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Pathway to Future Symbiotic Creativity

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Is a
‌
Academic paper
0

Academic Paper attributes

arXiv ID
2209.023880
arXiv Classification
Computer science
Computer science
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Publication URL
arxiv.org/pdf/2209.0...88.pdf0
Publisher
ArXiv
ArXiv
0
DOI
doi.org/10.48550/ar...09.023880
Paid/Free
Free0
Academic Discipline
‌
Human–computer interaction
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Artificial Intelligence (AI)
Artificial Intelligence (AI)
0
Computer science
Computer science
0
Submission Date
September 13, 2023
0
August 18, 2022
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Author Names
Qifeng Liu0
Yike Guo0
Xing Wu0
Jiji Zhang0
Wei Xue0
Fernando Rosas0
Henrik Jensen0
Jeffrey Shaw0
...
Paper abstract

This report presents a comprehensive view of our vision on the development path of the human-machine symbiotic art creation. We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist (Turing Artists) to a Machine artist in its own right. We begin with an overview of the limitations of the Turing Artists then focus on the top two-level systems, Machine Artists, emphasizing machine-human communication in art creation. In art creation, it is necessary for machines to understand humans' mental states, including desires, appreciation, and emotions, humans also need to understand machines' creative capabilities and limitations. The rapid development of immersive environment and further evolution into the new concept of metaverse enable symbiotic art creation through unprecedented flexibility of bi-directional communication between artists and art manifestation environments. By examining the latest sensor and XR technologies, we illustrate the novel way for art data collection to constitute the base of a new form of human-machine bidirectional communication and understanding in art creation. Based on such communication and understanding mechanisms, we propose a novel framework for building future Machine artists, which comes with the philosophy that a human-compatible AI system should be based on the "human-in-the-loop" principle rather than the traditional "end-to-end" dogma. By proposing a new form of inverse reinforcement learning model, we outline the platform design of machine artists, demonstrate its functions and showcase some examples of technologies we have developed. We also provide a systematic exposition of the ecosystem for AI-based symbiotic art form and community with an economic model built on NFT technology. Ethical issues for the development of machine artists are also discussed.

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