What is training data? An explanation from its 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 annotation?

First, let's organize how AI (artificial intelligence) works. The structure by which AI learns is the same as that of humans. AI also improves its judgment ability and processing speed through repeated training. This training is referred to as machine learning or ML (Machine Learning). The data used by AI when performing machine learning is called training data. The term annotation, which is often seen, refers to the process of creating training data.
Now, let's clarify the terminology.

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

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

1-2. Essential Training Data for AI Learning

Training data, as the name suggests, is the data that serves as a teacher when AI is learning.

For example, a human shows an AI a picture of "Mount Fuji" and teaches it both the question "What is this?" and the answer "This is Mount Fuji." The AI is shown a large number of pictures of Mount Fuji repeatedly. As a result, the AI gradually learns to recognize "Mount Fuji," and the accuracy of its answers when asked "What is this?" improves, with responses like "This is Mount Fuji" and "This is not Mount Fuji." The data that includes the questions and answers shown to the AI is called training data. Just like humans, the more the AI learns, the higher its accuracy becomes. To further improve the accuracy of the AI, it is necessary to repeatedly train it using a sufficient amount of training data.

1-3. How to Create Teacher Data

In annotation, we prepare the data that will serve as the material and add information to each one. The information is added as metadata such as tags and labels. This is a necessary process regardless of the form of the data, whether it is images, audio, or text. The term annotation originally means "note" or "comment" in English. The role of the annotation work is to give meaning and linkage to the data. The person responsible for the work is called an annotator.

Please refer to the following article for the meaning and types of annotations.

What is Annotation? An explanation from its meaning to its relationship with AI and machine learning.

1-4. Time-Consuming Annotations

Annotation is performed manually, requiring workers to possess not only accurate knowledge and judgment but also considerable perseverance. Behind the increasing capabilities of AI, this diligent process always exists.
In image data annotation, annotators manually specify certain areas within the images to add information. In the example of Mount Fuji above, the task involves visually inspecting each image and accurately selecting only the area where Mount Fuji is depicted.

1-5. How much is needed

How much training data is necessary? The answer varies depending on the project's objectives and the desired level of accuracy. We will actually train the AI with the available data to verify whether that amount can solve the issues or if further training is needed. If it seems insufficient, we will add more training data and continue the learning process. If it still does not work well, we may need to reconsider the rules for creating the training data. It is essential to consider both the quantity and quality of the training data.

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

The quality of training data has a significant impact on the accuracy of AI. High-quality training data requires both unbiased materials and consistent annotations. If you are training on Mount Fuji, it is essential to prepare a variety of photos of Mount Fuji taken at different locations and times, rather than just similar photos. On the operational side, it is important to establish clear rules regarding the selection of images and the method of data recording, so that all annotators work with the same criteria.

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

Even if you can define requirements that align with the purpose of AI development, if the quality of the training data is low, AI learning will not go well. If AI learns with low-quality training data, it will not achieve the accuracy that meets the development goals. This will necessitate re-annotation. Often, it is not possible to assign the same annotators again, which may require re-training of the annotators. Additionally, not only will there be a need to recreate the data, but also associated ancillary tasks will be added, leading to increased work costs and potential project delays. If the training data is of high quality from the start, it will be possible to develop AI that meets the objectives while minimizing costs and accelerating the development cycle.

Next, I will explain the benefits obtained by preparing high-quality training data.

1.Contributing to Improved AI Accuracy
With low-quality or inaccurate training data, AI learning does not progress as desired, and it cannot achieve the intended recognition accuracy. For example, consider the training data for image recognition AI using bounding boxes. If the accuracy of the box surrounding the target is poor, including background information or surrounding extra objects, the AI's recognition accuracy will naturally not improve. High-quality training data can help avoid such issues and contribute to accuracy improvement.

2. Capable of Handling Various Patterns of Data
Just as humans can respond to unknown objects and events through various experiences, AI can enhance its recognition accuracy for unknown data by learning from diverse training data. High-quality training data not only requires correct annotations but also needs to include a variety of data patterns (for example, images of cars in various orientations, in urban settings, on mountain roads, and inside tunnels). If only the same pattern of data is continuously provided, the model may maintain high accuracy for that pattern but will fail to respond to different pattern data, leading to overfitting. With high-quality training data, AI learning can progress to accommodate various data types.

3. Alignment with Development Objectives
High-quality training data is aligned with the objectives of the AI you want to develop. If you are developing an AI to recognize human faces from the front, it would be difficult to train the AI on images that only show profiles or the back of the head. By ensuring high-quality training data, accurate learning can be achieved.

