• 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.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.259-278
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• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.23-28
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• 2024

This study focuses on investigating and exploring solutions to problems related to the increase in the elderly population in modern society where aging is rapidly progressing. With the advent of an aging society the elderly population requires more attention and support. In response companies are attempting to provide elder care services using AI and robotic technology. These services can assist seniors not only with health management and daily life care but also with emotional health aspects. This paper analyzes various elder-related technologies available in the current market investigates their pros and cons and potential for development. The paper concludes that companies need to develop and provide more AI and robot-based elder care services to solve aging problems. Such services can alleviate social and emotional issues in an aging society enhancing the quality of life for seniors.

• The Journal of Bigdata
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• v.2 no.2
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• pp.9-16
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• 2017

Global Platform Solution Company (aka Amazon, Google, MS, IBM) who has cloud platform, are driving AI and Big Data service on their cloud platform. It will dramatically change Enterprise business value chain and infrastructures in Supply Chain Management, Enterprise Resource Planning in Customer relationship Management. Enterprise are focusing the channel with customers and Business Partners and also changing their infrastructures to platform by integrating data. It will be Digital Transformation for decision support. AI and Deep learning technology are rapidly combined to their data driven platform, which supports mobile, social and big data. The collaboration of platform service with business partner and the customer will generate new ecosystem market and it will be the new way of enterprise revolution as a part of the 4th industrial revolution.

• Smart Media Journal
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• v.12 no.9
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• pp.172-172
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• 2023

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.108-111
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• 2024

클라우드 사용이 보편화 되고 확대됨에 따라, 서비스를 유연하게 확장 및 축소하여 신속하게 시장의 수요에 대응할 수 있는 PaaS(Platform-as-a-Service) 형태의 서비스가 많은 기업에서 각광받고 있다. 그리고 이러한 PaaS 형 서비스의 핵심이 되는 기술인 컨테이너(Container)와 컨테이너 관리를 효율화 해주는 쿠버네티스(Kubernetes)가 실질적인 표준으로 사용되고 있다. 이때 쿠버네티스 기반의 환경에서 서비스 어플리케이션은 다양한 구성사례가 존재하나, DB 는 아직 안정성 및 데이터 정합성 등을 이유로 베어메탈(Baremetal)이나 VM(Virtual Machine)을 기반으로 구성하고 있는 상황이다. 그러나, 인프라 구성 및 운영에 있어서도 파드(Pod) 형태의 DB 구성은 베어메탈 및 VM 대비 장점이 존재한다고 생각하여 본 실험을 수행하였다. 본 논문에서는 서비스 응답시간 및 자원 사용의 효율성 측면에서 VM 기반의 DB 와 쿠버네티스 파드 기반의 DB 에 각각 트래픽을 발생시켜서 비교한 결과와 시사점을 제시한다.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.499-503
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• 2024

Broadcasting programs are provided to various media such as Internet replay, OTT, and IPTV services as well as self-broadcasting. In this case, it is very important to provide keywords for search that represent the characteristics of the content well. Broadcasters mainly use the method of manually entering key keywords in the production process and the archive process. This method is insufficient in terms of quantity to secure core metadata, and also reveals limitations in recommending and using content in other media services. This study supports securing a large number of metadata by utilizing closed caption data pre-archived through the DTV closed captioning server developed in EBS. First, core metadata was automatically extracted by applying Google's natural language AI technology. The next step is to propose a method of finding core metadata by reflecting priorities and content characteristics as core research contents. As a technology to obtain differentiated metadata weights, the importance was classified by applying the TF-IDF calculation method. Successful weight data were obtained as a result of the experiment. The string metadata obtained by this study, when combined with future string similarity measurement studies, becomes the basis for securing sophisticated content recommendation metadata from content services provided to other media.

• Review of KIISC
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• v.26 no.2
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• pp.35-38
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• 2016

본 논문에서는 핀테크 보안에 활용 가능한 딥러닝 기술을 살펴본다. 먼저 인공지능과 관련된 보안 이슈를 인공지능이 사람을 위협하는 상황에 대한 보안(Security FROM AI), 인공지능 시스템이나 서비스를 악의적인 공격으로부터 보호하는 이슈(Security OF AI), 인공지능 기술을 활용해 보안 문제를 해결하는 것(Security BY AI) 3가지로 구분하여 살펴본다. Security BY AI의 일환으로 딥러닝에 기반한 비정상탐지(anomaly detection)과 회귀분석(regression)기법을 설명하고, 이상거래탐지, 바이오인증, 피싱, 파밍 탐지, 본인확인, 명의도용탐지, 거래 상대방 신뢰도 분석 등 핀테크 보안 문제에 활용할 수 있는 방안을 살펴본다.

• Electronics and Telecommunications Trends
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• v.36 no.6
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• pp.88-95
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• 2021

Korea will face difficult social problems including population decline and climate change in the future. Artificial intelligence (AI)-powered ICT convergence services are expected to greatly help in overcoming these social challenges. Accordingly, we have derived key promising services (AI+x) in terms of individuals, industries, and countries and identified expectations and threats perceived by the general public. These findings provide policies and research directions for promising AI-based ICT convergence services for social goods.

• Smart Media Journal
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• v.7 no.4
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• pp.30-36
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• 2018

Single domain model like DataLake framework is in spotlight because it can improve data efficiency and process data smarter in big data environment, where large scaled business system generates huge amount of data. In particular, efficient operation of network, storage, and computing resources in logical single domain model is very important for physically partitioned multi-site data process. Based on the advantages of Data Lake framework, we define and extend the concept of Connected Data Architecture and functions of DataLake framework for integrating multiple sites in various domains and managing the lifecycle of data. Also, we propose the design of CDA-based AI service and utilization scenarios in various application domain.