The Basics of Generative AI: How It Works and How to Use It

What is generative AI?

Generative AI is a field of artificial intelligence (AI) that has been developing rapidly in recent years. While traditional AI focuses on data analysis and prediction, generative AI is characterized by its ability to generate new content.

Definition and characteristics of generative AI

Generative AI is an AI that uses machine learning to generate new content such as text, images, music, and videos. By learning from large amounts of data created by humans and capturing its patterns and features, it can generate content that looks as if it was created by a human.

Differences from conventional AI

Traditional AI has been used mainly for tasks such as classification and prediction, such as detecting spam emails, predicting customer behavior, diagnosing diseases, etc. These AIs make decisions based on existing data, so they cannot create anything new.

Generative AI, on the other hand, can generate new content based on patterns and features learned from existing data, making it possible to automate creative tasks that were previously impossible with traditional AI.

Why generative AI is attracting attention

The reason generative AI is attracting attention is because of its wide range of possible applications. In business, it is expected to be useful in improving the efficiency of various operations, such as creating marketing content, automatically generating product designs, and automating customer support.

It is also attracting attention in everyday life as a tool that supports creative activities such as writing, editing images, and composing music. It is also expected to contribute to solving social issues, such as diagnostic support and drug discovery in the medical field and the creation of educational materials in the field.

How generative AI works

The mechanisms of generative AI are complex and varied, but here we will explain the basic concepts and key technologies.

Machine Learning and Deep Learning

The core technologies of generative AI are machine learning and deep learning. Machine learning is a technology that allows computers to learn from data and discover patterns and regularities. Deep learning is a type of machine learning that uses neural networks that mimic the neural circuits of the human brain to learn more complex patterns.

Generative AI uses deep learning models to learn patterns and features from large amounts of data (text, images, music, etc.) and can then generate new content based on those learned patterns.

Large-scale language models

In recent years, the Large Language Model (LLM) has been attracting attention as a text generation AI. LLMs learn from huge amounts of text data and can generate natural-sounding sentences that sound like they were written by a human.

For example, the GPT (Generative Pre-trained Transformer) series developed by OpenAI is one of the most well-known LLMs. GPT has learned from a huge amount of text data on the Internet and can handle a variety of tasks, including question answering, translation, summarization, and text generation.

Generative Adversarial Networks (GANs)

One of the techniques often used in image generation AI is the Generative Adversarial Network (GAN), which is a learning mechanism in which two neural networks (a generative network and a discriminative network) compete with each other.

The generative network tries to create fake data that looks as good as the real thing, while the discriminative network learns to distinguish between real and fake data. As the two networks compete, the generative network is able to create better fake data, and ultimately generate images that are indistinguishable from the real thing to the human eye.

Applications of generative AI

Generative AI is highly versatile and is used in a variety of fields. Here, we will introduce some of the most common fields of use and some specific examples.

Text Generation

Text generation AI uses natural language processing technology to generate natural sentences that sound as if they were written by a human.

• Text Creation, Translation, and Summarization: Generative AI can help you create texts like blog posts, news articles, novels, poems, etc. It can also translate between different languages and summarize longer texts.
• Chatbots, dialogue systems: Chatbots that utilize generative AI can automatically answer customer inquiries and provide information about products and services, while dialogue systems can understand the needs of users and provide appropriate information through more natural conversations.
• Code generation: Tools are now available that allow AI to automatically generate code by simply providing instructions in natural language, even if you have no programming knowledge. This is expected to improve development efficiency and make app development easier even for programming novices.

Image Generation

Image generation AI can use machine learning models to generate a variety of images, including photographs, illustrations, and designs.

• Image creation, conversion and editing: Generate high-quality images from text instructions or simple sketches, convert existing images to a different style or remove unwanted parts.
• Design, illustration: Can be used for various design production such as advertising banners and posters, website designs, character illustrations, etc.
• Photo Enhancement: Automatically apply advanced photo enhancements such as adjusting color and brightness, removing unwanted objects, and blurring backgrounds.

Music Generation

Music generation AI uses machine learning models to generate melodies, chord progressions, accompaniments, and more to create original musical compositions.

