Machine Teaching

Machine teaching (MT) focuses on optimizing the process of training machine learning models by designing efficient teaching strategies and data sets. Current research emphasizes interactive methods, exploring how to best leverage human expertise to guide model learning, including techniques that adapt to diverse learner capabilities and utilize interactive feedback loops to improve both teaching and learning efficiency. This field is significant because it aims to reduce the substantial cost and time associated with training complex models, impacting various applications from robotics and code translation to educational tools and human-AI collaboration. Improved MT methods promise more efficient and effective AI development across numerous domains.