The current mainstream machine learning method is mainly supervised deep learning method, which has a strong dependence on labeled data. So, what is data annotation?
What is Data Labeling?
Data annotation is the process of processing unprocessed primary data, including speech, pictures, text, video, etc. (such as identifying the gender of the speaker, judging the type of noise, etc.), and converting it into machine-recognizable information.
Raw data that has not been labeled is mostly unstructured data, which cannot be recognized and learned by machines. Only the data that has been labeled and processed becomes structured data that can be used by algorithm training. Data labeling is the artificial intelligence trainer, which is the foundation of artificial intelligence, which is equivalent to the teacher of artificial intelligence products.
For example, Baidu’s function of searching for pictures by picture can immediately determine what brand of car it is by scanning it, so how does it know?
First of all, we have to have hundreds of millions of pictures of vehicle brands, and each picture has a corresponding brand, so that the machine can learn. Through a lot of learning, the machine has the characteristics of each brand, and then given a picture, the machine can know the brand. Among them, how to make each picture have a corresponding car brand, this requires manual data labeling.
The importance of data labeling:
In the development of artificial intelligence, data has always been regarded as its “blood”. Data labeling is a key link for the effective operation of artificial intelligence algorithms. To achieve artificial intelligence, it is necessary to let computers learn to understand and have the ability to judge things. As the cornerstone of the development of artificial intelligence, data annotation is an important link in the development of artificial intelligence.
The process of data labeling is to provide a large number of learning samples for the machine system through manual labeling. Data labeling is to label the data that needs to be recognized and distinguished by the machine, and then let the computer continuously learn the characteristics of these data, and finally realize the computer. identify.
In the testing process of deep learning models, high-quality data annotation can often better improve the quality of model training. It can be said that data determines the degree of implementation of AI, and accurate data set products and highly customized data services are valued by major companies.