As rumors circulate about new generative AI technologies like the promising Mamba, the "Mixture of Experts" (MoE) architecture emerges as the most promising approach in terms of efficiency and achieves excellent qualitative results with a limited number of parameters. This architecture has enabled models like Mixtral 8x7B to achieve remarkable results, approaching those of GPT-4, which itself relies on MoE.
MoE draws inspiration from the idea of "collaboration" among "expert" sub-models, which are smaller, specialized neural networks. These specializations can be diverse: syntactic, modal, domain-specific, etc. To simplify, MoE can be described as an assembly where different individuals, each with different strengths, handle a request and provide the appropriate response. A "gating network" (sometimes called a "router") calculates a weighting for each expert, and the outputs of the experts are then combined to generate a final output. This approach thus allows for a more efficient breakdown of user requests into multiple sub-domains.
This method of dividing requests is somewhat similar to the multi-agent approach and has several advantages:
- Increased efficiency: MoE distributes the computational load among the experts, resulting in improved efficiency and reduced training time.
- Better performance: Expert specialization leads to improved precision and adaptability to different types of tasks.
- Flexibility: The MoE architecture is flexible and can be easily adapted to different types of models and problems.
This modular approach also allows for the possibility of even more specialized MoEs, where workflows are divided and distributed across experts finely tuned to very specific partitions. The MoE architecture represents a promising advancement for generative AI. Its ability to enhance efficiency, performance, model flexibility, and especially its effectiveness brings us closer to a vision where the cost of using an AI solution based on specific use cases becomes increasingly accessible to all.