• 제목/요약/키워드: AI Generation

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Application of AI in Marketing Strategy: Insights from Millennials and Generation Z

  • Yooncheong CHO
    • The Journal of Economics, Marketing and Management
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    • 제12권1호
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    • pp.29-38
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    • 2024
  • Purpose: The purpose of this study is to explore the perceptions of millennials and Generation Z regarding AI applications in marketing, an area that has been rarely explored in previous researches. This study formulated research questions how millennials and Generation Z perceive the impact of brand image, AI-assistant customer service, affective factor, immersive experience, cognitive factor social factor and competitiveness of products and brands on overall attitude through the lens of AI applications in marketing. Additionally, this study also explored the influence of overall attitudes on satisfaction, intention to use, and loyalty towards AI applications. Research design, data and methodology: To gather data, this study employed an online survey conducted in collaboration with a reputable research organization. This study utilized factor analysis, ANOVA, and regression analysis for data analysis. Results: The findings revealed that the impact of brand image, AI-assistant customer service, and competitiveness on attitude demonstrated significance in both millennials and generation Z cohorts. The study identified that cognitive and social factors significantly influenced attitudes among millennials, whereas affective and immersive experiences showed significance in influencing attitudes among Generation Z. Conclusions: The findings offer valuable managerial implications, shedding light on the application of AI in marketing with distinct perspectives between millennials and Generation Z.

A Case Study of Creative Art Based on AI Generation Technology

  • Qianqian Jiang;Jeanhun Chung
    • International journal of advanced smart convergence
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    • 제12권2호
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    • pp.84-89
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    • 2023
  • In recent years, with the breakthrough of Artificial Intelligence (AI) technology in deep learning algorithms such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAE), AI generation technology has rapidly expanded in various sub-sectors in the art field. 2022 as the explosive year of AI-generated art, especially in the creation of AI-generated art creative design, many excellent works have been born, which has improved the work efficiency of art design. This study analyzed the application design characteristics of AI generation technology in two sub fields of artistic creative design of AI painting and AI animation production , and compares the differences between traditional painting and AI painting in the field of painting. Through the research of this paper, the advantages and problems in the process of AI creative design are summarized. Although AI art designs are affected by technical limitations, there are still flaws in artworks and practical problems such as copyright and income, but it provides a strong technical guarantee in the expansion of subdivisions of artistic innovation and technology integration, and has extremely high research value.

Header Text Generation based on Structural Information of Table (테이블 구조 정보를 활용한 헤더 텍스트 생성)

  • Haemin Jung;Myoseop Sim;Kyungkoo Min;Jooyoung Choi;Minjun Park;Stanley Jungkyu Choi
    • Annual Conference on Human and Language Technology
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    • 한국정보과학회언어공학연구회 2023년도 제35회 한글 및 한국어 정보처리 학술대회
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    • pp.415-418
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    • 2023
  • 테이블 데이터는 일반적으로 헤더와 데이터로 구성되며, 헤더는 데이터의 구조와 내용을 이해하는데 중요한 역할을 한다. 하지만 웹 스크래핑 등을 통해 얻은 데이터와 같이 다양한 상황에서 헤더 정보가 누락될 수 있다. 수동으로 헤더를 생성하는 것은 시간이 많이 걸리고 비효율적이기 때문에, 본 논문에서는 자동으로 헤더를 생성하는 태스크를 정의하고 이를 해결하기 위한 모델을 제안한다. 이 모델은 BART를 기반으로 각 열을 구성하는 텍스트와 열 간의 관계를 분석하여 헤더 텍스트를 생성한다. 이 과정을 통해 테이블 데이터의 구성요소 간의 관계에 대해 이해하고, 테이블 데이터의 헤더를 생성하여 다양한 애플리케이션에서의 활용할 수 있다. 실험을 통해 그 성능을 평가한 결과, 테이블 구조 정보를 종합적으로 활용하는 것이 더 높은 성능을 보임을 확인하였다.

