Recently, researchers from universities such as the University of California, Berkeley, published a paper stating that they have trained a deep learning model named RECAST to improve earthquake prediction. This model is built on the foundation of neural network generative models and can utilize larger-scale historical earthquake data for training. Compared to the existing standard model ETAS, RECAST offers higher flexibility. The researchers accelerated the training of this model using GPUs and tested it in multiple regions. The results show that RECAST's accuracy in earthquake prediction significantly outperforms ETAS. They plan to open source the model for more teams to test and iteratively improve, aiming to further enhance the state of earthquake prediction.