CG-Art: demystifying the anthropocentric bias of artistic creativity (original) (raw)
Related papers
AI Aesthetics and the Anthropocentric Myth of Creativity
Nodes, 2022
Suppose human creativity could be potentially replicated by mechanical processes. In that case, we would face a crossroads: either we could give up using the concept of creativity altogether, or if we hold to our common understanding of what creativity is, we could agree to apply this concept to non-human phenomena as well, as world champion Lee Sedol did when judging the performance of AlphaGo. However, the idea that artificial creativity discloses the mechanic nature of human creativity should also be met with a bit of critical detachment, particularly if we consider the specific case of the arts. In fact, artificial reproductions of human artifacts do not follow the same processes with which humans actually produced those artifacts. Nobody thinks that Mondrian followed procedures similar to the algorithm used in 1966 that generated pseudo-Mondrian, even though the public appreciated the artificial images more than the original ones....
Architectural Design, 2022
The current discussions about the adoption of AI (artificial intelligence) in visual arts, design, architecture, cinema, music and other arts often rely on widely accepted ideas about art and creativity. These ideas include the following: “Art is the most creative human domain.” “Art and creativity can’t be measured.” “Artists does not follow rules.” It is also commonly assumed that “computers can only follow rules,” and therefore “computers struggle to generate something novel and original.” Taken together, these ideas lead to a new assumption: “generation of original art is a great test of AI progress.” Where do these popular popular ideas about art and its relationship to creativity come from? Historically, they are quite recent. For thousands of years human creators in all human cultures made artifacts that today we put in museums and worship as great art. But their creators did not have modern concepts of art, artist, and creativity. Th goal of this text is to briefly discuss the historical origins of currently popular ideas about art and creativity, and suggest that these ideas limit our vision of cultural AI. There are a few dominant popular understandings of “art” today. Logically, they contradict each other. Despite this, they may perfectly co-exist in a single publication or conversation. Sometimes one idea dominates and others do not appear. But very often, all three are assumed to be valid in the same time. Because these ideas contradict each other, holding them together can lead to feelings of confusion and unease - and also big fears about “creative AI.”
(Un)creative Artificial Intelligence: A Critique of 'Artificial Art'
2020
I The arguments that follow are situated within a larger project entitled "Critique of Algorithmic Rationality." That project, drawing deliberately from Immanuel Kant, is an attempt to move beyond the technological, social or cultural critiques of digital rationalities usually found in social, media and cultural theories and take a critical look at the validity of algorithmic approaches. It explores the limitations of the performance and purview of algorithmic schematics in the realm of art and creativity.
A Digital Aesthetics? Artificial Intelligence and the Future of the Art
Artificial intelligence has brought about significant changes in various creative domains, sparking discussions about the nature of art and its authenticity in the era of AI. Some scholars assert that the computer monitor now serves as a canvas, a brush, a musical instrument, and even an art tutor, leading us to explore deeper connections between AI and creativity. However, in this presentation, we wish to emphasize the humanistic dimension of creative processes once more. we acknowledge the role of AI in enhancing creative endeavors, but we firmly believe that human creativity remains paramount in the production of artistic works. The current notion of machines replacing artists is, in our view, more of a media sensation than a reality. Examining the history of electronic arts, our paper argues that claims of AI's artistic superiority are not novel; they echo similar trends from the past. The current enthusiasm mirrors earlier media frenzies. While the sciences have made significant strides in unraveling the mysteries of the human brain, our understanding of the intricacies of our remarkably creative minds, their origins, and their fulfillment in our brains This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY NC), which permits distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
ISEA 2022, 2022
This paper is dedicated to the analysis of two works produced by two contemporary artists, more specifically “Rachael is not real” by Matthias Winckelmann and “Machine Hallucination”, by Refik Anadol, having as convergence point the generative method of production employed in both. The article describes both works using, as theoretical foundation, the most widely accepted definition of Generative Art and reveals nuances of both works that might not be fully comprehended by the current theory. Finally, the article looks at some of the most recent technological advances and their impact on the art world, such as Artificial Intelligence, which have been used to create art, and proposes a reflection on the importance of revisiting theoretical definitions in order for them to be able to be able to keep up with the technical evolution of art.
As machines take over more tasks previously done by humans, artistic creation is also considered as a candidate to be automated. But, can machines create art? This paper offers a conceptual framework for a philosophical discussion of this question regarding the status of machine art and machine creativity. It breaks the main question down in three sub-questions, and then analyses each question in order to arrive at more precise problems with regard to machine art and machine creativity: What is art creation? What do we mean by art? And, what do we mean by machines create art? This then provides criteria we can use to discuss the main question in relation to particular cases. In the course of the analysis, the paper engages with theory in aesthetics, refers to literature on computational creativity, and contributes to the philosophy of technology and philosophical anthropology by reflecting on the role of technology in art creation. It is shown that the distinctions between process versus outcome criteria and subjective versus objective criteria of creativity are unstable. It is also argued that we should consider non-human forms of creativity, and not only cases where either humans or machines create art but also collaborations between humans and machines, which makes us reflect on human-technology relations. Finally, the paper questions the very approach that seeks criteria and suggests that the artistic status of machines may be shown and revealed in the human/non-human encounter before any theorizing or agreement takes place; an experience which then is presupposed when we theorize. This hints at a more general model of what happens in artistic perception and engagement as a hybrid human-technological and emergent or even poetic process, a model which leaves more room for letting ourselves be surprised by creativity—human and perhaps non-human.
