MoE – Page 2 – Lechuck Park

This image depicts a conceptual diagram of the “MOE (Mixture of Expert)” system, effectively illustrating the similarities between human expert collaboration structures and AI model MoE architectures.

The key points of the diagram are:

The upper section shows a traditional human expert collaboration model:
- A user presents a complex problem (“Please analyze the problem now”)
- An intermediary agent distributes this to appropriate experts (A, B, C Experts)
- Each expert analyzes the problem and provides solutions from their specialized domain
The lower section demonstrates how this same structure is implemented in the AI world:
- When a user’s question or command is input
- The LLM Foundation Expert Model processes it
- The Routing Expert Model distributes tasks to appropriate specialized models (A, B, C Expert Models)

This diagram emphasizes that human expert systems and AI MoE architectures are fundamentally similar. The approach of utilizing multiple experts’ knowledge to solve complex problems has been used in human settings for a long time, and the AI MoE structure applies this human-centered collaborative model to AI systems. The core message of this diagram is that AI models are essentially performing the roles that human experts would traditionally fulfill.

This perspective suggests that mimicking human problem-solving approaches can be effective in AI system design.

With Claude

From Claude with some prompting
This image illustrates the key components of a Mixture of Experts (MoE) model architecture. An MoE model combines the outputs of multiple expert networks to produce a final output.

The main components are:

Expert Network: This represents a specialized neural network trained for a specific task or inputs. Multiple expert networks can exist in the architecture.
Weighting Scheme: This component determines how to weight and combine the outputs from the different expert networks based on the input data.
Routing Algorithm: This algorithm decides which expert network(s) should handle a given input based on the specific inputs. It essentially routes the input data to the appropriate expert(s).

The workflow is as follows: The specific inputs are fed into the routing algorithm (3), which decides which expert network(s) should process those inputs. The selected expert network(s) (1) process the inputs and generate outputs. The weighting scheme (2) then combines these expert outputs into a final output based on a small neural network.

The key idea is that different expert networks can specialize in different types of inputs or tasks, and the MoE architecture can leverage their collective expertise by routing inputs to the appropriate experts and combining their outputs intelligently.