With the rise of AI working itself into every crevice of our lives, there are several benefits of using active learning for data — more specifically, correctly labeled, valuable data. However, sourcing, identifying, and labeling data accurately can be tedious, time-consuming, and prone to human error when it comes to working with large quantities of data. With this in mind, ways to improve machine learning, such as active learning, or the use of an algorithm to condition machine learning models to accurately label data, have already been adopted by reputable companies such as Tesla. This being said, businesses should stem from the conventional approach and progress towards active learning.
In an article by Humanloop, Raza Habib discusses “three benefits of using active learning.
1. You spend less time and money on labelling data
Active learning has been shown to deliver large savings in data labeling across a wide range of tasks and data sets ranging from computer vision to NLP. Since data labeling is one of the most expensive parts of training modern machine learning models this should be enough justification on its own!
2. Faster feedback on model performance is another one of the benefits of using active learning
Usually, people label their data before they train any models or get any feedback. Often, it takes days or weeks of iterating on annotation guidelines and re-labeling only to discover that model performance falls far short of what is needed, or differently labeled data is required. Since Active Learning trains a model frequently during the data labeling process, it’s possible to get feedback and correct issues that might otherwise be discovered much later.
3. Your model will have higher final accuracy
People are often surprised that models trained with active learning not only learn faster but can actually converge to a better final model (with less data). We’re told so often that more data is better, that it’s easy to forget that the quality of the data matters just as much as the quantity. If the dataset contains ambiguous examples which are hard to label accurately this can actually degrade your final model performance.
The order the model sees examples also matters. There’s an entire sub-field of machine learning, called curriculum learning, which studies how you can improve model performance by first teaching about simple concepts before teaching complex ones. Think “Arithmetic before advanced calculus”. Active learning naturally enforces a curriculum on your models and helps them achieve better overall performance.”