What is Teacher Data? Explanation from the relationship with AI, machine learning, and annotation to how to create it.

1. What is Teacher Data?

1-1. What is the relationship between AI, machine learning, training data, and data annotation?

First, let's organize the mechanism of how AI (Artificial Intelligence) works. The structure in which AI learns its job is the same as humans. By going through training, AI can improve its decision-making ability and processing speed. This training is called machine learning or ML (Machine Learning). The data used by AI for machine learning is called training data. The term often seen, "annotation," refers to the process of creating training data.
Let's clarify the terminology here.

AI: Refers to artificial intelligence itself.
Machine learning: Training to improve the accuracy of AI.
Training data: Data used for machine learning.
Data annotation: The process of creating training data.

The positioning of data annotation in the process of AI development is as follows.

1-2. Essential Teacher Data for AI Learning

Teacher data is data that plays the role of a teacher when AI is learning, as the name suggests.

For example, humans teach AI both the question "What is this?" and the answer "This is Mount Fuji" by showing it photos of Mount Fuji multiple times. As AI sees more and more photos, it learns to recognize Mount Fuji and its accuracy in answering the question "What is this?" with "This is Mount Fuji" or "This is not Mount Fuji" increases. The data that contains the questions and answers shown to AI is called training data. Just like humans, the more AI learns, the higher its accuracy becomes. To improve the accuracy of AI, it is necessary to repeatedly train it using a sufficient amount of training data.

1-3. How to Create Teacher Data

In data annotation, data is prepared and information is added to each one. Information is added as metadata such as tags and labels. This is a necessary process regardless of the form of data, such as images, audio, and text. Annotation is originally an English word meaning "annotation" or "explanation". The role of annotation work is to give meaning and association to data. The person in charge of the work is called a data annotator.

Please also refer to the following article for the meaning and types of data annotation.

>>What is Data Annotation? Explanation from its meaning to its relationship with AI and machine learning.

1-4. Time-consuming data annotation

Data annotation is done manually, so workers need not only accurate knowledge and judgment, but also considerable patience. Behind the increasing capabilities of AI, this painstaking process always exists.
In annotation using image data, the annotator manually specifies specific areas within the image and adds information. In the example of Mount Fuji above, workers visually confirm each image and accurately select only the area where Mount Fuji is captured.

1-5. How much quantity is needed?

How much teacher data is needed? The answer varies depending on the project's objectives and desired accuracy. We will verify whether the problem can be solved with the amount of data actually trained by AI, or if more training is needed. If it is not enough, we will continue training by adding more teacher data. If it does not work well, we may also review the rules for creating teacher data. It is necessary to consider both the quantity and quality of teacher data.

1-6. What is High-Quality Teacher Data?

The quality of training data greatly affects the accuracy of AI. High-quality training data requires both unbiased materials and consistent annotations. For example, if we want to train AI to recognize Mount Fuji, we need to provide a variety of photos of Mount Fuji taken from different locations and at different times, rather than just using the same photos. It is important to establish clear rules for selecting images and recording data, so that all data annotators work with the same criteria.

1-7. Why is the quality of teacher data important?

Even if you can define requirements that align with the purpose of AI development, if the quality of the training data is low, the learning of AI will not go well. If AI learns with low-quality training data, it will not be able to achieve the desired accuracy for development. In that case, annotation will be required again. In many cases, the same annotator cannot be assigned, and retraining of the annotator may also be necessary. In addition to just recreating the data, there will also be additional ancillary tasks, which not only increases the cost of work, but also causes delays in the project. If the training data is of high quality from the beginning, it will be possible to develop AI that aligns with the purpose, minimize costs, and speed up the development cycle.

Next, we will explain the benefits of preparing high-quality teacher data.

1. Contributes to Improving AI Accuracy
When using low-quality and inaccurate training data, AI learning does not progress as intended and it is not possible to achieve the desired recognition accuracy. For example, let's consider training data using bounding boxes for image recognition AI. If the accuracy of the boxes surrounding the target is poor and includes background information or unnecessary objects, the recognition accuracy of the AI will naturally not improve. High-quality training data can avoid such problems and contribute to improving accuracy.

2. Capable of handling various patterns of data
Just as humans can adapt to unknown things and events through various experiences, AI can also improve its recognition accuracy for unknown data by learning from various training data. High-quality training data is not only important for ensuring correct data annotation, but also for having various patterns of data (taking car images as an example, images with various orientations, in the city, on mountain roads, in tunnels, etc.). If only the same patterns are given, overfitting may occur where the AI can maintain high accuracy for that pattern, but cannot adapt to different patterns of data. With high-quality training data, AI can continue to learn and adapt to various data.

3. Compliant with Development Objectives
High-quality training data is data that is suitable for the AI you want to develop. If you are developing AI to recognize faces from the front, it will be difficult for the AI to learn if you only collect images of side profiles or the back of the head. By having high-quality training data, you can conduct accurate learning.