• Composing and arranging: Just select your favorite genre and mood, and the AI will automatically compose and arrange the music.
• BGM generation: Generate background music that is perfect for games, videos, presentations, etc. to match the scene and atmosphere.

Video Generation

Video generation AI can generate short video clips and animations using machine learning models.

• Edit videos and add effects: You can combine multiple videos and images to create new videos and add effects.
• Animation generation: You can automatically generate character movements and expressions to create animations.

These are just a few of the use cases. Generative AI is expected to continue to be used in a variety of fields, greatly changing our lives and businesses.

Examples of generative AI in use

Generative AI has the potential to make our lives more convenient and enriching. Here, we will introduce some specific use cases. Through these examples, we will see how generative AI is helping us in our daily lives and work.

Business Use Cases

• Marketing: Generative AI tools can automate and streamline tasks previously performed by humans in marketing activities.
  • Automatic generation of ad copy and taglines: Effective ad copy and taglines can be automatically generated to match the features of the product and the needs of the target audience. This saves time and effort and allows you to test more ad variations. For example, one EC site used generative AI to automatically generate product descriptions, significantly reducing the time it took to create content and allowing them to efficiently introduce more products.
  • Create email newsletters tailored to the target audience: You can automatically generate personalized email newsletters based on the attributes and interests of your customers. This can improve open and click rates and increase customer engagement. For example, one travel agency automatically generated email newsletters introducing recommended travel plans based on customers’ past travel history and survey results, improving customer satisfaction.
  • Auto-generate and schedule social media posts: Based on themes and keywords, you can automatically generate attractive social media posts and post them at the optimal time. This will help you increase your social media presence and grow your following. For example, one restaurant used a generation AI to automatically post daily menu introductions and event information on social media, which helped attract more customers.
  • Landing page content creation: You can automatically generate landing page content that conveys the appeal of your products and services. This can improve the conversion rate of potential customers. For example, one online learning platform used generative AI to automatically generate course descriptions and learner testimonials to increase the number of sign-ups.
  • SEO: Generating SEO-friendly articles and content can increase the chances of ranking highly in search engines. For example, one web media company used generative AI to automate keyword research and article structure creation, streamlining SEO measures.
• Customer Support: Generative AI tools can help streamline customer support operations and improve customer satisfaction.
  • Automatic answers to frequently asked questions: By having an AI chatbot automatically answer frequently asked questions, you can shorten customer waiting times and reduce the burden on support staff. For example, one telecommunications company introduced an AI chatbot to respond to customer inquiries 24 hours a day, 365 days a year, improving customer satisfaction.
  • Customer support by chatbots: AI chatbots can improve customer satisfaction by responding to customer inquiries 24 hours a day, 365 days a year. AI chatbots are also now available that can handle complex questions that are difficult for human operators to handle. For example, one e-commerce site improved customer convenience by having an AI chatbot answer questions about product inventory and delivery status in real time.
  • Analyzing customer reviews and suggesting improvements: Generative AI tools can analyze customer feedback to identify areas for improvement or new needs in products and services. This can improve customer satisfaction and strengthen competitiveness. For example, one hotel chain used generative AI to analyze customer survey responses and improve its services.
• Content Creation: Generative AI tools can help make content creation more efficient and improve its quality.
  • Auto-generation of blog posts and news articles: High-quality articles can be automatically generated based on themes and keywords. This can reduce the time and cost of content creation. For example, one news site used generative AI to automatically generate breaking news articles, allowing them to deliver information as quickly as possible.
  • Creation of product descriptions and manuals: Content that clearly explains the features and usage of a product can be automatically generated. This will deepen customer understanding and increase purchasing motivation. For example, one home appliance manufacturer used generative AI to automatically generate instruction manuals to improve the efficiency of customer support.
  • Creation of scripts and presentation materials: Speech manuscripts, video scripts, presentation materials, etc. can be automatically generated. This reduces preparation time for presentations and makes presentations more effective. For example, one consulting company used generative AI to streamline the creation of proposals and strengthen sales activities.