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Artificial Intelligence-Based Video Content Generation (인공지능 기반 영상 콘텐츠 생성 기술 동향)

  • Son, J.W.;Han, M.H.;Kim, S.J.
    • Electronics and Telecommunications Trends
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    • 제34권3호
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    • pp.34-42
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    • 2019
  • This study introduces artificial intelligence (AI) techniques for video generation. For an effective illustration, techniques for video generation are classified as either semi-automatic or automatic. First, we discuss some recent achievements in semi-automatic video generation, and explain which types of AI techniques can be applied to produce films and improve film quality. Additionally, we provide an example of video content that has been generated by using AI techniques. Then, two automatic video-generation techniques are introduced with technical details. As there is currently no feasible automatic video-generation technique that can generate commercial videos, in this study, we explain their technical details, and suggest the future direction for researchers. Finally, we discuss several considerations for more practical automatic video-generation techniques.

A Research on 3D Texture Production Using Artificial Intelligence Softwear

  • Ke Ma;Jeanhun Chung
    • International Journal of Internet, Broadcasting and Communication
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    • 제15권4호
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    • pp.178-184
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    • 2023
  • AI image generation technology has become a popular research direction in the field of AI, which is widely used in the field of digital art and conceptual design, and can also be used in the process of 3D texture mapping. This paper introduces the production process of 3D texture mapping using AI image technology, and discusses whether it can be used as a new way of 3D texture mapping to enrich the 3D texture mapping production process. Two AI deep learning models, Stable Diffusion and Midjourney, were combined to generate high-quality AI textures. Finally, the lmage to material function of substance 3D Sampler was used to convert the AI-generated textures into PBR 3D texture maps. And applied in 3D environment. This study shows that 3D texture maps generated by AI image generation technology can be used in 3D environment, which not only has short production time and high production efficiency, but also has rich changes in map styles, which can be quickly adjusted and modified according to the design scheme. However, some AI texture maps need to be manually modified before they can be used. With the continuous development of AI technology, there will be great potential for further development and innovation of AI-generated image technology in the 3D content production process in the future.

Artificial Intelligence Application in City Marketing Strategies: Perspectives from Millennials and Generation Z

  • Yooncheong CHO
    • Korean Journal of Artificial Intelligence
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    • 제12권1호
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    • pp.7-16
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    • 2024
  • This study aims to explore driving factors of Artificial Intelligence application for city marketing strategy with perspectives of millennials and generation Z. This study proposed the following research questions: i) how perceived place branding factor, public service factor, affective factor, immersive experience factor, cognitive factor, cost benefit factor, social networking factor, and promotional value factor affect attitude toward AI application for city marketing; and ii) how attitude affect satisfaction and prospect toward AI application for city marketing? This study conducted an online survey with the assistance of a well-known research agency and applied factor and regression analysis to test hypotheses. The results found that effects of place branding, cognitive, social networking, and promotional value affect attitude significantly in the case of millennials, while effects of public service, affective, cost benefit, social networking, and promotional value affect attitude significantly in the case of generation Z. The results found that effects of attitude on satisfaction and prospect of AI showed significance. The results provide implications and different aspects for AI application of city marketing strategy with perspectives by generations, while millennials and generation Z perceived effects of promotional value as the most significant factor for AI application of city marketing strategy.

A Study on the Service Integration of Traditional Chatbot and ChatGPT (전통적인 챗봇과 ChatGPT 연계 서비스 방안 연구)

  • Cheonsu Jeong
    • Journal of Information Technology Applications and Management
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    • 제30권4호
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    • pp.11-28
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    • 2023
  • This paper proposes a method of integrating ChatGPT with traditional chatbot systems to enhance conversational artificial intelligence(AI) and create more efficient conversational systems. Traditional chatbot systems are primarily based on classification models and are limited to intent classification and simple response generation. In contrast, ChatGPT is a state-of-the-art AI technology for natural language generation, which can generate more natural and fluent conversations. In this paper, we analyze the business service areas that can be integrated with ChatGPT and traditional chatbots, and present methods for conducting conversational scenarios through case studies of service types. Additionally, we suggest ways to integrate ChatGPT with traditional chatbot systems for intent recognition, conversation flow control, and response generation. We provide a practical implementation example of how to integrate ChatGPT with traditional chatbots, making it easier to understand and build integration methods and actively utilize ChatGPT with existing chatbots.

