• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.7-15
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• 2024

Despite a recent surge in multifaceted research on AI-integrated language learning, empirical studies in this area remain limited. This study adopts a Human-Generative AI parallel processing model to examine students' perceptions, asking 182 college students to independently construct knowledge and then compare their efforts with the results generated through in-classroom conversations with ChatGPT 3.5. In questionnaire responses, most students indicated that they found these activities useful and expressed a keen interest in learning various ways to utilize generative AI for language learning with instructor guidance. The findings confirm that ChatGPT's potential as a virtual conversation partner. Identifying specific reasons for the perceived usefulness of conversation activities and drawbacks of ChatGPT, this study emphasizes the importance of teachers staying informed about both the latest advances in technology and their limitations. We recommend that teachers endeavor to creatively design various classroom activities using AI technology.

• Korean Journal of Radiology
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• v.25 no.3
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• pp.224-242
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• 2024

The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application. This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.3
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• pp.53-68
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• 2024

Generative AI is receiving a lot of attention around the world, and ways to effectively utilize it in the business environment are being explored. In particular, since the public release of the ChatGPT service, which applies the GPT-3.5 model, a large language model developed by OpenAI, it has attracted more attention and has had a significant impact on the entire industry. This study focuses on the emergence of Generative AI, especially ChatGPT, which applies OpenAI's GPT-3.5 model, to investigate its impact on the startup industry and compare the changes that occurred before and after its emergence. This study aims to shed light on the actual application and impact of generative AI in the business environment by examining in detail how generative AI is being used in the startup industry and analyzing the impact of ChatGPT's emergence on the industry. To this end, we collected company information of generative AI-related startups that appeared before and after the ChatGPT announcement and analyzed changes in industry, business content, and investment information. Through keyword analysis, topic modeling, and network analysis, we identified trends in the startup industry and how the introduction of generative AI has revolutionized the startup industry. As a result of the study, we found that the number of startups related to Generative AI has increased since the emergence of ChatGPT, and in particular, the total and average amount of funding for Generative AI-related startups has increased significantly. We also found that various industries are attempting to apply Generative AI technology, and the development of services and products such as enterprise applications and SaaS using Generative AI has been actively promoted, influencing the emergence of new business models. The findings of this study confirm the impact of Generative AI on the startup industry and contribute to our understanding of how the emergence of this innovative new technology can change the business ecosystem.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.279-291
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• 2023

In recent years, language model-based generative AI technologies have made remarkable progress. In particular, it has attracted a lot of attention due to its increasing potential in various fields such as summarization and code writing. As a reflection of this interest, the number of patent applications related to generative AI has been increasing rapidly. In order to understand these trends and develop strategies accordingly, future forecasting is key. Predictions can be used to better understand the future trends in the field of technology and develop more effective strategies. In this paper, we analyzed patents filed to date to identify the direction of development of language model-based generative AI. In particular, we took an in-depth look at research and invention activities in each country, focusing on application trends by year and detailed technology. Through this analysis, we tried to understand the detailed technologies contained in the core patents and predict the future development trends of generative AI.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.81-88
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• 2024

Despite the rapid growth of the generative AI market and significant interest from domestic companies and institutions, concerns about the provision of inaccurate information and potential information leaks have emerged as major factors hindering the adoption of generative AI. To address these issues, this paper designs and implements a question-answering system based on the Retrieval-Augmented Generation (RAG) architecture. The proposed method constructs a knowledge database using Korean sentence embeddings and retrieves information relevant to queries through optimized searches, which is then provided to the generative language model. Additionally, it allows users to directly manage the knowledge database to efficiently update changing business information, and it is designed to operate in a private network to reduce the risk of corporate confidential information leakage. This study aims to serve as a useful reference for domestic companies seeking to adopt and utilize generative AI.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.3
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• pp.303-323
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• 2023

