Active learning in the context of artificial intelligence (AI) refers to a specialized form of machine learning, where the algorithm proactively queries the user (often a human annotator) to provide outputs for selected inputs to gather new data. In contrast to traditional machine learning techniques where all training data is given upfront, active learning algorithms autonomously identify and select the specific data they need for learning. This strategy is of particular use when unlabeled data is abundant, but labeling is expensive or time-consuming.
The algorithm typically begins with a small set of labeled data and uses it to make predictions on unlabeled data. The uncertain predictions—those where the model has the most difficulty—are then flagged for human intervention. The human expert provides the correct labels, thus supplying new examples that the model can learn from. The idea is to improve the model’s performance much quicker than if it were trained on randomly selected examples due to its ability to identify ‘informative’ examples for which it seeks labels.
The active learning process allows machine learning models to optimize their learning, reducing the amount of training data required, and potentially improving the efficiency and performance of the training process. It is especially useful in scenarios where data is vast but the cost of labeling could be prohibitive. By allowing the AI to actively select the most ‘useful’ data to learn from, it begins to mimic human learning by focusing on areas where its knowledge is uncertain or lacking.
« Back to Glossary Index
