벤처창업연구 (Asia-Pacific Journal of Business Venturing and Entrepreneurship)

  • 제18권4호
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  • Pages.65-76
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  • 2023
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  • 1975-7557(pISSN)
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  • 2671-9509(eISSN)
한국벤처창업학회 (The Korean Society of Business Venturing)
권호 표지

빅카인즈를 활용한 GenAI(생성형 인공지능) 기술 동향 분석: ChatGPT 등장과 스타트업 영향 평가

GenAI(Generative Artificial Intelligence) Technology Trend Analysis Using Bigkinds: ChatGPT Emergence and Startup Impact Assessment

  • 이현주 (동국대학교 기술창업학과) ;
  • 성창수 (동국대학교 기술창업학과) ;
  • 전병훈 (동국대학교 기술창업학과)
  • Lee, Hyun Ju (Dongguk University, Department of Technology Startup) ;
  • Sung, Chang Soo (Department of Technology Entrepreneurship, Dongguk University) ;
  • Jeon, Byung Hoon (Department of Technology Entrepreneurship, Dongguk University)
  • 투고 : 2023.07.14
  • 심사 : 2023.08.28
  • 발행 : 2023.08.31
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초록

기술 창업 및 스타트업 분야에서는 인공지능(AI)의 발전이 사업 모델 혁신의 핵심 주제로 부상하였다. 이를 통해 벤처기업들은 경쟁력 확보를 위해 AI를 중심으로 다양한 노력을 기울이고 있다. 본 연구는 GenAI 기술의 발전과 스타트업 생태계 간의 관계를 국내 뉴스 기사를 분석하여, 기술 창업 분야의 동향을 파악하는 것을 목적으로 하였다. 본 연구는 빅카인즈(BIG Kinds)를 활용하여 1990년부터 2023년 8월 10일까지의 국내 뉴스 기사에서 ChatGPT의 등장 전후를 중심으로 GenAI 관련 뉴스 기사, 주요 이슈 및 트렌드의 변화를 조사하였으며, 네트워크 분석 및 키워드 시각화를 통해 관련성을 시각화하였다. 연구결과, 2017년부터 2023년까지 GenAI에 대한 언급이 기사 내에서 점차 증가하였다. 특히, OpenAI의 GPT-3.5를 기반으로 한 ChatGPT 서비스가 주요 이슈로 부각 되었는데, 이 서비스는 OpenAI의 DALL-E, Google의 MusicLM, VoyagerX의 Vrew 등과 같은 언어 모델 기반 GenAI 기술의 대중화를 시사하였다. 이로써 생성형 인공지능은 다양한 분야에서의 유용성을 입증하며, ChatGPT 출시 이후 국내 기업들의 한국어 언어 모델 개발 활동이 활발히 이루어지고 있는 것으로 확인되었다. 리튼 테크놀로지스와 같은 스타트업들도 GenAI를 활용하여 기술 창업 분야에서의 영역을 확장하고 있다. 본 연구에서는 GenAI 기술과 스타트업 창업 활동 간의 연관성을 확인하였으며, 이는 혁신적인 비즈니스 전략의 구축 지원을 시사하며 GenAI 기술의 발전과 스타트업 생태계의 성장을 지속해서 형성할 것으로 전망된다. 더 나아가 국제적 동향 및 다양한 분석 방법의 활용, 실제 현장에서의 GenAI 응용 가능성을 모색하는 연구가 요구 된다. 이러한 노력은 GenAI 기술의 발전과 스타트업 생태계의 성장 발전에 이바지할 것으로 기대된다.

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.

