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How is Data Annotation a part of AI Development?

It is easy to get confused when there are so many different areas of AI, but simply put, AI is the ability for a computer to do work normally done by humans by imitating human intelligence...

What does it take to build?

Artificial intelligence, or AI, is quickly changing the world and how we live in it. Its influence can be seen all around us in many different forms. From online shopping recommendations to self-driving cars and more, AI is already here and improving our lives in many ways. Given that so many aspects of modern society are steadily becoming integrated with AI, the importance of understanding the technology and its development has become clear.

What is Artificial Intelligence?

When met with the question “what is artificial intelligence” people often have some trouble finding the right answer. AI itself is a broad field and there are a number of different subsets to AI like machine learning, natural language processing, computer vision and so on. It is easy to get confused when there are so many different areas of AI,

“but simply put, AI is the ability for a computer to do work normally done by humans by imitating human intelligence.”

The key aspect separating AI from normal computer programs is that AI is able to perform tasks in unfamiliar situations and improve as it encounters more problems.

How AI is changing the way we live?

We are still some time away from being able to interact with AI as we see it in movies, but in many ways AI is already being used to make the world a better place. Click here to find out 10 ways AI is being used for good.

What does AI development look like?

As with any new technology, the development of AI poses significant challenges. An AI system is capable of identifying specific objects in images, understanding human speech, solving complex problems and much more, but before it can do any of that it often needs to be trained with vast amounts of data.

Training an algorithm to recognise patterns is an essential part of any AI project, but it’s not as easy as feeding a computer immense amounts of raw data.

“Data sets used for training need to be carefully labelled and annotated before it is passed into the computer so it can learn.”

Of course, if the data is inaccurately labelled it causes the AI to make incorrect assumptions and conclusions when met with ambiguous situations. This is why accurately labelled data is vital to creating AI.

The process of data labelling and annotation is simpler than it sounds, but that doesn’t make it a small task. For a data set to be useful in training an AI it usually needs at least one thousand data points and can go up to tens, hundreds of thousands or millions of data points to enable more complex problem solving. More data points, when annotated and used correctly, leads to better and more accurate AI models.

“AI modelling also suffers from diminishing returns - the effort and data required to get a model’s accuracy from 80 percent to 90 percent is a few fold that of getting the accuracy from 70 percent to 80 percent.”

What this means in traditional AI development projects is a team of data scientists sitting down and labelling all of these data points manually, which can be a big challenge for a small team. In fact, just cleaning and organising data often takes up more than eighty percent of an AI development project’s time. Clearly, data labelling and annotation remains a vital part of any AI project and has resulted in many new innovations designed to take on this challenge. As the influence of AI continues to grow and expand we will find ourselves faced with many new and interesting experiences. Progression in AI development and data annotation is a global effort, and it’s one that you can contribute to or benefit from. Find out more about how you can get involved while earning rewards at Tictag, or contact us to find out how to get your critical data labelled quickly and accurately.

by Delwin van Strien, Tictag Commercial


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