Importance of Human in the Loop (HITL) in the Data Annotation Process

Contrary to what we see in the movies, artificial intelligence (AI) today is not capable of doing and learning everything on its own. It greatly and principally depends on the feedback that it gets from people. “Human in the Loop” (HITL) refers to the role of human feedback in the AI training process.

Before getting into the concept of humans in the loop, let us understand the need for accurately annotated data and get the annotated data at speed.

The need for accurately annotated data

In training an AI model, a large amount of data is required. And this data should be accurately annotated for the AI model to be successful. We can’t emphasize enough the need for accurate data in the success of an AI model. For example, when developing an AI model to predict the probability of a disaster, one must have accurate data to work with. In the same way, we cannot let autonomous vehicles operate on roads with a 1% error in the training data sets of children running across the roads.

The need to get the annotated data at speed

Data scientists today spend more than 50% of their entire ML model development process improving their data sets. Imagine how much time is spent on preparing the data sets when new and latest data is required for running the ML models. According to Forbes, 90% of the data generated in the world was in the last 2 years alone. 2.5 quintillion bytes of data are produced by humans every day. 95 million photos and videos are shared every day on Instagram. Annotating even a fraction of this data using the skilled manpower available is humanely impossible.

Human in the loop in data annotation:

Human-in-the-Loop in data annotation has grown in significance as a component of machine learning. To correctly label the deluge of data now available for AI training, data scientists are increasingly leveraging the power of human-in-the-loop machine learning.

What is Human in the Loop?

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

1. Supervised learning – It is a process where AI data sets are fully labeled using manual efforts. This increases the accuracy of labeling the data sets.
2. Unsupervised learning – It is a process where AI is fed with unlabeled data sets. AI then divides the data into different categories based on its algorithmic process. This helps build the data set faster.

Human in the loop process use both of the above methods to generate accurate data as well as build the data set faster. Below is the pictorial representation of the Human in the Loop process:Human and ML capabilities are used in the above process to generate the desired output. Based on the initial annotated data, the model learns and annotates the remaining data. When the model outputs incorrect annotations, the HITL ensures that they are annotated appropriately. The model learns from the human in the loop and annotates the remaining images based on the new learnings. As more and more data are annotated, the accuracy of the model improves as the human in the loop annotates the low-confidence data. As a machine learning model is involved in the process of annotation, the output is generated faster than with manual annotation.

Zastra™

To make your data annotation accurate and deliver your AI/ML projects faster to market, Cigniti has developed an active learning-based, end-to-end data curation and annotation platform, Zastra™.

Benefits of Zastra™

Below are the benefits of Zastra™ that help you during your data annotation process:

• Reduce time in the data annotation process and thereby reduce the time taken for the ML project
• Reduce annotation efforts by up to 70%
• Delivers high-quality detection, and classification of image and video datasets
• Increase collaboration and effectiveness of the entire AI development and deployment process by providing a unified platform

Conclusion

The volumes of data available in the market are humongous, and it is the one who takes advantage of the data faster and gains the maximum.

Cigniti’s Zastra™ reduces your data annotation efforts and fastens your ML model deployment, thereby helping you achieve the true market potential you deserve in the global ecosystem.

Need help? Read more about Zastra™ to learn how it can accelerate your data annotation efforts.