Do you remember taking toys apart as a child, thinking, what was it that made them work? Well, the other day, after stepping out of my Tesla and listening to some of the amazing songs that my best friend Siri picked out for me. I started to think, “what is it that makes you tick?”
Rest assured, I didn’t take my iPhone apart with a hammer. However, I did some research that led me to discover what Data annotation is and some of its benefits. This discovery made me realize that AI data annotation was like childhood instances where my dad answered my numerous “What is that?” questions.
In this article, we’ll be walking around Data annotation boulevard, pointing out why everyone should visit sometime.
What is Data Annotation?
Data annotation is an aspect of machine learning where a person teaches a computer things by identifying particular objects to it, either to perform a specific task or just for the fun of it. In more technical terms, Data annotation is an aspect of machine learning where a person identifies an object, often by using data annotation methods so that the computer can identify and analyze it.
There are several types of data annotation modalities:
- image and video annotation
- sound annotation
- text annotation
- and semantic annotation
Without data annotation, machine learning would be impossible. Nowadays, data annotation has shifted beyond the scope of machine learning during the creation of an algorithm to include annotation even after the release of algorithms for consumer use.
Benefits of data annotation
Data annotation involves the process of data annotation at several stages of machine learning. Annotation in both phases provides several benefits, including:
1:Birth of the AI age:
Without data annotation, the Age of AI would not exist. The process of annotating data gave life to machines, dramatically improving the human experience. We have annotation to thank for some of the mainstays in our daily lives like Search engines, Digital Personal assistants, and autonomous vehicles.
2:Better personalization:
Data annotation allows easier personalization of machines. These days it is common for algorithms to leave “ports” for users to insert preferences which the AI uses to optimize each user’s experience—in the process, making each search more contextual and each playlist a little more relatable.
3:Time saving:
Data annotation removes the burden of repetition from processes, thereby saving a lot of time and effort. When a process is taught to a machine, the machine can repeat that process endlessly. This means that more manageable tasks can be automated, saving time and human resources for more worthwhile tasks.
Conclusion
The human experience is rapidly evolving, proving its effervescent dynamism. AI is at the forefront, leading the revolution. With several annotation techniques in the pipeline, we can only look to the future and wonder, “how much better does this get?”
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