Toward an Artist-Centred AI

Awareness about the immense impact that artificial intelligence (AI) might have or already has made on the social, economic, political, and cultural realities of our world has become part of the mainstream public discourse. Attributes such as ethical, responsible, or explainable emerge as associative and descriptive nominal references in guidelines that influence perspectives on AI application and development. This paper contextualizes the notions of suitability and desirability of principles, practices, and tools related to the use of AI in the arts. The result is a framework drafted as a set of atomic attributes that summarize the values of AI deemed important for artistic creativity. It was composed by examining the challenges that AI poses to art production, distribution, consumption, and monetization. Considering the differentiating potentials of AI and taking a perspective aside from the purely technical ontology, we argue that artistically pertinent AI should be unexpected, diversified, affordant, and evolvable

Learning Model for Pandava Five Puppetry Digital Creation: A Digital Literacy and Transformation of Traditional Characters Approach

The rapid evolution of information and communication technology has triggered a transformative revolution in contemporary society, impacting traditional Indonesian arts, notably Purwa Pandava Five shadow puppetry, leading to its decline among the younger generation. This research pioneers an innovative approach to revive the Purwa Pandava Five culture, targeting 50 fine arts students. The goal is to establish a digital literacy-based creative learning model in higher education, fostering an understanding of the aesthetics and moral fabric of Purwa Pandava Five shadow puppetry. Through a concurrent mixed-methods research design, 50 fine arts students craft digital puppetry works resonating with millennial comprehension. Data collection involves qualitative and quantitative methods such as interviews, surveys, and artistic assessments. The outcomes aim to ignite the younger generation's interest in traditional Indonesian arts, anticipating the evolution of fine arts programs in higher education to be responsive to changing cultural and technological dynamics. The aesthetic value and character of the Pandava Five shadow puppetry puppet are reflected through dramatic scenes and positive traits. Learning integrates creativity, digital literacy, and the transformation of Purwa Pandava Five shadow puppetry, creating innovative digital puppetry with a unique Pandava Five story interpretation. Implementing a digital creation learning model yields positive results, allowing students to collaboratively understand Pandava Five shadow puppetry's aesthetic and moral values. This stimulates millennial interest in the cultural heritage of Purwa Pandava Five shadow puppetry, producing works that blend traditional aesthetics with modern elements through digital literacy and advanced technology. The principles balance tradition and modern technology, preserving the visual authenticity of Purwa Pandava Five shadow puppetry while focusing on narrative and moral aspects. These outcomes aim to engage the younger generation in comprehending and safeguarding traditional Indonesian arts, with an anticipated evolution of fine arts programs in higher education to meet changing cultural and technological dynamics

Dataremix: Aesthetic Experiences of Big Data and Data Abstraction

This PhD by published work expands on the contribution to knowledge in two recent large-scale transdisciplinary artistic research projects: ATLAS in silico and INSTRUMENT | One Antarctic Night and their exhibited and published outputs. The thesis reflects upon this practice-based artistic research that interrogates data abstraction: the digitization, datafication and abstraction of culture and nature, as vast and abstract digital data. The research is situated in digital arts practices that engage a combination of big (scientific) data as artistic material, embodied interaction in virtual environments, and poetic recombination. A transdisciplinary and collaborative artistic practice, x-resonance, provides a framework for the hybrid processes, outcomes, and contributions to knowledge from the research. These are purposefully and productively situated at the objective | subjective interface, have potential to convey multiple meanings simultaneously to a variety of audiences and resist disciplinary definition. In the course of the research, a novel methodology emerges, dataremix, which is employed and iteratively evolved through artistic practice to address the research questions: 1) How can a visceral and poetic experience of data abstraction be created? and 2) How would one go about generating an artistically-informed (scientific) discovery? Several interconnected contributions to knowledge arise through the first research question: creation of representational elements for artistic visualization of big (scientific) data that includes four new forms (genomic calligraphy, algorithmic objects as natural specimens, scalable auditory data signatures, and signal objects); an aesthetic of slowness that contributes an extension to the operative forces in Jevbratt’s inverted sublime of looking down and in to also include looking fast and slow; an extension of Corby’s objective and subjective image consisting of “informational and aesthetic components” to novel virtual environments created from big 3 (scientific) data that extend Davies’ poetic virtual spatiality to poetic objective | subjective generative virtual spaces; and an extension of Seaman’s embodied interactive recombinant poetics through embodied interaction in virtual environments as a recapitulation of scientific (objective) and algorithmic processes through aesthetic (subjective) physical gestures. These contributions holistically combine in the artworks ATLAS in silico and INSTRUMENT | One Antarctic Night to create visceral poetic experiences of big data abstraction. Contributions to knowledge from the first research question develop artworks that are visceral and poetic experiences of data abstraction, and which manifest the objective | subjective through art. Contributions to knowledge from the second research question occur through the process of the artworks functioning as experimental systems in which experiments using analytical tools from the scientific domain are enacted within the process of creation of the artwork. The results are “returned” into the artwork. These contributions are: elucidating differences in DNA helix bending and curvature along regions of gene sequences specified as either introns or exons, revealing nuanced differences in BLAST results in relation to genomics sequence metadata, and cross-correlation of astronomical data to identify putative variable signals from astronomical objects for further scientific evaluation

