본문 바로가기
KERD
논문KCI 등재

Art Thinking on Data and AI

0

조회

0

다운로드

1

인용

0

참고문헌

키워드

초록

Nowadays, there are many IT-developed societies where people’s everyday thoughts, actions, decisions, and expressions are digitally collected into a massive capacity of data whether people realize it or not. Many of us now live in a data-saturated society, especially with the rise of the metaverse, which blurs the boundaries between the digital and material worlds. The reason for us to pay attention to data is that these data reflect our world and the people within. Furthermore, they are analyzed, processed, and interpreted for diverse causes such as infographics, and new product services for our well-being. As we are moving toward the algorithmic age from the age of big data, a deeper understanding of data, algorithmic culture, and technology in the field of art is needed for future designers and artists. Instead of focusing on the technicality of data, this paper discusses how data can be seen artistically and what the layers of data bias mean through my recent exploration of making art with data. Lastly, it suggests how art thinking on data and artificial intelligence (AI) can open new creative possibilities in art education.

Abstract

Nowadays, there are many IT-developed societies where people’s everyday thoughts, actions, decisions, and expressions are digitally collected into a massive capacity of data whether people realize it or not. Many of us now live in a data-saturated society, especially with the rise of the metaverse, which blurs the boundaries between the digital and material worlds. The reason for us to pay attention to data is that these data reflect our world and the people within. Furthermore, they are analyzed, processed, and interpreted for diverse causes such as infographics, and new product services for our well-being. As we are moving toward the algorithmic age from the age of big data, a deeper understanding of data, algorithmic culture, and technology in the field of art is needed for future designers and artists. Instead of focusing on the technicality of data, this paper discusses how data can be seen artistically and what the layers of data bias mean through my recent exploration of making art with data. Lastly, it suggests how art thinking on data and artificial intelligence (AI) can open new creative possibilities in art education.

참고문헌 (16)

  1. Aggarwal, C., "Data Mining: The Textbook" (2015), Springer
  2. "The Sea Between A and I" (2015)[URL]
  3. "Project Alors" (2020)[URL]
  4. Crawford. K., "Critiquing Big Data: Politics, Ethics, Epistemology, Special Section Introduction", International Journal of Communication, 8, pp.1663~1672 (2014)
  5. Freeman, J., "A Concise Taxonomy for Describing Data as an Art Material", Leonardo, 51(1), pp.75~79 (2018)
  6. "Zero Likes" (2017)[URL]
  7. Hawkins, D., "Identification of Outliers" (1980), springer
  8. "⑦ 데이터 편견, 데이터아트로 승화하다" (2020)[URL]
  9. Kim, Y., "Seeking For Outliers: Artistic Exploration of Data Through Creative Practice", Ph.D., pp.2020
  10. "Since 2009 - Seaon’s River" (2021)[URL]
  11. 데이터, 알고리즘, AI와 예술: 홍익대학교 디자인컨버전스학부 대학혁신지원사업 사이버강의콘텐츠개발 보고서, "Kim, Y.", 교육부, ~ (2022)
  12. "An Evolving GAIA" (2017)[URL]
  13. "Plug-In City" (2020)[URL]
  14. "The Fourth Industrial Revolution: what it means and how to respond" (2016)[URL]
  15. "A Synthetic Song Beyond The Sea" (2019)[URL]
  16. Unwin, A., "When is an Outlier an Outlier? The O3 plot", useR! International R User 2017 Conference (2017)

관련 논문AI추천

이 간행물의 다른 논문 (2건)