Human Labeling

Human labeling, the process of annotating data for machine learning, is a crucial but often expensive and time-consuming bottleneck in many AI applications. Current research focuses on reducing reliance on human labeling through techniques like leveraging large language models (LLMs) to generate or refine labels, employing self-supervised learning and unsupervised methods to discover underlying data structures, and developing active learning strategies to optimize human annotation efforts. These advancements aim to improve the efficiency and scalability of training machine learning models, impacting diverse fields from image recognition and natural language processing to robotics and healthcare.