From undesired flaws to esthetic assets: A digital framework enabling artistic explorations of erroneous geometric features of robotically formed molds

Until recently, digital fabrication research in architecture has aimed to eliminate manufacturing errors. However, a novel notion has just been established—intentional computational infidelity. Inspired by this notion, we set out to develop means than can transform the errors in fabrication from an undesired complication to a creative opportunity. We carried out design experiment-based investigations, which culminated in the construction of a framework enabling fundamental artistic explorations of erroneous geometric features of robotically formed molds. The framework consists of digital processes, assisting in the explorations of mold errors, and physical processes, enabling the inclusion of physical feedback in digital explorations. Other complementary elements embrace an implementation workflow, an enabling digital toolset and a visual script demonstrating how imprecise artistic explorations can be included within the computational environment. Our framework application suggests that the exploration of geometrical errors aids the emergence of unprecedented design features that would not have arisen if error elimination were the ultimate design goal. Our conclusion is that welcoming error into the design process can reinstate the role of art, craft, and material agency therein. This can guide the practice and research of architectural computing onto a new territory of esthetic and material innovation

From undesired flaws to esthetic assets: A digital framework enabling artistic explorations of erroneous geometric features of robotically formed molds

Until recently, digital fabrication research in architecture has aimed to eliminate manufacturing errors. However, a novel notion has just been established—intentional computational infidelity. Inspired by this notion, we set out to develop means than can transform the errors in fabrication from an undesired complication to a creative opportunity. We carried out design experiment-based investigations, which culminated in the construction of a framework enabling fundamental artistic explorations of erroneous geometric features of robotically formed molds. The framework consists of digital processes, assisting in the explorations of mold errors, and physical processes, enabling the inclusion of physical feedback in digital explorations. Other complementary elements embrace an implementation workflow, an enabling digital toolset and a visual script demonstrating how imprecise artistic explorations can be included within the computational environment. Our framework application suggests that the exploration of geometrical errors aids the emergence of unprecedented design features that would not have arisen if error elimination were the ultimate design goal. Our conclusion is that welcoming error into the design process can reinstate the role of art, craft, and material agency therein. This can guide the practice and research of architectural computing onto a new territory of esthetic and material innovation

An analysis of a choreographic work: fundamental and innovative methodology

The article analyzes the methodology of professional critical analysis of dance art work and outlines the prospects of development of this vector of choreological activities. The purpose of the article is a goal-oriented description of methods of professional analysis of choreographic works in the context of habit training of choreographers’ critical thinking. The methods used are as follows: method of synthesis, systematic approach, method of structural analysis, method of analysis of musical content, analysis of the specific character of audience perception, analysis of semiotic components of the work, analysis of the dialectic of a choreographic text. The criteria for the analysis of choreographic works can be worked out through the use of innovative technologies. it is possible to make additional arguments based on the experience of mathematical, music and linguistic methodology. The combination of innovative technologies with techniques that consider the quantitative and qualitative indicators of dance, allows the analysis of a choreographic work to move to a new more evidence-based level. Further development of this applied research issue can be aimed at improving the theoretical apparatus of the proposed innovative methods, or the development of new techniques based on technical developments in the field of GPS-devices

A Data Set and a Convolutional Model for Iconography Classification in Paintings

Iconography in art is the discipline that studies the visual content of artworks to determine their motifs and themes andto characterize the way these are represented. It is a subject of active research for a variety of purposes, including the interpretation of meaning, the investigation of the origin and diffusion in time and space of representations, and the study of influences across artists and art works. With the proliferation of digital archives of art images, the possibility arises of applying Computer Vision techniques to the analysis of art images at an unprecedented scale, which may support iconography research and education. In this paper we introduce a novel paintings data set for iconography classification and present the quantitativeand qualitative results of applying a Convolutional Neural Network (CNN) classifier to the recognition of the iconography of artworks. The proposed classifier achieves good performances (71.17% Precision, 70.89% Recall, 70.25% F1-Score and 72.73% Average Precision) in the task of identifying saints in Christian religious paintings, a task made difficult by the presence of classes with very similar visual features. Qualitative analysis of the results shows that the CNN focuses on the traditional iconic motifs that characterize the representation of each saint and exploits such hints to attain correct identification. The ultimate goal of our work is to enable the automatic extraction, decomposition, and comparison of iconography elements to support iconographic studies and automatic art work annotation.Comment: Published at ACM Journal on Computing and Cultural Heritage (JOCCH) https://doi.org/10.1145/345888

Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities

We present a framework for automating generative deep learning with a specific focus on artistic applications. The framework provides opportunities to hand over creative responsibilities to a generative system as targets for automation. For the definition of targets, we adopt core concepts from automated machine learning and an analysis of generative deep learning pipelines, both in standard and artistic settings. To motivate the framework, we argue that automation aligns well with the goal of increasing the creative responsibility of a generative system, a central theme in computational creativity research. We understand automation as the challenge of granting a generative system more creative autonomy, by framing the interaction between the user and the system as a co-creative process. The development of the framework is informed by our analysis of the relationship between automation and creative autonomy. An illustrative example shows how the framework can give inspiration and guidance in the process of handing over creative responsibility