What is The Importance of Human in the Loop or HITL in Data Annotation?

Contrary to what we can see in the movies, AI or artificial intelligence today is incapable of doing and also learning everything on its own. It depends greatly on the feedback that it gets from people. HITL or Human in the Loop refers to the responsibility of human feedback in the procedure of AI training.

Prior to getting into the specific concept of Humans in the Loop, let us discuss the requirement for correctly annotated data at speed and why many businesses trust a reliable annotation company.

·         The requirement to get the annotated data at speed

Data scientists of today spend more than 50% of their overall model development procedure that improves their data sets. You can imagine how much time is used on developing the data sets when the latest and new are needed for running the ML models properly. As per Forbes, 90% of the data that is generated in the world was just in the last two years.

2.5 quintillion bytes of vital data are created by humans every day .95 million videos and photos are shared every day on Instagram. Annotation of even a small fraction of this data that uses the skilled manpower available is humanely possible.

·         The requirement for correctly annotated data

In the procedure of training an AI model, a huge amount of data is required. Also, this data needs to be correctly annotated for the specific AI model to be successful. We cannot correctly emphasize the requirement for accurate data in the success of an AI model.

For instance, while developing an AI model that can predict the probability of a disaster, one should have the correct data to work with. In the same way, we cannot let autonomous vehicles function on roads with a 1% error in the sets of training data of children running across the roads.

HITL in data annotation

HITL, or Human in the Loop in data annotation, has grown up significantly as an element of machine learning. In order to correctly label the deluge of data that is now available for the purpose of AI training, data scientists are now rapidly leveraging the power of this Human in the Loop of machine learning.

What is HITL or Human in the Loop?

At its most basic level, this HITL machine learning is a specific kind of synthesis of the two primary methods of training AI systems.

1.    Unsupervised learning: It is a procedure where AI is being fed with data sets that are unlabeled. Then, AI divides the data into various categories depending on its algorithmic procedure. This eventually helps in building the data sets faster.

2.    Supervised learning: It is a procedure where AI data sets are completely labeled with the help of manual efforts. This can increase the accuracy of labeling the data sets.

The procedure of Human in the loop uses both of the above-mentioned methods to create accurate data set as well as build those sets faster. ML and Human capabilities are used in the procedure to create the desired output. Depending on the initial annotated data, the model can learn and annotate vital data. When the model offers incorrect annotations, this HITL makes sure that they are properly annotated.

The model learns from the HITL and can annotate the remaining images depending on new learning. As a huge amount of data is annotated, the accuracy of the model improves as the Human in the Loop starts to annotate the low-confidence data. As a model of machine learning is involved in the procedure of annotation, the output can be generated faster than can be done with manual annotation.

Therefore, HITL plays an important role in data annotation, and hence, many businesses depend on an annotation company to a great extent.