• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.57-69
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• 2023

In a situation where digital transformation using artificial intelligence is active around the world, the growth of domestic AI companies or AI industrial ecosystems is slow. Where a large amount of government funds related to AI are being invested to overcome the difficult economic situation, systematic research on the effect is insufficient. So, this study aimed to examine the policy effectiveness of the government artificial intelligence solution voucher support project for small and medium-sized enterprises (SMEs) using Propensity Score Matching (PSM) and Difference-in-Differences (DID) on the financial performance of beneficiary companies. For empirical analysis, PSM-DID analysis was performed using sales performance since 2019 for 461 companies with a history of voucher support among the AI SMEs data released by the National IT Industry Promotion Agency. As a result of the analysis, the beneficiary companies' asset growth, salary, and R&D expenses increased overall after government support, and no significant contribution could be confirmed in terms of profits. This study suggests that the voucher policy business directly contributed to the company's growth in the short term, but it requires a certain period of time to generate profits.

• The Journal of Bigdata
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• v.9 no.1
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• pp.169-186
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• 2024

This study aims to analyze the acceptance factors for expanding the adoption of AI by SMEs and draw practical and policy implications. To this, we conducted an empirical analysis of AI acceptance factors among 315 SMEs in various industries such as manufacturing, service, and information and communication sectors located in Korea. Based on the UTAUT, we examined the influence of decision-making reliability, perceived awareness, policy support, education and training, perceived cost, perceived risk, and system complexity, and found that decision-making reliability positively affects performance expectancy and social influence, perceived awareness positively affects performance expectancy and effort expectancy, policy support positively affects social influence and facilitating conditions, and education and training positively affects effort expectancy and facilitating conditions. Perceived cost had a negative effect on social influence and facilitating conditions, and perceived risk had a negative effect on performance expectancy and social influence. System complexity had a negative effect on effort expectancy but no effect on facilitating conditions. These results are expected to be widely utilized as basic research for the diffusion of AI in industry and provide practical and policy implications for promoting the adoption of AI in SMEs.

• Electronics and Telecommunications Trends
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• v.35 no.7
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• pp.56-66
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• 2020

As the development of artificial intelligence (AI) technology spreads to various industrial sectors, diversity in AI utilization rapidly increases, creating rich user experience. In addition, AI is required to solve various social problems through the use of public data. The spread of AI utilization across all sectors will continue, covering such industrial and public demands. This article examines the domestic and international trends in AI utilization technologies and establishes the direction of research and development (R&D), which is highly consistent with Korea's AI policy. ETRI, which leads AI's national R&D, has used its experience to establish AI R&D implementation strategies as well as technology roadmaps for the utilization of AI to improve individual quality of life, continuous growth in society, industrial innovation, and the solutions to public societal problems. In addition, it has derived tasks and implementation strategies for developing AI utilization technologies in 10 major areas including medical services.

• Review of KIISC
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• v.32 no.3
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• pp.57-64
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• 2022

우리 사회에서 AI 활용이 더욱 보편화 되어가고 있는 가운데 AI 신뢰에 대한 사회적 요구도 증가했다. 특히 최근 대화형 인공지능'이루다'사건으로 AI 윤리에 대한 논의가 뜨거워졌다. 금융 분야에서도 로보어드바이저, 보험 심사 등 AI가 다양하게 활용되고 있지만, AI 윤리 문제가 AI 활성화에 큰 걸림돌이 되고 있다. 본 논문에서는 인공지능으로 발생할 수 있는 윤리적 문제를 활용 도메인과 데이터 분석 파이프라인에 따라 나눈다. 금융 AI 기술 분야에 따른 윤리 문제를 분류했으며 각 분야별 윤리사례를 제시했고 윤리 문제 분류에 따른 대응 방안과 해외에서의 대응방식과 우리나라의 대응방식을 소개하며 해결방안을 제시했다. 본 연구를 통해 금융 AI 기술 발전에 더불어 윤리 문제에 대한 경각심을 고취시킬 수 있을 것으로 기대한다. 금융 AI 기술 발전이 AI 윤리와 조화를 이루며 성장하길 바라며, 금융 AI 정책 수립 시에도 AI 윤리적 문제를 염두해 두어 차별, 개인정보유출 등과 같은 AI 윤리 규범 미준수로 파생되는 문제점을 줄이며 금융분야 AI 활용이 더욱 활성화되길 기대한다.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.647-664
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• 2022

Purpose: The purpose of this study is to derive major policies that domestic small and medium-sized manufacturing companies should consider to maximize productivity and quality improvement by utilizing manufacturing data and AI, and to find priorities and implications. Methods: In this study, domestic and international issues and literature review by country were conducted to derive major considerations such as manufacturing AI technology, manufacturing AI talent, manufacturing AI data and manufacturing AI ecosystem. Additionally, the questionnaire survey targeting 46 experts of manufacturing data and AI industry were conducted. Finally, the major considerations and detailed factors importance were derived by applying the Analytic Hierarchy Process (AHP). Results: As a result of the study, it was found that 'manufacturing AI technology', 'manufacturing AI talent', 'manufacturing AI data', and 'manufacturing AI ecosystem' exist as key considerations for domestic manufacturing AI. After empirical analysis, the importance of the four key considerations was found to be 'manufacturing AI ecosystem (0.272)', 'manufacturing AI data (0.265)', 'manufacturing AI technology (0.233)', and 'manufacturing AI talent (0.230)'. The importance of the derived four viewpoints is maintained at a similar level. In addition, looking at the detailed variables with the highest importance for each of the four perspectives, 'Best Practice', 'manufacturing data quality management regime, 'manufacturing data collection infrastructure', and 'manufacturing AI manpower level of solution providers' were found. Conclusion: For the sustainable growth of the domestic manufacturing AI ecosystem, it should be possible to develop and promote manufacturing AI policies in a balanced way by considering all four derived viewpoints. This paper is expected to be used as an effective guideline when developing policies for upgrading manufacturing through domestic manufacturing data and AI in the future.

