This post was originally published by at Medium [AI]
Here is everything you need to know about data annotators and how they add tremendous value to this era of artificial intelligence.
Data — Isn’t that all we need?
In the past decades, we have seen many improvements in Artificial Intelligence (AI), Machine Learning (ML), and still, there’s a long way to go. The impact of AI on business is enormous. There’s always this hype that AI can reach anywhere and do anything. That’s true, but to achieve that stage, we need vast amounts of data. All the AI and ML models are fed with tons of data to give us the best or the sophisticated user experiences like personalized recommendations for products while shopping online or a chatbot that answers our questions.
Well, it’s of no use when the raw data is fed into the system directly, right? There should be some structured format or structural data that is easily understandable for the AI models.
And that’s a set of continuous processes which is called data annotation.
Entering the world of data annotation
Let me give you a simpler version of the definition of data annotation. Data annotation or data labeling is the process of labeling data for AI or ML models/applications. The data can be in various forms, such as texts, images, audio, and video clips.
The growing impact of AI on business is tremendous. Computers, technology, artificial intelligence, machine language, mobile applications, and much more! These are nothing without the humans-in-the-loop. So who deals with all the data that is fed into the AI or ML models? Who exactly is the human behind the AI model?
They are Data Annotators. Data annotators are the humans behind the AI. They deal with a large amount of data, label it, and are the backbone for any AI or ML applications that we use in our everyday lives. They are not just humans but the real heroes in this AI era.
When more data is annotated and fed into the AI models, the smarter the application becomes. And that’s why there’s a massive requirement for data annotators at present and will only increase in the future. The data annotation market is vast.
A real-life example of a data annotator
When I had a chance to connect with Rahul, a data annotator, I came to know many things about his job and his work life for the past two years.
He says, ‘As a data annotator, my work is hassle-free. But the effort that I put in is enormous. The main advantage of being a data annotator is that there is no prior work experience or any specialized skills required. All you need is general knowledge of machine learning, basic knowledge about computers, and good command over English. We, data annotators, are given much value.’
‘The only thing that any data annotator must be cautious about is the quality, as the product quality is way more important than the time we take to build an application or an ML model. Besides, we also have deadlines, like any other project in the IT & services industry.’
‘Some people even give the weirdest reaction while hearing the word: data annotator. There are still many people who don’t know about data annotation and who a data annotator is.’
All of us, in our everyday lives, interact with AI for some purpose. Be it, Alexa, to play our favorite song or our favorite gaming app that helps combat boredom. In fact, there will be 11.5 million jobs in the field of data annotation by the end of 2026, according to the US Bureau of Labor statistics. And yet, most people don’t know what happens behind an AI application and who these unsung heroes of AI are.
A day in the life of a data annotator
The data annotator first starts with the raw data collected from various sources like the internet, public domains, or satellite. These raw data should be processed or cleansed because they may come from multiple forms and is of varied quality. Low-quality data, like an unclear picture, is removed and kept for future reference. Once the data annotator checks all the images, texts, or video clips, he or she moves on to his next step, merging.
Post that, the data annotator moves on to the final and the most crucial process: Data labeling or data annotating. It requires a lot of time and effort, as it is the most time-consuming part of the entire process because they need clean data in the end.
There are several tools available to speed up the process. Some data annotators might even use tools to label or categorize to maintain the quality of data. The tools such as Label Maps, Classes, and Mojo, our data annotation platform help speed up the process and produce quality data.
Apart from labeling the data, these data annotators also deal with some crucial processes when ML fails to process. And that is done by data annotation specialists.
As I mentioned earlier, we still have a long way to go. We are yet to see a lot of innovations in the field of AI and ML. But in this journey where AI will leap, we should always remember that without data annotators’ sweat, there’s no innovation in AI. And that’s the reason we call them the real heroes in the AI era.
Dealing with large chunks of data?
If you are looking for a data annotation platform or worried about dealing with a large amount of raw data every day, Worry not! Mojo has got your back. Mojo, an end-to-end data annotation platform, helps to empower your business with its data abstraction, annotation, and enrichment features with humans-in-the-loop.
This post was originally published by at Medium [AI]