What AI and machine learning can do. 12 selected use cases by industry.

1. Relationship between AI and Machine Learning

1-1. Relationship between AI, Machine Learning, Training Data, and Data Annotation

To understand what AI can do, first we will organize how AI works. In order to run AI, the process of giving data to AI and training it is necessary. By repeatedly giving data containing information about problems and answers, AI will gain the ability to find patterns and features and make judgments. This training is called machine learning. Below, we will organize commonly used terms.

 

AI: Artificial Intelligence itself.
Machine Learning: Training for AI to operate.
Training Data: Data used for Machine Learning.
Data Annotation: The process of creating training data.

 

When illustrating the process of AI development, it looks like this.

Learn more about data annotation
>>What is data annotation? Explanation from its meaning to its relationship with AI and machine learning.

Click here for teacher data
>>What is teacher data? Explanation from the relationship with AI, machine learning, and data annotation to how to create it.

Case studies of business efficiency improvement using AI can be found here.
>> Introduction of 4 selected case studies of machine learning projects where 80% of users have experienced the effects of business efficiency improvement through AI utilization.

2. 12 Examples of AI Applications

As AI learns and gains decision-making abilities, it becomes possible to entrust it with various tasks. From here, we will introduce examples of how AI can be utilized in different industries.

2-1. View and Judge (Image Recognition)

You can identify where things are by looking at images and videos. It is also possible to track the movement of the subject.

2-2. Listen and Judge (Speech Recognition)

You can identify by listening to the voice.

2-3. Using Words (Natural Language Processing)

You can understand the meaning of words used by humans. AI can also write and speak.

3. Difficulties with AI

We have seen examples of AI utilization up to this point. We can see that things that were previously thought to be difficult for machines are now achievable. However, even within this, there are still things that are difficult to achieve with AI.

3-1. Creative Work

AI learns and achieves its goals based on given data. This applies to generative AI such as ChatGPT, which can generate text and images. While it may seem like AI is coming up with new ideas or creating creative works that have never been seen before, in reality, these are generated by algorithms based on past data. Humans use intuition and their own experiences to engage in creative work, but AI does not possess this ability. However, as a tool to complement human creativity, it is extremely capable.

3-2. Moral Judgment

AI does not possess ethical judgment or moral sense. If biased or discriminatory elements are included in the learning data, AI will learn them uncritically. Therefore, it is necessary for humans to consider and modify the learning data from ethical and moral perspectives.

3-3. Understanding Human Emotions

AI does not have emotions. Conversational AI can generate probabilistic responses to text input from users, but this does not mean that the AI itself understands emotions or engages in emotional conversations. It is important to note that AI is simply a program and cannot have emotions or consciousness.

4. Introduction Status by Industry for AI

4-1. Introduction Status of AI by Industry

This is data that investigates the implementation status of AI by industry.

Source: Created by the author from O'Reilly AI Adoption in the Enterprise 2021

 

In the computer, finance, and retail industries, AI implementation is already progressing. In the fields of education and administration, the implementation rate seems to be still low compared to the percentage of consideration and evaluation stages. Especially in these fields, ethical considerations such as handling personal information, social roles, and environmental considerations are expected to be challenges before implementation.

5. What is the background behind the growing attention to AI in Japan?

 

The following factors contribute to the growing interest in AI implementation in Japan:

5-1. Promotion of Work Style Reform

By assigning simple and time-consuming tasks to AI, humans can spend their time on creative tasks such as planning and learning in new fields. With the promotion of work style reform, it is predicted that the demand for AI will continue to increase in the future.

5-2. Realization of Business Improvement Effects

This is the result of a survey on the effects of introducing AI for Japanese companies.

 

Source: Author's creation from the 2021 Information and Communications White Paper by the Ministry of Internal Affairs and Communications

 

More than 80% of companies responded that the introduction of AI had either "very effective" or "somewhat effective" results. There were zero companies that reported negative effects. It can be said that there is a clearly positive mood surrounding the adoption of AI.

6. Free Thinking is Essential for Introducing AI

6-1. Flexible AI that changes the way we work

AI can expand its range of use by combining various methods. The 12 use cases introduced here are just a small part of the potential that AI holds. Originally, AI is capable of flexibly changing its way of working to meet human needs. Regardless of industry or previous examples, let's first consider introducing AI from the perspective of "wouldn't it be great if there was an AI like this?" with a free mindset.

7. For inquiries about AI implementation, contact Human Science Co., Ltd.

7-1. 48 million records of teacher data creation

"I want to introduce AI, but I don't know where to start."
"I don't know what to ask for when outsourcing."
If you are in such a situation, please consult with Human Science. At Human Science, we participate 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 a total of over 48 million high-quality training data. We can handle various annotation projects regardless of industry, from small-scale projects to long-term large-scale projects with 150 annotators.

7-2. 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.

7-3. 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.

7-4. 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.