Generative Pre Training

Generative pre-training (GPT) leverages large datasets to train models capable of generating various data types, including text, images, and even simulations of physical processes, by learning underlying patterns and relationships. Current research focuses on adapting GPT architectures, such as transformers and diffusion models, to diverse modalities and tasks, including medical image analysis, video understanding, and financial transaction processing. This approach offers significant advantages in scenarios with limited labeled data, improving model performance and generalizability across various applications while also enabling novel capabilities like uncertainty quantification and few-shot learning. The resulting models are proving valuable for both scientific discovery and practical applications across numerous fields.