This paper focuses on explicit attempts at developing artificial intelligence in the production of art that generate outcomes similar to, or even technically superseding, the works of human artists. We aim at revealing the underlying discourses that equate art production with transformation of information, artists with input/output systems, and artistic creativity with an unlimited and autonomous generation of art-like outcomes. As a point of departure, we begin from an exposition of Margaret Boden’s account of creativity and proceed by examining different arguments to the effect that computers can be truly creative, primarily those offered by Boden (2004, 2010). We question what the assumptions, operative in the discourse on artificial or computational creativity, entail. AI-agents can produce creative outcomes because they implement our best models of creativity. By implementing these models, however, AI-agents evidence a particular understanding of what art is and what constitutes artistic production. This understanding does not fully conform to how contemporary artistic practices are perceived and valued. As a result, we argue, better models to frame artistic AI and computational creativity are needed to fully appreciate the developments in this field and their articulation within the existing art world.
Art, Creativity and the Potential of Artificial Intelligence
Arts, 2019
Our essay discusses an AI process developed for making art (AICAN), and the issues AI creativity raises for understanding art and artists in the 21st century. Backed by our training in computer science (Elgammal) and art history (Mazzone), we argue for the consideration of AICAN’s works as art, relate AICAN works to the contemporary art context, and urge a reconsideration of how we might define human and machine creativity. Our work in developing AI processes for art making, style analysis, and detecting large-scale style patterns in art history has led us to carefully consider the history and dynamics of human art-making and to examine how those patterns can be modeled and taught to the machine. We advocate for a connection between machine creativity and art broadly defined as parallel to but not in conflict with human artists and their emotional and social intentions of art making. Rather, we urge a partnership between human and machine creativity when called for, seeing in this collaboration a means to maximize both partners’ creative strengths.
2019
Intelligence Everywhere: What artistic explorations can tell us through and about technological development presented on Sept 18 2019 during the Humanities and Public Life Conference at Dawson College, Montréal, Canada Recent developments in machine learning and what John McCarthy has named artificial intelligence in 1956 have repeatedly been portrayed in the media as competing with human creativity. Binary narratives that (narcissistically) anthropomorphize and present technological advancements as either miraculous or antagonistic spread fear and fascination amongst the public. Machines, some threaten, will take your job as an artist, a lawyer, a taxi driver, a doctor, an accountant, and govern us … In this presentation I wish to draw a historical lineage between ideas that were at the roots of the British branch of cybernetics comparing and contrasting the worldview that underlined it with the approach taken by the founders of the Artificial Intelligence project in 1956. I wish to establish the link between the cybernetic worldview and the recent developments in machine learning that we commonly refer to as Artificial Intelligence. (AI) These powerful discoveries are currently used to generate images, natural language, soundscapes and videos that can be mistaken to have been produced by people. This has pushed some to declare that the machines were themselves creative. I will argue that while the tools do display what N.Katherine Hayles calls non-conscious cognition, a process that is found everywhere in nature, creativity, in the realm of art, is a concept rooted in the self-reflexive sense-making ability of the person orchestrating it as well as in the social, cultural and political context in which it is being examined. Presenting creativity from the point of view of the art world, I will argue that the definition of art does not lie solely in the formal aesthetics of the object produced but is a shifting culturally constructed concept that is by no means negated by machine “imagination” or “creativity”. The notion of authorship in relation with automation in the creative process have been explored thoroughly in the realm of art ever since, for example, Marcel Duchamp presented his readymade, Walter Benjamin published his famous text in 1937 and Roland Barth examined aspects of the topic in 1967. Early cybernetic prototypes that displayed cognitive behaviours as well as artworks that use automation in their creative process will be presented as well as a selection of recent art practises that explore and comment on the use of statistical models or what Hunger calls “enhanced pattern recognition” systems such as artificial neural networks and adversarial neural networks. (Hunger 2017) These artworks often present advanced technical tools as one component of a network (Latour) /agencement (Deleuze) in which humans interact with them in complex and intricate ways. Through the examination of a selection of projects by artists from various backgrounds, such as the recent work and writings by indigenous artists as well as local and international artists, I wish to point to some of the shortcomings they bring to light as well as how they engage us into some much-needed reflection about the technologies we generate and how they hold the potential to redefine us and the environment.
Mind, Machine, and Creativity: An Artist's Perspective
Harold Cohen is a renowned painter who has developed a computer program , AARON, to create art. While AARON has been hailed as one of the most creative AI programs, Cohen consistently rejects the claims of machine creativity. Questioning the possibility for AI to model human creativity, Cohen suggests in so many words that the human mind takes a different route to creativity, a route that privileges the relational, rather than the computational, dimension of cognition. This unique perspective on the tangled web of mind, machine, and creativity is explored by an application of three relational models of the mind to an analysis of Cohen's talks and writings, which are available on his website: www.aaronshome.com.