Applications of Generative AI: From Business to Creative Fields

Examples of generative AI applications in business

Generative AI is being used in various areas of business, contributing to operational efficiency, cost reduction, improved customer experience, and even the creation of new business models. Here, we will take a closer look at specific application cases in marketing, customer service, content production, human resources, and other business fields, as well as their respective effects, challenges, and future prospects.

Marketing and Advertising

Generative AI can be a powerful ally for marketers, helping them create more effective advertising strategies.

• Creating ad copy tailored to the target: Traditionally, creating ad copy required the skills and time of an experienced copywriter. However, generative AI can analyze data such as the attributes, interests, and behavioral history of the target audience and automatically generate optimal ad copy. This makes it possible to create a large number of ad variations in a short time and identify effective copy through A/B testing.
  • Example: A major e-commerce site is using generative AI to automatically generate ad copy that takes into account product categories, price ranges, seasonality, etc. As a result, click-through rates increased by 15%, which contributed greatly to sales.
• Personalized email marketing: You can automatically generate personalized emails based on each customer’s interests and purchasing history, which can increase open and click rates and boost customer engagement.
  • Example: A travel company used generative AI to send emails with travel recommendations based on customers’ past travel history and survey responses, resulting in double the open rates and a significant increase in bookings.
• Automatic generation of SNS posts: Generation AI can automatically generate SNS posts based on product information, campaign details, etc. This can improve the efficiency of SNS marketing.
  • Example: An apparel brand uses generative AI to automatically post information about new products on Instagram. Generative AI automatically generates not only product images and descriptions, but also hashtags and captions, significantly reducing the burden on staff.
• Creating effective landing pages: Generative AI can automatically generate landing page content that conveys the appeal of your products and services. By optimizing the headline, body text, CTA (Call to Action), etc., you can improve the conversion rate of potential customers.
  • Example: An online learning platform is using generative AI to automatically generate course descriptions and learner testimonials, significantly reducing the time it takes to create landing page content and allowing them to promote more courses more efficiently.
• Automatic generation of ad creatives: Generative AI can automatically generate ad creatives such as images and videos. By creating creatives that match the attributes and interests of the target audience, you can increase the effectiveness of your advertising.
  • Example: An advertising agency is using generative AI to automatically generate targeted banner ads, reducing ad production costs and allowing them to test more ad variations.
• Marketing Data Analysis and Prediction: Generative AI can analyze large amounts of marketing data and predict customer behavior patterns and market trends, which can help develop and improve marketing strategies.
  • Case study: A major beverage manufacturer is using generative AI to analyze past sales data and social media trends to forecast demand for new products. By creating production plans and sales strategies based on these predictions, the company is increasing the success rate of new products.

Challenges and future prospects:

The use of generative AI in marketing is still in its infancy, and several challenges remain.

• Data quality and quantity: The performance of generative AI depends heavily on the quality and quantity of training data. The challenge is to collect and accumulate large amounts of high-quality data.
• Limited creativity: Because generative AI generates content based on past data, it can be hard for it to surpass human creativity.
• Ethical issues: Content generated by generative AI may be used as fake news or inappropriate advertising. Ethical guidelines must be established to promote the appropriate use of AI.

To solve these problems, not only technological advances but also discussion and rule-making across society are essential. Generative AI has the potential to dramatically change the way marketing works, and expectations are high for its future development.

Customer service

Generative AI is also attracting attention in the field of customer service as a powerful tool for achieving both improved customer satisfaction and improved operational efficiency.