키워드

참고문헌

  1. Aydin, O., & Karaarslan, E.(2022). OpenAI ChatGPT generated literature review: Digital twin in healthcare. Available at SSRN 4308687.
  2. BIG KINDS(2023). GenerativeAI. Retrieved 2023.05.19. from https://www.bigkinds.or.kr/v2/news/index.do/2023.02.23.
  3. Bommarito II, M., & Katz, D. M.(2022). GPT takes the bar exam. arXiv preprint arXiv:2212.14402.
  4. Byeon, J. H., & Kwon, Y. J.(2023). An Investigation of Generative AI in Educational Application: Focusing on the Usage of ChatGPT for Learning Biology. Brain, Digital, & Learning, 13(1), 1-17.
  5. Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L.(2023). A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt. arXiv preprint arXiv:2303.04226.
  6. Chi, D. C., & Kim, S. H.(2021). Home training trend analysis using newspaper big data and keyword analysis. Journal of the Korea Convergence Society, 12(6), 233-239.
  7. Cho, J., Zala, A., & Bansal, M.(2022). DALL-EVAL: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Models. arXiv preprint arXiv: 2202.04053.
  8. Choi, J. Y.(2023, March 3). Ministry of SMEs discusses policy support with generative AI startups. Munhwa Ilbo. Retrieved May 9, 2023, from https://www.munhwa.com/news/view.html?no=2023030301071507025001.
  9. Forti, S., Breitenbucher, U., & Soldani, J.(2022). Trending topics in software engineering. ACM SIGSOFT Software Engineering Notes, 47(3), 20-21.
  10. Go, S. Y.(2023, January 13). "AI generates English fingerprints and questions," Artifical Society, Service unveiled. MONEY TODAY(2023). Retrieved 2023.05.11 from https://news.mt.co.kr/mtview.php?no=2023013112281246955.
  11. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y.(2014). Generative adversarial nets. Advances in neural information processing systems, 27.
  12. Han, K. D., & Jeon, B. H.(2022). A Study on the Status of Startups and Their Nurturing Plans: Focusing on Startups in Seongnam City. Asia-Pacific Journal of Business Venturing and Entrepreneurship, 17(5), 67-80.
  13. Hataya, R., Bao, H., & Arai, H.(2022). Will Large-scale Generative Models Corrupt Future Datasets?. arXiv preprint arXiv:2211.08095.
  14. Heo, Y. J., Kim, B. G., & Roy, P. P.(2021). Frontal face generation algorithm from multi-view images based on generative adversarial network. Journal of Multimedia Information System, 8(2), 85-92.
  15. Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A.(2017). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1125-1134.
  16. Jung, S. W., Son, M. J., & Hwang, E. J.(2019). Variational Auto-Encoder Based Semi-supervised Learning Scheme for Learner Classification in Intelligent Tutoring System. Journal of Korea Multimedia Society, 22(11), 1251-1258.
  17. Kim, B. R.(2023, March 3). "Not being outdone by OpenAI" German AI translation 'Dark Horse'-'DeepL'. Unicorn Times, http://www.unicorntimes.kr/news/articleView.html?idxno=530
  18. Kim, C. S., Kim, N. G., & Kwahk, K. Y.(2019). Research trends analysis of machine learning and deep learning: Focused on the topic modeling. Journal of Korea Society of Digital Industry and Information Management, 15(2), 19-28.
  19. Kim, J. H., Jung, A. R., & Kim, S. W.(2022). A Study on Current Trends and Characteristics of Korean Unicorn Group. Asia-Pacific Journal of Business Venturing and Entrepreneurship, 17(1), 63-77.
  20. Kim, J. O., & Kwon, C. H.(2020). Comparative Analysis of News Articles related to Airlines and Staff the Previous Corona19(2019) and After Corona19(2020). Journal of The Korea Society of Computer and Information, 25(7), 167-173.
  21. Kim, N. H., & Oh, H. Y.(2021). Analysis of COVID-19 Pandemic based on Massive Big Data Analysis. Journal of the Korea Institute of Information and Communication Engineering, 25(4), 495-500.
  22. KoreaAIStartup(2022). 2022 Korea AI Startup 100 Finalists. Retrieved 2023.05.19. from https://aistartuptop100.co.kr/page/s1/s3.php
