Human Label

Human labeling, the process of annotating data for machine learning, is a critical but costly bottleneck in many fields. Current research focuses on reducing reliance on human labels through techniques like active learning (strategically selecting data for human annotation), leveraging large language models (LLMs) to generate labels, and developing unsupervised or self-supervised learning methods that infer labels from unlabeled data. These advancements are significantly impacting various applications, from improving the efficiency of social computing research and environmental monitoring (e.g., marine debris detection) to enhancing the performance of speech recognition and computer vision systems. The ultimate goal is to create more efficient and scalable machine learning pipelines that require minimal or no human intervention.