• 제목/요약/키워드: AI Technology

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ETRI AI Strategy #7: Preventing Technological and Social Dysfunction Caused by AI (ETRI AI 실행전략 7: AI로 인한 기술·사회적 역기능 방지)

  • Kim, T.W.;Choi, S.S.;Yeon, S.J.
    • Electronics and Telecommunications Trends
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    • v.35 no.7
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    • pp.67-76
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    • 2020
  • Because of the development and spread of artificial intelligence (AI) technology, new security threats and adverse AI functions have emerged as a real problem in the process of diversifying areas of use and introducing AI-based products and services to users. In response, it is necessary to develop new AI-based technologies in the field of information protection and security. This paper reviews topics such as domestic and international trends on false information detection technology, cyber security technology, and trust distribution platform technology, and it establishes the direction of the promotion of technology development. In addition, the development of international trends in ethical AI guidelines to ensure the human-centered ethical validity of AI development processes and final systems in parallel with technology development are analyzed and discussed. ETRI has developed AI policing technology, information protection, and security technologies as well as derived tasks and implementation strategies to prepare ethical AI development guidelines to ensure the reliability of AI based on its capabilities.

A Comparison for the Maturity Level of Defense AI Technology to Support Situation Awareness and Decision Making (상황인식 및 의사결정지원을 위한 국방AI기술의 성숙도 수준비교)

  • Kwon, Hyuk Jin;Joo, Ye Na;Kim, Sung Tae
    • Journal of the Korean Society of Systems Engineering
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    • v.18 no.1
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    • pp.90-98
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    • 2022
  • On February 12, 2019, the U.S. Department of Defense newly established and announced the "Defense AI Strategy" to accelerate the use of artificial intelligence (AI) technology for military purposes. As China and Russia invested heavily in AI for military purposes, the U.S. was concerned that it could eventually lose its advantage in AI technology to China and Russia. In response, China and Russia, which are hostile countries, and especially China, are speeding up the development of new military theories related to the overall construction and operation of the Chinese military based on AI. With the rapid development of AI technology, major advanced countries such as the U.S. and China are actively researching the application of AI technology, but most existing studies do not address the special topic of defense. Fortunately, the "Future Defense 2030 Technology Strategy" classified AI technology fields from a defense perspective and analyzed advanced overseas cases to present a roadmap in detail, but it has limitations in comparing private technology-oriented benchmarking and AI technology's maturity level. Therefore, this study tried to overcome the limitations of the "Future Defense 2030 Technology Strategy" by comparing and analyzing Chinese and U.S. military research cases and evaluating the maturity level of military use of AI technology, not AI technology itself.

Comparison of On-Device AI Software Tools

  • Song, Hong-Jong
    • International Journal of Advanced Culture Technology
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    • v.10 no.2
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    • pp.246-251
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    • 2022
  • As the number of data and devices explodes, centralized data processing and AI analysis have limitations due to the load on the network and cloud. On-device AI technology can provide intelligent services without overloading the network and cloud because the device itself performs AI models. Accordingly, the need for on-device AI technology is emerging. Many smartphones are equipped with On-Device AI technology to support the use of related functions. In this paper, we compare software tools that implement On-Device AI.

Discovering Essential AI-based Manufacturing Policy Issues for Competitive Reinforcement of Small and Medium Manufacturing Enterprises (중소 제조기업의 경쟁력 강화를 위한 제조AI 핵심 정책과제 도출에 관한 연구)

  • Kim, Il Jung;Kim, Woo Soon;Kim, Joon Young;Chae, Hee Su;Woo, Ji Yeong;Do, Kyung Min;Lim, Sung Hoon;Shin, Min Soo;Lee, Ji Eun;Kim, Heung Nam
    • 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.

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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    • v.12 no.2
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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.

Deep Analysis of Causal AI-Based Data Analysis Techniques for the Status Evaluation of Casual AI Technology (인과적 인공지능 기반 데이터 분석 기법의 심층 분석을 통한 인과적 AI 기술의 현황 분석)

  • Cha Jooho;Ryu Minwoo
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.19 no.4
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    • pp.45-52
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    • 2023
  • With the advent of deep learning, Artificial Intelligence (AI) technology has experienced rapid advancements, extending its application across various industrial sectors. However, the focus has shifted from the independent use of AI technology to its dispersion and proliferation through the open AI ecosystem. This shift signifies the transition from a phase of research and development to an era where AI technology is becoming widely accessible to the general public. However, as this dispersion continues, there is an increasing demand for the verification of outcomes derived from AI technologies. Causal AI applies the traditional concept of causal inference to AI, allowing not only the analysis of data correlations but also the derivation of the causes of the results, thereby obtaining the optimal output values. Causal AI technology addresses these limitations by applying the theory of causal inference to machine learning and deep learning to derive the basis of the analysis results. This paper analyzes recent cases of causal AI technology and presents the major tasks and directions of causal AI, extracting patterns between data using the correlation between them and presenting the results of the analysis.