• Chatbots for automated responses: Chatbots powered by generative AI respond to customer inquiries 24/7 and provide fast and accurate answers. Natural language processing technology enables natural, human-like conversations, guiding customers to problem-solving without stress.
  • Case study: A major airline introduced a generative AI chatbot to handle a wide range of customer inquiries, including flight reservation changes and cancellations, and baggage inquiries. This has significantly reduced customer waiting time and improved customer satisfaction. In addition, the quality of the customer experience is maintained by building a system that seamlessly transfers complex inquiries that the chatbot cannot handle to a human operator.
• Automatic generation and maintenance of FAQs: Generation AI can automatically generate FAQs (frequently asked questions and answers) by analyzing past inquiry data, product information, etc. This not only saves you the trouble of creating FAQs, but also allows you to maintain FAQs that always reflect the latest information.
  • Example: An e-commerce site uses generative AI to automatically generate FAQs and post them on its website. Generative AI analyzes customer inquiries and creates new FAQs or updates existing FAQs, providing an environment where customers can quickly resolve their questions.
• Analyzing and utilizing customer feedback: Generative AI can analyze customer feedback collected from various channels, such as social media, review sites, and surveys, and use it to improve products and services. Using techniques such as sentiment analysis and topic analysis, you can understand customer needs and dissatisfaction and consider specific improvement measures.
  • Example: A cosmetics manufacturer is using generative AI to analyze reviews on social media to understand customer ratings and requests for their products. Based on this information, they develop new products and improve existing products, leading to increased customer satisfaction.
• Multilingual Customer Support: Generative AI can help global companies with customer support by providing chatbots and translation tools that are multilingual, helping them provide quality support to customers around the world, regardless of language barriers.
  • Example: A global e-commerce site has introduced a multilingual chatbot that uses generative AI to respond to customers in over 20 languages. This has improved customer satisfaction in overseas markets and contributed to sales.
• Improving customer service efficiency through voice recognition: Generative AI can be used in conjunction with voice recognition technology to improve the efficiency of customer service. For example, by automatically transcribing telephone inquiries and analyzing customer emotions from their voice, it is possible to reduce the burden on operators and provide higher quality customer service.
  • Example: A call center introduced a speech recognition system using generative AI to transcribe agent calls in real time, allowing agents to serve more customers while reducing the time they spend reviewing and recording calls.

Challenges and future prospects:

The use of generative AI in customer service has greatly contributed to improving customer satisfaction and operational efficiency, but it also presents some challenges.

• Natural conversation generation: Generative AI cannot yet fully reproduce natural human conversation. Further technological development is needed to improve the accuracy of contextual understanding and emotion recognition.
• Personal information protection: Since we handle personal information of customers, we need to give careful consideration to privacy protection. We are required to comply with laws such as the Personal Information Protection Act and to implement thorough security measures.
• Collaboration between AI and humans: AI chatbots cannot handle all inquiries. Complex or emotional issues require human operators to handle them. It is important to clearly define the roles of AI and humans and build a smooth collaboration system.

By resolving these issues, it is expected that generative AI will play a more active role in the field of customer service and improve the customer experience.

Content Creation

Generative AI is also demonstrating its power in content production, where it is improving the quality of content and opening up new possibilities for expression by automating and streamlining tasks that previously required time and effort.

• Automated generation of blog posts and news articles: Generative AI can train large amounts of text data and generate natural-sounding, human-like sentences based on specific themes and keywords, freeing up writers and editors to focus on more creative tasks.
  • Example: The Associated Press is using an AI system to auto-generate formulaic news articles, such as corporate earnings reports, freeing up reporters to focus on investigative reporting and analysis.
• Creation of product descriptions and manuals: Generative AI can understand the features and functions of a product and automatically generate attractive product descriptions and easy-to-understand manuals tailored to the target audience, allowing marketers and engineers to focus on more specialized tasks.
  • Case study: A major home appliance manufacturer used generative AI to develop an automatic generation system for product manuals. This system automatically generates manuals in multiple languages based on product specifications and technical information, allowing for efficient global expansion.
• Creation of video scripts and presentation materials: Generative AI can automatically generate scripts and slides by simply specifying the video theme, structure, presentation objectives, etc. This allows content creators and presenters to focus on the content and structure of the content.
  • Example: Wibbitz provides an AI-powered video creation platform that automatically generates videos based on text input. The platform is used by a wide range of users, including news media and corporate marketers.
• Automated Translation and Localization: Generative AI can automate the translation and localization of content by providing translation tools that support multiple languages, accelerating your expansion into global markets.
  • Examples: DeepL provides highly accurate machine translation services for translating business and technical documents, while DeepL Write is an AI writing assistant that offers proofreading and suggestions to improve your English writing skills.
• Generate SEO-optimized content: Generator AI can automatically generate SEO-optimized content to rank highly in search engines. By automating keyword research, title creation, meta description creation, etc., you can reduce the time it takes to implement SEO measures.
  • Example: SurferSEO provides AI-based SEO tools, including keyword research, content composition, and competitive analysis. This tool improves the work efficiency of SEO professionals and allows them to implement more effective SEO strategies.

human resources

Generative AI also contributes to improving the efficiency of human resources departments. AI is increasingly being used in areas such as recruitment and human resource development.