4. Streamlining AI Development
If the quality of the training data is high, the accuracy of the AI will also improve quickly. By using planned human resources efficiently, objectives can be achieved. On the other hand, if the quality is low, it will consume extra resources, resulting in not only longer development time but also lower accuracy.

5. Risk Avoidance in Security
When handling data with high security requirements, it is crucial for operators and administrators to manage the data appropriately. Not only must the creation of training data be done correctly, but it is also essential to ensure that work content is not disclosed to others, to provide a work environment with comprehensive security measures, and to conduct training to maintain a high level of security awareness. These aspects can be considered conditions for creating high-quality training data.

6. Cost Reduction
If the quality of the training data is low, the cost of AI learning may increase. It will be necessary to redo the creation of training data, and time and costs will have to be spent on re-educating the annotators. By preparing high-quality training data from the beginning, these costs can be kept down.

By improving the quality of training data in this way, there are many benefits to be gained. It is important to prepare high-quality training data to ensure the success of AI development projects. Therefore, proper management of the annotation work becomes crucial.

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

Here, we will explain the difference between training data and learning data.

:Training Data
The set of data labeled by annotations is called training data. Based on this data, AI learns to recognize the subjects it should identify. Even if it only learns from labeled data, it cannot be evaluated whether it can recognize unlabeled data effectively based solely on the training data.

Training Data
Training data refers to the entire set of data used by AI for learning. It also includes unlabeled data that is not teacher data. The AI, which has learned to recognize targets from the teacher data, improves its recognition accuracy through unlabeled data. Additionally, depending on the learning method, there are also training data sets that do not have teacher data.

1-9. Three Approaches to Machine Learning

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

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

2. Unsupervised Learning
This is a method that uses training data without labeled data. It is a technique that finds patterns within the data and classifies the data according to those patterns, and is often used for AI learning aimed at anomaly detection and similar tasks.

3. Reinforcement Learning
Reinforcement learning is a method of learning in which a system repeatedly tries and errors to find the optimal solution. It is a technique used when an optimal solution is required for tasks with clearly defined rules. Examples include AI for winning in robot control and games like chess.

2. Three Important Points in Producing Teacher Data

2-1. Standardization of Work Rules

If the quality of the training data is inconsistent, AI cannot learn. Just like humans, when taught different things by multiple teachers, it becomes unclear whose advice to follow. To prevent this, it is important to create specific work guidelines before starting the actual work in annotation projects and share them with the entire team. In high-difficulty projects, a trial period may be established at the beginning, and the team may be formed only with annotators who pass the test.

2-2. Management System Suitable for Annotations

Annotation work requires considerable attention to detail and perseverance. Additionally, a correct understanding of the guidelines and knowledge and insight into the subjects being tagged are also required. In terms of resources, not only the annotators responsible for the work but also checkers who verify the deliverables, trainers who provide education, and project managers who oversee the entire process are necessary. Building an effective management system tailored to the characteristics of the project leads to ensuring quality and productivity.

2-3. Ensuring Security Levels

Annotations may involve handling highly confidential data and personal information. Therefore, security training for annotators is essential. At the same time, it is necessary to implement adequate security measures in the construction of the work environment and the selection of tools used. When outsourcing annotation projects to external service providers, it is crucial to thoroughly verify the level of security measures of the outsourcing partner.

3. Human Science Annotation Agency Services

3-1. Extensive track record of creating 48 million teacher data entries

At Human Science, we participate in AI development projects across various industries, including natural language processing, medical support, automotive, IT, manufacturing, and construction. To date, we have provided over 48 million high-quality training data through direct transactions with many companies, including GAFAM. We handle a wide range of annotation projects, from small-scale projects to long-term large-scale projects with 150 annotators, regardless of the industry. If your company wants to implement AI but doesn't know where to start, please feel free to consult with us.

3-2. Resource Management Without Using Crowdsourcing

At Human Science, we do not use crowdsourcing; instead, we advance projects with personnel directly contracted by our company. We form teams that can deliver maximum performance based on a solid understanding of each member's practical experience and their evaluations from previous projects.

3-3. Utilizing the latest data annotation tools

One of the annotation tools introduced by Human Science, AnnoFab, allows customers to receive progress checks and feedback from the cloud even during the project. By ensuring that work data cannot be saved on local machines, we also take security into consideration.

3-4. Complete with a security room in-house

Human Science has a security room that meets ISMS standards within our Shinjuku office. We can handle even highly confidential projects on-site. We consider the assurance of confidentiality to be extremely important for any project. Our staff undergoes continuous security training, and we exercise the utmost care in handling information and data, even for remote projects.