• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.18 no.4
• /
• pp.65-76
• /
• 2023

In the field of technology entrepreneurship and startups, the development of Artificial Intelligence(AI) has emerged as a key topic for business model innovation. As a result, venture firms are making various efforts centered on AI to secure competitiveness(Kim & Geum, 2023). The purpose of this study is to analyze the relationship between the development of GenAI technology and the startup ecosystem by analyzing domestic news articles to identify trends in the technology startup field. Using BIG Kinds, this study examined the changes in GenAI-related news articles, major issues, and trends in Korean news articles from 1990 to August 10, 2023, focusing on the emergence of ChatGPT before and after, and visualized the relevance through network analysis and keyword visualization. The results of the study showed that the mention of GenAI gradually increased in the articles from 2017 to 2023. In particular, OpenAI's ChatGPT service based on GPT-3.5 was highlighted as a major issue, indicating the popularization of language model-based GenAI technologies such as OpenAI's DALL-E, Google's MusicLM, and VoyagerX's Vrew. This proves the usefulness of GenAI in various fields, and since the launch of ChatGPT, Korean companies have been actively developing Korean language models. Startups such as Ritten Technologies are also utilizing GenAI to expand their scope in the technology startup field. This study confirms the connection between GenAI technology and startup entrepreneurship activities, which suggests that it can support the construction of innovative business strategies, and is expected to continue to shape the development of GenAI technology and the growth of the startup ecosystem. Further research is needed to explore international trends, the utilization of various analysis methods, and the possibility of applying GenAI in the real world. These efforts are expected to contribute to the development of GenAI technology and the growth of the startup ecosystem.

• International Journal of Internet, Broadcasting and Communication
• /
• v.16 no.1
• /
• pp.99-105
• /
• 2024

Runway released an updated version, Gen-2, in March 2023, which introduced new features that are different from Gen-1: it can convert text and images into videos, or convert text and images together into video images based on text instructions. This update will be officially open to the public in June 2023, so more people can enjoy and use their creativity. With this new feature, users can easily transform text and images into impressive video creations. However, as with all new technologies, comes the instability of AI, which also affects the results generated by Runway. This article verifies the feasibility of using Runway to generate the desired video from several aspects through personal practice. In practice, I discovered Runway generation problems and propose improvement methods to find ways to improve the accuracy of Runway generation. And found that although the instability of AI is a factor that needs attention, through careful adjustment and testing, users can still make full use of this feature and create stunning video works. This update marks the beginning of a more innovative and diverse future for the digital creative field.

• Annual Conference on Human and Language Technology
• /
• 2021.10a
• /
• pp.55-60
• /
• 2021

최근 한국어에 대한 자연어 처리 연구는 딥러닝 기반의 자연어 이해 모델을 중심으로 각 모델의 성능에 대한 비교 분석과 평가가 활발하게 이루어지고 있다. 그러나 한국어 생성 모델에 대해서도 자연어 이해 영역의 하위 과제(e.g. 감정 분류, 문장 유사도 측정 등)에 대한 수행 능력만을 정량적으로 평가하여, 생성 모델의 한국어 문장 구성 능력이나 상식 추론 과정을 충분히 평가하지 못하고 있다. 또한 대부분의 생성 모델은 여전히 간단하고 일반적인 상식에 부합하는 자연스러운 문장을 생성하는 것에도 큰 어려움을 겪고 있기에 이를 해결하기 위한 개선 연구가 필요한 상황이다. 따라서 본 논문은 이러한 문제를 해결하기 위해 한국어 생성 모델이 일반 상식 추론 능력을 바탕으로 문장을 생성하도록 KommonGen 데이터셋을 제안한다. 그리고 KommonGen을 통해 한국어 생성 모델의 성능을 정량적으로 비교 분석할 수 있도록 평가 기준을 구성하고, 한국어 기반 자연어 생성 모델의 개선 방향을 제시하고자 한다.