• Candidate Screening: Generative AI can automatically parse through large volumes of resumes and filter out candidates who match set criteria, allowing recruiters to focus on interviewing higher quality candidates.
  • Example: A recruitment agency introduced a resume screening system that uses generative AI. The system analyzes applicants’ skills, experience, and work history to extract candidates who match the company’s desired talent profile. This allows recruiters to more efficiently select candidates.
• Automatic generation and evaluation of interview questions: Generative AI can automatically generate effective interview questions based on past interview data and job content. It can also analyze the applicant’s facial expressions and tone of voice during the interview to make an objective evaluation.
  • Example: HireVue provides an AI-powered interview platform that automates the generation of interview questions, analyzes the facial expressions and tone of voice of applicants, etc. The platform is used by companies around the world.
• Creation and personalization of training materials: Generative AI can create personalized training materials based on employees’ skills, experience, and learning context, improving the learning outcomes for each employee.
  • Example: An IT company is using generative AI to automatically generate programming training materials for new employees. The materials are tailored to the skill level of each new employee, making training more effective.
• Content generation for employee engagement: Generative AI can automatically generate content for company newsletters and employee communication tools (such as chatbots), which can increase employee engagement and contribute to revitalizing organizational culture.
  • Example: A large company is using generative AI to automatically generate an in-house newsletter for its employees. This newsletter delivers articles tailored to employees’ interests, which has led to increased employee engagement.

Other Business Fields

The applications of generative AI are not limited to marketing, customer service, content creation, and human resources. Here are some examples of how generative AI can be used in other areas of business.

• Demand forecasting and inventory management: Generative AI can forecast product demand by analyzing past sales data, market trends, seasonal factors, etc. By optimizing inventory levels based on these forecast results, you can prevent cost increases and opportunity losses due to excess inventory.
  • Case study: A major supermarket chain introduced a demand forecasting system that uses generative AI and succeeded in reducing food waste. The system analyzes past sales data and weather information to predict demand for each product and calculate the optimal order quantity.
• Fraud detection systems: Generative AI can analyze data such as financial transactions and credit card usage to learn fraudulent patterns, which can improve the accuracy of fraud detection systems and prevent fraud and misuse before it occurs.
  • Case study: A credit card company introduced a fraud detection system that uses generative AI and significantly reduced losses due to fraud. The system learns fraud patterns from past fraud data and detects fraudulent use by monitoring transactions in real time.
• Molecular design in new drug development: Generative AI can learn from a huge amount of molecular structure data and design compounds that will become new drug candidates. In the conventional new drug development process, searching for candidate compounds took a lot of time and money, but the use of generative AI is expected to make new drug development more efficient.
  • Example: Insilico Medicine provides a drug development platform that utilizes generative AI and has already discovered several new drug candidate compounds. This platform contributes to accelerating new drug development by automating the process of identifying drug targets, designing candidate compounds, and evaluating efficacy using AI.
• Financial product development and risk management: Generative AI can analyze market data and economic indicators to predict price fluctuations of financial products and evaluate risks, enabling more accurate investment decisions and risk management.
  • Example: A hedge fund has implemented an algorithmic trading system that uses generative AI to achieve high performance. The system analyzes market conditions in real time and automatically executes optimal trading strategies.

Examples of generative AI applications in creative fields

Generative AI is opening up new possibilities for expression in creative fields such as art, music, video, and games. Here, we will explain specific application cases in each field, the reactions of artists and creators, and future prospects.