The purpose of this study is to introduce this language model in the era of generative AI such as ChatGPT, and to provide direction for data literacy education components in libraries using it. To this end, the following three research questions are proposed. First, the technical features of ChatGPT-like language models are examined, and then, it is argued that data literacy education is necessary for the proper and accurate use of information by users using a service platform based on generative AI technology. Finally, for library data literacy education in the ChatGPT era, it is proposed a data literacy education scheme including seven components such as data understanding, data generation, data collection, data verification, data management, data use and sharing, and data ethics. In conclusion, since generative AI technologies such as ChatGPT are expected to have a significant impact on users' information utilization, libraries should think about the advantages, disadvantages, and problems of these technologies first, and use them as a basis for further improving library information services.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.13-21
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• 2022

Recent deep learning-based research shows excellent performance in most natural language processing (NLP) fields with pre-trained language models. In particular, the auto-encoder-based language model proves its excellent performance and usefulness in various fields of Korean language understanding. However, the decoder-based Korean generative model even suffers from generating simple sentences. Also, there is few detailed research and data for the field of conversation where generative models are most commonly utilized. Therefore, this paper constructs multi-turn dialogue data for a Korean generative model. In addition, we compare and analyze the performance by improving the dialogue ability of the generative model through transfer learning. In addition, we propose a method of supplementing the insufficient dialogue generation ability of the model by extracting recommended response candidates from external knowledge information through a retrival model.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.129-164
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• 2023

In a situation where the use and introduction of Large Language Models (LLMs) is expanding due to recent developments in generative AI technology, it is difficult to find actual application cases or implementation methods for the use of internal company data in existing studies. Accordingly, this study presents a method of implementing generative AI services using the LLM application architecture using the most widely used LangChain framework. To this end, we reviewed various ways to overcome the problem of lack of information, focusing on the use of LLM, and presented specific solutions. To this end, we analyze methods of fine-tuning or direct use of document information and look in detail at the main steps of information storage and retrieval methods using the retrieval augmented generation (RAG) model to solve these problems. In particular, similar context recommendation and Question-Answering (QA) systems were utilized as a method to store and search information in a vector store using the RAG model. In addition, the specific operation method, major implementation steps and cases, including implementation source and user interface were presented to enhance understanding of generative AI technology. This has meaning and value in enabling LLM to be actively utilized in implementing services within companies.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.15-24
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• 2023

One of the objectives and aspirations of scientists and engineers ever since the development of computers has been to interact naturally with machines. Hence features of artificial intelligence (AI) like natural language processing and natural language generation were developed. The field of AI that is thought to be expanding the fastest is interactive conversational systems. Numerous businesses have created various Virtual Personal Assistants (VPAs) using these technologies, including Apple's Siri, Amazon's Alexa, and Google Assistant, among others. Even though many chatbots have been introduced through the years to diagnose or treat psychological disorders, we are yet to have a user-friendly chatbot available. A smart generative cognitive behavioral therapy with spoken dialogue systems support was then developed using a model Persona Perception (P2) bot with Generative Pre-trained Transformer-2 (GPT-2). The model was then implemented using modern technologies in VPAs like voice recognition, Natural Language Understanding (NLU), and text-to-speech. This system is a magnificent device to help with voice-based systems because it can have therapeutic discussions with the users utilizing text and vocal interactive user experience.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.56-61
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• 2023

The present study explored a comprehensive investigation of university professors' perspectives on the implementation of ChatGPT - an artificial intelligence-powered language model - in their teaching practices. A diverse group of 30 university professors responded to a questionnaire about the level of their interest in implementing the tool, willingness to apply it, and concerns they have regarding the intervention of ChatGPT in higher education setting. The results showed that the participants are highly interested in employing the tool into their teaching practice, and find that the students are likely to benefit from using ChatGPT in classroom settings. On the other hand, they displayed concerns regarding high depandency on data, privacy-related issues, lack of supports required, and technical contraints. In today's fast-paced society, educators are urged to mindfully apply this inevitable generative AI means with thoughtfulness and ethical considerations to and for their learners. Relevant topics are discussed to successfully intervene AI tools in teaching practices in higher education.