융합정보논문지 (Journal of Convergence for Information Technology)

중소기업융합학회 (Convergence Society for SMB)
권호 표지
DOI QR코드

DOI QR Code

무한연결시 4차 산업기술의 이용 가능성 분석을 통한 감성 인공 지능의 자율 결정권에 관한 연구

A Study on the Autonomous Decision Right of Emotional AI based on Analysis of 4th Wave Technology Availability in the Hyper-Linkage

  • 서대성 (성결대학교 파이데이아학부)
  • 투고 : 2019.07.04
  • 심사 : 2019.08.20
  • 발행 : 2019.08.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문은 인공 지능 기술의 효과는 산업에 미치는 영향과 일상생활의 변화 등에 관한 연구이다. 또한 감성 인공 지능 개발은 차세대 3D 벡터 감응 인공 지능을 대비한다. 이것은 인공 지능의 의사 결정 권력의 주요 키워드를 제시한다. 특히 비 윤리적 학습의 중요성과 윤리적 가치 판단을 반영하는 의사 결정 시스템의 구현으로 인해 중요한 결과가 달성된다. 이것은 데이터 기반 시뮬레이션이며 (1) 사용 가능한 데이터, (2) 시뮬레이션 목표를 위한 기술을 필요로 한다. 실제 의도된 시뮬레이션 기반 연구의 일반적인 내용을 고려한다. 현재 기존 연구는 의미있는 연구 동기에 중점을 두고있느나, 본 연구는 기술의 방향성을 제시하는 결과이다. 그 결과 실증분석은 각국이 인공 지능에 대한 신기술 기업의 윤리 책임감에 대한 의사 결정력과 일치한다. 결론적으로, AI / ML의 기술적 측면에 대한 윤리적 주제에 대해 달성 할 수 있는 구체적인 기여와 해석이 필요하다. 이는 인공지능 의사 결정에서 인간의 공감의 분석력이 더욱 부각될 수 있다.

The effects of artificial intelligence technology is social science research as research on the impact on industry and changes in daily life, etc. This means that developing 'emotion AI' will prepare 'next-generation 3D-vector-sensitive AI'. This suggests the main keywords of the tertiary AI decision-making power. Particularly important results will be achieved because of the importance of current unethical learning and the implementation of decision-making systems that reflect ethical value judgments. This is a data based simulation, and required (1)Available data, (2)the technology for the goal of simulation. This takes into account the general content of the intended simulation based research. Currently, existing researches focus on meaningful research motivation, but this study presents the direction of technology. So, empirical analysis is consistent with the decision-making power of each country vs. new technology firms for AI on ehtic responsibility. As a result, there is a need for a concrete contribution and interpretation that can be achieved for the ethic Responsibility, on the technical side of AI / ML. In AI decision making, analytic power of human empathy should be included tech own trust.

키워드

JKOHBZ_2019_v9n8_9_f0002.png 이미지

Fig. 1. The 4th Wave ethic conception of AI role on the decision making, (Emotion AI role vs. Human empathy) [13]

JKOHBZ_2019_v9n8_9_f0004.png 이미지

Fig. 2. Reliability of each countries vs. new technology firms on AI (author own made)

Table 1. Keyword data of SWOT analysis (google data, 2019.7: author own made)[13]

JKOHBZ_2019_v9n8_9_t0001.png 이미지

참고문헌

  1. A. M. Yazdani. (2012). Brain emotional learning based intelligent controller for stepper motor trajectory tracking. Int. J. of Physical sciences, 7(15), 2364-2386. https://doi.org/10.5897/IJPS11.1590
  2. W. J. BELL. (1991). Searching Behavior: The Behavioral Ecology of Finding Resources. Chapman and Hall, London, England.
  3. M. M. Cohen. & D. Massaro. (1993). Modelling co-articulation in synthetic visual speech. Springer-Verlag , 139-156.
  4. D. H. Kim. & Baranyi P. (2011). Novel emotional dynamic express for robot. SAMI2011, Jan, 27-30.
  5. D. S. Seo. (2018). Strategy of Market Spread-Commercialization in EVs Industry: Visegrad and Nordic Countries. The International Journal of Industrial Distribution & Business, 9(3), 57-68. https://doi.org/10.13106/ijidb.2018.vol9.no3.57.
  6. D. S. Seo. (2017). The investment point on cooperative innovation in EVs for the spoke-smart cities: focused on Nordic countries and Korea. The Journals of Economics, Marketing & Management, 5(3), 1-11.
  7. D. S. Seo. (2016). The Role of Innovative Energy Public Firms' Channels according to Shale Gas for E-Convergence Economy. The Journal of Distribution Science, 14(5), 17-26. https://doi.org/10.15722/jds.14.5.201605.17
  8. D. S. Seo. (2016). The Commerce Strategy towards Pan-European Innovation and Consumption: Spokes Partnership for FDI of Korea. Journal of Internet Banking and Commerce, 21(S2), 1-7.
  9. S. V. Valentin. (2018). Multivariate analytics of chromatographic data: Visual computing based on moving window factor models. Journal of Chromatography B, 1092, 179-190. https://doi.org/10.1016/j.jchromb.2018.06.010
  10. J. Velasque. (1997). Modeling emotions and other motivations in synthetic agents. In: Proceedings of AAAI-97, MIT Press, 10-15.
  11. D. H. Kim. & D. S. Seo. (2019). Vector based 3D Emotion Expression for Emotion Robot. ACM International Conference Proceeding Series, 113-117. DOI : 10.1145/3314493.3314499
  12. D. S. Seo. (2017). Vehicle occupant safety confirmation system. Seoul : Korean Intellectual Porperty Office (KIPO).
  13. D. H. Kim. (2017). Tuning Innovation with Biotechnology. Panstanford Publishing Co. - ISBN 978-981-4745-55-2.
  14. McKinsey Global Institute. (2013). Disruptive technologies: Advances that will transform life, business, and the global economy.
  15. C. Peter. (2006). The Role of Emotion in Human-Computer Interaction. The workshop's website is: http://www.emotion-in-hci.net.
  16. Deloitte. (2019). Global Automotive Consumer Study. https://www2.deloitte.com/us/en/pages/manufacturing/articles/automotive-trends-millennials-consumer-study.html(2019/3/1).
  17. H. K. Sim. (2008). Neural networks and machine learning. The Age of Artificial Intelligence, Pearson Prentice Hall.
  18. Atomico (2017). State of European Tech.
  19. EU center. (2018). Towards a European Strategy for Human- Centric Machines. November 30, 2018. https://ec.europa.eu/epsc/sites/epsc/files/epsc_strategicnote_ai.pdf.
  20. European Parliament. (2017). Commission's report on Saving Lives: Boosting Car Safety in the EU. November 30, 2018 retrieved from (COM(2016) 0787 final).
  21. European Commission. (2018). Responsible Research and Innovation workstream. retrieved from https://ec.europa.eu/programmes/horizon2020/en/h2020-section/responsible-research-innovation.
  22. S. Moran, D. Cropley & J. Kaufman. (2014). The Ethics of Creativity. London: Palgrave Macmillan UK.