• 한국벤처창업학회:학술대회논문집
• /
• 2024.04a
• /
• pp.75-79
• /
• 2024

생성형AI(Generative AI, GenAI) 서비스는 단순/반복적인 업무를 자동화하고, 많은 일자리의 생산성 향상에 도음을 주어 전반적으로 기존 일자리 감소에 영향을 줄 것이다. 반면에 높은 수준의 생성형AI 기술을 낮은 비용으로 활용할 수 있게 하고, 예술, 게임, 미디어 산업 등 창작 영역과 플랫폼 기반의 서비스 출시에 도움을 주어, 창조적 아이디어에 기반한 소수의 인원이 참여하는 1인 창조기업에서 새로운 일자리 창출에 긍정적인 영향을 줄 것이다. 본 연구의 목적은 1인 지식 창업자라고 할 수 있는 컨설턴트, 강사들이 생성형AI의 강점인 창의성을 활용하여 업무 효율을 높이는 데 적극적으로 활용할 수 있도록 생성형AI 서비스 특성이 지속이용의도에 미치는 영향을 분석하는데 있다. 본 연구에서는 기술수용모델(TAM)을 활용하여 생성형AI의 서비스 특성인 효율성, 창의성, 의인화, 개인화 및 자율성이 지속이용의도에 미치는 영향에 관한 연구를 인지된 유용성과 인지된 이용 용이성을 중심으로 진행하였다. 따라서 본 연구에서는 생성형AI의 서비스 특성이 인지된 유용성과 인지된 이용 용이성을 증진하고, 인지된 유용성과 인지된 이용 용이성이 지속 이용의도에 영향을 미칠 것이라는 인과적 경로를 검증하고자 한다. 1인 창업자가 외부의 도움없이 지속적으로 높은 수준의 업무효율를 유지할 수 있게 도움이 되는 지속사용의도를 종속변수로 설정하여 본 연구를 진행하였다.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.3
• /
• pp.723-730
• /
• 2024

In order to trace the changes in the concept and understanding of automatically generated images, this study analogously explores the creative methods of photography and cinema, which represent the existing image fields, in terms of AI-based image creation methods and 'automaticity', and discusses the understanding and possibilities of new automatic image creation. At the time of the invention of photography and cinema, the field of 'automatic creation' was established for them in comparison to traditional art genres such as painting. Recently, as AI has been applied to video production, the concept of 'automatic creation' has been expanded, and experimental creations that freely cross the boundaries of literature, art, photography, and film are active. By utilizing technologies such as machine learning and deep learning, AI automated creation allows AI to perform the creative process independently. Automated creation using AI can greatly improve efficiency, but it also risks compromising the personal and subjective nature of art. The problem stems from the fact that AI cannot completely replace human creativity.

• Convergence Security Journal
• /
• v.24 no.2
• /
• pp.89-104
• /
• 2024

This study is an empirical study to examine the factors that influence the intention to use artificial intelligence (AI) technology for SW engineering-related tasks, and the purpose of the study is to understand the key factors that influence the use in terms of AI augmentation characteristics and interactive UI/UX characteristics. For this purpose, a survey was conducted among information and communication workers who have experience in using AI-related technologies and the collected data was analyzed. The results of the empirical analysis showed that perceived usefulness was positively influenced by the factors of expertise, interestingness, realism, aesthetics, efficiency, and flexibility, and perceived ease of use was positively influenced by the factors of expertise, interestingness, realism, aesthetics, and flexibility. Variety had no effect on both perceived ease of use and perceived usefulness. Perceived ease of use had a significant effect on perceived immersion, which positively influenced intention to use. These findings are significant in that they provide an academic understanding of the factors that influence the use of AI-enhanced tools in SW engineering-related tasks such as application design, development, testing, and process automation, as well as practical directions for the creators of tools that provide AI-enhanced development services to develop user acquisition strategies.

• Microbiology and Biotechnology Letters
• /
• v.51 no.1
• /
• pp.69-82
• /
• 2023

Nitrilases are a hydrolase group of enzymes that catalyzes nitrile compounds and produce industrially important organic acids. The current objective is to optimize nitrilase production using statistical methods assisted with artificial intelligence (AI) tool from novel nitrile degrading isolate. A nitrile hydrolyzing bacteria Bacillus subtilis AGAB-2 (GenBank Ascension number- MW857547) was isolated from industrial effluent waste through an enrichment culture technique. The culture conditions were optimized by creating an orthogonal design with 7 variables to investigate the effect of the significant factors on nitrilase activity. On the basis of obtained data, an AI-driven support vector machine was used for the fitted regression, which yielded new sets of predicted responses with zero mean error and reduced root mean square error. The results of the above global optimization were regarded as the theoretical optimal function conditions. Nitrilase activity of 9832 ± 15.3 U/ml was obtained under optimized conditions, which is a 5.3-fold increase in compared to unoptimized (1822 ± 18.42 U/ml). The statistical optimization method involving Plackett Burman Design and Response surface methodology in combination with an AI tool created a better response prediction model with a significant improvement in enzyme production.

