Annotation work is necessary for AI development, and many people may be considering whether it can be automated due to the large volume of work involved. On the other hand, it is currently difficult to completely automate annotation tasks.
This article explains the strengths and weaknesses of manual versus automated annotation, and how automation can be implemented in annotation work.
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Annotation is the process of creating training data for AI learning. Specifically, it refers to the task of adding "labels" to data such as text, audio, and images.
Usually, data collected by companies often lacks labels, so they must be added by humans or machines to be used as training data. This process is known as annotation.
For more details on annotation, please refer to the following article.
| Related Article: What is annotation? Why is it necessary for AI use? Explaining the process and work involved |
Methods for performing annotation can be broadly divided into "manual" and "automated by machine." Below, we outline the strengths and weaknesses of both.
First, we introduce the method of manually adding label information to text, audio, and images. The strengths and weaknesses are as follows.
Currently, there are many cases where machine automation cannot guarantee sufficient quality, and manual implementation can result in higher quality. It is also possible to further improve quality by conducting double-checks and establishing annotation rules.
At present, machine automation can only handle a limited range of tasks. Manual implementation allows for annotation work that meets all needs.
As it is performed manually, labor costs are inevitable.
To perform annotation, you need annotators (= those who carry out the annotation work) who fully understand the implementation methods. In addition, personnel well-versed in the annotation process and project management, such as organizing annotation requirements and establishing annotation rules, are also required.
The other method is to perform annotation automatically by machine. Specifically, besides using programs to automate tasks, methods using generative models, which are an AI technology, are also being researched.
Generally, annotation work, which requires labeling large amounts of data, takes time. Automation can shorten the processing time.
Depending on the implementation method, automation may potentially lower annotation costs.
Basically, automated annotation methods are developed for specific domains. Implementing such initiatives requires individual development for each domain, which takes time and money.
It can be said that automated annotation often fails to guarantee sufficient quality at this point. In a study on tomato detection (*), the result showed that only 76.9% of the annotation was performed appropriately. The situation has not yet reached a point where it can match the level of annotation performed by humans.
So, how should manual work and machines be used effectively?
In conclusion, it is technically difficult to completely automate annotation work, and human intervention is unavoidable. While there are cases where research and development for automation are conducted for each domain, the current situation is that it can only be used in limited areas.
Although annotation work cannot be fully automated at this time, machines can be utilized partially to support tasks performed by humans. Specifically, various tools have been developed as annotation support tools, offering features such as GUI-based assistance, streamlining labeling and image segmentation, and tracking functions for videos.
Furthermore, some tools include project management functions for annotation work involving multiple people, such as managing work progress, review statuses, and quality control.
By having humans perform annotation work while utilizing these tools, efficiency and quality improvements can be expected.
Annotation work is a high-load task, and while various efficiencies are being considered, the path to complete automation is still incomplete. For example, a method called active learning has been developed, which uses a small amount of training data as a basis for AI to perform annotation on the remaining data.
In active learning, automation is used when judgments can be made with sufficient evidence, while humans perform correct labeling when evidence is insufficient. Regardless, complete automation remains difficult. Various methods and tools, including active learning, have been developed to improve the efficiency of annotation work, and progress is expected to continue.
Given these circumstances, it is important to find an outsourcing partner familiar with the latest status and procedures of annotation work to proceed effectively in the future. There will be a demand for outsourcing partners that can define annotation requirements, utilize excellent tools and AI technologies, and provide project management for annotation work. When considering outsourcing annotation work in the future, we recommend comparing companies based on these criteria.
In this article, we introduced the strengths, weaknesses, and effective usage of humans and machines in annotation work. While annotation work often becomes a bottleneck in AI development, complete automation is currently difficult. For the time being, selecting an outsourcing partner that can perform annotation work efficiently and with high quality will be important.