Mixing Music Genres & Model Experts

Dr. Ryan Ries back again this week with an architectural concept that I think deserves some more hype: the Mixture of Experts (MoE).

If you've been following the latest developments in AI, you might have heard about models like Mistral, which has garnered significant attention for its impressive performance and cost efficiency.

But you might be wondering, what is the secret sauce behind these models?

The answer lies in the Mixture of Experts architecture.

Quick sidebar before we dive into MoE. I’m heading out on vacation, so next week, you won’t see me popping up in your inbox. I’ll be back on 6/26 with the next Mission Matrix.

Ok, now back to our regularly scheduled programming.

The Mystery of Mixture of Experts

Imagine a panel of experts sitting at a long table, each specializing in a different area. When a problem arises, a moderator (or orchestrator) routes the problem to the expert best equipped to handle it.

This is a simplified and easy way to understand the basics of how the Mixture of Experts architecture works in LLMs.

Instead of relying on a single, monolithic model to handle all tasks, MoE distributes the workload among multiple specialized "experts."

These experts are not explicitly defined by human engineers but are rather encoded within the model during the training process.

Why You Should Care

AI is super exciting, but the cost and computational requirements of large language models can be a major barrier to entry (which really puts a damper on our excitement).

This is where the MoE architecture comes in, offering a game-changing solution that can help organizations of all sizes harness the power of AI without breaking the bank.

By distributing the workload among specialized experts, MoE allows models to achieve unprecedented levels of performance while keeping computational costs in check.

This means that even small and medium-sized enterprises can now access the benefits of advanced AI technologies, leveling the playing field and giving an opportunity for more companies to innovate.

Innovating on a Budget

Imagine being able to implement a state-of-the-art language model for customer support, content generation, or data analysis without worrying about the prohibitive costs of running a 100+ billion parameter model. With MoE, this is now a reality.

As we've seen with Mistral, a model with 176 billion parameters can be run with just 22 billion parameters active at any given time.

This represents a significant leap forward in terms of cost efficiency and opens up a world of possibilities for businesses looking to leverage AI to drive growth and innovation.

So, whether you're an early-stage startup or an established enterprise, the Mixture of Experts architecture is a development you can't afford to ignore.

In fact, I (well, my AI voice clone) recently had the opportunity to discuss the implications of MoE and the future of AI on our podcast, Mission: Generate.

If you found this week’s Matrix interesting, I highly recommend checking out the latest episode.

As always, if you have any questions or want to discuss how MoE or other cutting-edge AI technologies could benefit your organization, don't hesitate to reach out. At Mission Cloud, we're passionate about helping businesses succeed with AI.

Until next time,

Ryan

Now, here's this week’s AI-generated image & the prompt that I used. I got a good chuckle out of what was generated this week…

"Use a pop culture reference to generate an image that visually explains this concept: Imagine a panel of experts sitting at a long table, each specializing in a different area. When a problem arises, a moderator (or orchestrator) routes the problem to the expert best equipped to handle it. This is a simplified and easy way to understand the basics of how the Mixture of Experts architecture works in LLMs."