$NVIDIA(NVDA.US) Simple Explanation: What is AI Distillation Mentioned by Jensen Huang at the GTC Conference?

In the field of AI, distillation can be understood as using AI to train AI.

Knowledge distillation is a machine learning method used to compress and optimize large models.

The process begins by training a large, complex model, known as the teacher model, which learns a vast amount of knowledge and features. The teacher model is then used to generate soft labels for the training data, which are the probability distributions of the model's predictions for each sample. These soft labels are subsequently used to train a student model, which attempts to mimic the teacher model's behavior, learning similar feature extraction and decision-making processes. Finally, optimization algorithms are applied to adjust the parameters of the student model to make its performance as close as possible to that of the teacher model.

This approach allows smaller models to inherit the knowledge of larger models, providing high-quality AI services even when computational resources are limited.