Published on May 28th, 2021 | by Bibhuranjan0
The Unsung Heroes of AI Development: What is a Data Annotation Job?
Artificial Intelligence (AI) and Machine Learning (ML) are rapidly developing industries nowadays, which make various fields globally benefit from their innovations. In order to have such machines and applications created, they first need to go through a lot of data set training.
The data sets are organized depending on the type of annotation required (video, audio, image, and text annotation) and they aim to make certain objects detectable to computer vision for machine learning. This process is advantageous for the AL field as well as for other stakeholders. Present industry trends let us estimate that the Data Annotation Tools Market size will grow at a CAGR of over 30% between 2021 and 2027.
The manually labeled data annotation sector in the US held the biggest part of the market, estimated at over USD 277.49 million in revenues in 2020 due to its rising usage to provide high-quality input data. The data labeled manually include fewer errors because of the hard work of domain experts, who can supervise elaborate data labeling cases, where automated applications or machines would not be able to manage the tasks successfully.
In the article, we would provide a detailed explanation of the data annotation definition, tell more about professionals working in the field and elaborate on data annotation use cases.
Data annotation is defined as the process of labeling data to make it recognizable for machines in many different formats as text, video, and images. Humans have to label what is in the picture and other sources to create data sets, with the help of which hard work machines are able to differentiate and easily understand the input information. Data annotators apply various tools and techniques to form those data sets for the productive work of machines.
Data annotation specialists have to use special software to mark people, animals, things, and other objects in pictures or videos to find the common patterns and use that information for a data set, created for AI machines.
This is a very popular and sought-after job today, but it is truly demanding. It requires persistence, a lot of time, and much attention to detail. Normally companies don’t have so much time and resources to annotate everything by themselves and for that reason, they need data annotators.
Autonomous vehicles and companies, which produce them, such as Tesla and Waymo, are becoming more and more popular. AI plays an integral role in the development of their products. These cars are a step into the future as they can drive by themselves. For designing the algorithms, which power the self-driving cars, it is necessary to annotate a lot of videos and images before the system becomes capable of recognizing cars, street signs, and other significant objects on the road.
Healthcare starts relying on Artificial Intelligence which lets doctors devote more time to the patients and leave more computer work to machines. The companies develop AI products that are able to recognize medical images like X-rays, CT scans, mammography, and come up with the diagnosis. Doctors have to make a great contribution to annotating medical images, which will be used to train the machines. Anyway, human doctors have to confirm the diagnosis of the computers to provide quality medical treatment.
Agriculture is the industry that uses robotic systems, which rely on AI, to grow bigger amounts of heavy crops. The robots have various tasks such as fertilizing the soil, harvesting the crops by themselves, giving analysis to crops growth, and many more functions. Using AI in agriculture allows farmers to save a lot of money, as the machine can conduct the operations much quicker and cheaper than the group of people. Agricultural robots apply LiDAR technology in their work which produces a 3D Point Cloud as a representation of the robot’s vision of a physical world. The 3D Cloud requires annotation in order to allow robots to determine the objects around them and the proximity to those things.
In the present time, a lot of people are enjoying the benefits AI provides us with, big corporations are building and improving AI tools, but almost everyone forgets about the crucial part of the process: data annotation. The workers who invest their time and effort in it with their skills and knowledge make the life of everyone easier and more pleasant. Without their commitment to their work, the development of modern technology would be impossible.