Learning to see

Akten, Memo; Fiebrink, Rebecca; Grierson, Mick

doi:10.1145/3306211.3320143

Cited by 19 publications

(7 citation statements)

References 10 publications

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“…Documentation of the ML process were sometimes presented in metatext accompanying the work, such as in the websites for Machine Bias, Learning to See, POSTcard Landscapes from Lanzarote, Butcher's Son, ImageNet Roulette, in transitu, Unsupervised and Biometric Mirror. Further explanations are frequently offered elsewhere in texts written by the artists such as essays [28,88], academic papers [5,43,81,82,91] and a PhD dissertation [3].…”

Section: A Appendix A1 Method: Analysis Of Artworkmentioning

confidence: 99%

“…Learning to See (Fig. 2, bottom) is an interactive video installation by Memo Akten [4] which appropriates GAN models trained on images of waves, flowers, and fire and applies them to real-time video feeds of mundane everyday objects such as phone chargers, pens, and fabric; turning them into animated waves, flowers, or fire in a similar composition.…”

Section: Sample Ai Artmentioning

confidence: 99%

“…Year Technology Types of Ambiguity Tactics Butcher's Son [52] 2017 Image synthesis Information 1, 3 Learning to See [4] 2017 Image synthesis Information & Context 1, 2, 5 Machine Bias [94] 2018 Image synthesis Information & Relationship 1, 2, 4, 10 Biometric Mirror [91] 2018 Facial analysis Information, Context & Relationship 2, 3, 4, 5, 8, 10 ImageNet Roulette [69] 2019…”

Section: Workmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Processes As Sources of Ambiguity: Insights from AI Art

Sivertsen,

Salimbeni,

Løvlie

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Ongoing efforts to turn Machine Learning (ML) into a design material have encountered limited success. This paper examines the burgeoning area of AI art to understand how artists incorporate ML in their creative work. Drawing upon related HCI theories, we investigate how artists create ambiguity by analyzing nine AI artworks that use computer vision and image synthesis. Our analysis shows that, in addition to the established types of ambiguity, artists worked closely with the ML process (dataset curation, model training, and application) and developed various techniques to evoke the ambiguity of processes. Our finding indicates that the current conceptualization of ML as a design material needs to reframe the ML process as design elements, instead of technical details. Finally, this paper offers reflections on commonly held assumptions in HCI about ML uncertainty, dependability, and explainability, and advocates to supplement the artifact-centered design perspective of ML with a process-centered one.CCS Concepts: • Human-centered computing → HCI theory, concepts and models; • Applied computing → Media arts.

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Section: A Appendix A1 Method: Analysis Of Artworkmentioning

confidence: 99%

Section: Sample Ai Artmentioning

confidence: 99%

Section: Workmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Processes As Sources of Ambiguity: Insights from AI Art

Sivertsen,

Salimbeni,

Løvlie

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

show abstract

“…The building walls are transformed into a dynamic display of light and sound [13]. "You Are What You See" uses NLP and machine-learning algorithms to generate appropriate responses to audience communication [14]. It opens new possibilities for creating interactive installations that respond to the viewer's questions or commands, creating a more conversational and personal experience.…”

Section: Artificial Intelligence In Interactive Installation Artmentioning

confidence: 99%

A Comprehensive Study of Emotional Responses in AI-Enhanced Interactive Installation Art

Chen,

Ibrahim

2023

Sustainability

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This study presents a comprehensive literature review on the convergence of affective computing, interactive installation art, multi-dimensional sensory stimulation, and artificial intelligence (AI) in measuring emotional responses, demonstrating the potential of artificial intelligence in emotion recognition as a tool for sustainable development. It addresses the problem of understanding emotional response and measurement in the context of interactive installation art under artificial intelligence (AI), emphasizing sustainability as a key factor. The study aims to fill the existing research gaps by examining three key aspects: sensory stimulation, multi-dimensional interactions, and engagement, which have been identified as significant contributors to profound emotional responses in interactive installation art. The proposed approach involves conducting a process analysis of emotional responses to interactive installation art, aiming to develop a conceptual framework that explores the variables influencing emotional responses. This study formulates hypotheses that make specific predictions about the relationships between sensory stimulation, multi-dimensional interactions, engagement, and emotional responses. By employing the ASSURE model combined with experimental design, the research methodology ensures a systematic and comprehensive study implementation. The implications of this project lie in advancing the understanding of emotional experiences in interactive installation art under AI, providing insights into the underlying mechanisms that drive these experiences, and their influence on individual well-being from a sustainable perspective. The contributions of this research include bridging the identified research gaps, refining theoretical frameworks, and guiding the design of more impactful and emotionally resonant interactive artworks with sustainability in mind. This research seeks not only to fill the existing gaps in understanding emotional experiences in interactive installation art, but also to guide the development of immersive and emotionally engaging installations, ultimately advancing the broader field of human–computer interaction, promoting individual well-being, and contribute to sustainable development.

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“…The early examples of interactive video installations using generative deep neural networks were projects used live camera input to create closed-circuit video systems, exemplified by Memo Akten's "Learning to See" (2017) [1] and by Mario Klingerman's "Uncanny Mirror" (2018) [9]. Nevertheless, a myriad of alternative interaction methodologies remain to be explored within the realm of interactive art, particularly in investigating the symbiosis with generative AI to engender artworks.…”

Section: Introductionmentioning

confidence: 99%