Unbelievable advancements in technology open a magical door for AI and machine learning. These both are counting on one of the fastest growing technology that going to make a bright future for different kinds of sectors of the global economy. But, AI and machine learning are not like fairy tales. It requires countless training data to allow a particular machine to identify the thing we command. All of this data needs to be annotated by experts to prepare the data to be applied by machines. The simple meaning is that annotation is one of the pivotal aspects of any machine learning project. In this article, we are going to explain data annotation services, types, importance, etc.
Types of Data Annotations
As we discuss above, Data annotation is a such broad practice. Here is the list of some of the most common types of Data Annotations:
- Semantic annotation
This is a kind of annotation where people, company names, and places are labelled as text to help out machine categorize each of them in new concepts. This helps machine training to improve search relevance and chatbots.
- Image annotation
Image annotation is ensuring that learning machines exactly find out the annotated area as a distinct object. It is highly used to guide different kinds of autonomous vehicles. Moreover, it is also useful for facial recognition software.
- Entity annotation
Entity annotation is one process where it helps the machine to understand unstructured sentences. To create a better understanding such as NER (Named Entity Recognition) there are lots of different techniques available. An example of such annotation is a company and the exact place where it is located.
- Video annotation
The only difference between image and video annotation is like bounding boxes, video annotation uses frame by frame to acknowledge the movement. Object tracking and localization by AI and machines is the best example of video annotation.
Importance of Quality and Accuracy in Data Annotation
What is the heart of the supervised learning model? The answer is Annotated data. The quantity and quality of annotated data directly affect the performance and accuracy of any model. Here are two main reasons that value the annotated data:
- The use of machine learning models mostly in a variety of difficult applications
- To build the best machine learning models we need high-quality annotated data which is a basic and typical challenge for machine learning.
Data Annotation in AI Industry
While it is about data annotation or data. It constantly evolving respectively. Like, a decade ago; there was far enough to label a few points on the face. But now AI prototype requires nearly 20 dots on the lips alone.
Furthermore, our transition from scripted chatbots to conversational AI is a major deal to fill the gap between natural interaction and AI. As technology upgrades, consumer trust in artificial intelligence-based solutions is also increasing dramatically.
How We Can Help
Uniworld Outsourcing successfully provide you the most accurately annotated data to an associated company that adopts AI developments. We are a group of more than 2000+ highly skilled experts that provide annotating in every possible type for data like text, image, audio, video, and 3D point cloud annotation.
Bottom line:
To find out perfect data annotation company to outsource your work you need to check a few processes like Initial assessment, quality review, verification and validation, repeated quality training, etc. we provide our service in different sectors like Agriculture, healthcare, space exploration, and many more. Just contact us today to learn more about our services.
