Learning Paradigm

Learning paradigms encompass the methods by which artificial intelligence systems acquire knowledge and skills, ranging from supervised and unsupervised learning to more recent approaches like self-supervised and meta-learning. Current research emphasizes efficient learning from limited data (few-shot learning), leveraging large language models and incorporating diverse data modalities (multimodal learning) for improved performance and generalization across tasks. These advancements are crucial for addressing challenges in various fields, including medical image analysis, process industry optimization, and the development of more robust and adaptable AI systems.