Technology

Data Annotation In Artificial Intelligence

With the great leap of technology, people are used to getting and providing services instantly. Business today, get adapted to the digital flow and has enhanced their tone.

Technology like Artificial intelligence, Internet of Things, Augmented Reality, Virtual Reality, Blockchain technology are the rule today and gonna rule tomorrow.

Among others, Artificial Intelligence plays a predominant role in the development of all sectors.

Artificial Intelligence?? What’s it about??

Ina simple layman terms, artificial intelligence is an ability of machines to think, act and learn as humans do. To make it possible the computer has to learn how to react to different situations, this done with a given detailed set of instructions and then lots of data to work with it.

AI has widespread and has shown achievements in the field of finance, education, military, healthcare and other sectors.

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AI! Is It Possible?

Yes, AI is possible in today’s world with machine learning. Machine learning is nothing but a scientific study of algorithms and statistical models that rely only on pattern and interference instead of indulging into complex code that tells machines to perform a specific task. Those algorithms or models are designed based on sample data, in order to make decisions or predictions

In Machine learning, annotation helps in the process of labelling data and make it usable data sets. Irrespective of the form of data it can be easily labelling in the annotation process.

A clear data set, annotated dataset are required to train the machine learning models. There are many different types of data annotation, depending on what kind of form the data is in, and the end-use for machine learning. Some are semantic annotation, image and video annotation, text categorization, and content categorization.

Data annotation is important in machine learning because in many cases, it makes the work of the machine learning program much easier.

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Data Annotation Examples:

If the purpose of the program is to identify cars in images, the system already has a large number of photos tagged as cars or not, identify different types of cars. It then uses those examples to contrast new data to make its results.