By now, it’s an established fact that data is the primary as well as the most valuable asset to humans as well as machines. The concept of human-annotated data carefully brings together machine intelligence along with human supervision. This refers to a model involving a human’s judgment to constantly enhance the performance of any machine learning model.
Machine learning forms an integral part of most business operations today and AI and ML’s performance greatly depends on the quality of data they are working with. Hence, it is imperative to collect datasets that are quality-wise appropriate and at the same time, adopt suitable methods to collect data.
Typically, the datasets have high-quality labels; only in some rare cases, the labels (like categories or tags) added by humans could be a little biased or subjective, while in some; the data may be without labels. Let’s begin by understanding what annotated data is.
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What is Data Annotation?
It will be an understatement to say that the amount of data generated every day is colossal and to some extent, unstructured. In reality, we produce around 2.5 quintillion bytes of data every single day and most of it is not classified clearly or accurately.
This is where the data annotation comes into the picture. The process of data annotation consists of acquiring labels/relabeling and working on their quality to improve them. Basically, data annotation helps in improving model performance; enhancing data quality, and facilitating model training.
Data annotation plays a vital role in the success of AI and ML projects. With data annotation, you can identify the goals of raw data while tagging helps ML to ensure the projects sail smoothly.
Let’s consider the example of building an Artificial Intelligence project. Here, it is very important to feed accurate details into the algorithm for it to deliver the expected results. This is only possible if the algorithm is able to understand the information fed into it. If done correctly, the algorithm will label the data accordingly and enable the machine to use the appropriate dataset.
To further simplify the concept of data annotation, we are highlighting the kinds of data annotation.
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Types of Data Annotation
The primary function of data annotation is to label content in order to make it recognizable to machines. Data annotation depends on the kinds of data involved as data can be in any form – images, videos, text, audio, etc.
Video Annotation
Under this type, a method such as bounding boxes to identify the motion frame-by-frame is used. It provides you with the key data required for AI and ML models that take care of tracking and object location. Video annotation mainly helps in motion, searching objects, blur, etc in a given system.
Image Annotation
This kind of annotation allows ML models to view the annotated area as a different item. Ensure that you use captions, keywords, and alt text to describe the image when you are training such models. this is done to enable the algorithm to identify and understand the said images effortlessly. Image annotation includes AI-based applications for semantic segmentation and bounding boxes.
Text Annotation
Text annotation defines the process of designating the text in a specific document in various categories according to context and the topic. In this type of annotation, text often offers a crystal clear picture of the intentions and at the same time, it also enables you to extract valuable details. There’s a catch with text annotation. This process has several phases mainly because ML models do not have any idea about emotions and the concept.
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Audio Annotation
The primary feature of audio annotation is to decode the different parameters in the audio with the help of tagging. Tagging uses a host of techniques like timestamping and music tagging. Besides verbal cues, in audio annotation, you can also annotate silence and breadths.
Now that we have the types of data annotations clear, let’s move on to human-annotated data and address a few basic questions– firstly, what is human-annotated data? Where does human-annotated data come into play? Why is it important to ML and what are the benefits of using it?
What is human-annotated data?
Human-annotated data can be defined as the process of the annotation where human beings are the source. The basic explanation is that humans are far more capable of learning, recognizing, and comprehending things that unfortunately ML can’t. To better explain, we have put together a few things that humans will better understand as compared to AI and ML.
- To identify if a given data is suitable enough to solve a business problem.
- Ambiguous ideas, uncertainty.
- Purpose of the data.
- The subjectivity of the data.
- The relevant context regarding the issue at hand.
Besides the above-mentioned points, other key points like compliance, and regulation also require the involvement of a human in the ML workflow.
Benefits of Human Data Annotation
There are countless ways in which human data annotation can prove to be beneficial. Here are a few:
- Enhanced machine learning performance.
- Improved search results.
- Better data quality.
- Easy to understand structured data.
- Data customization to fulfill particular goals.
Conclusion
The process of data annotation enables AI and ML to understand the format they have received the data in – whether it is audio, video, text, etc, or a combination of all formats. Based on the parameters, the ML model categorizes the data and approves the execution of the tasks. If you want your machine to be trained properly to achieve the best outcomes, data annotation is the ideal way, to begin with streamlining.
[To share your insights with us, please write to sghosh@martechseries.com].
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