AI and ML have now appeared everywhere, becoming an indispensable part of human lives and work. ML helps to solve ever-more-complex algorithms. Have you ever imagined driving a completely automated vehicle with virtually little risk of accident? The world is changing, making the seemingly impossible more and more likely. Do you ever wonder why machines can assist people in completing certain "tasks"? We will introduce you to how AI and ML have "learned and done" in this post.
WHAT IS DATA ANNOTATION?
First, let's go through a basic definition section. In order for AI and ML to perform the tasks that we have given the example above, they must go through a process of "learning", with people, the definitions of things and things progress seem very simple. However, with an AI, without the learning process (hereinafter referred to as the Data Annotation process, the recognition of things and events is almost impossible. To be smarter, more accurate, AI needs to continuously go through the process of "learning" through accurately labeling data sets, helping AI to identify, analyze and make decisions when required.
For the time being, computers are unable to analyze visual information like the human brain does. Therefore, the computer needs to be informed of what it has to offer so that it can analyze, interpret, and provide context before making a decision about the requested task. To help computers perform these tasks correctly and logically, humans are responsible for labeling data so that the computer can recognize the things and events that it needs to analyze and learn.
Simply, Data Annotation is the process of labeling data (Labelling) in text, audio, image, video or mixed format, so that machines can understand it. Thereby making assessments, analyzing, processing and performing assigned tasks based on the provided data.
Currently, Data Annotation is considered the biggest step behind Artificial Intelligence (AI) and Machine Learning (ML) algorithms. It is helping to create high-precision experimental activity to improve as well as directly impact to algorithm performance. Annotated data sets are an integral part of many AI and ML applications, and also the most time- and resource-intensive.
You already have a basic definition of Data Annotation, and you should have some understanding of the role of Data Annotation already, but please don't stop. We will help you better understand the following content about the role of Data Annotation.
Data Annotation is integral to AI and ML because AI and ML models need to be trained consistently to become more efficient at making predictions. Especially in ML projects, the more annotated data is fed to the model, the sooner the model is able to continue learning actively.
Only through Data Annotation can make the models distinguish which is a cat or a dog; which is a noun or an adjective; which is a street or a sidewalk, etc. Without data annotation, all photographs will look the same to computers because the necessary qualities can't be easily computed by AI and ML algorithms.
For photos with correctly classified objects as well as images with unlabeled or incorrectly labeled objects, a computer vision model performs with varied degrees of accuracy. Therefore, the more standard the annotation, the higher the accuracy of the model.
Additionally, properly annotated data will enhance the user experience and make it easier for AI developers to apply mathematical models to any piece of data. In the present technological era, these must be noteworthy locations.
Here is a quick summary of data annotation and its crucial function. I hope this can assist you in getting a general idea of Data Annotation and pique your interest. See you in the next sharing post, we will go deeper into this process together. Stay tuned!