In Active Learning, you first provide a small number of labeled examples. The model is trained on this " Seed" dataset. Then, the model " asked questions" by selecting the unlabeled data points it is unsure about, So the human can "answer" the questions by providing labels for those points. The model updates again and the process is repeated until the performance is good enough. By having the human iteratively teach the model. it is possible to make a better model, in less time, with much less labeled data