Human Labelers

Human labelers are crucial for training machine learning models, particularly in areas like image and audio processing, but their cost and time constraints drive research into efficient labeling strategies. Current efforts focus on optimizing labeling workflows through active learning, leveraging AI to pre-screen data and guide human annotators towards the most informative samples, and developing systems that assist labelers in complex tasks like comparing robot trajectories. These advancements aim to improve both the speed and quality of data annotation, ultimately leading to more robust and cost-effective machine learning models across various applications.