4. Streamlining AI Development
If the quality of training data is high, the accuracy of AI will also be high at an early stage. By using planned human resources efficiently, the goal can be achieved. On the other hand, if the quality is low, not only will it take more time for development, but the accuracy will also not improve as extra resources will be used.

5. Risk Avoidance in Terms of Security
When handling data with high security requirements, it is extremely important for workers and administrators to manage the data properly. In addition to creating training data correctly, it is also important to not leak work content to others and to have a fully secure work environment. It is also necessary to have a high level of security awareness, such as implementing education to maintain this, in order to create high-quality training data.

6. Cost Reduction
If the quality of training data is low, there is a possibility that the cost of AI learning will increase. It will be necessary to redo the creation of training data, and it will be necessary to spend time and cost to re-educate the annotators for that purpose. By preparing high-quality training data from the beginning, it is possible to reduce these costs.

There are many benefits to improving the quality of training data in this way. In order to successfully complete an AI development project, it is important to provide high-quality training data. For this purpose, proper management in the annotation process is crucial.

1-8. What is High-Quality Teacher Data?

Here, we will explain the differences between teacher data and learning data.

:Teacher Data
The set of data labeled by annotation is called teacher data. AI learns the objects it should recognize based on this data. Even if only the labeled data is learned, it cannot be evaluated whether it can recognize unlabeled data well or not only with teacher data.

:Training Data
Training data refers to the entire set of data used for AI learning. This includes data without labels other than the training data. AI trained on the target to be recognized by the training data will improve its recognition accuracy through unlabeled data. In addition, there are also training data sets without training data depending on the learning method.

1-9. Three Approaches to Machine Learning

Here, we will explain the three representative approaches used in machine learning.

1. Supervised Learning
Supervised learning is a method that uses training data containing labeled correct answers. It is a commonly used method in AI development, and requires data annotation to create the training data. Supervised learning is often used in object detection.

2. Unsupervised Learning
This is a method that uses learning data that does not include teacher data. It is used to find patterns in the data and classify the data according to those patterns. It is often used for AI learning purposes such as anomaly detection.

3. Reinforcement Learning
Reinforcement learning is a learning method in which a system repeats trial and error to find the optimal solution. It is used for tasks where the rules are clearly defined and the optimal solution can be sought. A common example is AI that wins in games such as robot control and chess.

2. Three Key Points for Producing Teacher Data

2-1. Standardization of Work Rules

Without consistent quality in the teacher data, AI cannot learn. Just like humans, if taught different things by multiple teachers, it becomes difficult to know whose instructions to follow. To prevent this, in annotation projects, it is important for the entire team to create and share specific work guidelines before starting the actual work. In more challenging projects, a trial period may be set up initially and only annotators who pass the test will be selected to form the team.

2-2. Management System Suitable for Data Annotation

Data annotation work requires a considerable amount of attention and patience. In addition, it also demands a correct understanding of guidelines, knowledge and insight into the subject being tagged. In terms of resources, not only data annotators who are responsible for the work, but also checkers who review the results, trainers who provide education, and project managers who oversee and manage the entire process are necessary. Building an effective management structure tailored to the characteristics of the project leads to ensuring quality and productivity.

2-3. Ensuring Security Levels

Data annotation may involve handling highly confidential data and personal information. Therefore, it is important to provide security education to data annotators. It is also necessary to take sufficient security measures when setting up the work environment and selecting tools to use. When outsourcing annotation projects to external service providers, it is important to thoroughly check the level of security measures of the subcontractor.

3. Data Annotation Outsourcing Service by Human Science Co., Ltd.

3-1. Rich track record of creating 48 million pieces of teacher data

At Human Science, we are involved in AI development projects in various industries such as natural language processing, medical support, automotive, IT, manufacturing, and construction. Through direct transactions with many companies including GAFAM, we have provided over 48 million high-quality training data. We handle various annotation projects regardless of industry, from small-scale projects to large-scale projects with 150 annotators. If your company is interested in introducing AI but unsure where to start, please consult with us.

3-2. Resource Management without Using Crowdsourcing

At Human Science, we do not use crowdsourcing and instead directly contract with workers to manage projects. We carefully assess each member's practical experience and evaluations from previous projects to form a team that can perform to the best of their abilities.

3-3. Utilizing the Latest Data Annotation Tools

One of the annotation tools introduced by Human Science, AnnoFab, allows customers to check progress and provide feedback on the cloud even during project execution. By not allowing work data to be saved on local machines, we also consider security.

3-4. Equipped with a security room within the company

At Human Science, we have a security room that meets the ISMS standards in our Shinjuku office. We can handle highly confidential projects on-site. We consider ensuring confidentiality to be extremely important for all projects. We continuously provide security education to our staff and pay close attention to handling information and data, even for remote projects.