Explain : What active learning in artificial intelligence.
Active learning is an approach in machine learning that is focused on reducing the amount of labeled data required to train a model. In traditional machine learning, a large amount of labeled data is necessary to train a model. However, labeling data can be expensive and time-consuming.
Active learning aims to reduce this cost by selecting the most informative data points to label. The algorithm selects a small set of unlabeled data points and presents them to an oracle, such as a human expert or a pre-trained model, to be labeled. The model is then trained on the newly labeled data.
Next, the algorithm selects the next set of unlabeled data points to be labeled, based on how informative they are likely to be in improving the model's performance. This process is iterative, continuing until the model reaches a satisfactory level of performance or the available budget for labeling is exhausted.
The selection of the most informative data points is a key part of active learning. The algorithm uses different strategies to select these data points, such as uncertainty sampling, diversity sampling, and query-by-committee. These strategies are designed to choose data points that are most likely to improve the model's performance.
Active learning is a powerful approach that can significantly reduce the amount of labeled data required to train a model. This makes it particularly useful in scenarios where labeled data is scarce or expensive to obtain.
Here's an example to illustrate how active learning can be applied in practice:
Let's say we have a dataset of images and we want to train a model to classify the images into different categories (e.g., cat, dog, bird, etc.). We have a limited budget for labeling, so we cannot label all the images in the dataset. Instead, we decide to use active learning to select the most informative images for labeling.
We start by randomly selecting a small subset of images from the dataset and labeling them. We use these labeled images to train our initial model.
Next, we use the model to predict the labels of the remaining unlabeled images in the dataset. The model will likely make mistakes on some of these predictions, which means these images are more informative than others and should be labeled next.
To select the most informative images, we use a strategy called uncertainty sampling. This involves selecting the images for which the model is most uncertain about the correct label. For example, if the model is 50% sure that an image is a cat and 50% sure that it's a dog, then this image is more informative than an image for which the model is 100% sure about the correct label.
We present these selected images to a human annotator to label, and we use the newly labeled data to retrain the model. We repeat this process of selecting informative images, labeling them, and retraining the model until we have a model that performs well on the task.
In summary, active learning allows us to train a model using a smaller amount of labeled data by selecting the most informative examples for labeling. This can significantly reduce the cost of data labeling and improve the efficiency of the machine learning process.