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Mixture of Experts in AI: What it is and Why it Matters
- Mixture of experts models have some limitations and gate networks are difficult to train correctly with the experts.
- To train AI systems, we need data, a model, and an optimization function that calculates the difference between the model’s output and the expected output.
- The optimization function for a mixture of experts model is complicated as loss function has to be calculated for two models, gate and chosen expert.
- Training the expert is straightforward, but optimizing the model by reflecting both gate and expert performance is “dirty” loss and is less efficient.
- The second technique starts with training all of the experts on the same data and then train gate on these outputs and losses without a dirty loss function.
- This technique reduces the inefficiency of training mixture of experts models.
- MoE has proved itself in some of the most successful AI models in production such as Mixtral 8x7B, Google V-Moe, and GPT 4o.
- AI is for everyone to use and develop by exploring unanswered problems with MoE models.
- Exploring other AI techniques like quantization, pruning, and knowledge distillation is recommended.
- Convolution, variational autoencoding, gradient boosting, and q-learning are also incredible techniques.
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