Demystifying the Mixture of Experts in AI: Not What You Think

Introduction

In this world of fast evolution in artificial intelligence, misconceptions may arise about many things. A very good example of that is the "Mixture of Experts" technique in large language models. As the name might have suggested, that would be a team of specialized models, but the reality is actually much simpler and ingenious. Let us delve deep into this interesting aspect of AI and clear up some misunderstandings.

The True Nature of "Experts" in AI

The name "Mixture of Experts" makes it sound like teams of AI models are present, each really good at a specific task. That is not how this goes whatsoever. In fact, the "experts" are actually other subparts of one big AI system that work with each other in order to get the job done more efficiently.

Imagine there is an orchestra, and ideally, every musician should be only a solo player, but instead, they all play a symphony. In a similar way, the specialists at MoE collaborate to process information more effectively without them having their own specializations.

The Scale of Modern AI is Mind-Boggling

To get into this complexity we are faced with, consider:

• GPT-4-like model: estimated to have close to 1.8 trillion parameters.
• That is 1.8 million million!
• If a human were to process one parameter per second non-stop, it would take about 57,000 years.

Remarkably, transformer models using MoE can process this astronomical number of parameters in milliseconds. How? The answer lies in the brilliant engineering behind these systems.

Inside the Transformer Model

Large transformer models power many of today's sophisticated AI systems. Their process involves several steps:

1. Tokenization: Breaking down text input into smaller units, similar to words or phrases.
2. Embedding: Creating numerical representations for each token. Here’s more about Embeddings
3. Positional Encoding: Adding information about the position of each token in the sequence.
4. Transformer Block Processing: This is where the magic happens, featuring:
• Attention Mechanism: Identifying important parts of the input.
• Feed-Forward Network: Further processing each token.

Enter the Mixture of Experts

In models like Mixtral, the MoE technique replaces a single feed-forward network with multiple smaller networks or "experts." This approach offers several advantages:

• Parallel Processing: Multiple experts work simultaneously, improving efficiency.
• Scalability: The system can handle enormous volumes of data.
• Sparse Activation: Only a subset of experts processes each token, saving computational resources.

Debunking the Specialization Myth

Despite the name "experts," these components don't actually specialize in particular tasks. Research has shown that:

• The router network randomly routes tokens to experts.
• There's no observable pattern of specialization among experts.

Instead of having specialists, the MoE approach creates a more generalized, randomized approach to information processing.

The Evolution of Mixture of Experts

The MoE concept isn't new in AI. It started with early work in 2013 on small, confined environments. As AI technology scaled up, this concept was adapted to transformer models, leading to powerful systems like GPT-4 and Mixtral.

Conclusion: A Fresh Perspective

Understanding the true nature of the Mixture of Experts technique helps us appreciate the ingenious ways AI researchers are pushing boundaries. By using many small networks instead of a single large one, these models become more efficient and scalable.

As AI continues to advance, new methods will emerge, challenging our understanding of technology. The Mixture of Experts is just one example of how innovative thinking in AI can lead to remarkable advancements—even if it means redefining what we thought we knew.