Self-Supervised Learning in AI: Transforming data efficiency and model generalization in machine learning

Self-supervised learning (SSL) represents a revolutionary AI paradigm which lets machines acquire significant data representations directly from unlabeled information through unsupervised learning approaches. SSL uses contrastive learning and masked data modeling and predictive learning approaches to optimize data efficiency thereby improving model generalization between multiple domains. This paper evaluates the core concepts of SSL alongside its superiority to supervised and unsupervised learning and its usage in different fields such as NLP, computer vision, speech recognition, healthcare, finance and robotics. The paper focuses on analysis of essential techniques and architectures which include SimCLR, MoCo, BERT, MAE, BYOL and approaches combining SSL with reinforcement learning and weak supervision methods. The research analyzes SSL's current challenges including operational expenses and representation degeneration as well as the assessment obstructions while proposing future uses for the method in mixed-data learning and minimal-resource contexts and artificial general intelligence (AGI). The adoption of SSL in real-world AI applications depends on effectively dealing with ethical matters that include bias issues and responsible AI practices and fairness assurance.