Mixture of Experts Explained — Switch Transformers

AI Impact Summary

The Mixture of Experts (MoE) architecture is being introduced, leveraging sparse activation and conditional computation to train massive models efficiently. This involves replacing dense feed-forward network layers with MoE layers, which consist of a gate network and multiple experts. The goal is to achieve the quality of larger dense models with significantly reduced compute costs, exemplified by models like Switch Transformers. This shift represents a key advancement in scaling transformer models, particularly in NLP, and is driven by the need for faster inference and reduced memory requirements.