The development of Artificial Intelligence and Machine Learning became the top subject and already taking a dominant place in our daily routines. It revolutionized the way we work and live, regardless of the geographic point we live on, the type of business we work in, or the gadgets we use on a daily basis. Examples are numerous: smart replies or predictions on text-based conversations and programming features, autonomous vehicles, emojis, maps, digital assistants, etc. All of them are powered by Artificial Intelligence which is supported through Machine Learning, Deep Learning, and Computer Vision processing. But let’s back to your question answer. Machine learning and Artificial Intelligence processing are highly dependable on well-annotated and unbiased data. Unbiased means properly segregating data from previously done research results (which can contain data waste) and incorporating it into the Machine Learning process.
In order to train the model so it can produce a trustworthy data outcome, you need to feed your model with high-quality data inputs, and annotating it is one of the main parts of that process. If data isn’t well-annotated or contains bugs and biases, the data outcome will not be valid and it will result in time and money wasted. Simply to be said, data annotation and labeling terms are interchangeably used in order to refer to the technique of tagging labels in different types of content. This process is using data annotation tools for making objects of interest (video, text, image) recognizable to machines through audio processing, Natural Language Processing, and Computer Vision processing. They are all well connected and dependable on each other when it comes to Artificial Intelligence development, of which they are an integral part. For example, text-based annotations’ common feature is adding the metadata for creating the keywords which are easily recognized by machines in order to make valid decisions and outcomes. The video annotation process is supported with a feature of Computer Vision, by following the moving object(s) through the video. It can be also used for autonomous vehicles’ vision perception.