a comprehensive survey of MoE architectures, tracing their evolution from early neural network implementations to modern large-scale applications in language models, time series forecasting, and tabular data analysis. The paper then explores diverse applications across domains such as natural language processing, computer vision, finance, and healthcare. We discuss key challenges including routing imbalances, memory fragmentation, and training instability, while reviewing recent solutions proposed in the literature. Finally, we identify promising future research directions and the potential impact of MoE models on the next generation of artificial intelligence systems.