Art and Design

• Image generation, transformation, and editing: Generative AI can generate high-quality images from text instructions or simple sketches. It can also convert existing images into a different style or remove unwanted parts, giving artists and designers more freedom in their work.
  • Example: OpenAI’s DALL-E 2 is an AI that can generate high-quality images from text. Artists use it to visualize unrealistic landscapes and abstract concepts, or transform existing artwork into a new style.
• Auto-generation of designs: Generative AI can auto-generate designs such as logos, web designs, product designs, etc. This allows designers to focus on more creative work.
  • Example: Wix ADI is an AI-powered website builder that automatically generates a professionally designed website for you by asking a few simple questions.
• Creating works of art: Generative AI can create works of art such as paintings, sculptures, and installations. AI-generated works can be of such high quality that they are indistinguishable from the work of human artists.
  • Example:
    • In 2018, an AI-generated painting made headlines when it was sold at auction for $432,500.
    • A collective of artists called Obvious are shaking up the art world with their use of GANs to generate portraits.
• Fashion Design: Generative AI can suggest new fashion designs. It can learn from past trends and designers’ work to generate new designs with its own unique style.
  • Example: Stitch Fix offers an AI-powered personal styling service that helps customers choose clothes based on their tastes and body types.
• Architectural design: Generative AI can be used to optimize spatial design, proposing optimal layouts and exteriors that take into account the building’s purpose, surrounding environment, and legal regulations.
  • Example: Autodesk offers AI-powered architectural design software that helps architects design more efficient and sustainable buildings.

These examples show that generative AI is bringing new possibilities to the fields of art and design. AI will be a powerful tool to expand the creativity of artists and designers and generate new forms of expression that have never been seen before.

music

In music production, generative AI has a wide range of possibilities, from composing, arranging, and performing to creating new musical experiences.

• Composing and arranging: Generative AI can compose and arrange original music simply by specifying a specific genre, mood, instrument arrangement, etc. It can also analyze existing music and generate music that imitates its style.
  • Examples: Amper Music is an AI composition tool used to create background music for movies and games. OpenAI’s Jukebox can generate music in a variety of genres, expanding the possibilities of music production.
• BGM Generation: Generative AI can automatically generate background music for video works, games, presentations, etc. By analyzing the content and atmosphere of the video and generating music that matches it, you can enhance the appeal of the content.
  • Example: Epidemic Sound uses AI to provide royalty-free background music to video creators. AI analyzes the content of the video and suggests the best background music, which contributes to the efficiency of video production.
• Musical style transformation: Generative AI can transform an existing piece of music into a different musical style, for example creating a jazz arrangement of a classical piece or an orchestral version of a pop song.
  • Example: OpenAI’s Jukebox has a music style conversion function, allowing you to freely arrange music of various genres.
• Virtual Singer: Generative AI can use voice synthesis technology to create the voices of non-existent singers, creating new musical experiences.
  • Example: Yamaha’s VOCALOID is a widely known singing voice synthesis software that has given birth to many virtual singers.

movie

In video production, generative AI also makes editing more efficient and enables unprecedented visual expression.

• Video editing, adding effects: The generation AI can automatically cut and edit videos, insert subtitles, add effects, etc. This allows you to create high-quality videos even if you have no knowledge or experience of video editing.
  • Example: RunwayML provides an AI-powered video editing tool with features such as video background removal, object tracking, and style transfer.
• Generative animation: Generative AI can auto-generate character movements and expressions to create animations, allowing animators to focus on more creative tasks.
  • Example: DeepMotion develops AI-powered motion capture technology that can generate animations that realistically reproduce human movement.
• Virtual YouTuber: Generative AI enables Virtual YouTuber (VTuber) character creation, motion generation, voice synthesis, etc. This makes it easy for anyone to become a VTuber.
  • Case study: Kizuna AI is known as the world’s first virtual YouTuber, with over 3 million subscribers on her YouTube channel.
• Film production: Generative AI is used in many aspects of film production, including script generation, CG production, and special effects. Generative AI has the potential to improve the efficiency of film production and broaden the scope of expression.
  • Example: Disney Research is researching AI-based filmmaking technology, using AI for character animation, background generation, and more.

game

In the gaming industry, generative AI is contributing to more efficient game development and diversifying gameplay.

• Automatic generation of game characters and stages: Generative AI can automatically generate game characters and stages, allowing game developers to focus more on game design and story creation.
  • Example: No Man’s Sky uses a technique called procedural generation to automatically generate a nearly infinite number of planets and creatures, allowing players to experience a unique gameplay experience every time they play.
• Game scenario creation: Generative AI can automatically generate game stories, plots, character dialogue, etc. This allows game scenario writers to focus on more creative tasks.
  • Example: AI Dungeon is an AI-generated text-based adventure game where the player has free reign to navigate the story and the AI generates a story to suit them.
• NPC Conversation Generation: Generative AI can automatically generate conversations for in-game non-player characters (NPCs), making conversations more natural and realistic, and deepening the worldview of the game.
  • Example: Replica Studios provides an AI-powered dialogue generation tool that allows game developers to easily create lines and conversations for NPCs.
• Game Balancing: Generative AI can adjust the difficulty and balance of the game. By analyzing player behavior data and optimizing game balance, we can provide a more fun and challenging gaming experience.
  • Example: Ubisoft is using AI to develop a system that automatically adjusts the difficulty of its games, adjusting things like enemy strength and frequency of appearance based on the player’s skill level.