A Study of an AI-Based Content Source Data Generation Model using Folk Paintings and Genre Paintings (민화와 풍속화를 이용한 AI 기반의 콘텐츠 원천 데이터 생성 모델의 연구)

  • Yang, Seokhwan;Lee, Young-Suk
    • Journal of Korea Multimedia Society
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    • 제24권5호
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    • pp.736-743
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    • 2021
  • Due to COVID-19, the non-face-to-face content market is growing rapidly. However, most of the non-face-to-face content such as webtoons and web novels are produced based on the traditional culture of other countries, not Korean traditional culture. The biggest cause of this situation is the lack of reference materials for creating based on Korean traditional culture. Therefore, the need for materials on traditional Korean culture that can be used for content creation is emerging. In this paper, we propose a generation model of source data based on traditional folk paintings through the fusion of traditional Korean folk paintings and AI technology. The proposed model secures basic data based on folk tales, analyzes the style and characteristics of folk tales, and converts historical backgrounds and various stories related to folk tales into data. In addition, using the built data, various new stories are created based on AI technology. The proposed model is highly utilized in that it provides a foundation for new creation based on Korean traditional folk painting and AI technology.

A Study on Korean Poetry Generation System Based on Artificial Intelligence (인공지능 기반 한국어 시 생성 시스템 개발 연구)

  • Myung-sun Kim;Woo-Hyuk Jung;Jihwan Woo
    • Information Systems Review
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    • 제25권3호
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    • pp.43-57
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    • 2023
  • In this study, we developed an AI-based system to generate sentences that assist in creating Korean poetry. Instead of replacing the creative aspect of composition, which is considered a unique domain of humans, the focus was on generating foundational sentences to enhance human imagination efficiently. By conducting interviews with poets, the researchers extracted sentences from eight distinct datasets, enabling the generation of poetry across eight different genres. This study stands out for its innovation in developing a method for crafting literary works in Korean. Its significance lies in its potential to facilitate the creation of diverse literary forms such as essays, prose, or novels.

Research on Generative AI for Korean Multi-Modal Montage App (한국형 멀티모달 몽타주 앱을 위한 생성형 AI 연구)

  • Lim, Jeounghyun;Cha, Kyung-Ae;Koh, Jaepil;Hong, Won-Kee
    • Journal of Service Research and Studies
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    • 제14권1호
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    • pp.13-26
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    • 2024
  • Multi-modal generation is the process of generating results based on a variety of information, such as text, images, and audio. With the rapid development of AI technology, there is a growing number of multi-modal based systems that synthesize different types of data to produce results. In this paper, we present an AI system that uses speech and text recognition to describe a person and generate a montage image. While the existing montage generation technology is based on the appearance of Westerners, the montage generation system developed in this paper learns a model based on Korean facial features. Therefore, it is possible to create more accurate and effective Korean montage images based on multi-modal voice and text specific to Korean. Since the developed montage generation app can be utilized as a draft montage, it can dramatically reduce the manual labor of existing montage production personnel. For this purpose, we utilized persona-based virtual person montage data provided by the AI-Hub of the National Information Society Agency. AI-Hub is an AI integration platform aimed at providing a one-stop service by building artificial intelligence learning data necessary for the development of AI technology and services. The image generation system was implemented using VQGAN, a deep learning model used to generate high-resolution images, and the KoDALLE model, a Korean-based image generation model. It can be confirmed that the learned AI model creates a montage image of a face that is very similar to what was described using voice and text. To verify the practicality of the developed montage generation app, 10 testers used it and more than 70% responded that they were satisfied. The montage generator can be used in various fields, such as criminal detection, to describe and image facial features.