by Analytics Insight
November 25, 2021
Key Roles of Data Accuracy in Delivering Effective Data Annotation Services with Artificial Intelligence
There is so much buzz around artificial intelligence (AI) and machine learning today. It’s no longer surprising that most of the tools you use online, from your smartphones, most websites, and various devices, use AI-powered machine learning to improve your interaction with multiple apps. Some machine learning applications include facial recognition, voice recognition, financial security, bus schedules, traffic forecasting, medical services, social media, customer support, and retail. Additionally, writing tools such as Spell check are developed using machine learning.
Another great use of machine learning applications is predictive analytics. Suppose you log in to your social network account, the system analyzes your interaction with the social network application. He can make smart choices about your friends and the kind of sponsored content he can send through your News Feed.
What is Data Annotation?
When a company trains a machine model for a specific application, it uses huge volumes of data. For example, an automotive company is developing a self-driving car. The machine learning model will need all the information to allow the autonomous vehicle to recognize all the objects and situations it will encounter in the real world.
A data annotation service provider will use whatever available data they can provide to the machine. The information they provide to the device is not the usual data that programmers encode in machine learning. They will use various types of data, including photographs, videos, and text. Therefore, annotators add labels to elements of an image, photo, or video to allow the machine to recognize the information. They must ensure that all labels or annotations are accurate. The work is tedious and attention to detail is essential. Annotators must also ensure that the spelling of labels is correct so that the machine model learns correctly.
Humans are responsible for annotating the data. They have the skills and training to classify and decipher data that machines can understand. For example, an image or photograph can have multiple objects such as humans, animals, and others. The annotator should label each recognizable and partially hidden element in the image. The tags are generic – male for male, female for female, and so on, adding tags to every tree, lamp post, cat, dog, etc.
Considering the example above, you can understand the volume of data they have to manage and annotate to train a machine learning model. But due to the sheer volume of data they have to annotate, there will be instances where they may make labeling and spelling mistakes. So concentration and using tools like a spell checker can help.
Why is human intervention in data annotation essential?
Humans can do various things and can exercise their subjectivity. They can manage, understand intent, and deal with challenges and uncertainty. However, even if you enter everything into an automated system, it lacks the insight of a human. Therefore, data beacons must be precise so that machines can accurately learn different objects and develop the ability to provide accurate and targeted predictions and responses.
Humans perform data annotation better because they can annotate each type of data with care and precision. They think critically as they work, manually check spelling and tag placement, and validate everything they do.
Share this article
Do the sharing
About the Author
More info about the author