The End Of Knowledge - Vault 5/97 - CVPR - 2024 - Entanglements, Exploring Artificial Biodiversity

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Biodiversity] Main --> A[AI in Art Creation] A --> A1[Tech-driven art creation 1] A --> A2[Humans drive AI art process 2] A --> A3[AI extracts invisible patterns 4] A --> A4[2D AI creatures evolve 3D 6] A --> A5[AI-marine life fusion creatures 5] A --> A6[Cyanotype merges AI-generated images 8] Main --> B[Inspiration and Techniques] B --> B1[Microscopes shaped nature perception 3] B --> B2[Traditional species reimagination inspires 7] B --> B3[Animated cyanotypes: microscopic motion 9] B --> B4[Personal underwater data collection 10] Main --> C[Scientific Collaboration] C --> C1[Immersive marine environment collaboration 11] C --> C2[Oceanic data visualization scientists 16] C --> C3[Artistic twilight zone ocean exploration 17] C --> C4[Depicting organic matter ocean descent 18] C --> C5[Artistic-scientific accurate representations 19] Main --> D[Public and Environmental Art] D --> D1[AI art reaches public masses 14] D --> D2[AI artwork integrates Gaudí forms 15] D --> D3[Incomplete data affects nature representation 12] D --> D4[AI limitations: endangered species depiction 13] D --> D5[Embracing unattractive subjects messages 23] Main --> E[Ethical and Practical Considerations] E --> E1[AI art computational resource use 20] E --> E2[AI training consent ethics 21] E --> E3[Tangible artworks complement digital 22] E --> E4[Funding unconventional art project difficulties 24] E --> E5[Artists control AI dataset work 26] Main --> F[Artist Development and Challenges] F --> F1[Rapid AI art evolution perspective 25] F --> F2[Exploring subjective art evaluation processes 27] F --> F3[Cross-disciplinary inadequacy without training 28] F --> F4[Artist-friendly technical explanations needed 29] F --> F5[AI tool workshops recommended 30] class Main main class A,B,C,D,E,F tech class A1,A2,A3,A4,A5,A6,B1,B2,B3,B4 creation class C1,C2,C3,C4,C5,D1,D2,D3,D4,D5 collab class E1,E2,E3,E4,E5,F1,F2,F3,F4,F5 ethics

Resume:

1.- Artistic practice with technology: Using AI and other tech tools to create art, without necessarily demonstrating technical aspects.

2.- Human intention in AI art: Emphasizing that humans, not computers, are the creators of AI-generated art.

3.- Microscope as inspiration: Early exposure to microscopes influenced the artist's perception of patterns and structures in nature.

4.- Neural networks as pattern extractors: AI viewed as a tool to see and extract patterns invisible to the naked eye.

5.- Neural Zoo series: Project combining marine life datasets with AI to create new, imaginary creatures.

6.- 3D creature generation: Evolution of 2D AI-generated creatures into 3D models using GAN interpolations and 3D style transfer.

7.- Historical inspiration: Drawing from traditional artistic practices of combining and reimagining species, like medieval bestiaries.

8.- Cyanotype printing: Incorporating traditional photographic techniques with AI-generated images to create unique artworks.

9.- Temporally Uncaptured series: Animating cyanotype prints to represent the motion of microscopic life.

10.- Diving and data collection: Personally gathering underwater imagery for datasets, emphasizing connection to subject matter.

11.- Virtual ocean space: Collaboration to create immersive experiences combining visual and audio elements of marine environments.

12.- Incomplete natural world data: Exploring how incomplete or distorted data affects our understanding and representation of nature.

13.- Critically Extant series: Highlighting limitations of AI by generating inaccurate depictions of endangered species using incomplete data.

14.- Public art installations: Showcasing AI-generated art in public spaces like Times Square to reach wider audiences.

15.- Casa Batlló projection: Creating a site-specific AI artwork for Gaudí's architectural masterpiece, integrating with its organic forms.

16.- Ocean visualization: Collaborating with scientists to visualize oceanic data, such as surface temperatures and currents.

17.- Mesopelagic zone exploration: Artistic interpretation of the ocean's "twilight zone" and its importance in carbon sequestration.

18.- Marine snow visualization: Artistically representing the often-overlooked phenomenon of organic matter falling through ocean layers.

19.- Collaborative scientific art: Working closely with researchers to create accurate yet artistic representations of scientific concepts.

20.- Resource consciousness: Addressing concerns about computational resources used in AI art creation.

21.- Artist consent in AI training: Discussing the ethics of using artists' work to train AI models without permission.

22.- Physical art objects: Exploring the creation of tangible artworks to complement digital pieces.

23.- Ugly art: Embracing less aesthetically pleasing subjects to convey important scientific or environmental messages.

24.- Funding challenges: Addressing difficulties in securing support for less conventionally attractive or marketable art projects.

25.- Veteran vs. newcomer perspectives: Reflecting on the rapid evolution of AI art and the artist's position within the field.

26.- Opting out of AI training: Mentioning tools and discussions around artists' control over their work in AI datasets.

27.- Defining "good art": Exploring the subjective nature of art evaluation and the role of curators and society.

28.- Imposter syndrome: Discussing feelings of inadequacy when working across disciplines, particularly without formal technical training.

29.- Accessibility of technical knowledge: Addressing the need for more artist-friendly explanations of complex AI and computer vision concepts.

30.- Learning resources for artists: Recommending workshops and online materials to help artists understand and utilize AI tools.

Knowledge Vault built byDavid Vivancos 2024