JEPA, or Joint Embedding Predictive Architecture, is a research framework in machine learning that enables models to learn representations of data by predicting the relationships between different inputs. As a promising approach to self-supervised learning, JEPA is relevant to the tech community because it has the potential to improve the efficiency and accuracy of AI models, particularly in applications where labeled training data is scarce, such as computer vision and natural language processing.