Artist Support Networks: Implications for Future Creativity Support Tools

Chung, John Joon Young; He, Shiqing; Adar, Eytan

doi:10.1145/3532106.3533505

Cited by 18 publications

(7 citation statements)

References 29 publications

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“…Interactive Systems: Interactive systems allow for real-time collaboration between humans and machines, enabling artists to create new artworks that respond to user input [8], [14].…”

Section: Findings Methodologies For Using Ai In Artmentioning

confidence: 99%

Reflections on the Use of Artificial Intelligence in Works of Art

Yusa¹,

Yu²,

Sovhyra³

2022

JADAM

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Purpose: This paper aims to reflect on the use of Artificial Intelligence (AI) in works of art, examining its impact on the creative process, aesthetics, and audience reception, then provide a critical reflection on the use of AI in works of art, examining its potential benefits and drawbacks and exploring the ethical considerations involved. The paper examines the use of AI in works of art, with a focus on the methodologies and case studies that demonstrate the potential and challenges of this emerging field. Research methods: The paper presents case studies of notable artworks that utilize AI, discusses the potential benefits and drawbacks of AI in art, and explores the ethical considerations involved. Through a review of the literature and analysis of six case studies, we explore the aesthetic, technical, and social dimensions of AI-generated art, as well as the ethical and critical debates surrounding its production and reception. Findings: The paper concludes by emphasizing the importance of critical engagement and continued reflection on the use of AI in art. Our findings suggest that AI can offer new modes of creative expression and engagement, but also raise questions about the role of human agency and interpretation in the artistic process. Implications: AI-generated art offers new opportunities and challenges for artists, scholars, and audiences, and that future research should focus on developing ethical guidelines, exploring collaborative practices, and examining the social and political implications of AI-based art. Case studies of AI-generated art can help us understand the potentials and limitations of this field by providing specific examples of how AI is being used to create art, and how artists and audiences are engaging with these works. These studies can provide insights into the creative processes, technical challenges, and social implications of AI-generated art.

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“…Interactive Systems: Interactive systems allow for real-time collaboration between humans and machines, enabling artists to create new artworks that respond to user input [8], [14].…”

Section: Findings Methodologies For Using Ai In Artmentioning

confidence: 99%

Reflections on the Use of Artificial Intelligence in Works of Art

Yusa¹,

Yu²,

Sovhyra³

2022

JADAM

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“…The system faithfully reproduced user input by that control, resulting in a final output that closely mirrors the concept in their mind. These generated outputs helped users actualize their existing ideas, more supporting implementation [17,18]. Dynamic Brushes [36] or Framer [51] had a similar approach to creativity in terms of this.…”

Section: Creativeconnect Vs Baseline -Two Different Types Of Creativi...mentioning

confidence: 99%

“…Conversely, CreativeConnect stimulates creativity by providing inspiration [17,18]. Instead of requiring users to provide detailed input, CreativeConnect accepts keywords and deliberately refrains from exact expression, generating a wider range of outcomes, potentially with serendipity.…”

Section: Creativeconnect Vs Baseline -Two Different Types Of Creativi...mentioning

confidence: 99%

Regional classification according to PM10 concentrations in the Seoul Metropolitan and Chungcheong Area, Republic of Korea

Ho¹

2023

Preprint

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Since the Seoul Metropolitan Area, Republic of Korea, is highly populated, a large number of people are frequently exposed to high concentrations of particulate matter (PM) with mean aerodynamic diameters of <= 10 mm (PM10) in cold season. The concentration of PM10 in Seoul Metropolitan Area increases by transboundary transport, local direct emissions, and chemical reactions of aerosol precursors in the atmosphere. Here, the Seoul Metropolitan Area (Seoul, Gyeonggi-do, and Incheon) and surrounding region (Chungcheongnam-do, Daejeon, and Sejong) are regionally classified by clustering analysis based on the variability of PM10 concentrations. According to the inertia score by the number of clusters, the optimum cluster number of regional variability of PM10 is four. The region of cluster 1 is divided into southern Gyeonggi-do and eastern Chungcheongnam-do, the cluster 2 is mainly classified Incheon, western Gyeonggi-do, and Seoul. The cluster 3 region is western Chungcheongnam-do adjacent to Yellow Sea, and the cluster 4 is classified into eastern Gyeonggi-do. The variability of PM10 in each region is distinguished by the local chemical pollutants emission and weather conditions such as wind speed and direction in each region. This regional classification of PM10 variability is different from administrative districts. Considering most of policies for responding to high concentrations of PM10 are being prepared by administrative districts, this study suggests that a response on the basis of these regional PM10 distribution would be more effective way to improve air quality in the Seoul Metropolitan and Chungcheong Area.

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“…On the other hand, there are many papers studied artist's overall circumstance or several implications these AI models can bring up. complex relationship between artists needed for an art-making [15]. Through this analysis, they tried to inform the design of creativity support tools in connection with the network.…”

Section: Ai Models To Support Visual Artists' Creative Workmentioning

confidence: 99%

Large-scale Text-to-Image Generation Models for Visual Artists' Creative Works

Ko¹,

Park²,

Jeon³

et al. 2022

Preprint

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Large-scale Text-to-image Generation Models (LTGMs) (e.g., DALL-E), self-supervised deep learning models trained on a huge dataset, have demonstrated the capacity for generating high-quality opendomain images from multi-modal input. Although they can even produce anthropomorphized versions of objects and animals, combine irrelevant concepts in reasonable ways, and give variation to any user-provided images, we witnessed such rapid technological advancement left many visual artists disoriented in leveraging LT-GMs more actively in their creative works. Our goal in this work is to understand how visual artists would adopt LTGMs to support their creative works. To this end, we conducted an interview study as well as a systematic literature review of 72 system/application papers for a thorough examination. A total of 28 visual artists covering 35 distinct visual art domains acknowledged LTGMs' versatile roles with high usability to support creative works in automating the creation process (i.e., automation), expanding their ideas (i.e., exploration), and facilitating or arbitrating in communication (i.e., mediation). We conclude by providing four design guidelines that future researchers can refer to in making intelligent user interfaces using LTGMs.

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Artist Support Networks: Implications for Future Creativity Support Tools

Cited by 18 publications

References 29 publications

Reflections on the Use of Artificial Intelligence in Works of Art

Reflections on the Use of Artificial Intelligence in Works of Art

Regional classification according to PM10 concentrations in the Seoul Metropolitan and Chungcheong Area, Republic of Korea

Large-scale Text-to-Image Generation Models for Visual Artists' Creative Works

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