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What does artificial intelligence data labeling mean, and which data should be labeled

In recent years, artificial intelligenc  data has developed rapidly, and the application of AI in various industries has become more and more extensive. However, data labeling is a job with a very high technical threshold. Many novices do not understand the AI ​​industry, and often cause mistakes due to labeling mistakes. So today I will take you to learn about artificial intelligence data annotation.

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What does artificial intelligence data labeling mean?

Data annotation generally refers to the process and work of manually collecting, sorting, categorizing and analyzing the data or content required by artificial intelligence, and giving relevant suggestions, explanations or evaluations on this basis. There are two basic types of data annotation : one is non-automated, which refers to the annotation formed without any human factors involved. The other is automatic annotation, which is formed based on methods such as machine learning and deep learning, and forms an answer to a question by analyzing a given data set or question.


What data needs to be labeled?

Annotated data can be divided into two categories: basic data and application data. Basic data refers to the objects that need to be marked, including pictures, videos, audio, text, etc. These data will be stored in the cloud server in a certain format; application data refers to the users actively uploaded to the cloud server, such as e-commerce Platforms, medical devices, educational software, and more. In addition, labelers also need to conduct reasonable analysis and processing of application data.

What data should be labeled?

1. Image data: Image annotation is to process unprocessed image data and convert it into machine-recognizable information.

2. Speech data: Speech annotation means that the annotator first “extracts” the text information and various sounds contained in the speech, and then transcribes or synthesizes them.

3. Text data: Text annotation is the process of characterizing text, and labeling it with specific data such as semantics, composition, context, purpose, and emotion.

How to label?

The steps of labeling are as follows: test a pre-trained model on the prepared data set, and labeling is required at this time. Label the pretrained model and submit the data to the machine learning algorithm. A machine learning algorithm will generate a label for the task.

How to choose the right supplier and platform?


This is a very complex process involving various software and hardware. Therefore, be cautious when choosing a supplier, don’t be blind, and first understand whether the supplier has a complete team. Also, think carefully when choosing a platform. The development speed and quality of the platform have a great impact on the company.

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