Art Notions in the Age of (Mis)anthropic AI
Abstract
:1. Introduction
2. Generative AI
3. Art Notions
3.1. Conceptual Aspects
3.1.1. Ontological
3.1.2. Taxonomic
3.2. The Cultural Normalization of AI
3.3. Authorship and Agency
3.4. Tricky Distinctions
3.4.1. Porous Perimeters
3.4.2. Critical Approaches
4. Undercurrents
4.1. The Machinic Agency Fetishism
4.2. The Objectivization of Humans
4.2.1. Computers as Humans
The separation between “reasonable” and “unreasonable” ideas [in AI science], which we might call superstition is less clear than one might expect. In Computing Machinery and Intelligence, Alan Turing considers the use of a “telepathy-proof room” to protect the integrity of his Imitation Game from players exhibiting extrasensory perception. This may cause us to cringe in hindsight—it’s uncomfortable to imagine heroes of science believing such unlikely things. But good science demands open-mindedness and the courage to challenge accepted truths. AI researchers are in a difficult position, expected to dismiss “silly” ideas like telepathy and yet take seriously the idea that bits of metal and silicon might become intelligent if you program them the right way.
4.2.2. Sociotechnical Blindness
4.3. Ideologies
5. Discussion
5.1. Generative AI as a Cultural Conduit
5.2. Generative AI, Art, and Democracy
5.3. The Elusive Paradigms
6. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
1 | Epstein et al. (2023), McCormack et al. (2023), Sanchez (2023), and Totlani (2023) provide concise overviews of generative AI and its common issues. |
2 | Trained on a dataset of 400 million Internet-scraped image-text pairs, OpenAI’s CLIP (Contrastive Language–Image Pre-training) extends the earlier models’ object recognition capabilities by retrieving abstract concepts such as context and style (Radford et al. 2021). Other large-scale language-vision models include ALIGN, BASIC, GLIDE, Flamingo, and Imagen. |
3 | Diffusion models inject noise interference into data and generate samples in a gradual denoising process that involves predicting the next datum based on prior information found in the dataset. Over time, the model’s prediction improves in “filling in the blanks” by calculating the most probable configuration of numerical representations in the data space (see Yang et al. 2022). |
4 | Censorship has been integral to the Internet since its outset but has intensified since the mid-1990s with the privatization of the Internet’s backbone network, Domain Name System, and the Internet Protocol. It escalated after the release of the Digital Millennium Copyright Act in 1998, and the introduction of Web 2.0 in the mid-2000s (Faris et al. 2008; Cobbe 2021). |
5 | Computational creativity is a subfield of computer science that examines AI systems’ creative potentials. |
6 | For a summary of the common problems in CC studies, see Moruzzi (2020, pp. 162–64; 2022, pp. 183–84) and Issak and Varshney (2022). |
7 | The notional realm of “art” in AI research has mostly favored popular artists’ paintings from the Western art canon on account of other (visual) artists, art forms, and cultural domains, often interpreted with simplistic categorization taxonomies and anachronistic interpretations of art-historical concepts (Wasielewski 2023). While its scope expanded beyond well-known artists and styles with the large/foundation models, the field’s overall approach to art remains superficial. |
8 | Most notably the commercialization of personal computers in the 1980s, Apple’s doctrine of user-friendly computation in the mid-1980s, the Internet in the 1990s, Web 2.0 technologies in the mid-2000s, blockchain in the late 2000s, and the NFTs and AI media generators since the mid-2010s. |
9 | The term art brut (“raw art” or “rough art”) was introduced in the 1940s by French artist Jean Dubuffet. |
10 | Although kitsch is often derogated as culturally detrimental (see Quaranta 2023, pp. 5–6), some authors believe it plays positive social roles by enabling less privileged groups to access art and own the affordable interpretations of artworks, which facilitates a sense of community and promotes wellbeing (Jerrentrup 2024, pp. 4–5). |
11 | China, Denmark, Finland, France, Germany, Holland, Iceland, Italy, Kenya, Portugal, Russia, Turkey, Ukraine, and the United States. |
12 | While acknowledging the caveats of retrospective diagnoses, O’Connell and Fitzgerald’s (2003) analysis of Turing’s biography and contemporaneous accounts concludes that he met Gillberg, ICD-10, and DSM-IV criteria for Asperger’s syndrome, which places him within the autism spectrum disorder. |
13 | For example, see the reiterations of the computer-as-a-person concept by the computer science/AI giant Marvin Minsky in Elis (2014) and vivid accounts of the prevalence of similar mindsets among the Silicon Valley hackers in Jaron Lanier’s autobiography Dawn of the New Everything (Lanier 2017). |
14 | The AI-powered statistical reductionism is not exclusive to businesses and can be radicalized by authoritarian regimes. For example, the Social Credit System and the “innovative development pilot zones”, implemented by the Chinese government and AI industry in 2014 and 2019, respectively, are based on a state-wide networked surveillance of citizens’ social and business activities with practical repercussions such as the availability of jobs, education, bank loans, electronic services, transportation, and travel (Yang 2022). |
15 | With large models such as Open AI’s GPT-4, this process is somewhat streamlined by using datasets of users’ feedback to build separate “training reward” models. |
16 | Golumbia also monetizes his essays behind the paywall on Medium, while Marx hosts his podcast Tech Won’t Save Us on YouTube and monetizes it additionally through Patreon. |
17 | After enjoying exciting but highly privileged and surreally insulated academic lives, art school graduates quickly learn that talent and hard work do not guarantee success as most of them fail to become emerging artists and only a fraction of those who do maintain mid- or long-term professional careers that can be broadly characterized as artistic. |
18 | Deeply embedded in and emerging from capitalist profit-seeking motivations that tend to overlook social justice, AI entrepreneurships often gravitate towards free markets, such as the US, to avoid regulation and accountability, and leverage legal loopholes and the absence of ethical vetting (McElroy 2024). |
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Grba, D. Art Notions in the Age of (Mis)anthropic AI. Arts 2024, 13, 137. https://doi.org/10.3390/arts13050137
Grba D. Art Notions in the Age of (Mis)anthropic AI. Arts. 2024; 13(5):137. https://doi.org/10.3390/arts13050137
Chicago/Turabian StyleGrba, Dejan. 2024. "Art Notions in the Age of (Mis)anthropic AI" Arts 13, no. 5: 137. https://doi.org/10.3390/arts13050137