The artificial intelligence industry is one of the fastest-growing sectors in technology today. Valued at $10.1 billion in 2018, industry experts believe it will grow to more than $126 billion in 2025. Data annotation is a critical subsection of this industry.
The image below explains how data annotation fits into the world of artificial intelligence.
Source: Statista
Knowing where it slots in is interesting, but it doesn’t explain what data annotation is. In this article, we’ll explain what the concept entails and why it’s vital.
What is Data Annotation?
According to Label Your Data, data annotation or labeling assigns a name to a specific dataset. The label helps the algorithm learn to identify the dataset. At a later stage, the program can recognize similar sets and apply the same classification to them.
Think of it as you would flashcards to teach children to read. A flashcard contains an image and its name so that the child makes a visual link between the two. Data annotation helps a machine make connections in much the same way.
The difference is that you may label any type of data. It doesn’t matter if it’s text, audio, images, or videos.
Why is it Important to Label the Data?
Labeling the data helps AI to recognize what it is and identifies subsets if it encounters them later. It’s an essential part of the learning process.
Say, for example, that you’re designing an autonomous vehicle. How do you teach the computer what a tree is? Using data annotation, you assign a specific set of values that define a tree. You could do this using text or upload images of trees.
It’s crucial to name the data correctly so that your new car understands that it should avoid trees. The more accurate the labeling, the more effective the program will be.
The Benefits of Professional Annotation
A More Accurate Output
The better the annotation, the more accurate the algorithm’s results become. Let’s go back to the example of the tree for a minute. If you defined a tree using a picture of an oak tree, would the car recognize a palm tree as something similar?
Professional annotators create meta tags that give your car enough information to find the data it needs within its database.
A Better Experience for Users
Machine learning changes the stakes when it comes to application performance. If you think back to the earliest iterations of virtual assistants like Siri, you’ll understand why. The VA could understand the context of some queries but required that you use the exact command. It also didn’t understand heavily accented English.
As machine learning advances, our virtual assistants become capable of doing more. Better annotation allows them to learn a broader range of skills.
Types of Data Annotation
The type of data determines which labeling method annotators employ. They fall into the following categories:
- Bounding Boxes: You enclose the data to annotate within a rectangle when you need to detect locations and objects.
- Semantic Segmentation: You use this type when it’s important to note the environmental context. With this method, you assign a class to each pixel in an image.
- 3D Cuboids: This is similar to using bounding boxes except that it has a third dimension, i.e. the depth, to work with as well.
- Polygonal Segmentation: When precision is crucial, this method replaces the rectangle. Instead of a four-sided shape, it has as many sides as you require. It helps you to trace the object more precisely.
- Landmark and Key-Point: Commonly used in facial recognition programs, this type allows you to mark the object’s key points. You might, for example, note where the nose starts and ends, the shape of the eyes, or any other feature you deem necessary.
- Entity Annotation: This format is used when you have to label unstructured sentences.
Final Notes
Properly annotating data paves the way for efficient machine learning. To give your new AI-based application the best chance of success, it’s vital to hire a skilled, professional team.
