Separate and Reassemble
Generative AI through the lens of art and media histories
Bibliographic Data
| ID | 12995609 |
|---|---|
| Authors | Lev Manovich (0000-0003-0667-7584, City University of New York, corresponding author) |
| Year | 2024 |
| Volume | 18 |
| Issue | 2 |
| Pages | 7-18 |
| Publication date | 2024-08-30 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Matrizes (JOURNAL) |
| Journal identifiers | ISSN: 1982-2073 • E-ISSN: 1982-8160 |
| Publisher | Universidade de São Paulo. Agência de Bibliotecas e Coleções Digitais (PUBLISHER) |
| DOI | 10.11606/issn.1982-8160.v18i2p7-18 |
| OpenAlex | W4402123148 |
| Language | EN |
| Citations received | 3 |
AI image generation represents a logical evolution from early digital media algorithms, starting with basic paint programs in the 1970s and advancing to sophisticated 3D graphics and media creation software by the 1990s. Early algorithms struggled to simulate materials and effects, but advances in the 1970s and 1980s led to realistic simulations of natural phenomena and artistic techniques. Generative AI continues this trend, using neural networks to combine and interpolate visual patterns from extensive datasets. This method of digital media creation underscores the modular and discrete nature of computer-generated imagery, distinguishing it from traditional optical media
Generative grammar · Lens (geology · Optics · Physics · Aesthetic Perception and Analysis · Computer Science · Digital Humanities and Scholarship · Ethics and Social Impacts of AI · Artificial Intelligence
| Unique citing works | 3 |
|---|---|
| Citations per year | 3 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |