• 한국학교수학회논문집
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• 제23권2호
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• pp.223-237
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• 2020

4차 산업혁명 시대를 대표하는 인공지능 기술은 이제 우리 삶에 깊숙이 관여되고 있고 미래 교육은 이러한 인공지능의 원리와 활용에 대한 학생들의 역량 함양을 중시하고 있다. 따라서 본 연구의 목적은 인공지능 역량과 가장 밀접한 교과인 수학에서 다루어야 하는 인공지능 관련 교육 내용을 고찰하는데 있다. 이를 위해 인공지능의 핵심 기술인 기계학습(machine learning)의 원리를 수학기반으로 학습할 수 있는 인공지능 교과를 수학과의 과목으로 신설할 것과, '인공지능과 데이터 과학을 위한 수학' 교과에서 다루어야 하는 주요 수학 내용들을 제안하였다.

• 품질경영학회지
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• 제47권4호
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• pp.837-846
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• 2019

Purpose: The purpose of this study is to define various concepts, features, and scopes by examining various previous studies on AI services that are completely different from existing services. It also examines the limitations of existing service quality evaluation methods and studies the characteristics by combining them with various cases of new AI services. And this is to derive and propose quality evaluation attributes of AI service. Methods: The concept and characteristics of artificial intelligence were derived through research and analysis of various previous studies related to artificial intelligence. The key characteristics and quality evaluation items were derived through the KJ method and matching based on the keywords and characteristics derived from previous studies and various cases. Results: Based on the review of various previous studies on the quality of artificial intelligence services, this study presents the main characteristics and quality evaluation items of new artificial intelligence services, which are completely different from existing service quality evaluations. Conclusion: The quality measurement model of AI service is very useful when planning and developing AI-based new products or services because it can accurately evaluate the requirements of consumers using the services of the new AI era. In addition, consumers can be recommended a customized service according to the situation or taste, and can be provided with a customized service based on this.

• 한국인공지능학회지
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• 제11권2호
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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.

• 디지털융복합연구
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• 제20권3호
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• pp.241-249
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• 2022

인공지능은 개인의 일상생활뿐 아니라 전 산업 분야에 적용되며 인공지능 시대라 해도 과언이 아닌 시기가 도래하였다. 그러므로 인공지능 수용에 영향을 주는 요인 파악은 중요하다. 본 연구는 상용화되거나 익숙해진 인공지능은 더는 인공지능이라 인식하지 못하는 AI Effect 현상으로 인공지능 사전경험이 무시되었을 때 인공지능 수용에 어떠한 영향을 미치는지를 분석하였다. 이를 위해 두 번의 실험을 수행하였다. 105명의 성인을 대상으로 한 첫 번째 실험 결과는 실험 대상자 중 32.4%(34명)가 AI Effect가 존재하였고, 이 중 여성이 43.6%(24명), 남성은 20%(10명)가 AI Effect가 존재하는 것을 나타나 여성이 약 2배 정도 높았고, 인공지능 지식 정도가 낮을수록 AI Effect가 존재하는 것으로 나타났다. 두 번째 실험 결과는 성인 240명의 참가자 중 AI Effect가 존재하는 85명만이 대상이었고, 인공지능 경험인지는 인공지능을 적극적으로 수용하게 하는 것으로 나타났다. 본 연구를 통한 AI Effect 이해는 기업에 인공지능의 적극적 수용방안 설정에 도움을 줄 수 있을 것이라 기대된다. 더불어 사용자의 개인 차이와 AI Effect의 관계 규명, AI Effect가 다양한 수용 태도에 미치는 영향 등을 고려한 연구로의 확장을 기대한다.

• 시스템엔지니어링학술지
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• 제18권2호
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• pp.11-23
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• 2022

Although artificial intelligence(AI) is a key technology in the digital transformation among the emerging technologies, there are concerns about the use of AI, so many countries have been trying to set up a proper regulation system. This study analyzes the cases of the regulation policies on AI in USA, EU and Korea with the aim to set up and improve proper AI policies and strategies in Korea. In USA, the establishment of the code of ethics for the use of AI is led by private sector. On the other side, Europe is strengthening competitiveness in the AI industry by consolidating regulations that are dispersed by EU members. Korea has also prepared and promoted policies for AI ethics, copyright and privacy protection at the national level and trying to change to a negative regulation system and improve regulations to close the gap between the leading countries and Korea in AI. Moreover, this study analyzed the course of policy changes of AI regulation policy centered on ACF(Advocacy Coalition Framework) model of Sabatier. Through this study, it proposes hyper-scale AI regulation policy recommendations for improving competitiveness and commercialization in Korea. This study is significant in that it can contribute to increasing the predictability of policy makers who have difficulties due to uncertainty and ambiguity in establishing regulatory policies caused by the emergence of hyper-scale artificial intelligence.

• International journal of advanced smart convergence
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• 제10권3호
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• pp.59-65
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• 2021

In this paper, we propose the AI certification standardization activities for systematic research and planning for the standardization of trustworthy artificial intelligence (AI). The activities will be in two-fold. In the stage 1, we investigate the scope and possibility of standardization through AI reliability technology research targeting international standards organizations. And we establish the AI reliability technology standard and AI reliability verification for the feasibility of the AI reliability technology/certification standards. In the stage 2, based on the standard technical specifications established in the previous stage, we establish AI reliability certification program for verification of products, systems and services. Along with the establishment of the AI reliability certification system, a global InterOp (Interoperability test) event, an AI reliability certification international standard meetings and seminars are to be held for the spread of AI reliability certification. Finally, TAIPP (Trustworthy AI Partnership Project) will be established through the participation of relevant standards organizations and industries to overall maintain and develop standards and certification programs to ensure the governance of AI reliability certification standards.

• International Journal of Internet, Broadcasting and Communication
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• 제12권4호
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• pp.232-237
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• 2020

Given the recent pace of development and expansion of Artificial Intelligence (AI) technology, the influence and ripple effects of AI technology on the whole of our lives will be very large and spread rapidly. The National Artificial Intelligence R&D Strategy, published in 2019, emphasizes the importance of artificial intelligence education for K-12 students. It also mentions STEM education, AI convergence curriculum, and budget for supporting the development of teaching materials and tools. However, it is necessary to create a new type of curriculum at a time when artificial intelligence curriculum has never existed before. With many attempts and discussions going very fast in all countries on almost the same starting line. Also, there is no suitable professor for K-12 students, and it is difficult to make K-12 students understand the concept of AI. In particular, it is difficult to teach elementary school students through professional programming in AI education. It is also difficult to learn tools that can teach AI concepts. In this paper, we propose an educational model for elementary school students to improve their understanding of AI through play or experience. This an experiential education model that combineds exploratory learning and discovery learning using multi-intelligence and the PLAY teaching-learning model to undertand the importance of data training or data required for AI education. This educational model is designed to learn how a computer that knows only binary numbers through UA recognizes images. Through code.org, students were trained to learn AI robots and configured to understand data bias like play. In addition, by learning images directly on a computer through TeachableMachine, a tool capable of supervised learning, to understand the concept of dataset, learning process, and accuracy, and proposed the process of AI inference.

• 전자통신동향분석
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• 제35권7호
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• pp.13-22
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• 2020

There is no denying that computing power has been a crucial driving force behind the development of artificial intelligence today. In addition, artificial intelligence (AI) semiconductors and computing systems are perceived to have promising industrial value in the market along with rapid technological advances. Therefore, success in this field is also meaningful to the nation's growth and competitiveness. In this context, ETRI's AI strategy proposes implementation directions and tasks with the aim of strengthening the technological competitiveness of AI semiconductors and computing systems. The paper contains a brief background of ETRI's AI Strategy #2, research and development trends, and key tasks in four major areas: 1) AI processors, 2) AI computing systems, 3) neuromorphic computing, and 4) quantum computing.

• 정보교육학회논문지
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• 제25권4호
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• pp.611-619
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• 2021

4차 산업혁명의 중심에는 인공지능이 있다. 미래 인공지능 기반 사회에 필요한 역량을 기르기 위해 교육은 변화해야 한다. 본 연구는 초등학교 학생들을 대상으로 Teachable machine을 활용한 인공지능 체험 수업을 개발 및 적용하고, 학생들의 인공지능 흥미도 및 이해도 변화를 분석하였다. 총 10차시의 인공지능 수업 중 4차시는 다양한 인공지능 교육 플랫폼을 이용하였고, 6차시는 Teachable machine 체험을 중심으로 진행하였다. 프로그램 적용 전과 후에 학생들의 인공지능 흥미도와 이해도를 검사하였으며 양적 연구와 질적 연구를 동시에 진행하였다. 연구 결과, 프로그램 적용 후 학생들의 인공지능 흥미도와 이해도가 모두 향상되었음을 확인할 수 있었다. 또한, 연구 결과를 바탕으로 인공지능 교육 프로그램 개발을 위한 후속 연구를 제언하는 바이다.

• 전자통신동향분석
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• 제38권5호
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• pp.1-11
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• 2023

The emergence of generative hyperscale artificial intelligence (AI) has enabled new services, such as image-generating AI and conversational AI based on large language models. Such services likely lead to the influx of numerous users, who cannot be handled using conventional AI models. Furthermore, the exponential increase in training data, computations, and high user demand of AI models has led to intensive hardware resource consumption, highlighting the need to develop domain-specific semiconductors for hyperscale AI. In this technical report, we describe development trends in technologies for hyperscale AI processors pursued by domestic and foreign semiconductor companies, such as NVIDIA, Graphcore, Tesla, Google, Meta, SAPEON, FuriosaAI, and Rebellions.