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In recent years, Generative AI has developed rapidly, contributing to the improvement of operational efficiency in companies and the creation of new business opportunities.
However, many people may have questions such as "What exactly is Generative AI?", "What are its merits and demerits?", and "How can it be utilized?"
Therefore, in this article, we will provide an easy-to-understand explanation of the basic concepts, main types, mechanisms, and merits/demerits of Generative AI. We will also introduce hints for companies to utilize Generative AI more effectively, such as examples of business applications and points to note when using it.
This content is useful for those who want to cover the basic information of Generative AI.
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【Table of Contents】 |
1. What is Generative AI?
Generative AI refers to AI technology that automatically generates new content such as text and images based on vast amounts of learning data. It is a type of AI model based on "deep learning" that patterns large amounts of data to derive solutions.
Specifically, the following are possible with Generative AI:
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In recent years, the generative capabilities of Generative AI have improved, and it is evolving into a partner that supports creative content production even in business scenes.
Mechanism of Generative AI
Generative AI is a mechanism in which the AI learns using vast amounts of data and creates new information based on that knowledge. Specifically, it operates according to the following flow:
① Learning with Large Amounts of Data
It takes in large amounts of diverse data such as text, images, and audio, and automatically learns patterns and characteristics. This is to make the AI understand "what is correct" and "how things should be expressed."
② Extracting Features
Neural networks (particularly Transformers and others) are used to extract "features" that numerically represent important information and relationships within the data.
③ Prompt Analysis
It analyzes the input (prompt) from the user and extracts related information.
④ Content Generation
It creates new text, images, or audio. For example, in the case of text, it generates sentence by sentence while predicting the next word to follow according to the given keywords and context.
⑤ Output Optimization
A mechanism is also incorporated to further improve accuracy through feedback and evaluation so that the generated content becomes natural and meaningful.
What is important for creating advanced Generative AI is high-quality data. Not only the quantity of data used for learning, but also the diversity and accuracy significantly affect the understanding of the Generative AI model.
It is essential to continuously collect and update the latest and broadest data that reflects changes in the real world. In addition, by applying accurate data annotation such as data preprocessing and labeling, the AI can perform more sophisticated feature extraction, enabling natural and high-precision output.
What is the Decisive Difference Between Conventional AI and Generative AI?
Conventional AI focuses on analyzing existing data, predicting the future based on existing data, or recognizing patterns. Typical examples include image recognition, speech recognition, and data analysis.
On the other hand, the major characteristic of Generative AI is that it "generates" new content that did not exist before based on given information and learning data.
Due to these differences, Generative AI is opening up new possibilities in fields that require creativity, such as advertising, entertainment, and education, where it was difficult to utilize conventional AI.
Main Types of Generative AI
Generative AI is classified into various types according to its application.
The following table summarizes the main categories, their overviews, and representative model examples.
| Type | Overview | Major Model Examples |
| Text Generation AI | Automatically generates text data such as news articles, blog posts, and chatbot conversation text. |
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| Image Generation AI | Generates images based on specified themes or keywords. |
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| Video Generation AI | Automatically generates videos with motion based on still images or continuous images of scenes. |
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| Audio Generation AI (Music Generation) | Automatically generates audio content such as narration, music, and dialogue audio. |
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| Multimodal AI | Generates multiple types of data such as text, images, audio, and video. |
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2. Benefits that Generative AI Brings to Business
Generative AI does not stop at being a mere creative tool; it improves the efficiency of entire business processes and expands the possibilities for new data utilization, bringing various benefits to companies. Here, we introduce the benefits that Generative AI brings to business.
Improving Efficiency of Routine Tasks
Because Generative AI has high natural language processing capabilities, it promotes automation in text-based repetitive work and routine tasks.
Specifically, the following routine tasks can be automated:
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Routine work that occurs daily like the above can be automated, allowing employees to focus more on creative work, leading to improved productivity of the entire business.
Idea Generation for Planning and Content
Depending on the instructions in the prompt, Generative AI can propose novel ideas and highly creative compositions that humans might not even think of.
In fact, application examples utilizing the creativity of Generative AI are being reported one after another. For example, books partially written with ChatGPT have won the Akutagawa Prize, and the high creativity of Generative AI is starting to be recognized.
In this way, Generative AI can also be utilized as a tool for obtaining new ideas in creative fields such as advertising copy, product design, and script creation.
Easy Code Creation for Anyone
With the appearance of Generative AI, the barrier to programming has dropped significantly. Even without specialized knowledge, you can create simple codes, macros, and scripts necessary for business just by giving instructions in natural language.
Cost reduction is possible by enabling software development within the company without hiring specialized programmers. Additionally, since each department can handle simple requests, the burden on the IT department will also be reduced.
Speeding Up Trouble Response
The search capabilities and natural language processing power of Generative AI are useful for responding to internal and external troubles. This is because it can handle a series of tasks necessary for resolution as follows:
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The speed of initial response and the efficiency of troubleshooting are improved, significantly shortening the time to problem resolution.
