Introduction to Generative AI: A Simple Explanation of the World of Text and Image Generation

Greetings,

I am Kakeya, the representative of Scuti Jsc.

At Scuti, we specialize in offshore and lab-based development in Vietnam, leveraging the power of generative AI. Our services include not only development but also comprehensive generative AI consulting. We specialize in system development using generative AI with tools like Azure OpenAI Service and AWS Bedrock

Generative AI refers to artificial intelligence that can automatically create new content such as text, audio, and images.

Tools like ChatGPT, for instance, are widely used as free-to-access chatbots by many people. However, generative AI is not yet perfect and can sometimes produce inaccurate or inappropriate results.

Therefore, when using generative AI, it is important to select training data carefully, customize models, and have human oversight. We are in an era where humans need to work well with AI, keeping these points in mind.

Generative AI technology is evolving rapidly and is expected to continue providing many new opportunities in the future.

In this article, I would like to introduce the basics of generative AI and some use cases for readers who want to know what generative AI is all about.


Basics and Applications of Generative AI: The New Era of Text and Image Generation

Generative AI: An Innovative Content Creation Tool

Generative AI is rapidly gaining attention as a technology that enables new content creation. This technology can generate a variety of content, including audio, programs, images, text, and videos.

Services using generative AI, such as ChatGPT, have the ability to create interesting content like new art, music, and virtual worlds. These applications are not limited to entertainment but are also used for practical purposes, such as creating new product designs and optimizing business processes.

ChatGPT, in particular, is attracting attention for its ability to generate answers to questions, making it popular among many users. These tools can generate programs, university-level essays, poetry, and jokes, but they can also produce inaccurate or inappropriate results. Therefore, these models are heavily influenced by the quality of the data used for training.

Generative AI is part of a broader category of machine learning, which aims to mimic human intelligence by learning patterns from large amounts of data. While previously limited mainly to predictive models, the advent of generative AI has added the ability to create images or text descriptions on demand, rather than just recognizing and classifying photos.

The development of generative AI is primarily undertaken by major technology companies. These companies employ world-class computer scientists and engineers, with OpenAI, DeepMind, and Meta being representative examples. However, the use of generative AI is also expanding in the business world, enabling the instant generation of a wide range of written content that can be edited to fit specific purposes. This benefits many industries, including IT and software.

Finally, while the output of generative AI models can be very persuasive, it can sometimes contain incorrect information or biases. These risks can be mitigated by selecting initial data carefully, using specialized models, and customizing general models. Additionally, it is important to maintain human intervention for critical decisions and to address the ethical and legal risks of using generative AI models.

Use Cases of ChatGPT: New Possibilities in Text Generation

ChatGPT is bringing innovation to the field of text generation. This system has the ability to generate responses to a variety of questions.

Based on prompts provided by users, this service can generate diverse text content such as programs, university-level essays, poetry, and jokes.

In addition, leveraging its text generation capabilities, ChatGPT can be used to ask questions or request summaries of publicly available information. This significantly reduces the effort previously required to search for information using various keywords or to ask knowledgeable individuals. Now, you can get a quick overview just by asking ChatGPT.

ChatGPT is useful not only for personal use but also for business purposes. For example, it can be used for creating meeting minutes, drafting outlines for proposals, blog posts, system requirement definitions, brainstorming ideas, and many other applications.

By automating some of the tasks that were previously done manually with considerable accuracy, productivity can be greatly improved. Conversely, failing to effectively utilize ChatGPT in business can lead to significant productivity losses.

The Potential of Image-Generating AI: Applications in Business and Entertainment

Image-generating AI is attracting attention in both the business and entertainment sectors. This technology enables the automatic creation of various types of images by AI, including photo-realistic images, art-style images, and anime-style images.

Image-generating AI has made possible new visual expressions that were unthinkable with traditional methods. In business, it is used for prototyping product designs and generating marketing materials, while in the entertainment industry, it contributes to the creation of original visual effects for movies and games.

However, the quality and practicality of the generated images depend on the AI model and training data used, so attention must be paid to these aspects. This field is rapidly developing, and further possibilities are expected in the future.​​

Evolution of Learning and Model Development by AI

Learning and model development by AI have undergone significant evolution in recent years. Especially in the field of machine learning, models are learning patterns from vast datasets, enabling approaches that were previously impossible.