These examples show that generative AI is bringing new possibilities to the gaming industry. AI is not only making game development more efficient, but also contributing to diversifying gameplay and creating more immersive gaming experiences.

Examples of generative AI applications in solving social problems

Generative AI is contributing not only to business and creative fields, but also to solving social issues. Here, we will explain examples of how generative AI is used in various fields, such as medicine, education, environmental issues, and disaster prevention, as well as its effects, challenges, and future prospects.

medical care

• Medical imaging: Generative AI can analyze medical images such as X-rays, CT scans, and MRIs, helping to detect diseases earlier and improve the accuracy of diagnoses.
  • Examples: Google Health is using deep learning to improve breast and lung cancer detection, and Stanford University is developing an AI app to help diagnose skin cancer.
• Drug discovery: Generative AI is used to design compounds that are candidates for new drugs and predict their efficacy. In the conventional drug development process, searching for candidate compounds took a lot of time and money, but the use of generative AI is expected to make new drug development more efficient.
  • Example: Insilico Medicine provides a drug development platform that uses generative AI and has already discovered several new drug candidate compounds.
• Personalized medicine: Generative AI can analyze a patient’s genetic information, medical history, lifestyle, and other data to suggest the best treatment for the individual, potentially leading to more effective treatments with fewer side effects.
  • Example: Tempus provides an AI-powered personalized medicine platform that analyzes the genetic information of cancer patients and helps them select the most appropriate treatment.
• Telemedicine: Generative AI is used to support diagnosis and communicate with patients in telemedicine systems. In the medical field, which faces challenges such as a shortage of doctors and regional disparities, generative AI is expected to contribute to improving access to medical care.
  • Example: Babylon Health is an AI-powered telemedicine app that uses chatbots to ask questions, check symptoms, and connect with doctors via video.

education

• Personalized learning: Generative AI can generate optimal learning materials and assignments based on each student’s learning situation and level of understanding, making it possible to provide learning support tailored to individual learning needs.
  • Example: Knewton provides an AI-powered adaptive learning platform that adapts the difficulty and content of learning materials based on a student’s learning history and progress.
• Automated grading: Generative AI can automate the grading of essay questions and the evaluation of programming assignments, reducing the burden on teachers and allowing them to spend more time communicating with students.
  • Example: Gradescope provides an AI-based automated marking tool that can automatically mark handwritten answers and programming code.
• Learning support chatbots: Chatbots powered by generative AI can not only answer students’ questions but also provide advice and encouragement as students progress.
  • Case study: Ivy.ai provides AI chatbots for universities to answer student questions 24/7.
• Support for creating teaching materials: Generative AI can also be used as a support tool for teachers when creating teaching materials. For example, text generation AI can help with writing and summarizing teaching materials, and image generation AI can create illustrations and diagrams for teaching materials.
  • Example: An education startup is using generative AI to develop a system that automatically generates elementary school math teaching materials. The system automatically generates questions and explanations according to the unit and level of difficulty specified by the teacher.

Environment issues

• Climate change prediction: Generative AI can predict future climate change by analyzing past meteorological data and simulation results. Based on these prediction results, measures against global warming and disaster prevention can be developed.
  • Example: ClimateAI provides an AI-based climate change prediction platform that is used in sectors such as agriculture, energy, and insurance.
• Optimizing renewable energy: Generation AI can predict the amount of power generated by renewable energy sources such as solar and wind power, contributing to the stable operation of power grids.
  • Example: Google DeepMind uses AI to predict wind power output 36 hours in advance to optimize power consumption at Google’s data centers.
• Environmental monitoring: Generative AI can analyze satellite imagery, sensor data, and more to monitor environmental issues like deforestation and marine pollution.
  • Case study: Rainforest Connection is developing a system that uses AI to monitor illegal logging in rainforests. The system records forest sounds and detects sounds such as chainsaws, allowing it to detect illegal logging at an early stage.