• Human Resources: Generative AI tools can also help improve the efficiency of human resources department operations, including recruitment and training.
  • Screening candidate resumes: AI can scan large volumes of resumes and extract candidates who match the set criteria, reducing the burden on recruiters and making recruitment more efficient. For example, one IT company used generative AI to automatically evaluate the skills and experience of applicants and narrow down the candidates who proceed to interviews, significantly shortening the recruitment process.
  • Automatic generation of interview questions: Effective interview questions can be automatically generated based on past interview data and job content. This reduces the burden on interviewers and allows for more objective evaluation. For example, one recruitment agency used generative AI to create a set of interview questions for each job type, reducing the time interviewers spent preparing questions.
  • Creating in-house newsletters and training materials: By automatically generating in-house newsletter articles and training materials, you can increase employee engagement and promote skill development. For example, one manufacturing company used generative AI to automatically generate training materials for new employees, improving the quality of training.

Examples of use in everyday life

Generative AI is becoming more prevalent not only in business but also in our daily lives. Below are some examples of how it can be used in everyday life.

• Learning support: Generative AI can improve your learning efficiency and help you acquire new knowledge and skills.
  • Conversation practice in language learning apps: By talking to an AI chatbot, you can acquire practical language skills. It can be used for various learning purposes such as practicing pronunciation, checking grammar, and understanding different cultures. For example, one language learning app has a function where AI evaluates the user’s pronunciation and gives advice on areas for improvement.
  • Code generation and error checking in programming learning: AI can improve the efficiency of programming learning by automatically generating code and checking errors. Even beginners can easily understand complex code and acquire programming skills. For example, in one online programming learning platform, AI automatically generates tasks tailored to the learner’s level and provides individual learning plans.
  • Creation and explanation of questions for qualification exam preparation: AI can analyze past questions, automatically generate similar questions, and provide explanations for answers, enabling efficient exam preparation. For example, in one qualification exam preparation app, AI analyzes the user’s weak areas and suggests questions that should be focused on.
• Creative Activities: Generative AI can inspire your creativity and open up new possibilities for expression.
  • Novel, poetry, and script writing: AI can help you with plot suggestions and writing ideas, helping you when you’re stuck or need new inspiration. It can also help you proofread and polish your writing. For example, one novel-writing tool uses AI to provide character and story development ideas.
  • Music production (composing, arranging): AI generates melodies and chord progressions to support composing and arranging. You can easily create original music even without knowledge of music theory. For example, some music production software has a function where AI generates automatic accompaniment to match the user’s performance.
  • Create illustrations, paintings, and designs: AI generates illustrations, paintings, and designs based on your sketches and ideas. You can create high-quality works even if you are not a professional designer. For example, with one image generation tool, you can simply enter text and AI will generate an image that matches your image.
• Communication: Generative AI can facilitate and enrich communication.
  • Conversing with an AI chatbot: Talking to an AI chatbot about various topics such as hobbies and worries can help relieve stress and reduce feelings of loneliness.
  • Helping you communicate in foreign languages: AI translation tools allow you to overcome language barriers and communicate with people all over the world.
  • Assistance in creating presentation materials: AI will assist you in creating presentation materials, allowing you to give more effective presentations.

Generative AI has the potential to make our lives more convenient and enriching. Take a look at these use cases and try incorporating it into your own life.

Challenges and future prospects for generative AI

Generative AI has the potential to dramatically change our lives and businesses, but at the same time, it also faces several challenges. Here, we will explain the ethical and technical challenges facing generative AI, as well as future prospects.

Ethical issues

The use of generative AI raises ethical questions, including:

• Creation of fake news and deepfakes: Generative AI can create fake news articles, images, and videos that are indistinguishable from the real thing. This fake content can cause social unrest or defame individuals.
• Copyright infringement: Generative AI may learn from existing works and generate content that infringes copyright. As there are no clear rules yet regarding copyright for content generated by generative AI, caution is required.
• Privacy Invasion: Generative AI may collect personal information and invade privacy. For example, there are concerns that generative AI equipped with facial recognition technology may collect and use facial images without the consent of individuals.

To address these ethical issues, developers and users of generative AI must adhere to ethical guidelines and establish transparent development and usage processes. It is also important for society as a whole to deepen discussion and establish appropriate rules and regulations.