Machine learning, which was traditionally limited to predictive models, has expanded to include the generation of new images and text with the advent of generative AI. This has improved the accuracy of natural language processing and image recognition, making it possible to solve more complex tasks. However, these advancements require large datasets and advanced computational power, leading to a tendency for major technology companies to lead the way.​​

Practicality and Limitations of Generative AI: Impact on Business and Society

Practical Applications of Generative AI: Optimizing Business Processes

Generative AI plays a crucial role in optimizing business processes. By utilizing this technology, companies can significantly enhance productivity in areas such as product design, marketing strategies, and customer service.

In particular, the automatic generation of text and images by AI significantly reduces the time and cost of content creation, creating new business opportunities across various industries. However, there are limitations to the capabilities of generative AI, and attention must be paid to the accuracy, appropriateness, and ethical considerations of the generated content.

This means that businesses need to appropriately distinguish between tasks that should be done by humans and those that can be delegated to generative AI, leading to a need to review and optimize the business processes themselves based on this premise.

As the optimization of business processes with the assumption of delegating some tasks to generative AI progresses, the productivity of organizations is likely to increase significantly.

Inaccurate Results and Countermeasures: Challenges of Generative AI

Generative AI holds great potential, but it can also produce inaccurate results. While the text may appear very natural, it can sometimes provide answers that are factually incorrect. This issue arises largely due to the quality and biases of the data used to train the AI.

For instance, ChatGPT may fail to solve basic math problems (the author has experienced incorrect answers to simple addition problems and the inability to calculate the area of a triangle multiple times) or provide responses reflecting the biases present in online content related to gender or race.

To address these challenges, it is necessary to carefully select training data and customize the models from a technical standpoint. From the user’s perspective, it is important not to take AI outputs at face value and to have humans verify the final outputs. This approach helps enhance the accuracy and appropriateness of AI-generated content

Ethical Concerns and Risk Management: Precautions in the Use of Generative AI

Using generative AI involves important ethical concerns and risk management. AI models can potentially reflect biases related to gender, race, and other aspects present in the training data.

To mitigate these risks, it is crucial to carefully select training data, consider using specialized models, and customize general models. Additionally, human checks on AI outputs are necessary, and AI should be avoided in making critical decisions or matters affecting human welfare. This approach helps address the ethical and legal risks posed by generative AI.

ChatGPT appears to pay considerable attention to this aspect. For instance, when potentially biased, discriminatory, or violent terms are entered, it does not provide a response.

In my experience, I tried uploading a photo of Kinkaku-ji and requested an image generation showing Kinkaku-ji being attacked by Godzilla. However, this request was declined as it was interpreted as violent expression (which might actually be violent), and the output was refused

Future Prospects of Generative AI: Potential and Challenges of Technological Evolution

Impact of Generative AI on Society: Education, Entertainment, and Other Fields

​Generative AI is also having a significant impact on education, entertainment, and other fields.

In the field of education, it is expected to enhance the quality of education by providing personalized learning experiences tailored to individual abilities and interests, as well as by automatically generating teaching materials.

In the entertainment industry, it has made new forms of artistic expression possible through real-time video generation and music creation.

The application of generative AI in these fields offers new approaches different from traditional methods, paving the way for further innovation.​

Rapid Evolution of Generative AI Technology and Future Expectations

Generative AI technology is rapidly evolving, and its potential is immense.

In fact, since the launch of ChatGPT in December 2022 to the publication of this article in December 2023, the capabilities realized and the accuracy and variety of the generated content have significantly changed.

Stable Diffusion, known for image generation, has also seen a remarkable improvement in the quality of generated images over the past year.

This field is opening up new possibilities not only in content generation but also in predictive analysis, data analysis, and the development of interactive applications.

Advances in natural language processing and image recognition, in particular, are expanding the range of AI applications. However, these advancements also bring new challenges, such as the need for high-quality data, ethical issues, and the requirement for computational resources.

This field is expected to continue attracting significant attention and to have a substantial impact on business and society in the future.

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