Can Mixture of Experts (MoE) Models Push GenAI to the Next Level?
Source: tai_blog

Having worked in the AI/ML field for many years  I vividly recall the early days of GenAI when creating even simple coherent text was a Herculean task. I worked on a project where we had to generate summaries of large sales documents  and Ill never forget the puzzled look on the clients face when our model spat out awkward  noncoherent summaries. Those were challenging times  but they also taught us a lot. Fast forward to today  and its unbelievable how far weve come over the past couple of years. Now  we have models that can write like humans  create breathtaking images  and even compose music. However  these advancements come with their own set of challenges. GenAI models still struggle with scalability  require massive computational power  and often fall short of tackling diverse tasks. These hurdles are significant roadblocks to achieving what we dream of as Artificial General Intelligence (AGI). But our journey is far from over.   If youre interested in the fascinating journey towards AGI  you might enjoy reading my article: The Quest for Artificial General Intelligence (AGI): When AI Achieves Superpowers   In my experience leading AI/ML teams on large-scale projects  Ive discovered that one of the most promising solutions to these challenges is the Mixture of Experts (MoE) model. Picture a team of specialized experts  each excelling in specific tasks  working seamlessly together  guided by a system that knows precisely which expert to deploy and when. This is the essence of MoE models. Although the concept was introduced in 1991 by Jacobs et al.  its only now  with todays powerful GPUs and vast datasets  that we can fully understand and leverage its potentials. As generative AI continues to evolve  the ability of MoE models to employ specialized sub-models for different tasks makes them incredibly relevant. So  lets dive deep into what MoEs are and how they are leveraged in language  vision  and recommender models.   Over the past few years  weve witnessed the rise of ever-larger models  each striving to surpass the previous best in various benchmarks. However  it appears that these GenAI models eventually hit a plateau  and moving the needle becomes even more challenging. In my opinion  the more recent GenAI models face significant challenges in scalability  computational efficiency  and generalization. MoE models offer a solution by using multiple specialized sub-models  or experts  each handling different aspects of a task. This approach not only optimizes performance but also ensures efficient resource utilization  distinguishing MoE models from traditional monolithic AI models.   Lets take a closer look at the architecture of a typical MoE model. Imagine a team of experts  each one a specialist in a particular area. These experts are the specialized neural networks. Then  theres the gating network  like a smart manager who knows exactly which expert to call on based on the task at hand. Finally  the combiner acts like a project coordinator  pulling together the inputs from each expert into a seamless  cohesive output (not like my document summarization project a few years ago!).   The MoE concept isnt limited to just Transformer architecture; it can be applied to various neural network setups. However  its most exciting recent applications have been with Transformer-based models. Transformers architecture  introduced back in 2017  revolutionized AI  particularly in language models. They use a lot of computational power to handle massive datasets and parameters. MoE models build on this by enhancing the architecture. Transformers use self-attention mechanisms to figure out which parts of the input data are most important. By integrating MoE  these layers can call on multiple experts. The gating network acts like a dispatcher  directing each piece of data to the right expert  optimizing both efficiency and performance. MoE in transformers is illustrated below.   MoE in Language ModelsSome of my favorite uses of MoE are in language models. These models have experts specializing in different linguistic features  like syntax  semantics  and sentiment analysis. For instance  if an MoE model is processing a complex sentence  it might send tricky phrases to syntax experts and emotional words to sentiment experts. This not only makes the model more efficient but also more accurate. One standout example is Googles Switch Transformer  which uses this approach brilliantly.   MoE in Vision ModelsWhats my next favorite topic? Yes  vision! Vision models apply similar principles. Vision Transformers (ViTs) break down an image into smaller patches  processing each one independently. In an MoE-enhanced ViT  the gating network evaluates each patch and assigns it to the most suitable expert based on characteristics like texture  color  shape  and motion. This selective activation allows MoE models to handle high-resolution images and large datasets efficiently  making them highly effective for tasks like image classification and object detection. Vision MoE (V-MoE) is a good example of this approach.   MoE in Recommender SystemsRecommender systems are making a comeback again to the front row with applications of Mixture of Experts (MoE). Traditional recommendation algorithms often struggle with personalization and scalability. MoE models address this by using specialized experts  each focusing on different user behaviors and preferences  for example short-term interests vs long-term habits  leading to a better user experience. Multi-gate MoE (MMeE)  illustrated below  is a successful implementation of this concept for recommender systems. This architecture enhances multi-task learning by sharing expert submodels across all tasks  with a gating network trained to optimize performance for each specific task.   Some of the Noteworthy MoE Models (As of August 2024)Now that weve explored what MoE models are and how they help scale GenAI  lets take a look at some of the most noteworthy MoE models that have been widely adopted by the AI community.   Mistral Mixtral 8x7B made a big splash back in Dec 2023  when they released stunning evaluation metrics. It is an advanced MoE model developed by Mistral AI  comprising eight distinct expert modules  each with 7 billion parameters (thus the name 8x7B). Its performance has set a new benchmark in the field.   Switch Transformers was eveloped by Google and released back in 2021. It employs a MoE approach to achieve impressive scalability with a 1.6 trillion parameter model (   ). It uses a sparse activation method  where only a subset of experts is activated for each input.   V-MoE (Vision Mixture of Experts) was developed for computer vision tasks and released in 2021  and what I love about it is that it applies the MoE architecture to Vision Transformers (ViT). It partitions images into patches and dynamically selects the most appropriate experts for each patch.   GShard is another model from Google and is a framework for scaling large models efficiently using MoE. It allows for the training of models with up to trillions of parameters (_) by dividing them into smaller  specialized expert networks.   Z-code is Microsofts initiative that leverages MoE architecture for natural language processing tasks  such as translation. It supports massive scales of model parameters while keeping computational requirements constant  enhancing efficiency and performance.   MMoE (Multi-Gate Mixture of Experts) was proposed by Google researchers for YouTube video recommendation systems back in 2018  and it uses multiple gating networks to optimize predictions for different user behaviors  such as engagement and satisfaction  improving the accuracy of recommendations.   If youve had experience with any other MoE models  Id love to hear about them! Feel free to share your thoughts in the comments below.   Final Thoughts Mixture of Experts (MoE) models are a game-changer for GenAI. Ive watched AI grow from simple tasks to creating complex art and text  but it hits a wall with scalability and efficiency. MoE models offer a smart way around this by using specialized experts that handle different parts of a task  making everything faster and more efficient. MoE models have been applied in LLMs  computer vision  and recommendation systems by improving accuracy and speed while reducing computational load.   I believe as generative AI continues to evolve  the role of MoE models will become even more crucial. We might soon see these models tackling even more complex tasks with ease  pushing the boundaries of what we thought possible to the next level. BUT  WHAT IS THE NEXT LEVEL AI? \_()_/ Only time will tell.