Interactive Learning

Interactive learning focuses on improving machine learning models through iterative feedback and interaction, aiming for more efficient and effective learning processes compared to traditional passive approaches. Current research emphasizes developing algorithms and model architectures (including transformers, Bayesian networks, and Gaussian processes) that effectively incorporate human feedback, whether in the form of labels, demonstrations, natural language instructions, or other forms of guidance, across diverse applications like robotics, natural language processing, and education. This field is significant because it addresses limitations of purely data-driven methods by leveraging human expertise to enhance model performance, robustness, and explainability, leading to more reliable and adaptable AI systems.