Hey, Remotasker! If you’ve been hanging around Remotasks for a while, chances are you’ve heard the term “data labeling” used in the context of a Task. Of course, literally speaking, “data labeling” might have to do with labeling data, right? This is correct at some level, but there’s a deeper meaning behind the concept that enables us Remotaskers to help improve smart technology today. 

Wait, what does labeling have to do with AI and machine learning? 

First Things First: What Is Labelling?

In the context of artificial intelligence and machine learning, Data Labelling is the process of identifying and attaching informative and meaningful labels to raw data like videos, images, and text. That way, AI and machine learning can use the labels and the raw data to learn certain functions and mechanisms. For instance, data labeling can pave the way to help a self-driving car separate other cars from the road, pedestrians, and traffic elements. 

Data Labelling And AI: What’s It For?

Data labeling can help artificial intelligence and machine learning models make more insightful insights towards elements interacting in various media. However, since there are different kinds of raw data available, different kinds of data labeling use exist for AI and machine learning models to use. Here are some of the mechanisms used for data labeling:

• Natural language processing: One of the most common ways of data labeling in terms of AI and machine learning has to do with natural language processing. In essence, this involves tagging or segmenting sections of a text with specific labels to identify and label their uses in the context of the text. These can be parts of speech, noun classification, blurbs, and other categories such as places or people. NLP can help in terms of optical character recognition, entity name recognition, or even sentiment analysis.
• Computer vision: Data labeling in images and video usually comes with the help of computer vision. In these kinds of labeling processes, identifiers such as key points, borders, pixels, or parts of an image are used to create datasets. In turn, these can help classify images depending on the content, quality, or even pixels. Data labeling in this manner can create a computer vision model that can automatically categorize images as soon as they locate these key points.
• Audio processing: Contrary to computer vision, audio processing is data labeling that makes use of sounds such as wildlife noises or speech. In this way, sounds are converted into a proper format that can be used in machine learning. Systems that can categorize these kinds of audio according to content, quality, or information can help systems make use of images to gather accurate data. 

Labelling And Remotasks

If you’re interested in helping AI identify objects more accurately, then you might want to become a part of the Remotasks team! In our earnings platform, we pay Remotaskers to do a variety of Tasks and project types related to AI - including Data Labelling! In fact, Data Labelling is one of the most popular project types among our repertoire of task categories. Just sign up on our website for free, get trained without charge, and start getting paid for doing Data Labelling tasks!