ETRI AI Strategy #1: Proactively Securing AI Core Technologies (ETRI AI 실행전략 1: 인공지능 핵심기술 선제적 확보)

  • Kim, S.M.;Yeon, S.J.
    • Electronics and Telecommunications Trends
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    • v.35 no.7
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    • pp.3-12
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    • 2020
  • In this paper, we introduce ETRI AI Strategy #1, "Proactively Securing AI Core Technologies." The first goal of this strategy is to innovate artificial intelligence (AI) service technology to overcome the current limitations of AI technologies. Even though we saw a big jump in AI technology development recently due to the rise of deep learning (DL), DL still has technical limitations and problems. This paper introduces the four major parts of the advanced AI technologies that ETRI will secure to overcome the problems of DL and harmonize AI with the human world: post DL technology, human-AI collaboration technology, intelligence for autonomous things, and big data platform technology.

Analysis of AI Model Hub

  • Yo-Seob Lee
    • International Journal of Advanced Culture Technology
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    • v.11 no.4
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    • pp.442-448
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    • 2023
  • Artificial Intelligence (AI) technology has recently grown explosively and is being used in a variety of application fields. Accordingly, the number of AI models is rapidly increasing. AI models are adapted and developed to fit a variety of data types, tasks, and environments, and the variety and volume of models continues to grow. The need to share models and collaborate within the AI community is becoming increasingly important. Collaboration is essential for AI models to be shared and improved publicly and used in a variety of applications. Therefore, with the advancement of AI, the introduction of Model Hub has become more important, improving the sharing, reuse, and collaboration of AI models and increasing the utilization of AI technology. In this paper, we collect data on the model hub and analyze the characteristics of the model hub and the AI models provided. The results of this research can be of great help in developing various multimodal AI models in the future, utilizing AI models in various fields, and building services by fusing various AI models.

Exploring AI Principles in Global Top 500 Enterprises: A Delphi Technique of LDA Topic Modeling Results

  • Hyun BAEK
    • Korean Journal of Artificial Intelligence
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    • v.11 no.2
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    • pp.7-17
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    • 2023
  • Artificial Intelligence (AI) technology has already penetrated deeply into our daily lives, and we live with the convenience of it anytime, anywhere, and sometimes even without us noticing it. However, because AI is imitative intelligence based on human Intelligence, it inevitably has both good and evil sides of humans, which is why ethical principles are essential. The starting point of this study is the AI principles for companies or organizations to develop products. Since the late 2010s, studies on ethics and principles of AI have been actively published. This study focused on AI principles declared by global companies currently developing various products through AI technology. So, we surveyed the AI principles of the Global 500 companies by market capitalization at a given specific time and collected the AI principles explicitly declared by 46 of them. AI analysis technology primarily analyzed this text data, especially LDA (Latent Dirichlet Allocation) topic modeling, which belongs to Machine Learning (ML) analysis technology. Then, we conducted a Delphi technique to reach a meaningful consensus by presenting the primary analysis results. We expect to provide meaningful guidelines in AI-related government policy establishment, corporate ethics declarations, and academic research, where debates on AI ethics and principles often occur recently based on the results of our study.

Exploring the Convergence and Innovation of AI Technology in Short Dramas Production

  • Jiayuan Liang;Xinyi Shan;Jeanhun Chung
    • International journal of advanced smart convergence
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    • v.13 no.3
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    • pp.199-204
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    • 2024
  • In the context of exploring how Artificial Intelligence(AI) can revolutionize the entertainment industry, more and more film and television productions have begun to try to intervene AI technology in various aspects of content creation. However, despite the fact that AI can generate a large amount of textual content and dynamic visual effects, it still faces challenges in terms of plot expression and delivery. This thesis explores the strengths and weaknesses, innovations, and future developments of AI technology in plot production by analyzing existing film and television productions and production practices generated using AI technology. The study proves that as AI technology continues to improve, its use in short-form production will become more and more prevalent in the future, helping human creators become more efficient and even able to produce Short Dramas in full flow.