• Informatization Policy
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• v.27 no.2
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• pp.3-19
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• 2020

Artificial intelligence(AI) and blockchain are developing as the core technology leading the Fourth Industrial Revolution. However, AI is still showing limitations in securing and verifying data and explaining the evidence for the results, and blockchain also has some drawbacks such as excessive energy consumption and lack of flexibility in data management. This study analyzed technological limitations of AI and blockchain and convergence trends to overcome them, and finally suggested ways to improve Korea's related policies. Specifically, in terms of R&D reinforcement, we proposed 1) mid- and long-term AI /blockchain convergence research at the national level and 2) blockchain-based AI data platform development. In terms of creating an innovative ecosystem, we also suggested 3) development of AI/blockchain convergence applications by industry, and 4) Start-up support for developing AI/blockchain convergence business models. Lastly, in terms of improving the legal system, we insisted that 5) widening the application of regulatory sandboxes and 6) improving regulations related to privacy protection is necessary.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.545-547
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• 2019

Policy Gradient 방식의 학습은 최근 강화학습 분야에서 많이 연구되고 있는 주제로, 본 논문에서는 강화학습을 적용시킬 수 있는 OpenAi Gym 의 'CartPole-v0' 와 'Pendulum-v0' 환경에서 Policy Gradient 방식의 Asynchronous Advantage Actor-Critic (A3C) 알고리즘과 Proximal Policy Optimization (PPO) 알고리즘의 학습 성능을 비교 분석한 결과를 제시한다. 딥러닝 모델 등 두 알고리즘이 동일하게 지닐 수 있는 조건들은 가능한 동일하게 맞추면서 Episode 진행에 따른 Score 변화 과정을 실험하였다. 본 실험을 통해서 두 가지 서로 다른 환경에서 PPO 가 A3C 보다 더 나은 성능을 보임을 확인하였다.

• Smart Media Journal
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• v.10 no.2
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• pp.48-54
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• 2021

Sepsis is one of the leading causes of mortality globally, and it costs billions of dollars annually. However, treating septic patients is currently highly challenging, and more research is needed into a general treatment method for sepsis. Therefore, in this work, we propose a reinforcement learning method for learning the optimal treatment strategies for septic patients. We model the patient physiological time series data as the input for a deep recurrent Q-network that learns reliable treatment policies. We evaluate our model using an off-policy evaluation method, and the experimental results indicate that it outperforms the physicians' policy, reducing patient mortality up to 3.04%. Thus, our model can be used as a tool to reduce patient mortality by supporting clinicians in making dynamic decisions.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.588-591
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• 2021

4차 산업혁명 시대가 도래하면서 세상이 빠른 속도로 변하고 있다. 특히 데이터·인공지능(AI, Artificial Intelligence)의 활용이 적극적으로 다양한 분야에서 적용되기 시작하고 있다. 하지만 산림수종을 분석하는 업무를 수행하는 과정은 수작업으로 진행하다 보니 오류가 다수 발생하고 있다. 따라서 본 논문에서는 수도권 항공사진을 이용하여 소나무, 낙엽송, 침엽수, 활엽수를 대상으로 자동으로 분석하는 AI 학습용 데이터 약 60,000장을 구축하고, 수종을 구분할 수 있는 AI 모델을 개발하였다. 이를 통해 산림변화탐지 및 산림 분야 주제도 제작 시 수종 분할 이미지를 기초자료로 활용함으로써 업무효율 증대를 기대할 수 있다.

• Informatization Policy
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• v.30 no.2
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• pp.22-45
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• 2023

In research on the use of AI-based voice assistant services, problems related to the user's trust and privacy protection arising from the experience of service use are constantly being raised. The purpose of this study was to investigate empirically the effects of individual trust in AI and online privacy concerns on the continued use of AI-based voice assistants, specifically the impact of their interaction. In this study, question items were constructed based on previous studies, with an online survey conducted among 405 respondents. The effect of the user's trust in AI and privacy concerns on the adoption and continuous use intention of AI-based voice assistant services was analyzed using the Heckman selection model. As the main findings of the study, first, AI-based voice assistant service usage behavior was positively influenced by factors that promote technology acceptance, such as perceived usefulness, perceived ease of use, and social influence. Second, trust in AI had no statistically significant effect on AI-based voice assistant service usage behavior but had a positive effect on continuous use intention. Third, the privacy concern level was confirmed to have the effect of suppressing continuous use intention through interaction with trust in AI. These research results suggest the need to strengthen user experience through user opinion collection and action to improve trust in technology and alleviate users' concerns about privacy as governance for realizing digital government. When introducing artificial intelligence-based policy services, it is necessary to disclose transparently the scope of application of artificial intelligence technology through a public deliberation process, and the development of a system that can track and evaluate privacy issues ex-post and an algorithm that considers privacy protection is required.