Significance of Labeled Data in Training AI Models

Artificial Intelligence (AI) is being adopted at an accelerating pace, and is poised to have a staggering impact on the world. The AI models being used are backed by sophisticated algorithms which make the difficult look easy. But the algorithms and models can only do their jobs well with the help of high-quality labelled data. Data is being created at an ever-accelerating pace. According to Forbes, more than 2.5 quintillion bytes of data is created every day. The creation, storage, management, and accessibility of data has become easy, driving down costs. AI models can use all of that data if it is properly labelled, meaning it groups of samples of that data has been tagged or labelled. Labelled data acts as a “supervisor” to the learning algorithm for finding deep relationships between the features of training instances and the associated class labels which, once trained, can be further used to classify unlabeled data.

The learning patterns of AI algorithms (especially neural network-based algorithms) are based upon the learning processes the human brain. When a child is born, it has no idea what a dog is. While growing up, the child comes to know the four-legged animal we have “labelled” dog and they continue observing different dogs from multiple angles, understanding their individual, discrete features and hence, distinguishing them from other animals. The same concept is applied to the current generation of AI models.

Labeled data helps the AI model in developing the right understanding by utilizing a repetitive training and feedback mechanism. Once the model is trained on the historical data, it develops an understanding of the patterns in the data that would be impossible for a human to uncover. When fed with a data set that it has never seen before, the trained AI model is then able to make predictions based on its training, behaving just like an experienced and trained human brain.

This concept is applicable to every form of data, be it structured or unstructured. In the field of computer Vision, one needs a humongous amount of labelled training data to allow the AI model to detect low-level features and finding associations among pixels to detect a particular image class. In natural language processing (NLP), AI model needs labelled contextual examples to learn what words actually mean. The same goes for speech recognition, which needs labelled audio data to decipher nuances in different speech styles.

While data is plentiful, the process of labeling data can be expensive and time-consuming to do in volume because it might at first be a manual effort. Unlabeled data is abundant and cheap. This fact is helping to drive advances in semi-supervised learning to improve AI algorithms which make use of fewer labelled data points to achieve a desired level of performance. Useful techniques like ‘pseudo-labeling” have emerged, where approximate labels are automatically applied in place of manually labeling the data. This is of great use in data preparation of AI model training.

Labeled data plays a pivotal role in creating smart AI models even when the availability of training is limited. An AI model can be trained from a large generic labelled data set and then further tuned to identify a specific set of classes that were not present in the original data. This method is referred to as transfer learning. As AI algorithms and models have advanced and matured, so to have the methods and approaches used to gather, label, and curate the data needed.