• The Plant Pathology Journal
• /
• v.18 no.6
• /
• pp.317-322
• /
• 2002

An isolate of Odontoglossum ringspot virus (ORSV) was identified from Cymbidium var. 'Grace Kelly' showing ringspot symptom on the floral and leaf parts, and was denoted as cymbidium ringspot isolate (ORSV-CR). In ultrathin sections of leaf tissue from diseased Cymbidium plants, clusters of virus particles were observed in the vacuole and cytoplasm. In the Western blot hybridization, the virus strongly reacted with ORSV-specific antiserum indistinguishable from ORSV, suggesting that the vims is serologically identical with ORSV. ORSV-CR sap was inoculated onto 20 species belonging to 12 genera. Systemic infection occurred in Cymbidium sp., Nicotiana benthamiana and N. clevelandii, the host of which was found to be different from that of ORSV-Cy, the Korean strain of ORSV. The analysis of coat protein (CP) gene showed that ORSV-CR was highly homologous to the known isolates of ORSV, with over 95.6% identity in amino acid level. Phylogenetic tree analysis of CP showed that ORSV-CR was clustered with the known ORSV isolates, suggesting that ORSV is a very stable tobamovirus.

• Asian Pacific Journal of Cancer Prevention
• /
• v.13 no.6
• /
• pp.2527-2532
• /
• 2012

Objectives: The aim of the present study was to explore mechanisms underlying the effects of down-regulating ${\beta}$-catenin expression on esophageal carcinoma (EC) cells. Methods: Cell cycle distribution and apoptosis were determined using flow cytometry and annexin V apoptosis assay, respectively. Transmission electron microscopy (TEM) was used to examine changes in ultrastructure, while expression of cyclin D1 protein and mRNA was detected by western blot and real-time PCR. Proliferating cell nuclear antigen (PCNA) and extracellular signal-regulated kinase (ERK) 1-2 were evaluated by Western blot analysis. PCNA labeling index (LI) was determined by immunocytochemistry. Results: Compared with pGen-3-con transfected and Eca-109 cells, the percentage of G0/G1-phase pGen-3-CTNNB1 transfected cells was obviously increased (P<0.05), with no significant difference among the three groups with regard to apoptosis (P>0.05). pGen-3-CTNNB1 transfected cells exhibited obvious decrease in cyclin D1 mRNA and protein expression (P<0.05) and the ultrastructure of Eca-109 cells underwent a significant change after being transfected with pGen-3-CTNNB1, suggesting that down-regulating ${\beta}$-catenin expression can promote the differentiation and maturation. The expression of PCNA and the ERKI/2 phosphorylation state were also down-regulated in pGen-3-CTNNB1 transfected cells (P<0.05). At the same time, the PCNA labeling index was decreased accordingly (P<0.05). Conclusion: Inhibition of EC Eca-109 cellproliferation by down-regulating ${\beta}$-catenin expression could improve cell ultrastructure by mediating blockade in G0/G1 through inhibiting cyclin D1, PCNA and the MAPK pathway (p-ERK1/2).

• Annual Conference on Human and Language Technology
• /
• 2022.10a
• /
• pp.176-181
• /
• 2022

일반 상식 추론 능력은 가장 사람다운 능력 중 하나로써, 인공지능 모델이 쉽게 모사하기 어려운 영역이다. 딥러닝 기반의 언어 모델은 여전히 일반 상식에 기반한 추론을 필요로 하는 분야에서 부족한 성능을 보인다. 특히, 한국어에서는 일반 상식 추론과 관련한 연구가 상당히 부족한 상황이다. 이러한 문제 완화를 위해 최근 생성 기반의 일반 상식 추론을 위한 한국어 데이터셋인 Korean CommonGen [1]이 발표되었다. 그러나, 해당 데이터셋의 평가 지표는 어휘 단계의 유사성과 중첩에 의존하는 한계를 지니며, 생성한 문장이 일반 상식에 부합한 문장인지 측정하기 어렵다. 따라서 본 논문은 한국어 일반 상식 추론 및 생성 능력에 대한 평가 지표를 개선하기 위해 문장 성분의 의미역과 자모의 형태 변화를 바탕으로 생성 결과를 평가하는 SRLev, 사람의 평가 결과를 학습한 BIH, 그리고 두 평가 지표의 장점을 결합한 SRLev-BIH를 제안한다.