Hello, I am Kakeya, the representative of Scuti.
Our company provides services such as Vietnamese offshore development and lab-type development, with a focus on generative AI. Recently, we have been fortunate to receive numerous requests for system development that integrates generative AI.
Implementing generative AI presents a significant opportunity for companies to enhance productivity and streamline operations.
However, if one does not correctly understand the methods of implementing and utilizing generative AI, it will not be possible to fully leverage its potential value.
Generative AI refers to the technology that generates new content and data using AI generation techniques, capable of outputting various formats such as text, images, audio, and even video. Many companies are utilizing this technology to automate business processes and achieve innovations in creative fields, but proper implementation methods and responses to risks are essential.
Basic Knowledge of Generative AI
What is Generative AI?
Generative AI is a type of artificial intelligence (AI) that can generate new content and data based on patterns learned from existing data.
This technology can generate various formats of data, including text, images, audio, and video, and is expected to be utilized in various business scenarios. While traditional AI analyzes and predicts based on existing data, generative AI can produce creative outputs.
Specifically, by learning a large volume of text data, image data, audio data, etc., it can generate natural sentences, images, and sounds that seem to have been created by humans.
For example, a text generation AI can create various genres of writing, such as blog articles, novels, poems, and scripts. Likewise, an image generation AI can produce images in various styles, including photographs, illustrations, and paintings.
Mechanism of Generative AI
Generative AI is realized using a type of machine learning called deep learning.
In deep learning, a model that mimics the neural circuits of the human brain, known as a neural network, is trained using large amounts of data, enabling it to recognize complex patterns. In generative AI, this deep learning is utilized to learn the characteristics of input data and output new data based on those characteristics.
Among deep learning techniques, “Generative Adversarial Networks (GANs)” and “Variational Autoencoders (VAEs)” are particularly central to generative AI.
GANs use two networks called the generative network and the discriminative network for training. The generative network learns to create data that resembles real data from noise, while the discriminative network learns to determine whether the input data is real or not.
By having these two networks compete against each other during training, it becomes possible to generate data with higher accuracy. On the other hand, VAEs learn to map input data into a lower-dimensional space called latent variables and then restore data close to the original from these latent variables.
Through this learning process, it is possible to capture the latent characteristics of data and generate new data.
At our company, we have created a document titled “How to Use Generative AI by Purpose! A Collection of Generative AI Use Cases,” summarizing how ChatGPT and generative AI can be utilized in business. It is available for free download, so please make good use of it!
Use Cases of Generative AI in Companies
Text Generation: Improving Operational Efficiency and Customer Experience
Generative AI can be utilized for various text processing tasks such as automatic text generation, summarization, translation, and proofreading.
For example, it is used widely in tasks such as chatbots that automatically respond to customer inquiries, assistance in creating internal documents, and automatic generation of marketing content. By automating these tasks, both operational efficiency and improved customer experience can be achieved simultaneously.
For instance, in the field of customer support, chatbots utilizing generative AI can automatically respond to customer inquiries, shortening customer wait times while simultaneously alleviating the burden on customer support representatives.
Additionally, in the marketing sector, using generative AI to automatically generate personalized advertising text and newsletters based on customer attributes and purchase history is expected to enhance marketing effectiveness.
Image Generation: New Possibilities for Expression
Generative AI has brought significant innovations in the field of image processing, including image generation, editing, and transformation. Techniques such as automatic generation of realistic images, creation of fictional character images, enhancement of image resolution, and style transfer are utilized to provide new expressive possibilities across various industries, including advertising, gaming, and entertainment.
For instance, in the advertising industry, the use of generative AI has made it possible to produce high-quality advertising banners and posters in a short amount of time. In the gaming industry, the automatic generation of 3D models for characters and backgrounds reduces costs and development time.
In the entertainment industry, generative AI is leveraged to create images of non-existent characters or to combine CGI with live-action footage, resulting in more realistic and impactful visuals.
As a result, new possibilities are expanding throughout the creative industry.
Voice Generation: Evolution of Communication
Generative AI is also applied in voice generation, allowing for natural speech synthesis that is indistinguishable from human voices, the ability to learn and reproduce specific speaking styles, and noise reduction.
This evolution has advanced communication using voice, with applications such as voice assistants, voice translation, and voice content production.
For example, voice assistants are embedded in smartphones and smart speakers, automatically executing tasks in response to user voice commands. Voice translation enables real-time communication between people speaking different languages, facilitating smooth intercultural exchanges.
Moreover, in voice content production, generative AI can generate high-quality audio without the need for narrators or voice actors, contributing to cost and time savings in advertising, education, and entertainment sectors.