Technical challenges

Generative AI also presents technical challenges.

• Bias: Generative AI may reflect biases contained in the training data. For example, AI trained with training data that contains prejudices against a particular gender or race may generate discriminatory content. To eliminate bias, it is necessary to train with diverse data and develop technology to detect and correct bias.
• Explainability: It can be difficult to explain why generative AI has output a certain result. This is because deep learning models have a complex structure. There is a demand for the development of “Explainable AI (XAI)” that can explain the basis for AI’s decisions.

To solve these technical challenges, we need to further advance AI research and development and develop more advanced algorithms and models. It is also important to collaborate with not only AI experts, but also experts in various fields, such as ethicists and sociologists.

Future outlook

Generative AI is still a developing technology, but its possibilities are endless. Here, we will explain the future prospects for generative AI.

• More accurate generation: As generative AI advances, the quality of generated content will continue to improve. In all fields, including text, images, music, and videos, it may become possible to generate content that is indistinguishable from that created by humans. For example, it will be possible to generate high-resolution, realistic images and videos, and create natural-looking sentences with an understanding of context.
• Use in a variety of fields: Generative AI is currently being used in a variety of fields, but it is expected to be used in even more diverse fields in the future. For example, in the medical field, we may see the emergence of AI that predicts the possibility of illness from a patient’s symptoms and test data, and suggests optimal treatment methods. In the education field, we may see the development of AI that generates optimal teaching materials and assignments based on the learning situation of each individual student.
• Personal AI Assistants: With the development of generative AI technology, personal AI assistants will emerge that support various tasks according to personal preferences and needs. We expect AI assistants to make our lives more convenient, such as schedule management, information gathering, and communication support. For example, an AI assistant may automatically reply to your emails or suggest the best outfit to wear based on today’s schedule.
• Real-time generation: In the past, content generation sometimes took time, but in the future, it will be possible to generate content in real time. For example, real-time generation, such as automatically generating minutes during a meeting or automatically generating slides during a presentation, could greatly improve the efficiency of our work. In addition, AI that translates and interprets in real time will also appear, and it is expected that international communication will become smoother.
• Collaboration with humans: Generative AI will not take away jobs from humans, but will expand human capabilities and provide an environment where humans can focus on more creative activities. It is expected that collaboration between humans and AI will create new value that has never been seen before. For example, a designer will use AI to expand on a design idea, or a writer will use AI to develop a novel plot. Generative AI will stimulate human creativity and open up new possibilities.

Generative AI is a technology that has the potential to dramatically change our future. By overcoming the ethical and technical challenges and maximizing the benefits of generative AI, we can create a richer and more creative society.

Summary: Use generative AI to create new possibilities

Generative AI is a revolutionary technology that has the potential to dramatically change our lives and businesses. With the ability to generate a variety of content, including text, images, music, and videos, generative AI can be a powerful tool to stimulate our creativity and create new value.

In this article, we have explained the basic mechanism of generative AI, various use cases, and future prospects. Generative AI is still a developing technology, but its potential is endless. By overcoming ethical issues and technical challenges and using generative AI correctly, we can build a richer and more creative future.

In order to use generative AI effectively, it is important to first understand its basic mechanisms. By understanding how technologies such as machine learning, deep learning, large-scale language models, and generative adversarial networks generate new content, you can make the most of generative AI’s capabilities.

Also, by learning about examples of how generative AI is used, you can get a concrete image of how you can use it in your own life and work. In the business world, generative AI is used in a variety of tasks, including marketing, customer support, content creation, and human resources. In everyday life, generative AI is useful in a variety of situations, including learning support, creative activities, and communication.

However, the use of generative AI also brings with it ethical issues and points to be aware of. It is necessary to be constantly aware of the potential for misuse of generative AI, such as the creation of fake news and deep fakes, copyright infringement, and privacy violations, and to use it responsibly.

Generative AI is a technology that has the potential to dramatically change our future. By mastering generative AI, we can generate new ideas, unleash our creativity, and live richer lives. We invite you to explore the possibilities of generative AI and enjoy its benefits.