A Mixture of Experts is a way of organizing an
AI model that combines multiple specialized systems ("experts") with a system that decides which expert is best suited for each task, like a director coordinating a team of specialists to solve problems more efficiently.
In an MoE system, each "expert" is a
neural network trained to handle specific types of tasks or data. A component called a "router" analyzes each input and decides which expert or combination of experts should process it, thus optimizing the system's performance and efficiency. Think of a hospital where different specialists treat different types of medical cases, and a medical director decides which doctor is most appropriate for each patient.
This architecture is more efficient than using a single large model because it only activates the experts needed for each task. For example, in a
large language model using MoE, it would be like having specialized experts: some in grammar, others in mathematics, others in scientific knowledge or literary creativity. This allows complex problems to be solved more effectively and with fewer computational resources.