Implementation Methods and Detailed Steps for Introducing Generative AI
Clarifying Objectives: What Do You Want to Achieve?
The first important step when introducing generative AI is to clarify the objectives. If the objectives are unclear, there is a risk of not achieving the expected results.
For example, setting specific goals such as “improving customer service efficiency,” “enhancing marketing effectiveness,” or “promoting new product development” can provide appropriate direction.
By clarifying the issues you want to solve and the expected outcomes using generative AI, you can select the optimal tools and data and formulate an effective implementation plan.
Moreover, sharing goals helps unify the understanding among stakeholders within the organization, facilitating a smoother implementation process.
Data Preparation: Learning Materials for Generative AI
To enhance the accuracy of generative AI, it is essential to have a large quantity of high-quality data.
Collecting data that aligns with the purpose and area of implementation and organizing and processing it appropriately is key to success. Particularly, the quality and quantity of data are directly linked to the performance of generative AI, necessitating careful handling.
When collecting data, it is essential to ensure comprehensiveness, accuracy, and timeliness, requiring the gathering of diverse data.
Furthermore, during the data organization and processing stage, tasks such as standardizing formats, handling missing values, and removing noise should be conducted to create an environment where the AI can learn efficiently.
Additionally, when handling data that includes personal or sensitive information, it is necessary to ensure compliance with laws and to adequately consider privacy protection.
Selecting Tools: Choosing Those That Fit Your Purpose and Budget
There is a wide variety of tools available for implementing generative AI, each with different characteristics. When selecting tools, it is important to consider the purpose of implementation, necessary features, and budget to choose the most suitable tools for your company.
There are a wide range of options, from open-source tools available for free to high-functionality commercial tools. When selecting tools, not only the completeness of features but also usability, support systems, and security measures are important factors to consider.
Especially when introducing commercial tools, it is crucial to confirm whether appropriate support and updates are provided in case of issues. Additionally, compatibility with the company’s system environment should not be overlooked.
Evaluation and Improvement: Measuring Effectiveness and Continuous Improvement
After implementing generative AI, it is important to regularly measure its effectiveness and make improvements as necessary.
It is essential to evaluate the extent of the results achieved against the goals set at the time of implementation, and, if necessary, to add data or retrain the model to improve the accuracy of the generative AI. Setting specific KPIs (Key Performance Indicators) for effectiveness measurement and evaluating based on quantitative data is effective.
Moreover, if the expected effects are not achieved, it is necessary to analyze the causes and implement appropriate improvement measures.
Generative AI is not just a tool to be operated as is after implementation; it is a tool that can produce higher results through continuous improvement. A long-term perspective on review and optimization is key to unlocking the true potential of generative AI.
Risks of Generative AI
Ethical Issues
While generative AI is a powerful technology, it also poses risks of ethical issues.
For example, the spread of misinformation and privacy violations are instances of this risk. Malicious users can utilize generative AI to create fake videos or images impersonating real individuals, which can lead to defamation and confusion.
Furthermore, when used to generate and spread fake news, biased information can permeate society and exacerbate anxiety.
Additionally, there are privacy risks. The misuse of data related to personal privacy by generative AI or its use without permission heightens the risk of personal information leakage or infringement.
To address these ethical issues, it is necessary to establish regulations and rules regarding the appropriate use of technology.
Impact on Employment
The proliferation of generative AI has the potential to significantly impact the labor market.
In particular, jobs that involve many routine or simple tasks are at risk of being replaced by automation through generative AI, leading to a reduction in employment opportunities.
For example, simple tasks such as data entry, text generation, and image processing can increasingly be delegated to AI, which may result in a decreased need for human labor.
On the other hand, there is also the possibility that new jobs utilizing new technologies may emerge, but concerns remain high about individuals in existing occupations being replaced by generative AI.
Particularly, low-skilled workers and those in certain industries are likely to be affected by the evolution of AI, and may need to undergo re-employment or retraining.
To address the impact on employment, it is essential to implement measures such as skill training and career transition support associated with the introduction of AI.
Conclusion
Generative AI has the potential to bring about innovative changes in modern business.
Through proper implementation and utilization, it can create unprecedented business opportunities and strengthen corporate competitiveness.
However, it is also true that generative AI is still an evolving technology with several challenges remaining.
For instance, there are concerns about the accuracy of generated content, ethical issues, and privacy protection, necessitating companies to explore ways to utilize this technology while addressing these challenges.
Nonetheless, the potential of generative AI is extremely high, and more companies are expected to proceed with its implementation in the future. The key to success lies in correctly understanding generative AI and effectively utilizing it according to business needs.
By doing so, companies can expect to seize new growth opportunities.