Disaster Prevention

• Disaster prediction: Generative AI can analyze past disaster data and meteorological data to predict the risk of disasters such as earthquakes, tsunamis, and floods. Evacuation plans and disaster prevention measures can be created based on the prediction results.
  • Example: IBM’s Deep Thunder is an AI-based weather forecasting system that can predict localized heavy rain and thunderstorms with high accuracy.
• Evacuation guidance: When a disaster occurs, generative AI can help ensure smooth evacuation by optimizing evacuation routes and predicting congestion at evacuation shelters.
  • Example: A local government has introduced an AI chatbot to automatically respond to inquiries about evacuation in the event of a disaster.
• Damage assessment: Generative AI can analyze damage from drone and satellite images to support rapid rescue and recovery efforts.
  • Example:
    • The University of Tokyo is developing a system that uses AI to automatically assess the damage caused to buildings by tsunamis.
    • Drone manufacturer DJI provides disaster response solutions that utilize AI, helping to assess the situation in affected areas and save lives.

These examples are just a few of the ways that generative AI can contribute to solving social issues. It is expected that generative AI will continue to be used in a variety of fields and contribute to the realization of a better society.

Future Prospects and Challenges of Generative AI

Generative AI has the potential to dramatically change our lives and society, but at the same time, it also faces several challenges. Here, we will explain the future outlook for generative AI and the challenges that need to be resolved.

Further evolution and expansion of application range

Generative AI is still in its development stages and is expected to continue to evolve in the future.

• More accurate and diverse content generation: Generative AI will be able to generate more accurate and diverse content by learning from more data and developing more advanced algorithms. For example, text generation AI will be able to generate more natural and creative sentences, and image generation AI will be able to generate more high-resolution and realistic images.
• Opening up new application fields: Generative AI is currently being used in a variety of fields, but in the future, new application fields will likely be opened up. For example, in the medical field, generative AI could be used to diagnose diseases and develop treatments. In the education field, we may see the emergence of AI that generates teaching materials and learning plans tailored to each individual student.
• Collaboration with humans: Generative AI will not take away human jobs, but will expand human capabilities and provide an environment in which 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.

Addressing ethical issues

As generative AI evolves, addressing ethical issues is becoming increasingly important.

• Countermeasures against fake content: Generative AI can be misused to create malicious content, such as deepfakes, which are fake videos that synthesize the faces of real people, or fake news. This fake content is a serious problem because it can cause social unrest and damage the reputation of individuals. To counter fake content, there is a need to develop deepfake detection technology and promote information literacy education.
• Copyright issues: Generative AI may learn from existing works and generate content that infringes copyright. There is an urgent need to create rules regarding copyright, such as who owns the copyright for content generated by generative AI and the extent to which AI can use it as training data.
• Bias and discrimination: Generative AI can reflect the biases contained in the training data. This can lead to the generation of discriminatory content against certain genders or races. Efforts to mitigate bias are required, including fairness in algorithms and the use of diverse datasets.

Overcoming technical challenges

Further development of generative AI also requires overcoming technical challenges.

• Explainability: It can be difficult for humans to understand why generative AI has output such results. This “black box problem” is an important challenge because it raises questions about the trustworthiness and accountability of AI. There is a demand for the development and deployment of explainable AI (XAI).
• Controllability: It is difficult to fully control the content generated by generative AI. Since unintended results may be output, safety measures and risk management are important. There is a need to develop technology to control the output of AI and to formulate ethical guidelines.
• Ensuring high-quality data: The performance of generative AI depends heavily on the quality and quantity of training data. However, collecting and annotating high-quality data is costly and time-consuming. Challenges include making data collection and annotation more efficient and building a data sharing platform.

Summary: Generative AI opens up new possibilities for business and creativity

Generative AI is being used in a wide range of fields, from business to the creative sector and even to solve social issues, to generate new value. As AI evolves, its range of applications will continue to expand, with the potential to bring about major changes to our lives and society.

However, the use of generative AI also entails ethical and technical issues. To resolve these issues and maximize the benefits of generative AI, not only technological development but also discussion and cooperation across society is essential.

Generative AI is a powerful tool that can expand our creativity and open up new possibilities. We need to correctly understand the possibilities and challenges of generative AI and actively participate in its development.