Expansion of Diverse Learning Data
Generative AI can also be used as a tool to supplement learning data for training AI when existing data is insufficient.
For example, when building a visual inspection system, a challenge that often arises is the lack of defective product data. Therefore, by using image generation AI to generate diverse defective product image data and evaluation data, it is possible to lead to an improvement in inspection precision.
In this way, by expanding learning datasets and generating simulation data with Generative AI, the problem of data shortage is resolved, and the construction of high-precision AI models and simulations is realized.
3. 4 Examples of Business Application Fields for Generative AI
In recent years, the utilization of Generative AI has spread across many fields, and its usefulness has been demonstrated in various business scenes. Here, we introduce application examples in four fields that are attracting particular attention.
For actual corporate examples, please see the following:
A summary of the latest examples of companies using generative AI! A thorough explanation of the benefits and implementation methods.
Software Development
Code generation AI can generate code from instructions in natural sentences. Moreover, it can support development work widely, from creating code to debugging and deciphering technical reports, and is widely utilized in software development.
Specifically, it can be utilized in software development tasks such as the following:
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It can be used widely from design to coding and testing, advancing business efficiency in software development.
Marketing
In the marketing field, Generative AI is widely utilized from research work to strategy planning, advertisement and content production, and data analysis.
Specifically, it can be utilized in tasks such as the following:
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By leaving the above work to Generative AI, you can focus on strategy formulation and creative work.
Internal and Customer Inquiries
Generative AI can be utilized in internal help desks and customer support work. Specifically, it can be utilized in inquiry work such as the following:
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By utilizing Generative AI, the speed of customer response improves, leading to an improvement in customer experience. At the same time, the burden on support responders can be reduced, enabling the allocation of human resources to more important work.
Sales
In sales, the utilization of Text Generation AI is particularly increasing for communication with customers using standard phrases and for document creation, as follows:
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By utilizing Generative AI, sales representatives can spend more time on dialogue and relationship building with customers, enabling a significant improvement in business efficiency.
4. Points to Note When Introducing Generative AI to Business
We introduce the points to note when companies utilize Generative AI.
A System Where Humans Can Monitor and Intervene is Essential
When a company utilizes Generative AI for work, the construction of a system that incorporates human checks and feedback is important to guarantee the quality and accuracy of the automatically generated content. Content output by Generative AI may include hallucinations (responses that are not facts) or discriminatory expressions.
Content output by Generative AI looks well-formed and high-quality at first glance. However, it is necessary to be careful because specific AI-related discomfort can occur in details.
Releasing non-factual expressions or unnatural images in advertisements or PR will lead to a decrease in trust from customers and loss of brand. A famous example is a major fast-food company's CM where a model with one extra finger was used.
Instead of using results output by Generative AI as they are, a mechanism where experts or representatives necessarily confirm the content and make corrections or supplements as needed is necessary. Especially when utilizing it for specialized information or critical decision-making, thorough verification of output content is indispensable.
| Related Article: Does generative AI constitute copyright infringement? Explaining the problematic conditions, examples, and countermeasures! |
Responding to Security Risks
When utilizing Generative AI models, various security risks arise. What is highly likely to occur in many companies is information leakage.
There are two patterns in which in-house information leaks through Generative AI models.
First is the pattern where an employee utilizing Generative AI enters in-house information when creating planning documents or minutes, and it leaks.
Second is the pattern where Generative AI suffers a cyberattack and information is leaked. In recent years, cases where information is leaked through DDoS attacks targeting Generative AI or "prompt injection," where company information is extracted by malicious prompts, are seen.
Against these security risks of Generative AI models, countermeasures for both employees and tools are necessary, such as employee education regarding Generative AI and the use of secure services with perfect data encryption and access control.
Beware of Data Poisoning
Data poisoning is an attack method that mixes malicious data into the learning data of an AI model. If malicious data is mixed into the learning data of an AI model, it causes performance degradation of the model or illegal output, increasing security risks.
Especially when a company performs fine-tuning using unique data, the detection of illegal data included within that data becomes difficult.
Therefore, it is essential to thoroughly verify learning data in advance and use only highly reliable data. Additionally, measures to ensure the reliability and safety of the AI model are required, such as constructing a check system by a third party, for example, by outsourcing to a company that specializes in data verification and selection.
5. Summary
Generative AI is an advanced AI technology that brings major benefits to business, such as automatic content generation, improvement of business process efficiency, and creation of new ideas.
It has high natural language processing capabilities, enables diverse outputs such as images, videos, codes, and text, and its utilization is progressing in a wide range of business fields such as software development, marketing, and sales.
On the other hand, Generative AI has many challenges such as data copyright issues, security risks, and hallucinations, and when introducing it, it is necessary to thoroughly manage risks by constructing a human monitoring system and through internal education.
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