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What is data annotation and why is it important in AI development?

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Anastasiya Slipenko
What is data annotation and why is it important in AI development?

Since the turn of the 20th century, humanity has been fascinated with the idea of superhumans. Looking to the skies is making us miss out on reality; we are in the age of cyborgs.

Human learning is pushing boundaries like never before. We are reaching new frontiers thanks to AI. For instance, if you have issues with spelling, all you need to do is download a spell checker, and you can pontificate with confidence

Individuals, Corporations, and Governments now use AI at varying levels in everyday life, in areas like Agriculture, Defense, Transportation, and Communication at all levels. 

These feats would have been impossible without a small part of machine learning, known as Data Annotation.

What is Data Annotation?

Data annotation is a principle in machine learning that is similar to what labeling is for children. It is the process whereby humans identify and label grouped data, known as datasets, intended to be fed to the machine to teach it specific tasks.

A computer cannot make sense of data by itself. It needs the training to identify specific characteristics and make decisions based on the traits it identifies.

Depending on what you want to teach a machine, datasets and the methods used for annotation may vary. Generally, the quality and quantity of the data and annotation are proportionate to the eventual quality of the AI.

Types of Data Annotation

There are different approaches to data annotation depending on the type of AI you are trying to create. Some of these approaches are:

  • Image annotation: image annotation is the process where still images are annotated either by using bounding boxes(boxes drawn around an item and labeled), or segmentation (assigning meaning to individual pixels in an image)
  • Video annotation: This is a process where a video is annotated on a frame-by-frame basis using bounding boxes or a video annotation tool. 
  • Text annotation – Text annotation involves labeling phrases, keywords, or sentences. For example, developers use this annotation system to train chatbots to understand people's sentiment through text better.
  • Audio annotation – Audio annotation involves labeling soundwaves and frequencies to allow machines to understand and identify unique sounds. Therefore using this technology, an algorithm can determine what type of sounds a person prefers.
  • LiDAR – LiDAR is short for Light Detection and Ranging. This technology employs laser technology by sending out laser pulses and measuring the reflections of the light to create 3D images. The technology LiDAR uses is similar to how bats "see" with sound but with laser.

Data Annotation in Different Industries 

AI has varied applications; it can be employed in nearly any field in existence today. Consequently, we'll be evaluating some areas of AI data annotation is used in such as:

  • Communication: In the field of communication, developers use text annotation to great effect. Some of the machines we have today can analyze texts and extract information concerning the tone and urgency of the text. In the future, experts expect chatbots to take on full roles assigned to people today.
  • Transportation: recently, Telsa shook the world with the unveiling of cars capable of autopilot. Using real-time object detection software known as YOLO (you only look once) to navigate the world in real-time. YOLO uses bounding boxes on a dataset of traffic-related data to teach the AI what to do when it sees traffic objects in real-time
  • Agriculture: Various data annotation methods are used in the agricultural sector today. For instance, TerraSentia employs LiDAR to gather data regarding plant health and physiology.
  • Music: Audio annotation is one of the more recent forms of data annotation. Streaming companies like Apple and Spotify use this technology to identify this technology to predict what kind of sounds that their users may prefer.

Conclusion

In a world that is constantly integrating AI to make it faster and more efficient, the role of data annotation cannot be understated. There are already impressive methods of annotation in the world today, such as text, video image, and sound annotations.

As a child's potentials are limited to the quality of its education, the AI is limited only by the efficiency and quality of the AI's data annotation. Therefore we must constantly explore ways to improve data annotation.

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Anastasiya Slipenko
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