Can Computers Create Art?

This essay discusses whether computers, using Artificial Intelligence (AI), could create art. First, the history of technologies that automated aspects of art is surveyed, including photography and animation. In each case, there were initial fears and denial of the technology, followed by a blossoming of new creative and professional opportunities for artists. The current hype and reality of Artificial Intelligence (AI) tools for art making is then discussed, together with predictions about how AI tools will be used. It is then speculated about whether it could ever happen that AI systems could be credited with authorship of artwork. It is theorized that art is something created by social agents, and so computers cannot be credited with authorship of art in our current understanding. A few ways that this could change are also hypothesized.Comment: to appear in Arts, special issue on Machine as Artist (21st Century

Pathway to Future Symbiotic Creativity

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

Deep Learning of Individual Aesthetics

Accurate evaluation of human aesthetic preferences represents a major challenge for creative evolutionary and generative systems research. Prior work has tended to focus on feature measures of the artefact, such as symmetry, complexity and coherence. However, research models from Psychology suggest that human aesthetic experiences encapsulate factors beyond the artefact, making accurate computational models very difficult to design. The interactive genetic algorithm (IGA) circumvents the problem through human-in-the-loop, subjective evaluation of aesthetics, but is limited due to user fatigue and small population sizes. In this paper we look at how recent advances in deep learning can assist in automating personal aesthetic judgement. Using a leading artist's computer art dataset, we investigate the relationship between image measures, such as complexity, and human aesthetic evaluation. We use dimension reduction methods to visualise both genotype and phenotype space in order to support the exploration of new territory in a generative system. Convolutional Neural Networks trained on the artist's prior aesthetic evaluations are used to suggest new possibilities similar or between known high quality genotype-phenotype mappings. We integrate this classification and discovery system into a software tool for evolving complex generative art and design

Understanding Aesthetic Evaluation using Deep Learning

A bottleneck in any evolutionary art system is aesthetic evaluation. Many different methods have been proposed to automate the evaluation of aesthetics, including measures of symmetry, coherence, complexity, contrast and grouping. The interactive genetic algorithm (IGA) relies on human-in-the-loop, subjective evaluation of aesthetics, but limits possibilities for large search due to user fatigue and small population sizes. In this paper we look at how recent advances in deep learning can assist in automating personal aesthetic judgement. Using a leading artist's computer art dataset, we use dimensionality reduction methods to visualise both genotype and phenotype space in order to support the exploration of new territory in any generative system. Convolutional Neural Networks trained on the user's prior aesthetic evaluations are used to suggest new possibilities similar or between known high quality genotype-phenotype mappings

Artificial Intelligence as a Substitute for Human Creativity

Creativity has always been perceived as a human trait, even though the exact neural mechanisms remain unknown, it has been the subject of research and debate for a long time. The recent development of AI technologies and increased interest in AI has led to many projects capable of performing tasks that have been previously regarded as impossible without human creativity. Music composition, visual arts, literature, and science represent areas in which these technologies have started to both help and replace the creative human, with the question of whether AI can be creative and capable of creation more realistic than ever. This review aims to provide an extensive perspective over several state-of-the art technologies and applications based on AI which are currently being implemented into areas of interest closely correlated to human creativity, as well as the economic impact the development of such technologies might have on those domains

Who gets credit for AI-generated art?

The recent sale of an artificial intelligence (AI)-generated portrait for $432,000 at Christie's art auction has raised questions about how credit and responsibility should be allocated to individuals involved and how the anthropomorphic perception of the AI system contributed to the artwork's success. Here, we identify natural heterogeneity in the extent to which different people perceive AI as anthropomorphic. We find that differences in the perception of AI anthropomorphicity are associated with different allocations of responsibility to the AI system and credit to different stakeholders involved in art production. We then show that perceptions of AI anthropomorphicity can be manipulated by changing the language used to talk about AI—as a tool versus agent—with consequences for artists and AI practitioners. Our findings shed light on what is at stake when we anthropomorphize AI systems and offer an empirical lens to reason about how to allocate credit and responsibility to human stakeholders

Images Big and Soft: The Digital Archive Rendered Cinematic

 In his recent immersive media art project titled Machine Hallucinations, artist Refik Anadol collected over 100 million images of New York City from social media and, using machine learning, created a 30-minute immersive experimental cinema experience that visualized the database. On his website, Anadol explains that computation allows “a novel form of synesthetic storytelling through its multilayered manipulation of a vast visual archive beyond the conventional limits of the camera and the existing cinematographic techniques.” With this project, Anadol demonstrates a tendency shared by a group of contemporary media artists who work at the intersection of cinema and the digital archive and who use machine learning and generative adversarial networks to render specific somatic experiences in relation to thousands of images. This essay discusses this shared focus by examining projects by three artists who use computational processes to assemble, manipulate, and then exhibit an archive of images as a part of their practice and output, translating the archival into the cinematic. The projects are significant in their evocation of what has been named by Ingrid Hoelzl the “soft-image” or “post-image,” shifting from the single image as a solid, stable representation within a collection of similarly single images, to that of the distributed, in-process experiential image. Further, each example approaches the creation of the collection with varied intentions; and each presents the material in disparate modalities that, while deeply connected to the cinematic, produce very different sensory experiences. Together, the examples offer a perspective on the archive in our current moment’s transition from representation to computation

From Artificial Intelligence to Artificial Art: Deep Learning with Generative Adversarial Networks

Neural Network had a great impact on Artificial Intelligence and nowadays the Deep Learning algorithms are widely used to extract knowledge from huge amount of data. This thesis aims to revisit the evolution of Deep Learning from the origins till the current state-of-art by focusing on a particular prospective. The main question we try to answer is: can AI exhibit artistic abilities comparable to the human ones? Recovering the definition of the Turing Test, we propose a similar formulation of the concept, indeed, we would like to test the machine's ability to exhibit artistic behaviour equivalent to, or indistinguishable from, that of a human. The argument we will analyze as a support for this debate is an interesting and innovative idea coming from the field of Deep Learning, known as Generative Adversarial Network (GAN). GAN is basically a system composed of two neural network fighting each other in a zero-sum game. The ''bullets'' fired during this challenge are simply images generated by one of the two networks. The interesting part in this scenario is that, with a proper system design and training, after several iteration these fake generated images start to become more and more closer to the ones we see in the reality, making indistinguishable what is real from what is not. We will talk about some real anecdotes around GANs to spice up even more the discussion generated by the question previously posed and we will present some recent real world application based on GANs to emphasize their importance also in term of business. We will conclude with a practical experiment over an Amazon catalogue of clothing images and reviews with the aim of generating new never seen product starting from the most popular existing ones