There is an option that many businesses and researchers must make when developing AI or machine learning algorithms.Since self-learning models need a large amount of annotated data to train before going live, the question arises,whether the model can be trained on manually labeled data or automated labeled data or both.

Data Labeling

First of all let’s understand the importance of data labeling for artificial intelligence.Data labeling is the process of making the objects recognizable to machines through Computer Vision. is the process of labelling images, video frames, audio, and text data that is mainly used in supervised machine learning to train the datasets

Labeled Data

Labeled data, which is a collection of data samples that have been tagged with one or more labels, play an important role in many software organizations in today’s market. It can help in solving automation problems, training and validating machine learning models, or analysing data. Many organizations therefore set up their own labeled data gathering system which supplies them with the data they require. Labeling data can either be done by humans or be done via some automated process

Manual Labeling

The first and most well-known approach to labeling visual data is manual: people are tasked with manually identifying objects of interest in the image, adding metadata to each image corresponding to the nature and/or position of these objects. Manual data labeling generally means individual annotators identifying objects in images or video frames. This is a labor intensive and time consuming process.

While each labelling instance can only take a few seconds, the cumulative effect of thousands of images may cause a backlog and impede a project. As a result, a growing number of AI developers are turning to skilled data annotation services like TagX .There are organizations today which focus solely on providing dataset labeling services.

When it comes to precision and accuracy in training datasets, well-trained human annotators remain the gold standard. Manual marking captures the edge cases that automated systems overlook, and experienced human managers can ensure data consistency across massive quantities of data.

Automatic Labeling

Automatic labeling refers to any data point labeling that is not conducted by humans. This could mean labeling by machine learning models, by heuristic approaches, or a combination of the two. A heuristic approach refers to passing single data points through a predefined set of rules that determine the label. These rules are often set up by human experts that can recognize the underlying factors that determine the label of the data point. Heuristic approaches have the advantage of being cost efficient since for each type of data point the rules can be set up by only a single or a few human experts. The labeling itself is also relatively efficient since each data point will be passed through a limited number of rules.

However, if the structure of the data of interest is changing over time, these rules may become irrelevant or even faulty which will decrease the accuracy of the labels or even render the algorithm unusable until the changes are accounted for. Furthermore, the data may be of such a nature that it is difficult to express these rules or the experts do not know the individual algorithmic steps they themselves take to evaluate a data point. For example, humans have an easy time recognizing the difference between a dog and a cat in an image, but do not know exactly what steps the brain takes to make this distinction.

Semi-Automatic Labeling

Curating training data is typically thought of as a strictly manual process. However, predictions subverts this notion by adding a machine into this labor intensive development process. Given that training data takes the same form as predictions, a model’s output can be used to make an initial annotation of raw data in real time. This data can then be fed through the training data pipeline where it can be improved upon by a labeling review team. The improved annotations would then be fed back into the model to increase its prediction accuracy. This tight feedback loop is referred to as semi-automatic labeling.

As opposed to the traditional process of labeling being a purely human process, predictions inserts mechanical automation into the training data loop. And, as opposed to models in production being a purely machine driven process, assuring quality predictions with low confidence scores are ways to improve and update increasingly performant models.

Conclusion

In manual data labeling, these two steps are very crucial – first, data labeling, and second, checking and verifying to ensure the quality of annotations. In automated data labeling, the data labeling and verification process take lesser time compared to manual annotation. Though the automated data labeling process provides multiple times faster speed labeling and the advancement of technology brings more efficiency and quality, Human-in-the-loop is important to ensure quality and accuracy while labeling the data for Machine Learning.

TagX provides you with high-quality training data by integrating our human-assisted approach with machine-learning assistance. Our text, image, audio, and video annotations will give you the courage to scale your AI and ML models. Regardless of your data annotation criteria, our managed service team is ready to support you in both deploying and maintaining your AI and ML projects.

Shireen Mirza - Author