  23. Kwon, J. H., & Lee, H.(2019). A study on trends and perceptions in smart factory: news network analysis. Journal of Knowledge Information Technology and Systems, 14(6), 605-614.
  24. Kwon, M. J.(2019). Identifying Seoul city issues based on topic modeling of news article. In Proceedings of the Korean Society of Broadcast Engineers Conference. 11-13, The Korean Institute of Broadcast and Media Engineers.
  25. Lee, E. Y., Chu, K. H., & Lee, D. H.(2019). A study on recent trend analysis in consumer research applying Word Cloud. Journal of product Research, 37(1), 1-7.
  26. Lee, S. Y.(2021). A Discourse Analysis on Human Rights Education on Labor through Big Data Analysis of News Articles. Journal of Social Studies Education Research, 28(4), 33-45.
  27. MicroSoft(2023). Microsoft combines mega AI into all its workplace productivity tools, Copilot. Retrieved 2023.05.19. from https://news.microsoft.com/ko-kr/2023/03/17/introducing-microsoft-365-copilot/.
  28. Oh, S. Y.(2023, April 4). 'Korea's NVIDIA' Rebellion, leaps forward with AI semiconductor 'Atom'. Asia Economy. Retrieved May 19, 2023, from https://www.asiae.co.kr/article/2023040409194345764.
  29. OpenAI(2022). ChatGPT. Retrieved 2022.12.10. from https://openai.com.
  30. Park, J. H.(2023, June 27). Ministry of Science and ICT selects 15 companies including Luton for ICT unicorn fostering project. ET News, https://www.etnews.com/20230627000058
  31. Park, J. S., & Kim, C. S.(2019). Research trends analysis of big data: focused on the topic modeling. Journal of Korea Society of Digital Industry and Information Management, 15(1), 1-7.
  32. Park, S. H.(2023, May 19). "'ChatGPT' is 6500 times better in Korean"... Naver jumps into the conversational AI war with 'HyperClovaX'. Chosun Biz, https://biz.chosun.com/it-science/ict/2023/02/27/6Y46R5SPXFFYBCJGXG7M6ZU62E/
  33. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I.(2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.
  34. Reddy, M. D. M., Basha, M. S. M., Hari, M. M. C., & Penchalaiah, M. N.(2021). Dall-e: Creating images from text. UGC Care Group I Journal, 8(14), 71-75.
  35. Sung, H. H.(2022, July 19). Naver 'Clova Studio', 100 startups participate in CBT... Accelerating 'AI popularization'. ET News, Retrieved May 19, 2023, from https://www.etnews.com/20220719000093.
  36. Suzuki, K., Roseboom, W., Schwartzman, D. J., & Seth, A. K.(2017). A Deep-Dream Virtual Reality Platform for Studying Altered Perceptual Phenomenology. Scientific reports, 7(1), 15982. https://doi.org/10.1038/s41598-017-16316-2
  37. Transformer(2022). Transformer. https://blogs.nvidia.co.kr/2022/04/01/what-is-a-transformer-model/2022.05.18
  38. Wikipedia(2023). AIVA. Retrieved June 10, 2023, from https://en.wikipedia.org/wiki/AIVA.
  39. Wolf, M. J., Miller, K., & Grodzinsky, F. S.(2017). Why we should have seen that coming: comments on Microsoft's tay" experiment," and wider implications. Acm Sigcas Computers and Society, 47(3), 54-64.
  40. Wu, J., Gan, W., Chen, Z., Wan, S., & Lin, H.(2023). Ai-generated content(aigc): A survey. arXiv preprint arXiv:2304.06632.
  41. Yoon, H. Y.(2021). An analysis of trends in international trade issues using big data-based news articles: From 2011 to 2020. International Commerce and Information Review, 23(1), 113-136. https://doi.org/10.15798/kaici.2021.23.1.113
  42. Yun, S. M., & Sung, C. S.(2023). A Study on the Success Factors of Co-Founding Start-up by Step: Focusing on the Case of Opportunity-type Start-up. Asia-Pacific Journal of Business Venturing and Entrepreneurship, 18(1), 141-158.
  43. Zheng, Q., Xia, X., Zou, X., Dong, Y., Wang, S., Xue, Y., ... & Tang, J.(2023). Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x. arXiv preprint arXiv:2303.17568.